CSE Syllabus BTech NEW 2023

HERITAGE INSTITUTE OF TECHNOLOGY
(An Autonomous Institute Under MAKAUT)
DEPARTMENT OF
COMPUTER SCIENCE AND ENGINEERING
B.Tech Course Structure
v.28
Dept. of CSE, HIT-K B. Tech in CSE, Course Structure Revised: October 2022

COURSE STRUCTURE
Department of Computer Science & Engineering
Applicable for B. Tech 2018-2022 (v .28) Page 1 of 130

FIRST YEAR
FIRST SEMESTER
Contacts Credit
Sl. Code Subject Periods/ Week Points
L T P Total
A. Theory
1 CHEM1001 Chemistry-I 3 1 0 4 4
2 MATH1101 Mathematics-I 3 1 0 4 4
3 ELEC1001 Basic Electrical Engineering 3 1 0 4 4
Total Theory 9 3 0 12 12
B. Practical
1 CHEM1051 Chemistry I Lab 0 0 3 3 1.5
2 ELEC1051 Basic Electrical Engineering Lab 0 0 2 2 1
3 MECH1052 Engineering Graphics & Design 1 0 4 5 3
Total Practical 1 0 9 10 5.5
Total of Semester without Honors 10 3 9 22 17.5
C. Honors
1 HMTS1011 Communication for Professionals 3 0 0 3 3
2. HMTS1061 Professional Communication Lab 0 0 2 2 1
Total Honors 3 0 2 5 4
Total of Semester with Honors 13 3 11 27 21.5

FIRST YEAR
SECOND SEMESTER
Contacts
Credit
L T P Total
A. Theory
1 PHYS1001 Physics I 3 1 0 4 4
2 MATH1201 Mathematics II 3 1 0 4 4
3 CSEN1001 Programming for Problem Solving 3 0 0 3 3
4 HMTS1202 Business English 2 0 0 2 2
B. Practical
1 PHYS1051 Physics I Lab 0 0 3 3 1.5
2 CSEN1051 Programming for Problem Solving Lab 0 0 4 4 2
3 MECH1051 Workshop / Manufacturing Practice 1 0 4 5 3
4 HMTS1252 Language Lab 0 0 2 2 1
Total Practical 1 0 13 14 7.5
Total of Semester without Honors 12 2 13 27 20.5
C. Honors
1 ECEN1011 Basic Electronics 3 0 0 3 3
2 ECEN1061 Basic Electronics Lab 0 0 2 2 1
Total of Semester with Honors 15 2 15 32 24.5

SECOND YEAR
THIRD SEMESTER
Contacts
Credit
L T P Total
A. Theory
1 CSEN2101 Data Structures and Algorithms 4 0 0 4 4
2 CSEN2102 Discrete Mathematics 4 0 0 4 4
3 ECEN2101 Analog Circuits 3 0 0 3 3
4 ECEN2104 Digital Logic 3 0 0 3 3
5 HMTS2001 Human Values and Professional Ethics 3 0 0 3 3
B. Practical
1 CSEN2151 Data Structures and Algorithms Lab 0 0 3 3 1.5
2 CSEN2152 Software Tools Lab 0 0 3 3 1.5
3 ECEN2154 Digital Logic Lab 0 0 2 2 1
Total Practical 0 0 8 8 4
Total of Semester without Honors 17 0 8 25 21
C. Honors
1 MATH2111 Probability and Statistical Methods 4 0 0 4 4
Total of Semester with Honors 21 0 8 29 25

SECOND YEAR
FOURTH SEMESTER
Contacts
Credit
L T P Total
A. Theory
1 CSEN2201 Design & Analysis of Algorithms 4 0 0 4 4
2 CSEN2202 Computer Organization and Architecture 4 0 0 4 4
3 CSEN2203 Operating Systems 3 0 0 3 3
4 MATH2201 Algebraic Structures 4 0 0 4 4
5 AEIE2205 Microprocessors and Microcontroller 2 0 0 2 2
6 EVSC2016 Environmental Sciences (Mandatory) 2 0 0 2 0
B. Practical
1 CSEN2251 Design & Analysis of Algorithms Lab 0 0 3 3 1.5
2 CSEN2252 Computer Architecture Lab 0 0 2 2 1
3 CSEN2253 Operating Systems Lab 0 0 3 3 1.5
4 AEIE2255 Microprocessors & Microcontroller Lab 0 0 2 2 1
Total of Semester 19 0 10 29 22

THIRD YEAR
FIFTH SEMESTER
Contacts
Credit
L T P Total
A. Theory
1 CSEN3101 Database Management Systems 4 0 0 4 4
2 CSEN3102 Formal Language & Automata Theory 4 0 0 4 4
3 CSEN3103 Object Oriented Programming 4 0 0 4 4
4 ECEN3106 Electronic Design Automation 2 0 0 2 2
5 CSEN3131- Professional Elective - I 3 0 0 3 3
CSEN3140
CSEN3131 Computer Graphics & Multimedia
CSEN3132 Data Mining & Knowledge Discovery
CSEN3133 Web Technologies
CSEN3134 Graph Algorithms
CSEN3135 Introduction to Data Analysis with Python
and R
B. Practical
1 CSEN3151 Database Management Systems Lab 0 0 3 3 1.5
2 CSEN3153 Object Oriented Programming Lab 0 0 3 3 1.5
3 ECEN3156 Electronic Design Automation Lab 0 0 2 2 1
C. Honors
1 CSEN3111 Artificial Intelligence 3 0 0 3 3
2 CSEN3161 Artificial Intelligence Lab 0 0 2 2 1

THIRD YEAR
SIXTH SEMESTER
Contacts
Credit
L T P Total
A. Theory
1 CSEN3201 Software Engineering 4 0 0 4 4
2 CSEN3202 Computer Networks 4 0 0 4 4
3 HMTS3201 Economics for Engineers 3 0 0 3 3
4 CSEN3231 - Professional Elective - II 3 0 0 3 3
CSEN3240
CSEN3231 Advanced Operating System
CSEN3232 Enterprise Application in Java EE
CSEN3233 Machine Learning
CSEN3234 Computational Geometry
CSEN3235 Cloud Computing
CSEN3236 Big Data
5 Open Elective - I 3 0 0 3 3
AEIE3221 Fundamentals of Sensors and
Transducers
CHEN3221 Water and Liquid Waste Management
ECEN3221 Artificial Intelligence in Radio
Communication
ECEN3222 Designing with Processors and
Controllers
ECEN3223 Analog and Digital Communication
MATH3221 Computational Mathematics
MATH3223 Scientific Computing
Indian Constitution and Civil Society
6 INCO3016 2 0 0 2 0
(Mandatory)
B. Practical
1 CSEN3251 Software Engineering Lab 0 0 3 3 1.5
2 CSEN3252 Computer Networks Lab 0 0 3 3 1.5
C. Sessional
1 CSEN3293 Term Paper and Seminar 0 0 4 4 2
Total Sessional 0 0 4 4 2
Total of Semester 19 0 10 29 22

FOURTH YEAR
SEVENTH SEMESTER
Contacts Credit
L T P Total
A. Theory
1 HMTS4101 Principles of Management 3 0 0 3 3
2 CSEN4131- Professional Elective - III 3 0 0 3 3
CSEN4140
CSEN4131 Soft Computing
CSEN4132 Cryptography & Network Security
CSEN4133 Image Processing
CSEN4134 Approximation Algorithms
CSEN4135 Information Retrieval
CSEN4136 NoSQL Database with MongoDB
3 Open Elective - II 3 0 0 3 3
AEIE4121 Instrumentation and Telemetry
BIOT4124 Biosensor
ECEN4121 Software Defined Radio
MATH4121 Methods in Optimization
MECH4124 Engineering Computational Techniques
4 Open Elective - III 3 0 0 3 3
AEIE4127 Introduction to Embedded System
BIOT4126 Biopolymer
ECEN4127 Ad-Hoc Wireless Networks
MATH4126 Linear Algebra
MECH4130 Ecology and Environmental Engineering
B. Sessional
1 CSEN4191 Industrial Training / Internship - - - - 2
2 CSEN4195 Project-I 0 0 8 8 4
C. Honors
1 CSEN4111 Compiler Design 3 0 0 3 3
2 CSEN4161 Compiler Design Lab 0 0 2 2 1

FOURTH YEAR
EIGHTH SEMESTER
Contacts Credit
L T P Total
A. Theory
1 CSEN4231- Professional Elective - IV 3 0 0 3 3
CSEN4240
CSEN4231 Distributed Algorithms
CSEN4232 Mobile Computing
CSEN4233 Pattern Recognition
CSEN4234 Computational Complexity
CSEN4235 Social Network Analysis
CSEN4236 Robotics
CSEN4237 Web Development with Node and
Express
CSEN4241-
2 Professional Elective - V 3 0 0 3 3
CSEN4250
CSEN4241 Distributed Databases
CSEN4242 Natural Language Processing
CSEN4243 Parallel Algorithms
CSEN4244 Real Time & Embedded System
CSEN4245 Quantum Computing
CSEN4246 Computer Vision
3 Open Elective - IV 3 0 0 3 3
AEIE4221 Process Instrumentation
AEIE4222 Medical Instrumentation
BIOT4221 Computational Biology
BIOT4222 Non-conventional Energy
BIOT4223 Biology For Engineers
CHEN4222 Introduction to Solar and Wind
Technology
ECEN4221 Low Power High Performance Digital
VLSI Circuit Design
ECEN4222 Cellular and Mobile Communication
ECEN4223 Optical Fiber Communication
HMTS4222 Introduction to French Language
B. Sessional
1 CSEN4295 Project-II 0 0 16 16 8
2 CSEN4297 Comprehensive Viva-voce - - - - 1
C. Honors
1 HMTS4011* Disaster Response Services and 4 0 0 4 4
Technologies
*N.B.: HMTS4011 Honors Course is for Lateral Entry Students Only

Open Electives to be offered by Computer Science and Engineering department for

Non-departmental students
Contact Hours / Week Credit
Sl. Semester Paper Code Course Title
L T P Total Points
1 6th CSEN3221 Fundamentals of RDBMS 3 0 0 3 3
2 7th CSEN4121 Fundamentals of Operating Systems 3 0 0 3 3
3 7th CSEN4126 Intelligent Web and Big Data 3 0 0 3 3
4 8th CSEN4221 Basics of Mobile Computing 3 0 0 3 3
Credit Summary for B Tech Programme with effect from 2018-2019

Credit
Sl. Course Type
Points
1 Humanities and Social Sciences including Management Courses 12
2 Basic Science Courses 23
Engineering Science Courses including Workshop, Drawing, Basics
3 29
of Electrical / Mechanical / Computer, etc.
4 Professional Core Courses 52
Professional Elective Courses relevant to chosen
5 15
Specialization / Branch
Open Subjects – Electives from other Technical and/or Emerging
6 12
Subjects
7 Project Work, Seminar and Internship in industry or elsewhere 17
Mandatory Courses (Non-credit)
8 [Environmental Sciences, Induction Program, Indian Constitution, 0
Essence of Indian Traditional Knowledge]
Total 160
9 Honors Courses 20
Grand Total 180
Honors Course for B. Tech Computer Science & Engineering Students
Contact Hours / Credit

Sl. Semester Paper Code Course Title Week Points
L T P
1 HMTS1011 Communication for Professionals 3 0 0 3
1st
2 HMTS1061 Professional Communication Lab 0 0 2 1
3 ECEN1011 Basic Electronics 3 0 0 3
2nd
4 ECEN1061 Basic Electronics Lab 0 0 2 1
5 3rd MATH2111 Probability and Statistical Methods 4 0 0 4
6 CSEN3111 Artificial Intelligence 3 0 0 3
5th
7 CSEN3161 Artificial Intelligence Lab 0 0 2 1
8 CSEN4111 Compiler Design 3 0 0 3
7th
9 CSEN4161 Compiler Design Lab 0 0 2 1
10 8th HMTS4011* Disaster Response Services and 4 0 0 4
Technologies
Total 20/24*

Definition of Credit (as per AICTE):

• 1 Hour Lecture (L) per Week = 1 Credit
• 1 Hour Tutorial (T) per Week = 1 Credit
• 1 Hour Practical (P) per Week = 0.5 Credits
• 2 Hours Practical (Lab) per Week = 1 Credit
Range of Credits (as per AICTE):

• A total of 160 credits will be necessary for a student to be eligible to get B Tech degree.
• A student will be eligible to get B Tech degree with Honors if he/she completes an additional 20 credits. These could be
acquired through various Honors Courses offered by the respective departments.
• A part or all of the above additional credits may also be acquired through MOOCs. Any student completing any course
through MOOC will have to submit an appropriate certificate to earn the corresponding credit.
• For any additional information, the student may contact the concerned HODs .
Swayam/MOOCs Courses recommended to the students of CSE department
Sl. Credit
Code Name Corresponding Online Course Offered by Platform
Points
Communication for
1 HMTS1011 3 IIM Bangalore
Professionals Effective Business Communication AND
Swayam
Professional Developing Soft Skills and Personality
2 HMTS1061 1 IIT Kanpur
Communication Lab
3 ECEN1011 Basic Electronics 3
Fundamentals of Semiconductor Devices IISc Bangalore NPTEL
Basic Electronics
4 ECEN1061 1
Lab
5 Probability and
MATH2111 4 Stochastic Processes IIT Delhi Swayam
Statistical Methods
Artificial
6 CSEN3111 3
Intelligence Artificial Intelligence Search Methods for
IIT Madras NPTEL
Artificial Problem Solving
7 CSEN3161 1
Intelligence Lab
CSEN4111 Compiler Design 3
8
CSEN4161 Compiler Design Compiler Design IIT Kharagpur NPTEL
9 1
Lab


SYLLABUS OF 1ST SEMESTER

A. THEORY COURSES
Course Name: Chemistry-I

Course Code: CHEM1001
L T P Total Credit points
Contact Hours per week:
3 1 0 4 4
1. Course Outcomes
After completion of the course, students will be able to:
CHEM1001.1. Knowledge of understanding the operating principles and reaction involved in batteries and fuel cells and their
application in automobiles as well as other sectors to reduce environmental pollution.
CHEM1001.2. An ability to analyse microscopic chemistry in terms of atomic and molecular orbitals and intermolecular forces
for engineering applications.
CHEM1001.3. Have knowledge of synthesizing nano materials and their applications in industry, carbon nano tube technology
is used in every industry now-a-days.
CHEM1001.4. Understanding of bulk properties and processes using thermodynamic considerations.
CHEM1001.5. 5 Elementary knowledge of IR, UV, NMR and X-ray spectroscopy is usable in structure elucidation and
characterisation of various molecules.
CHEM1001.6. Knowledge of electronic effect and stereochemistry for understanding mechanism of the major chemical
reactions involved in synthesis of various drug molecules.
2. Detailed Syllabus
Module 1 [10L]
Atomic structure and Wave Mechanics: Brief outline of the atomic structure, Dual character of electron, De Broglie’s
equation, the Heisenberg uncertainty principle, brief introduction of quantum mechanics, the Schrodinger wave equation,
Hermitian operator, solution of the Schrodinger equation for particle in a one-dimensional box, interpretation of the wave
function Ψ, concept of atomic orbital.
Thermodynamics: Carnot cycle, 2nd law of thermodynamics, entropy, Clausius inequality, free energy and work function,
Clausius Clapeyron Equation, Chemical Potential, Activity and Activity coefficient. Gibbs Duhem Relation.
Spectroscopic Techniques & Application: Electromagnetic spectrum: EMR interaction with matter - absorption and emission
of radiation. Principle and application of UV- visible and IR spectroscopy, Principles of NMR Spectroscopy and X-ray
diffraction technique.
Module 2 [10L]
Chemical Bonding: Covalent bond, VSEPR Theory, hybridization, molecular geometries, Dipole moment, Intermolecular
forces, V.B. and M.O. Theory and its application in Homo and Heteronuclear diatomic molecules, Band theory of solids, Pi-
molecular orbitals of ethylene and butadiene.
Periodicity: Effective nuclear charge, electronic configurations, atomic and ionic sizes, ionization energies, electron affinity
and electro-negativity, inert pair effect.
Ionic Equilibria: Acid Base Equilibria, Salt Hydrolysis and Henderson Equation, Buffer solutions, pH indicator, Common
ion Effect, Solubility product, Fractional Precipitation.
Module 3 [10L]
Conductance: Conductance of electrolytic solutions, Strong and Weak electrolytes, effect of temperature and concentration.
Kohlrausch’s law of independent migration of ions, transport numbers and hydration of ions. Application of conductance Acid-
base and precipitation titration.
Electrochemical Cell: Thermodynamic derivation of Nernst equation, Electrode potential and its application to predict redox
reaction; Standard Hydrogen Electrode, Reference electrode, cell configuration, half-cell reactions, evaluation of
thermodynamic functions; Reversible and Irreversible cells; Electrochemical corrosion. Electrochemical Energy
Conversion: Primary & Secondary batteries, Fuel Cells.
Reaction dynamics: Rate Laws, Order & Molecularity; zero, first and second order kinetics. Pseudo unimolecular reaction,
Arrhenius equation. Mechanism and theories of reaction rates (Transition state theory, Collison theory). Catalysis:
Homogeneous catalysis (Definition, example, mechanism, kinetics).
Module 4 [10L]
Stereochemistry: Representations of 3- dimensional structures, structural isomers and stereoisomers, configurations and
symmetry and chirality, enantiomers, diastereomers, optical activity, absolute configurations and conformational analysis.
Structure and reactivity of Organic molecule: Inductive effect, resonance, hyperconjugation, electrometric effect,
carbocation, carbanion, free radicals, aromaticity.
Organic reactions and synthesis of drug molecule: Introduction to reaction mechanisms involving substitution, addition,
elimination and oxidation- reduction reactions. Synthesis of commonly used drug molecules.

3. Textbooks
1. Atkins’ Physical Chemistry, P.W. Atkins (10th Edition).
2. Organic Chemistry, I. L. Finar, Vol-1 (6th Edition).
3. Engineering Chemistry, Jain & Jain, (16 th Edition).
4. Fundamental Concepts of Inorganic Chemistry, A. K. Das, (2 nd Edition).
5. Engineering Chemistry -I, Gourkrishna Dasmohapatra, (3rd Edition).
4. Reference Books
1. General & Inorganic Chemistry, R. P. Sarkar.
2. Physical Chemistry, P. C. Rakshit, (7th Edition).
3. Organic Chemistry, Morrison & Boyd, (7th Edition).
4. Fundamentals of Molecular Spectroscopy, C.N. Banwell, (4th Edition).
5. Physical Chemistry, G. W. Castellan, (3rd Edition).
6. Basic Stereo chemistry of Organic Molecules, Subrata Sen Gupta, (1 st Edition).
Course Name: Mathematics-I

Course Code: MATH1101
3 1 0 4 4
1. Course Outcomes
MATH1101.1. Apply the concept of rank of matrices to find the solution of a system of linear simultaneous equations.
MATH1101.2. Develop the concept of eigen values and eigen vectors.
MATH1101.3. Combine the concepts of gradient, curl, divergence, directional derivatives, line integrals, surface integrals and
volume integrals.
MATH1101.4. Analyze the nature of sequence and infinite series
MATH1101.5. Choose proper method for finding solution of a specific differential equation.
MATH1101.6. Describe the concept of differentiation and integration for functions of several variables with their applications
in vector calculus.
Module 1 [10L]
Matrix: Inverse and rank of a matrix; Elementary row and column operations over a matrix; System of linear equations and
its consistency; Symmetric, skew symmetric and orthogonal matrices; Determinants; Eigen values and eigen vectors;
Diagonalization of matrices; Cayley Hamilton theorem; Orthogonal transformation.
Module 2 [10L]
Vector Calculus: Vector function of a scalar variable, Differentiation of a vector function, Scalar and vector point functions,
Gradient of a scalar point function, divergence and curl of a vector point function, Directional derivative, Related problems on
these topics.
Infinite Series: Convergence of sequence and series; Tests for convergence: Comparison test, Cauchy’s Root test, D’
Alembert’s Ratio test (statements and related problems on these tests), Raabe’s test; Alternating series; Leibnitz’s Test
(statement, definition); Absolute convergence and Conditional convergence.
Module 3 [10L]
First order ordinary differential equations: Exact, linear and Bernoulli’s equations, Euler’s equations, Equations not of first
degree: equations solvable for p, equations solvable for y, equations solvable for x and Clairaut’s type.
Ordinary differential equations of higher orders: General linear ODE of order two with constant coefficients, C.F. & P.I.,
D-operator methods, Method of variation of parameters, Cauchy-Euler equations.
Module 4 [10L]
Calculus of functions of several variables: Introduction to functions of several variables with examples, Knowledge of limit
and continuity, Determination of partial derivatives of higher orders with examples, Homogeneous functions and Euler’s
theorem and related problems up to three variables.
Multiple Integration: Concept of line integrals, Double and triple integrals. Green’s Theorem, Stoke’s Theorem and Gauss
Divergence Theorem.
3. Textbooks
1. Higher Engineering Mathematics, B.S. Grewal, Khanna Publishers, 2000.
2. Advanced Engineering Mathematics, E. Kreyszig, John Wiley & Sons, 2006 .
4. Reference Books
1. Engineering Mathematics for first year, Veerarajan T., Tata McGraw-Hill, New Delhi, 2008.
2. Higher Engineering Mathematics, Ramana B.V., Tata McGraw Hill New Delhi, 11th Reprint, 2010.
3. Mathematical Methods for Physics and Engineering, K. F. Riley, M. P. Hobson, S. J. Bence., Cambridge University
Press, 23-Mar-2006.
4. Differential Equations, S. L. Ross, Wiley India, 1984.
5. Differential Equations, G.F. Simmons and S.G. Krantz, McGraw Hill, 2007.
6. Vector Analysis (Schaum’s outline series): M. R. Spiegel, Seymour Lipschutz, Dennis Spellman (McGraw Hill
Education).
7. Engineering Mathematics: S. S. Sastry (PHI).
8. Advanced Engineering Mathematics: M.C. Potter, J.L. Goldberg and E.F. Abonfadel (OUP), Indian Edition.
9. Linear Algebra (Schaum’s outline series): Seymour Lipschutz, Marc Lipson (McGraw Hill Education).
Course Name: Basic Electrical Engineering

Course Code: ELEC1001
3 1 0 4 4
1. Course Outcomes
ELEC1001.1. Analyze DC electrical circuits using KCL, KVL and network theorems like Superposition Theorem, Thevenin’s
Theorem, Norton’s Theorem and Maximum Power Transfer Theorem.
ELEC1001.2. Analyze DC Machines; Starters and speed control of DC motors.
ELEC1001.3. Analyze magnetic circuits.
ELEC1001.4. Analyze single and three phase AC circuits.
ELEC1001.5. Analyze the operation of single-phase transformers.
ELEC1001.6. Analyze the operation of three phase induction motors.
Module 1 [11L]
DC Network Theorem: Kirchhoff’s laws, Nodal analysis, Mesh analysis, Superposition theorem, Thevenin’s theorem,
Norton’s theorem, Maximum power transfer theorem, Star-Delta conversion.
Electromagnetism: Review of magnetic flux, Force on current carrying conductors, Magnetic circuit analysis, Self and Mutual
inductance, B-H loop, Hysteresis and Eddy current loss, Lifting power of magnet.
Module 2 [10L]
AC single phase system: Generation of alternating emf, Average value, RMS value, Form factor, Peak factor, representation
of an alternating quantity by a phasor, phasor diagram, AC series, parallel and series-parallel circuits, Active power, Reactive
power, Apparent power, power factor, Resonance in RLC series and parallel circuit.
Module 3 [11L]
Three phase system: Generation of three-phase AC power, Balanced three phase system, delta and star connection,
relationship between line and phase quantities, phasor diagrams, power measurement by two wattmeter method.
DC Machines: Construction, EMF equation, Principle of operation of DC generator, Open circuit characteristics, External
characteristics, Principle of operation of DC motor, speed-torque characteristics of shunt and series machine, starting of DC
motor, speed control of DC motor.
Module 4 [10L]
Transformer: Construction, EMF equation, no load and on load operation and their phasor diagrams, Equivalent circuit,
Regulation, losses of a transformer, Open and Short circuit tests, Efficiency, Introduction to three phase transformers.
Three-phase induction motor: Concept of rotating magnetic field, Principle of operation, Construction, Equivalent circuit
and phasor diagram, torque-speed/slip characteristics.
3. Textbooks
1. Basic Electrical engineering, D.P Kothari & I.J Nagrath, TMH, Second Edition.
2. Basic Electrical Engineering, V.N Mittle & Arvind Mittal, TMH, Second Edition.
3. Basic Electrical Engineering, Hughes.
4. Electrical Technology, Vol-I, Vol-II, Surinder Pal Bali, Pearson Publication.
5. A Textbook of Electrical Technology, Vol. I & II, B.L. Theraja, A.K. Theraja, S. Chand & Company.

4. Reference Books
1. Electrical Engineering Fundamentals, Vincent Del Toro, Prentice-Hall.
2. Advance Electrical Technology, H. Cotton, Reem Publication.
3. Basic Electrical Engineering, R.A. Natarajan, P.R. Babu, SciTech Publishers.
4. Basic Electrical Engineering, N.K. Mondal, Dhanpat Rai.
5. Basic Electrical Engineering, Nath & Chakraborti.
6. Fundamental of Electrical Engineering, Rajendra Prasad, PHI, Edition 2005.
B. LABORATORY COURSES
Course Name: Chemistry–I Lab

Course Code: CHEM1051
0 0 3 3 1.5
1. Course Outcomes
CHEM1051.1. Knowledge to estimate the hardness of water which is required to determine the usability of water used in
industries.
CHEM1051.2. Estimation of ions like Fe2+, Cu2+ and Cl- present in water sample to know the composition of industrial water.
CHEM1051.3. Study of reaction dynamics to control the speed and yield of various manufactured goods produced in polymer,
metallurgical and pharmaceutical industries.
CHEM1051.4. Handling physio-chemical instruments like viscometer, stalagmometer, pH-meter, potentiometer and
conductometer.
CHEM1051.5. Understanding the miscibility of solutes in various solvents required in paint, emulsion, biochemical and
material industries.
CHEM1051.6. Knowledge of sampling water can be employed for water treatment to prepare pollution free water.
1. Estimation of iron using KMnO4 self-indicator.
2+
2. Iodometric estimation of Cu .
3. Determination of Viscosity.
4. Determination of surface tension.
5. Adsorption of acetic acid by charcoal.
6. Potentiometric determination of redox potentials.
7. Determination of total hardness and amount of calcium and magnesium separately in a given water sample.
8. Determination of the rate constant for acid catalyzed hydrolysis of ethyl acetate.
9. Heterogeneous equilibrium (determination of partition coefficient of acetic acid in n-butanol and water mixture).
10. Conductometric titration for the determination of strength of a given HCl solution against a standard NaOH solution.
11. pH-metric titration for determination of strength of a given HCl solution against a standard NaOH solution.
12. Determination of chloride ion in a given water sample by Argentometric method (using chromate indicator solution)
3. Textbooks
1. Vogel’s Textbook of Quantitative Chemical Analysis-G. H. Jeffery, J. Bassett, J. Mendham, R. C. Denney.
4. Reference Books
1. Advanced Practical Chemistry- S. C. Das.
2. Practicals in Physical Chemistry- P. S. Sindhu.

Course Name: Basic Electrical Engineering Lab

Course Code: ELEC1051
0 0 2 2 1
1. Course Outcomes
ELEC1051.1. Get an exposure to common electrical apparatus and their ratings.
ELEC1051.2. Make electrical connections by wires of appropriate ratings.
ELEC1051.3. Understand the application of common electrical measuring instruments.
ELEC1051.4. Understand the basic characteristics of different electrical machines.
1. Characteristics of Fluorescent lamps.
2. Characteristics of Tungsten and Carbon filament lamps.
3. Verification of Thevenin’s & Norton’s theorem.
4. Verification of Superposition theorem.
5. Verification of Maximum Power Transfer theorem.
6. Calibration of ammeter and voltmeter.
7. Open circuit and Short circuit test of a single-phase Transformer.
8. Study of R-L-C Series / Parallel circuit.
9. Starting and reversing of speed of a D.C. shunt Motor.
10. Speed control of DC shunt motor.
11. No load characteristics of D.C shunt Generators
12. Measurement of power in a three-phase circuit by two wattmeter method.
3. Textbooks and References

Lab Manual to be provided.
Course Name: Engineering Graphics & Design
Course Code: MECH1052
1 0 4 5 3
1. Course Outcomes
MECH1052.1. To understand the meaning of engineering drawing.
MECH1052.2. To have acquaintance with the various standards (like lines, dimensions, scale etc.) and symbols
followed in engineering drawing.
MECH1052.3. To represent a 3-D object into 2-D drawing with the help of orthographic and isometric projections.
MECH1052.4. To read and understand projection drawings.
MECH1052.5. To draw the section view and true shape of a surface when a regular object is cut by a section plane.
MECH1052.6. To use engineering drawing software (CAD).
Lecture Plan [13L]
1. Importance and principles of engineering drawing (1 L)
2. Concepts of Conic sections and Scale (1 L)
3. Introduction to concept of projection (Projections of points, lines and surfaces) (4 L)
4. Definitions of different solids and their projections (1 L)
5. Section of solids and sectional view (1 L)
6. Isometric projection (2 L)
7. Introduction to CAD (2 L)
8. Viva Voce (1 L)

Lab Hours [52 Hours]
Module 1 [8H]
Introduction to Engineering Drawing covering Principles of Engineering Graphics and their significance, usage of Drawing
instruments, lines, lettering & dimensioning, Conic section like Ellipse (General method only); Involute; Scales – Plain,
Diagonal.
Module 2 [12H]
Orthographic Projections covering Principles of Orthographic Projections - Conventions - Projections of Points and lines
inclined to both planes; Projections on Auxiliary Planes. Projection of lamina.
Module 3 [8H]
Projections of Regular Solids covering those inclined to both the Planes- Auxiliary Views.
Module 4 [4H]
Sections and Sectional Views of Right Angular Solids covering Prism, Cylinder, Pyramid, Cone – Auxiliary Views;
Development of surfaces of Right Regular Solids - Prism, Pyramid, Cylinder and Cone; Draw the sectional orthographic views
of geometrical solids.
Module 5 [8H]
Isometric Projections covering Principles of Isometric projection – Isometric Scale, Isometric Views, Conventions; Isometric
Views of lines, Planes, Simple and compound Solids; Conversion of Isometric Views to Orthographic Views and Vice-versa,
Conventions.
Module 6 [4H]
Overview of Computer Graphics covering listing the computer technologies that impact on graphical communication,
Demonstrating knowledge of the theory of CAD software [such as: The Menu System, Toolbars (Standard, Object Properties,
Draw, Modify and Dimension), Drawing Area (Background, Crosshairs, Coordinate System), Dialog boxes and windows,
Shortcut menus (Button Bars), The Command Line (where applicable), The Status Bar, Different methods of zoom as used in
CAD, Select and erase objects.; Isometric Views of lines, Planes, Simple and compound Solids.
Module 7 [4H]
Customization & CAD Drawing consisting of set up of the drawing page and the printer, including scale settings, Setting up of
units and drawing limits; ISO and ANSI standards for coordinate dimensioning and tolerancing; Orthographic constraints, Snap
to objects manually and automatically; Producing drawings by using various coordinate input entry methods to draw straight
lines, Applying various ways of drawing circles.
Annotations, layering & other functions covering applying dimensions to objects, applying annotations to drawings; Setting up
and use of Layers, layers to create drawings, Create, edit and use customized layers; Changing line lengths through modifying
existing lines (extend/lengthen); Printing documents to paper using the print command; orthographic projection techniques;
Drawing sectional views of composite right regular geometric solids and project the true shape of the sectioned surface;
Drawing annotation.
Module 8 [4H]
Demonstration of a simple team design project that illustrates Geometry and topology of engineered components: creation of
engineering models and their presentation in standard 2D blueprint form and as 3D wire-frame.
3. Reference Books
1. Elementary Engineering Drawing, Bhatt, N.D., Panchal V.M. & Ingle P.R., (2014), Charotan Publishing House.
2. Engineering Graphics, Narayana, K.L. and Kannaaiah P, TMH.
3. Engineering Graphics, Lakshminarayanan, V. and Vaish Wanar, R.S, Jain Brothers.
4. Engineering Drawing and Computer Graphics, Shah, M.B. & Rana B.C. (2008), Pearson Edication.
5. Engineering Graphics, Agarwal B. & Agarwal C. M. (2012), TMH Publications .

C. HONORS COURSES
Course Name: Communication for Professionals

Course Code: HMTS1011
3 0 0 3 3
1. Course Outcomes
HMTS1011.1. Write business letters and reports
HMTS1011.2. Communicate in an official and formal environment.
HMTS1011.3. Effectively use the various channels of communication at workplace.
HMTS1011.4. Use language as a tool to build bridges and develop interpersonal relations in multi-cultural
environment.
HMTS1011.5. Learn to articulate opinions and views with clarity.
HMTS1011.6. Use various techniques of communication for multiple requirements of globalized workplaces.
Module 1 [9L]
Introduction to Linguistics: Phonetics- Vowel and Consonant Sounds (Identification & Articulation); Word- stress, stress in
connected speech; Intonation (Falling and Rising Tone); Voice Modulation; Accent Training; Vocabulary Building; The
concept of Word Formation; Root words from foreign languages and their use in English; Acquaintance with prefixes and
suffixes from foreign languages in English to form derivatives; Synonyms, Antonyms and standard abbreviations.
Module 2 [10L]
Communication Skills: Definition, nature & attributes of Communication; Process of Communication; Models or Theories of
Communication; Types of Communication; Levels or Channels of Communication; Barriers to Communication.
Module 3 [10L]
Professional Writing Skills: Letter Writing: Importance, Types, Process, Form and Structure, Style and Tone; Proposal
Writing: Purpose, Types of Proposals, Structure of Formal Proposals; Report Writing: Importance and Purpose, Types of
Reports, Report Formats, Structure of Formal Reports, Writing Strategies.
Module 4 [10L]
Communication Skills at Work: Communication and its role in the workplace; Benefits of effective communication in the
workplace; Common obstacles to effective communication; Approaches and Communication techniques for multiple needs at
workplace: persuading, convincing, responding, resolving conflict, delivering bad news, making positive connections; Identify
common audiences and design techniques for communicating with each audience.
3. Reference Books
1. Communication Skills, Kumar, S. &Lata, P., OUP, New Delhi2011.
2. Effective Technical Communication, Rizvi, Ashraf, M., Mc Graw Hill Education (India) Pvt. Ltd..Chennai,2018.
3. Technical Communication: Principles and Practice, Raman, M. and Sharma, S., 2nd Ed., 2011.
Course Name: Professional Communication Lab

0 0 2 2 1
1. Course Outcomes
HMTS1061.1. Communicate in an official and formal environment.
HMTS1061.2. Effectively communicate in a group and engage in relevant discussion.
HMTS1061.3. Engage in research and prepare presentations on selected topics.
HMTS1061.4. Understand the dynamics of multicultural circumstances at workplace and act accordingly.
HMTS1061.5. Organize content in an attempt to prepare official documents.
HMTS1061.6. Appreciate the use of language to create beautiful expressions.

Module 1 [4L]
Techniques for Effective Speaking, Voice Modulation: Developing correct tone, Using correct stress patterns: word stress,
primary stress, secondary stress, Rhythm in connected speech.
Module 2 [6L]
Effective Speaking and Social awareness; The Art of Speaking: Encoding Meaning Using Nonverbal Symbols, How to Improve
Body Language, Eye Communication, Facial Expression, Dress and Appearance, Posture and Movement, Gesture,
Paralanguage, Encoding meaning using Verbal symbols: How words work and how to use words, Volume, Pace, Pitch and
Pause, Cross-Cultural Communication: Multiple aspects/dimensions of culture, Challenges of cross-cultural communication,
Improving cross-cultural communication skills at workplace.
Module 3 [6L]
Group Discussion: Nature and purpose; Characteristics of a successful Group Discussion; Group discussion Strategies: Getting
the GD started, contributing systematically, moving the discussion along, promoting optimal participation, Handling conflict,
Effecting closure
Module 4 [10L]
Professional Presentation Skills: Nature and Importance of Presentation skills; Planning the Presentation: Define the purpose,
analyze the Audience, Analyze the occasion and choose a suitable title; Preparing the Presentation: The central idea, main
ideas, collecting support material, plan visual aids, design the slides; Organizing the Presentation: Introduction-Getting
audience attention, introduce the subject, establish credibility, preview the main ideas, Body-develop the main idea, present
information sequentially and logically, Conclusion-summaries, re-emphasize, focus on the purpose, provide closure; Improving
Delivery: Choosing Delivery methods, handling stage fright; Post-Presentation discussion: Handling Questions-opportunities
and challenges.
3. Reference Books
1. The Cambridge guide to Teaching English to Speakers of Other Languages, Carter, R. And Nunan, D. (Eds), CUP, 2001.
2. Writing and Speaking at Work: A Practical Guide for Business Communication, Edward P. Bailey, Prentice Hall, 3rd Ed.,
2004.
3. Guide to Managerial Communication: Effective Business Writing and Speaking, Munter, M., Prentice Hall, 5th Ed., 1999.
4. Job Readiness for IT & ITES- A Placement and Career Companion, R. Anand, McGraw Hill Education.2015.
5. Campus Placements, Malhotra, A., McGraw Hill Education.2015.


SYLLABUS OF 2nd SEMESTER

A. THEORY COURSES
Course Name: Physics-I

Course Code: PHYS1001
3 1 0 4 4
1. Course Outcomes
PHYS1001.1. To develop basic understanding of the modern science to the technology related domain.
PHYS1001.2. Analytical & logical skill development through solving problems.
PHYS1001.3. To impart idea of concise notation for presenting equations arising from mathematical formulation
of physical as well as geometrical problems percolating ability of forming mental pictures of them.
PHYS1001.4. Imparting the essence and developing the knowledge of controlling distant object like satellite,
data transfer through optical fibre, implication of laser technology, handling materials in terms of
their electrical and magnetic properties etc.
PHYS1001.5. To understand how the systems under force field work giving their trajectories which is the basic of
classical Field theory.
PHYS1001.6. To impart basic knowledge of the electric and magnetic behaviour of materials to increase the
understanding of how and why electronic devices work.
Module 1 [12L]
Mechanics: Elementary concepts of grad, divergence and curl. Potential energy function; F=-grad V, Equipotential surfaces
and meaning of gradient; Conservative and non-conservative forces, Curl of a force field; Central forces; conservation of
angular momentum; Energy equation and energy diagrams; elliptical, parabolic and hyperbolic orbit; Kepler Problem;
Application: Satellite maneuvers.
Non-inertial frames of reference; rotating coordinate system; five term acceleration formula- centripetal and Coriolis
accelerations; applications: Weather system, Foucault pendulum.
Module 2 [12L]
Oscillatory Motion: Damped harmonic motion – Over damped, critically damped and lightly damped oscillators; Forced
oscillation and resonance. Electrical equivalent of mechanical oscillator, Wave equation, plane wave solution.
Optics: Elementary features of polarization of light waves. Double refraction, Production and analysis of linearly, elliptic and
circularly polarized light, Polaroid and application of polarizations, Polarimeter.
Laser & Fiber Optics: Characteristics of Lasers, Spontaneous and Stimulated Emission of Radiation, Meta-stable State,
Population Inversion, Lasing Action, Einstein’s Coefficients and Relation between them, Ruby Laser, Helium-Neon Laser,
Semiconductor Diode Laser, Applications of Lasers.
Fiber optics - principle of operation, numerical aperture, acceptance angle, Single mode, graded indexed fiber.
Module 3 [12L]
Electrostatics in free space: Calculation of electric field and electrostatic potential for a charge distribution, Divergence and
curl of electrostatic field, Laplace’s and Poisson’s equation for electrostatic potential. Boundary conditions of electric field and
electrostatic potential. Method of images, energy of a charge distribution and its expression in terms of electric field.
Electrostatics in a linear dielectric medium: Electrostatic field and potential of a dipole, Bound charges due to electric
polarization, Electric displacement, Boundary conditions on displacement, Solving simple electrostatic problem in presence of
dielectric – point charge at the center of a dielectric sphere, charge in front of dielectric slab, Dielectric slab and dielectric
sphere in uniform electric field.
Module 4 [12L]
Magnetostatics: Biot-Savart law, divergence and curl of static magnetic field; vector potential and calculating it
for a given magnetic field using Stokes’ theorem; equation for vector potential and its solutions for given current
densities.
Magnetostatics in a linear magnetic medium: Magnetization and associated bound currents; Auxiliary magnetic
field H ; boundary conditions on B and H . Solving for magnetic field due to simple magnet like a bar magnet;
Magnetic susceptibility; ferromagnetic, paramagnetic and diamagnetic materials; Qualitative discussion of
magnetic field in presence of magnetic materials.
Faraday’s Law: Differential form of Faraday’s law expressing curl of electric field in terms of time derivative of
magnetic field and calculating electric field due to changing magnetic fields in quasi static approximation. Energy
stored in a magnetic field.
3. Reference Books
1. Optics –Eugene Hecht Pearson Education India Private Limited.
2. Introduction to Electrodynamics, David J. Griffiths, Pearson Education India Learning Private Limited.
3. Waves and Oscillations by N.K. Bajaj.
4. Principles of Physics, 10ed, David Halliday, Robert Resnick Jearl Walker, Wiley.
5. Electricity, Magnetism, and Light, Wayne M. Saslow, Academic Press.
6. Classical mechanics, Narayan Rana, Pramod Joag, McGraw Hill Education.
7. Introduction to Classical Mechanics, R Takwale, P Puranik, McGraw Hill Education.
8. Optics, Ghatak, McGraw Hill Education India Private Limited.
9. Refresher Course in B.Sc. Physics –Vol1 and Vol 2 –C.L.Arora.
Course Name: Mathematics-II

3 1 0 4 4
1. Course Outcomes
MATH1201.1. Demonstrate the knowledge of probabilistic approaches to solve wide range of engineering problem.
MATH1201.2. Recognize probability distribution for discrete and continuous variables to quantify physical and engineering
phenomenon.
MATH1201.3. Develop numerical techniques to obtain approximate solutions to mathematical problems where analytical
solutions are not possible to evaluate.
MATH1201.4. Analyse certain physical problems that can be transformed in terms of graphs and trees and solving problems
involving searching, sorting and such other algorithms.
MATH1201.5. Apply techniques of Laplace Transform and its inverse in various advanced engineering problems.
MATH1201.6. Interpret differential equations and reduce them to mere algebraic equations using Laplace Transform to solve
easily.
Module 1 [10L]
Basic Probability: Random experiment, Sample space and events, Classical and Axiomatic definition of probability, Addition
and Multiplication law of probability, Conditional probability, Bayes’ Theorem, Random variables, General discussion on
discrete and continuous distributions, Expectation and Variance, Examples of special distribution: Binomial and Normal
Distribution.
Module 2 [10L]
Basic Numerical Methods: Solution of non-linear algebraic and transcendental equations: Bisection Method, Newton-
Raphson Method, Regula-Falsi Method. Solution of linear system of equations: Gauss Elimination Method, Gauss-Seidel
Method, LU Factorization Method, Matrix Inversion Method. Solution of Ordinary differential equations: Euler’s Method,
Modified Euler’s Method, Runge-Kutta Method of 4th order.
Module 3 [10L]
Basic Graph Theory: Graph, Digraph, Weighted graph, Connected and disconnected graphs, Complement of a graph, Regular
graph, Complete graph, Sub-graph, Walk, Path, Circuit, Euler Graph, Cut sets and cut vertices, Matrix representation of a
graph, Adjacency and incidence matrices of a graph, Graph isomorphism, Bipartite graph, Dijkstra’s Algorithm for shortest
path problem. Definition and properties of a Tree, Binary tree and its properties, Spanning tree of a graph, Minimal spanning
tree, Determination of spanning trees using BFS and DFS algorithms, Determination of minimal spanning tree using Kruskal’s
and Prim’s algorithms.
Module 4 [12L]
Laplace Transformation: Basic ideas of improper integrals, working knowledge of Beta and Gamma functions (convergence
to be assumed) and their interrelations. Introduction to integral transformation, Functions of exponential order, Definition and
existence of Laplace Transform(LT) (statement of initial and final value theorem only), LT of elementary functions, Properties
of Laplace Transformations , Evaluation of sine , cosine and exponential integrals using LT, LT of periodic and step functions,
Definition and properties of inverse LT, Convolution Theorem (statement only) and its application to the evaluation of inverse
LT, Solution of linear ODEs with constant coefficients (initial value problem) using LT.
3. Textbooks
1. Advanced Engineering Mathematics, E. Kreyszig, Wiley Publications.
2. Engineering Mathematics, B.S. Grewal, S. Chand & Co.

4. Reference Books
1. Introduction to Probability and Statistics for Engineers and Scientists, S. Ross, Elsevier.
2. Introductory methods of Numerical Analysis, S.S. Sastry, PHI learning.
3. Introduction to Graph Theory, D. B. West, Prentice-Hall of India.
Course Name: Programming for Problem Solving

Course Code: CSEN1001
3 0 0 3 3
1. Course Outcomes
CSEN1001.1. Understand and remember functions of the different parts of a computer.
CSEN1001.2. Understand and remember how a high-level language (C Programming language, in this course)
works, different stages a program goes through.
CSEN1001.3. Understand and remember syntax and semantics of a high-level language (C programming language,
in this course).
CSEN1001.4. Understand how code can be optimized in high-level languages.
CSEN1001.5. Apply high-level language to automate the solution to a problem.
CSEN1001.6. Apply high-level language to implement different solutions for the same problem and analyze why
one solution is better than the other.
Module 1 [10L]
Fundamentals of Computer: History of Computers, Generations of Computers, Classification of Computers.
Basic Anatomy of Computer System, Primary & Secondary Memory, Processing Unit, Input & Output devices. Basic Concepts
of Assembly language, High level language, Compiler and Assembler.
Binary & Allied number systems (decimal, octal and hexadecimal) with signed and unsigned numbers (using 1’s and 2’s
complement) - their representation, conversion and arithmetic operations. Packed and unpacked BCD system, ASCII. IEEE-
754 floating point representation (half- 16 bit, full- 32 bit, double- 64 bit).
Basic concepts of operating systems like MS WINDOWS, LINUX.
How to write algorithms & draw flow charts.
Module 2 [10L]
Basic Concepts of C: C Fundamentals: The C character set identifiers and keywords, data type & sizes, variable names,
declaration, statements.
Operators & Expressions: Arithmetic operators, relational and logical operators, type, conversion, increment and decrement
operators, bit wise operators, assignment operators and expressions, precedence and order of evaluation. Standard input and
output, formatted output -- printf, formatted input scanf.
Flow of Control: Statement and blocks, if-else, switch-case, loops (while, for, do-while), break and continue, go to and labels.
Module 3 [10L]
Program Structures in C: Basic of functions, function prototypes, functions returning values, functions not returning values.
Storage classes - auto, external, static and register variables – comparison between them. Scope, longevity and visibility of
variables; C preprocessor (macro, header files), command line arguments; Arrays and Pointers: One dimensional arrays,
pointers and functions – call by value and call by reference, array of arrays. Dynamic memory usage– using malloc(), calloc(),
free(), realloc(). Array pointer duality; String and character arrays; C library string functions and their use.
Module 4 [10L]
User defined data types and files: Basic of structures; structures and functions; arrays of structures.
Files – text files only, modes of operation. File related functions – fopen(), fclose(), fscanf(), fprintf(), fgets(), fputs(), fseek(),
ftell().
3. Textbooks
1. Schaum’s outline of Programming with C – Byron Gottfried.
2. Teach Yourself C- Herbert Schildt.
3. Programming in ANSI C – E Balagurusamy.
4. Reference Books
1. C: The Complete Reference – Herbert Schildt.
2. The C Programming Language- D.M.Ritchie, B.W. Kernighan.
Course Name: Business English

2 0 0 2 2
1. Course Outcomes
HMTS1202.1. Acquire competence in using English language to communicate.
HMTS1202.2. Be aware of the four essential skills of language usage-listening, speaking, reading and writing.
HMTS1202.3. Be adept at using various modes of written communication at work.
HMTS1202.4. Attain the skills to face formal interview sessions.
Module 1 [6L]
Grammar (Identifying Common Errors in Writing): Subject-verb agreement, Noun-pronoun agreement, Misplaced
Modifiers, Articles, Prepositions, Redundancies.
Module 2 [6L]
Basic Writing Strategies: Sentence Structures, Use of phrases and clauses in sentences, Creating coherence, Organizing
principles –accuracy, clarity, brevity, Techniques for writing precisely, Different styles of writing: descriptive, narrative,
expository, Importance of proper punctuation.
Module 3 [8L]
Business Communication- Scope & Importance: Writing Formal Business Letters: Form and Structure-Parts of a
Business letter, Business Letter Formats, Style and Tone, Writing strategies.
Organizational Communication: Agenda & minutes of a meeting, Notice, Memo, Circular.
Organizing e-mail messages, E-mail etiquette.
Job Application Letter: Responding to Advertisements and Forced Applications, Qualities of well-written Application Letters:
The You-Attitude, Length, Knowledge of Job Requirement, Reader-Benefit Information, Organization, Style, Mechanics –
Letter Plan: Opening Section, Middle Section, Closing Section.
Resume and CV: Difference, Content of the Resume – Formulating Career Plans: Self Analysis, Career Analysis, Job Analysis,
Matching Personal Needs with Job Profile – Planning your Resume – Structuring the Resume: Chronological Resume, The
Functional Resume, Combination of Chronological and Functional Resume, Content of the Resume: Heading, Career Goal or
Objectives, Education, Work Experience, Summary of Job Skills/Key Qualifications, Activities, Honors and Achievements,
Personal Profile, Special Interests, References.
Module 4 [6L]
Writing skills: Comprehension: Identifying the central idea, inferring the lexical and contextual meaning, comprehension
passage – practice. Paragraph Writing: Structure of a paragraph, Construction of a paragraph, Features of a paragraph, Writing
techniques/developing a paragraph.
Précis: The Art of Condensation-some working principles and strategies. Practice sessions of writing précis of given passages.
Essay Writing: Characteristic features of an Essay, Stages in Essay writing, Components comprising an Essay, Types of Essays-
Argumentative Essay, Analytical Essay, Descriptive Essays, Expository Essays, Reflective Essays.
3. Reference Books
1. Theories of Communication: A Short Introduction, Armand Matterlart and Michele Matterlart, Sage Publications Ltd.
2. Professional Writing Skills, Chan, Janis Fisher and Diane Lutovich. San Anselmo, CA: Advanced Communication Designs.
3. Hauppauge, Geffner, Andrew P. Business English, New York: Barron’s Educational Series.
4. Kalia, S. & Agarwal, S. Business Communication, Wiley India Pvt. Ltd., New Delhi, 2015.
5. Mukherjee, H.S., Business Communication- Connecting at work., Oxford University Press.2nd Edition.2015.
6. Raman, M. and Sharma, S., Technical Communication: Principles and Practice, 2nd Ed., 2011.
Course Name: Physics-I Lab

Course Code: PHYS1051

0 0 3 3 1.5
1. Course Outcomes
PHYS1051.1. To gain practical knowledge by applying the experimental methods to correlate with the Physics theory.
PHYS1051.2. To learn the usage of electrical and optical systems for various measurements.
PHYS1051.3. Apply the analytical techniques and graphical analysis to the experimental data.
PHYS1051.4. Understand measurement technology, usage of new instruments and real time applications in engineering
studies.
PHYS1051.5. To develop intellectual communication skills and discuss the basic principles of scientific concepts in a group.
Module 1 Experiments in General Properties of matter:
Determination of Young’s modulus by Flexure Method.
Determination of bending moment and shear force of a rectangular beam of uniform cross- section.
Determination of modulus of rigidity of the material of a rod by static method.
Determination of rigidity modulus of the material of a wire by dynamic method.
Determination of coefficient of viscosity by Poiseulle’s capillary flow method.
Module 2 Experiments in Optics:

Determination of dispersive power of the material of a prism.
Determination of wavelength of light by Newton’s ring method.
Determination of wavelength of light by Fresnel’s bi-prism method.
Determination of the wavelength of a given laser source by diffraction method.
Module 3 Electricity & Magnetism experiments:

Determination of dielectric constant of a given dielectric material.
Determination of resistance of ballistic galvanometer by half deflection method and study of variation of logarithmic decrement
with series resistance.
Determination of the thermo-electric power at a certain temperature of the given thermocouple.
Determination of specific charge (e/m) of electron.
Module 4 Quantum Physics Experiments:

Determination of Planck’s constant.
Determination of Stefan’s radiation constant.
Verification of Bohr’s atomic orbital theory through Frank-Hertz experiment.
Determination of Rydberg constant by studying Hydrogen/ Helium spectrum.
Determination of Hall co-efficient of semiconductors.
Determination of band gap of semiconductors.
To study current-voltage characteristics, load response, areal characteristics and spectral response of photo voltaic solar cells.
Minimum of six experiments to be performed taking at least one from each module mentioned above.
3. Textbooks
1. Advanced Practical Physics (vol.1 and vol.2) B. Ghosh and K. G. Mazumdar.
2. Advanced course in practical physics D. Chattopadhyay and P. C. Rakshit .
4. Reference Books
1. Optics –Eugene Hecht Pearson Education India Private Limited.
2. Introduction to Electrodynamics, David J. Griffiths, Pearson Education India Learning Private Limited.
3. Waves and Oscillations by N.K. Bajaj.
4. Principles of Physics, 10ed, David Halliday, Robert Resnick Jearl Walker, Wiley.
5. Electricity, Magnetism, and Light, Wayne M. Saslow, Academic Press.
6. Classical mechanics, Narayan Rana, Pramod Joag, McGraw Hill Education.
7. Introduction to Classical Mechanics, R Takwale, P Puranik, McGraw Hill Education.
8. Optics, Ghatak, McGraw Hill Education India Private Limited.
9. Refresher Course in B.Sc. Physics –Vol1 and Vol 2 –C.L.Arora.
Course Name: Programming for Problem Solving Lab



0 0 4 4 2
1. Course Outcomes
CSEN1051.1. To write simple programs relating to arithmetic and logical problems.
CSEN1051.2. To be able to interpret, understand and debug syntax errors reported by the compiler.
CSEN1051.3. To implement conditional branching, iteration (loops) and recursion.
CSEN1051.4. To decompose a problem into modules (functions) and amalgamating the modules to generate a complete
program.
CSEN1051.5. To use arrays, pointers and structures effectively in writing programs.
CSEN1051.6. To be able to create, read from and write into simple text files.
Topic 1: LINUX commands and LINUX based editors
Topic 2: Basic Problem Solving
Topic 3: Control Statements (if, if-else, if-elseif-else, switch-case)
Topic 4: Loops - Part I (for, while, do-while)
Topic 5: Loops - Part II
Topic 6: One Dimensional Array
Topic 7: Array of Arrays
Topic 8: Character Arrays/ Strings
Topic 9: Basics of C Functions
Topic 10: Recursive Functions
Topic 11: Pointers
Topic 12: Structures
Topic 13: File Handling
3. Textbooks
1. Schaum’s outline of Programming with C – Byron Gottfried.
2. Teach Yourself C- Herbert Schildt.
3. Programming in ANSI C – E Balagurusamy.
4. Reference Books
1. C: The Complete Reference – Herbert Schildt.
2. The C Programming Language- D.M.Ritchie, B.W. Kernighan.
Course Name: Workshop /Manufacturing Practices

1 0 4 5 3
1. Course Outcomes
MECH1051.1. The students will gain knowledge of the different manufacturing processes which are commonly employed in
the industry, to fabricate components using different materials.
MECH1051.2. The students will be able to fabricate components with their own hands.
MECH1051.3. They will also get practical knowledge of the dimensional accuracies and dimensional tolerances possible with
different manufacturing processes.
MECH1051.4. By assembling different components, they will be able to produce small devices of their interest.
MECH1051.5. The students will be able to describe different components and processes of machine tools.
MECH1051.6. The students will be able to apply the knowledge of welding technology and they can perform arc and gas
welding to join the material.
Lecture [13 Hours]

1. Introduction on Workshop and Safety Precautions. (1 L)
2. Manufacturing Methods- casting, forming, machining, joining, advanced manufacturing methods (3 L)
3. CNC machining, Additive manufacturing (1 L)
4. Fitting operations & power tools (1 L)
5. Electrical & Electronics (1 L)
6. Carpentry (1 L)
7. Plastic moulding, glass cutting (1 L)
8. Metal casting (1 L)
9. Welding (arc welding & gas welding), brazing (2 L)
10. Viva-voce (1 L)
Workshop Practice [52 Hours]

1. Machine shop (12 H)
2. Fitting shop (8 H)
3. Carpentry (4 H)
4. Electrical & Electronics (4 H)
5. Welding shop (Arc welding + gas welding) (8 H)
6. Casting (4 H)
7. Smithy (4 H)
8. Plastic moulding & Glass Cutting (4 H)
9. Sheet metal Shop (4 H)
Examinations could involve the actual fabrication of simple components, utilizing one or more of the techniques covered
above.
3. Reference Books
1. Elements of Workshop Technology, Hajra Choudhury S.K., Hajra Choudhury A.K. and Nirjhar Roy S.K., Vol. I 2008 and
Vol. II 2010, Media promoters and publishers private limited, Mumbai.
2. Manufacturing Engineering and Technology, Kalpakjian S. And Steven S. Schmid, 4th edition, Pearson Education India
Edition, 2002.
3. Manufacturing Technology – I, Gowri P. Hariharan and A. Suresh Babu, Pearson Education, 2008.
4. Processes and Materials of Manufacture, Roy A. Lindberg, 4th edition, Prentice Hall India, 1998.
5. Manufacturing Technology, Rao P.N., Vol. I and Vol. II, Tata McGraw Hill House, 2017.
Course Name: Language Lab

0 0 2 2 1
1. Course Outcomes
HMTS1252.1. Acquire the techniques to become an effective listener.
HMTS1252.2. Acquire the skill to become an effortless speaker.
HMTS1252.3. Organize and present information for specific audience.
HMTS1252.4. Communicate to make a positive impact in professional and personal environment.
HMTS1252.5. Engage in research and prepare authentic, formal, official documents.
HMTS1252.6. Acquire reading skills for specific purpose.
Module 1 [4L]
Listening Skills: Principles of Listening: Characteristics, Stages; Types of Listening: Passive listening, Marginal or superficial
listening, Projective Listening, Sensitive or Empathetic Listening, Active or Attentive listening; Guidelines for Effective
Listening; Barriers to Effective Listening; Listening Comprehension.
Module 2 [8L]
Interviewing: Types of Interviews, Format for Job Interviews: One-to-one and Panel Interviews, Telephonic Interviews,
Interview through video conferencing; Interview Preparation Techniques, Frequently Asked Questions, Answering Strategies,
Dress Code, Etiquette, Questions for the Interviewer, Simulated Interviews.
Module 3 [6L]

Public Speaking: The Speech Process: The Message, The Audience, The Speech Style, Encoding, Feedback; Characteristics
of a good speech : content and delivery, structure of a speech; Modes of delivery in public speaking: Impromptu,
Extemporaneous, Prepared or Memorized, Manuscript; Conversation: Types of conversation: formal and informal, Strategies
for effective conversation, Improving fluency; Situational conversation practice: Greetings and making introductions, Asking
for information and giving instructions, agreeing and disagreeing; Conversational skills in the business scenario: One-to-one
and Group communication, Gender and Culture Sensitivity, Etiquette, Sample Business Conversation, Telephonic
Conversation.
Module 4 [8L]
Presentation skills: Speaking from a Manuscript, Speaking from Memory, Impromptu Delivery, Extemporaneous Delivery,
Analyzing the Audience, Nonverbal Dimensions of Presentation; Organizing the Presentation: The Message Statement,
Organizing the Presentation: Organizing the Speech to Inform, The Conclusion, Supporting Your Ideas – Visual Aids:
Designing and Presenting Visual Aids, Selecting the Right Medium; Project Team/Group Presentations.
3. Reference Books
1. The Cambridge guide to Teaching English to Speakers of Other Languages, Carter, R. And Nunan, D. (Eds), CUP, 2001.
2. Writing and Speaking at Work: A Practical Guide for Business Communication, Edward P. Bailey, Prentice Hall, 3rd Ed.
3. Guide to Managerial Communication: Effective Business Writing and Speaking, Munter, M., Prentice Hall, 5th Ed., 1999.
4. Communication and Language Skills, Sen, S., Mahendra, A. & Patnaik, P., Cambridge University Press, 2015.
5. Business and Administrative Communication, Locker, Kitty O., McGraw-Hill/ Irwin.
6. Intercultural Business Communication, Chaney,L. and Martin, J., Prentice Hall.
C. HONORS COURSES
Course Name: Basic Electronics
Course Code: ECEN1011
3 0 0 3 3
1. Course Outcomes
ECEN1011.1. Categorize different semiconductor materials based on their energy bands and analyse the characteristics of
those materials for different doping concentrations based on previous knowledge on semiconductors acquired.
ECEN1011.2. Describe energy band of P-N Junction devices and solve problems related to P-N Junction Diode both from
device and circuit perspectives.
ECEN1011.3. Design different application specific circuits associated with diodes operating both in forward and reverse bias.
ECEN1011.4. Analyse various biasing configurations of Bipolar Junction Transistor and categorize different biasing circuits
based on stability.
ECEN1011.5. Categorize different field-effect transistors based on their constructions, physics and working principles and
solve problems associated with analog circuits based on operational amplifiers.
ECEN1011.6. Design and implement various practical purpose electronic circuits and systems meant for both special purpose
and general purpose and analyse their performance depending on the type of required output and subsequently
the applied input.
Module 1 [10L]
Basic Semiconductor Physics: Crystalline materials, Energy band theory, Conductors, Semiconductors and Insulators,
Concept of Fermi Energy level, intrinsic and extrinsic semiconductors, drift and diffusion currents in semiconductor.
Diodes and Diode Circuits: Formation of p-n junction, Energy Band diagram, forward & reverse biased configurations, V-I
characteristics, load line, breakdown mechanisms, Zener Diode and its Application; Rectifier circuits: half wave & full wave
rectifiers: ripple factor, rectification efficiency.
Module 2 [8L]
Bipolar Junction Transistors (BJT): PNP & NPN BJT structures, current components in BJT, CE, CB, CC configurations,
V-I Characteristics of CB & CE modes, regions of operation, Base width modulation & Early effect, thermal runaway, Concept
of Biasing: DC load line, Q-point, basics of BJT amplifier operation, current amplification factors, different biasing circuits:
fixed bias, collector to base bias, voltage divider bias.

Module 3 [9L]
Field Effect Transistors (FET): n-channel Junction Field Effect Transistor (JFET) structure & V-I characteristics.
Metal Oxide Semiconductor Field Effect Transistor (MOSFET): enhancement & depletion type MOSFETs (for both n & p
channel devices), drain & transfer characteristics; MOSFET as a digital switch, CMOS inverter, voltage transfer characteristic
(VTC), NAND & NOR gate realization using CMOS logic; Moore’s Law, evolution of process node, state of integration (SSI,
MSI, LSI, VLSI, ULSI); Classification of Integrated circuits (IC) and their applications.
Module 4 [9L]
Feedback in amplifiers: Concept of feedback, advantages of negative feedback (qualitative), Barkhausen criteria.
Operational Amplifier: Ideal OPAMP characteristics, OPAMP circuits: inverting and non-inverting amplifiers, Adder,
Subtractor, Integrator, Differentiator, Basic Comparator.
Special Semiconductor Devices: Light Emitting Diode (LED), Silicon Controlled Rectifier (SCR), Photodiode: Operations,
characteristics & applications.
3. Reference Books
1. Electronic Devices & Circuit Theory, Boylestad & Nashelsky.
2. Op Amps and Linear IC’s, R.A Gayakwad, PHI.
3. Electronics Fundamentals and Applications, D. Chattopadhyay, P. C. Rakshit.
4. Microelectronics Engineering, Adel S. Sedra, Kenneth Carless Smith.
5. Integrated Electronics, Millman & Halkias.
6. Electronics Devices & Circuits, Salivahanan.
7. Electronic Principle, Albert Paul Malvino.
Course Name: Basic Electronics Lab

0 0 2 2 1
1. Course Outcomes
ECEN1061.1. The students will correlate theory with diode behaviour.
ECEN1061.2. They will design and check rectifier operation with regulation etc.
ECEN1061.3. Students will design different modes with BJT and FET and check the operations.
ECEN1061.4. They will design and study adder, integrator etc. with OP-AMPs.
List of Experiments
1. Familiarization with passive and active electronic components such as Resistors, Inductors, Capacitors, Diodes, Transistors
(BJT) and electronic equipment like DC power supplies, multi-meters etc.
2. Familiarization with measuring and testing equipment like CRO, Signal generators etc.
3. Study of I-V characteristics of Junction diodes.
4. Study of I-V characteristics of Zener diodes.
5. Study of Half and Full wave rectifiers with Regulation and Ripple factors.
6. Study of I-V characteristics of BJTs in CB mode
7. Study of I-V characteristics of BJTs in CE mode
8. Study of I-V characteristics of Field Effect Transistors.
9. Determination of input-offset voltage, input bias current and Slew rate of OPAMPs.
10. Determination of Common-mode Rejection ratio, Bandwidth and Off-set null of OPAMPs.
11. Study of OPAMP circuits: Inverting and Non-inverting amplifiers, Adders, Integrators and Differentiators.
3. Reference Books
1. Electronic Devices & Circuit Theory, Boylestad & Nashelsky.
2. Op Amps and Linear IC’s, R.A Gayakwad, PHI.
3. Electronics Fundamentals and Applications, D. Chattopadhyay, P. C. Rakshit.
4. Microelectronics Engineering, Adel S. Sedra, Kenneth Carless Smith.
5. Integrated Electronics, Millman & Halkias.
6. Electronics Devices & Circuits, Salivahanan.
7. Electronic Principle, Albert Paul Malvino.


SYLLABUS OF 3rd SEMESTER

A. THEORY COURSES
Course Name: Data Structures & Algorithms

4 0 0 4 4
1. Course Outcomes
CSEN2101.1. Understand and remember the basics of data structures and how time complexity analysis is applicable to
different types of algorithms.
CSEN2101.2. Understand the significance and utility of different data structures and the context of their application. (For
example, the queue in front of ticket counters uses first-in-first-out paradigm in a linear data structure)
CSEN2101.3. Apply different types of data structures in algorithms and understand how the data structures can be useful in
those algorithms.
CSEN2101.4. Analyse the behaviour of different data structures in algorithms. (For example, given an algorithm that uses a
particular data structure, how to calculate its space and time complexity.)
CSEN2101.5. Evaluate solutions of a problem with different data structures and thereby understand how to select suitable
data structures for a solution. (For example, what are the different ways to find the second largest number from
a list of integers and which solution is the best.)
CSEN2101.6. Evaluate different types of solutions (e.g. sorting) to the same problem.
Module 1 [8L]
Introduction: Why do we need data structure? Concepts of data structures: a) Data and data structure b) Abstract Data Type
and Data Type; Algorithms and programs, basic idea of pseudo-code. Algorithm efficiency and analysis, time and space
analysis of algorithms – Big O, , , notations.
Array: Different representations – row major, column major. Sparse matrix - its implementation and usage. Array
representation of polynomials.
Linked List: Singly linked list, circular linked list, doubly linked list, linked list representation of polynomial and applications.
Module 2 [8L]
Stack and Queue: Stack and its implementations (using array, using linked list), applications. Queue, circular queue, deque.
Implementation of queue- both linear and circular (using array, using linked list), applications. Implementation of deque-
with input and output restriction.
Recursion: Principles of recursion – use of stack, differences between recursion and iteration, tail recursion. Applications -
The Tower of Hanoi, Eight Queens Puzzle (Concept of Backtracking).
Module 3 [13L]
Trees: Basic terminologies, forest, tree representation (using array, using linked list). Binary trees - binary tree traversal (pre-
, in-, post- order), threaded binary tree (left, right, full) - non-recursive traversal algorithms using threaded binary tree,
expression tree. Binary search tree- operations (creation, insertion, deletion, searching). Height balanced binary tree – AVL
tree (insertion, deletion with examples only). B- Trees – operations (insertion, deletion with examples only).
Graphs: Graph definitions and Basic concepts (directed/undirected graph, weighted/un-weighted edges, sub-graph, degree,
cut vertex/articulation point, complete graph, simple path, simple cycle). Graph representations/storage implementations –
adjacency matrix, adjacency list, Graph traversal and connectivity – Depth-first search (DFS), Breadth-first search (BFS) –
concepts of edges used in DFS and BFS (tree-edge, back-edge, cross-edge, forward-edge), applications.
Module 4 [11L]
Sorting Algorithms: Bubble sort and its optimizations, Cocktail Shaker Sort, Insertion sort, Selection sort,
Quicksort (Average Case Analysis not required), Heap sort (concept of max heap, application – priority queue),
Counting Sort, Radix sort.
Searching: Sequential search, Binary search, Interpolation search.
Hashing: Hashing functions, collision resolution techniques (Open and closed hashing).
3. Textbooks
1. Fundamentals of Data Structures of C, Ellis Horowitz, Sartaj Sahni, Susan Anderson-freed.
2. Data Structures in C, Aaron M. Tenenbaum.
3. Data Structures, S. Lipschutz.
4. Introduction to Algorithms, Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein.

4. Reference Books
1. Data Structures and Program Design In C, 2/E, Robert L. Kruse, Bruce P. Leung.
Course Name: Discrete Mathematics

4 0 0 4 4
1. Course Outcomes
CSEN2102.1. Interpret the problems that can be formulated in terms of graphs and trees.
CSEN2102.2. Explain network phenomena by using the concepts of connectivity, independent sets, cliques, matching, graph
coloring etc.
CSEN2102.3. Achieve the ability to think and reason abstract mathematical definitions and ideas relating to integers through
concepts of well-ordering principle, division algorithm, greatest common divisors and congruence.
CSEN2102.4. Apply counting techniques and the crucial concept of recurrence to comprehend the combinatorial aspects of
algorithms.
CSEN2102.5. Analyze the logical fundamentals of basic computational concepts.
CSEN2102.6. Compare the notions of converse, contrapositive, inverse etc. in order to consolidate the comprehension of the
logical subtleties involved in computational mathematics.
Module 1 [10L]
Graph Theory: Tree, Binary Tree, Spanning Tree. Walk, Path, Cycle, Hamiltonian Graph, The Travelling Salesman Problem,
Euler Graph, The Chinese Postman Problem. Planar Graph, Euler’s Formula for Planar Graph and Related Problems. Examples
of Non-Planar Graphs. Kuratowski’s Theorem. Matching and Augmenting Paths, Hall’s Marriage Theorem and Related
Problems. Vertex Coloring, Chromatic Polynomials.
Module 2 [10L]
Number Theory: Well Ordering Principle, Principle of Mathematical Induction, Divisibility theory and properties of
divisibility, Fundamental Theorem of Arithmetic, Euclidean Algorithm for finding greatest common divisor (GCD) and some
basic properties of GCD with simple examples, Congruence, Residue classes of integer modulo 𝑛 (ℤ𝑛 ) and its examples.
Module 3 [10L]
Combinatorics: Counting Techniques: Permutations and Combinations, Distinguishable and Indistinguishable Objects,
Binomial Coefficients, Generation of Permutations and Combinations, Pigeon-hole Principle, Generalized Pigeon-Hole
Principle, Principle of Inclusion and Exclusion, Generating Functions and Recurrence Relations: Solving Recurrence Relations
Using Generating Functions and other Methods, Divide-and-Conquer Methods, Formulation and Solution of Recurrence
Relations in Computer Sorting, Searching and other Application Areas.
Module 4 [12L]
Propositional Calculus: Propositions, Logical Connectives, Truth Tables, Conjunction, Disjunction, Negation, Implication,
Converse, Contra positive, Inverse, Biconditional Statements, Logical Equivalence, Tautology, Normal Forms, CNF and DNF,
Predicates, Universal and Existential Quantifiers, Bound and Free Variables, Examples of Propositions with Quantifiers.
3. Textbooks
1. Discrete Mathematics and its Applications, Kenneth H. Rosen, Tata McGraw- Hill.
2. Discrete Mathematics, T Veerarajan, Tata McGraw- Hill.
4. Reference Books
1. Elements of Discrete Mathematics: A Computer Oriented Approach, C L Liu and D P Mohapatra, McGraw Hill.
2. Discrete Mathematical Structure and Its Application to Computer Science, J.P. Tremblay and R. Manohar, McGraw Hill.
3. Discrete Mathematics for Computer Scientists and Mathematicians, J.L.Mott, A. Kandel and T.P.Baker, Prentice Hall
4. Discrete Mathematics,Norman L. Biggs, Seymour Lipschutz, Marc Lipson, Oxford University Press, Schaum’s Outlines
Series.
5. Higher Algebra (Classical), S.K. Mapa, Sarat Book Distributors.
6. Introduction to Graph Theory (2nd Ed), D G West, Prentice-Hall of India, 2006.

Course Name: Analog Circuits

3 0 0 3 3
1. Course Outcomes
ECEN2101.1. Apply the previous knowledge gathered from Basic Electrical and Basic Electronics papers.
ECEN2101.2. Understand the concepts of BJT, MOSFET and biasing techniques of BJT and MOSFET based amplifier
circuits.
ECEN2101.3. Analyse frequency response of amplifier circuits.
ECEN2101.4. Design different types sinusoidal oscillators and multi-vibrator circuits.
ECEN2101.5. Construct algebraic equations-based amplifier and analog computers using OP-AMP
ECEN2101.6. Design stable high-gain amplifier circuits.
Module 1 [9L]
Basic concepts and device biasing: Analog, discrete and digital signals. Diode: piecewise-linear model, clipping and clamping
operation. BJT biasing circuits, Q-point and stability.
Small Signal analysis of Amplifiers: Small signal (h-parameter and re model) analysis of BJT CE mode amplifier circuit
(derive input impedance, output impedance, voltage gain, current gain for the amplifiers).
Module 2 [9L]
Frequency Responses of Amplifiers: Frequency response of CE mode RC-coupled amplifier; effect of external and parasitic
capacitors on cut-off frequencies.
Feedback & Oscillator Circuits: Concept of feedback, Effects of negative feedback in amplifiers, Oscillators circuits: Phase-
shift, Wien-Bridge, Hartley, Colpitts and crystal Oscillators.
Module 3 [7L]
Fundamentals of OPAMP: Basic building blocks of OPAMP: Differential Amplifiers, Current source and current mirror
circuits. Types of differential amplifiers, AC and DC analysis of differential amplifiers; Characteristics of an ideal OPAMP.
Applications of OPAMP: Inverting and non-inverting OPAMP amplifiers, Log-antilog amplifiers, Instrumentation amplifier,
Precision rectifiers, basic comparator, Schmitt Trigger.
Module 4 [7L]
Power Amplifiers: Concepts and operations of Class A, B and AB amplifiers; Calculation of DC power, AC power and
efficiency of these amplifiers.
Applications Analog IC: Description of 555 Timer IC, astable and mono-stable operations using 555. Study of 78XX and
79XX voltage regulator ICs.
3. Textbooks
1. Microelectronic Circuits by Adel S. Sedra, Kenneth C. Smith.
2. Electronics Devices and Circuits by Robert L. Boylestad, Louis Nashelskey.
3. Fundamentals of Microelectronics by Behzad Razavi.
4. Integrated electronics by Jacob Millman, Christos C. Halkias.
Course Name: Digital Logic

3 0 0 3 3
1. Course Outcomes
ECEN2104.1. Students will learn Binary Number system, and logic design using combinational gates.
ECEN2104.2. Students will design applications of Sequential Circuits.
ECEN2104.3. Students will design Finite State Machines.
ECEN2104.4. Students will learn Memory classifications.
ECEN2104.5. Students will learn basics of CMOS logic.
ECEN2104.6. Students will be prepared to learn various digital component design as used in VLSI applications.

Module 1 [10L]
Binary System, Boolean Algebra and Logic Gates: Data and number systems; Binary, Octal and Hexadecimal
representation and their conversions, BCD, Gray codes, excess 3 codes and their conversions; Signed binary number
representation with 1’s and 2’s complement methods, Binary arithmetic. Boolean algebra, De-Morgan’s theorem,
Various Logic gates- their truth tables and circuits, universal logic gates, Representation in SOP and POS forms;
Minimization of logic expressions by algebraic method, Karnaugh-map method, Quine-McCluskey method.
Module 2 [10L]
Arithmetic Circuits: Adder circuit – Ripple Carry Adder, CLA Adder, CSA, and BCD adder, subtractor circuit.
Combinational Circuit: Encoder, Decoder, Comparator, Multiplexer, De-Multiplexer and parity Generator. Shannon’s
Expansion Theorem, Realization of logic functions using Mux, Parity Generators.
Module 3 [10L]
Sequential Logic: Basic memory elements, S-R, J-K, D and T Flip Flops, Sequential circuits design methodology: State table
and state diagram, State Reduction Method, Circuit Excitation and Output tables, Derivation of Boolean functions; Finite State
Machine Design using Sequential circuit design methodology, various types of Registers (with Parallel load, shift Registers)
and Counters (asynchronous ripple counters, synchronous counters: binary, BCD, Johnson).
Module 4 [6L]
Memory Systems: Concepts and basic designs of RAM (SRAM & DRAM), ROM, EPROM, EEPROM, Programmable logic
devices and gate arrays (PLAs and PLDs)
Logic families: NMOS and CMOS, their operation and specifications. Realization of basic gates using above logic families,
Open collector & Tristate gates, wired-AND and bus operations.
3. Textbooks
1. Digital Logic and Computer Design, Morris M. Mano, PHI.
2. Digital Principles & Applications, 5th Edition, Leach & Malvino, Mc Graw Hill Company.
3. Modern Digital Electronics, 2nd Edition, R.P. Jain. Tata Mc Graw Hill Company Limited.
4. Digital Logic Design, Fourth Edition - Brian Holdsworth & Clive Woods.
5. Digital Integrated Electronics, H.Taub & D.Shilling, Mc Graw Hill Company Limited.
4. Reference Books
1. Digital Design: Principles and Practices: John F. Wakerly.
2. Fundamental of Digital Circuits, A. Anand Kumar, PHI.
Course Name: Human Values and Professional Ethics

3 0 0 3 3
1. Course Outcomes
HMTS2001.1. Be aware of the value system and the importance of following such values at workplace.
HMTS2001.2. Learn to apply ethical theories in the decision-making process.
HMTS2001.3. Follow the ethical code of conduct as formulated by institutions and organizations.
HMTS2001.4. Implement the principles governing work ethics.
HMTS2001.5. Develop strategies to implement the principles of sustainable model of development.
HMTS2001.6. Implement ecological ethics wherever relevant and also develop eco-friendly technology.
Module 1 [10L]
Human society and the Value System: Values: Definition, Importance and application, Formation of Values: The process of
Socialization, Self and the integrated personality, Morality, courage, integrity.
Types of Values: Social Values: Justice, Rule of Law, Democracy, Indian Constitution, Secularism; Aesthetic Values:
Perception and appreciation of beauty; Organizational Values: Employee: Employer--- rights, relationships, obligations;
Psychological Values: Integrated personality and mental health; Spiritual Values and their role in our everyday life; Value
Spectrum for a Good Life, meaning of Good Life.
Value Crisis in Contemporary Society: Value crisis at: Individual Level, Societal Level, Cultural Level; Value Crisis
management: Strategies and Case Studies.

Module 2 [10L]
Ethics and Ethical Values, Principles and theories of ethics, Consequential and non-consequential ethics, Egotism,
Utilitarianism, Kant’s theory and other non-consequential perspectives, Ethics of care, justice and fairness, rights and duties.
Ethics: Standardization, Codification, Acceptance, Application.
Types of Ethics: Ethics of rights and Duties, Ethics of Responsibility, Ethics and Moral judgment, Ethics of care
Ethics of justice and fairness, Work ethics and quality of life at work.
Professional Ethics: Ethics in Engineering Profession; moral issues and dilemmas, moral autonomy (types of inquiry),
Kohlberg’s theory, Gilligan’s theory (consensus and controversy), Code of Professional Ethics Sample Code of ethics like
ASME, ASCE. IEEE, Institute of Engineers, Indian Institute of materials management, Institute of Electronics and
telecommunication engineers, Violation of Code of Ethics---conflict, causes and consequences
Engineering as social experimentation, engineers as responsible experimenters (computer ethics, weapons development),
Engineers as managers, consulting engineers, engineers as experts, witnesses and advisors, moral leadership, Conflict between
business demands and professional ideals, social and ethical responsibilities of technologies.
Whistle Blowing: Facts, contexts, justifications and case studies
Ethics and Industrial Law: Institutionalizing Ethics: Relevance, Application, Digression and Consequences.
Module 3 [10L]
Science, Technology and Engineering: Science, Technology and Engineering as knowledge and profession: Definition,
Nature, Social Function and Practical application of science; Rapid Industrial Growth and its Consequences; Renewable and
Non- renewable Resources: Definition and varieties; Energy Crisis; Industry and Industrialization; Man and Machine
interaction; Impact of assembly line and automation; Technology assessment and Impact analysis; Industrial hazards and safety;
Safety regulations and safety engineering; Safety responsibilities and rights; Safety and risk, risk benefit analysis and reducing
risk; Technology Transfer: Definition and Types; The Indian Context.
Module 4 [6L]
Environment and Eco- friendly Technology: Human Development and Environment, Ecological Ethics/Environment ethics
Depletion of Natural Resources: Environmental degradation, Pollution and Pollution Control, Eco-friendly Technology:
Implementation, impact and assessment, Sustainable Development: Definition and Concept, Strategies for sustainable
development, Sustainable Development: The Modern Trends, Appropriate technology movement by Schumacher and later
development, Reports of Club of Rome.
3. Reference Books
1. Human Values, Tripathi, A.N., New Age International, New Delhi, 2006.
2. Classical Sociological Theory, Ritzer, G., The McGraw Hill Companies, New York,1996.
3. Postmodern Perspectives on Indian Society, Doshi, S.L., Rawat Publications, New Delhi,2008.
4. Sustainable Development, Bhatnagar, D.K., Cyber Tech Publications, New Delhi, 2008.
5. The age of Spiritual Machines, Kurzwell, R., Penguin Books, New Delhi,1999.
6. Social Problems in Modern Urban Society, Weinberg, S.K., Prentice Hall,Inc.,USA, 1970.
7. Sociology, Giddens, Anthony 2009, London: Polity Press (reprint 13th Edition).
Course Name: Data Structure & Algorithms Lab

0 0 3 3 1.5
1. Course Outcomes
CSEN2151.1. To understand linear and non-linear data structures.
CSEN2151.2. To understand different types of sorting and searching techniques.
CSEN2151.3. To know how to create an application specific data structure.
CSEN2151.4. To solve the faults / errors that may appear due to wrong choice of data structure.
CSEN2151.5. To analyse reliability of different data structures in solving different problems.
CSEN2151.6. To evaluate efficiency in terms of time and space complexity, when different data structures are used to solve
same problem.

Day 1: Time and Space Complexity
Lab Assignment
Create three different 10; 000 10; 000 matrices matrixOne, matrixTwo and result-Matrix, using dynamic memory allocation.
Initialize matrixOne and matrixTwo by using rand() or srand() function, limit the values from 0 to 9. Multiply matrixOne
and matrixTwo into resultMatrix.
While execution, open another terminal and use top command to see the usage of memory by the process. Calculate the time
taken for the execution of the program.
Repeat the same exercise for 100,000 x 100,000matrices.
Home Assignment
Write a program (WAP) to check whether a matrix is i) identity, ii) diagonal. WAP to reverse the elements of an array
without using any other variable.
Day 2: Array
Lab Assignment
WAP to add two polynomials using array. Minimize the memory usage as much as you can.
WAP to convert a matrix into its sparse representation (triple format). Once represented in sparse format, do not revert back
to the matrix format any-more. Manipulate the sparse representation to find the transpose of the matrix (which should also
be in sparse representation).
Calculate and find out whether using triple format for your example is advantageous or not.
Home Assignment
WAP to multiply two polynomials. Minimize usage of memory.
WAP to add two matrices using sparse representation. Manipulation of data should be done in sparse format.
Day 3: Singly Linked List

Lab Assignment
Write a menu driven program to implement a singly linked list with the operations:
i) create the list ii) insert any element in any given position (front, end or intermediate)
iii) delete an element from any given position (front, end or intermediate) iv)display the list
Home Assignment
Write a menu driven program to implement a singly linked list with the operations:
i) count the number of nodes ii) reverse the list
Day 4: Circular and Doubly Linked List

Lab Assignment
Write a menu driven program to implement a circular linked list with the operations:
Home Assignment
Write a menu driven program to implement a doubly linked list with the operations:
Day 5: Stack, Queue - with array

Lab Assignment
Write a menu driven program to implement stack, using array, with
i) push, ii) pop, iii) display, iv) exit operations.
WAP to evaluate a postfix expression.
Write a menu driven program to implement a queue, using array, with
i) insert, ii) delete, iii) display, iv) exit operations
Home Assignment
WAP to convert an infix expression to its corresponding postfix operation.
Write a menu driven program to implement a double-ended queue, using array, with the following operations:
i) insert (from front, from rear) ii) delete (from front, from rear)
iii) display iv) exit operations
Day 6: Stack, Queue - with linked list

Lab Assignment
Write a menu driven program to implement a stack, using linked list, with
i) push, ii) pop, iii) exit operations
Home Assignment
Write a menu driven program to implement a queue, using linked list, with
i) insert, ii) delete, iii) exit operations

Day 7: Circular Queue, Deque - with linked list
Lab Assignment
Write a menu driven program to implement a circular queue using linked list with
i) insert, ii) delete, iii) exit operations
Home Assignment
Write a menu driven program to implement a double-ended queue, using linked list, with the following operations:
i) insert (from front, rear), ii) delete (from front, rear), iii) exit operations
Day 8: Binary Search Tree (BST)

Lab Assignment
Write a program, which creates a binary search tree (BST). Also write the functions to insert, delete (all possible cases) and
search elements from a BST.
Home Assignment
Write three functions to traverse a given BST in the following orders:
i) in-order, ii) pre-order, iii) post-order.
Display the elements while traversing.
Day 9: Searching
Lab Assignment
WAP to implement,
i) Linear Search, ii) Binary Search (iterative)
NB: As a pre-processing step, use bubble-sort to sort the elements in the search space.
WAP to generate integers from 1 to n (input parameter) in random order and guarantees that no number appears twice in the
list. While the number sequence is being generated, store it in a text file.
Home Assignment
WAP to implement binary search recursively.
Day 10: Sorting

Lab Assignment
Write different functions for implementing,
i) Bubble sort, ii) Cocktail shaker sort, iii) Quick Sort.
Plot a graph of n vs. time taken, for n= 100, 1000, 10,000 and 100,000 to com-pare the performances of the sorting methods
mentioned above. Use the second assignment of Day 9 to generate the data, using the given n values.
Home Assignment
Write different functions for implementing,
i) Insertion sort, ii) Merge sort.
Day 11: Graph Algorithms

Lab Assignment
Read a graph (consider it to be undirected) from an edge-list and store it in an adjacency list.
Use the adjacency list to run DFS algorithm on the graph and print the node labels. Detect and count the back-edges.
Home Assignment
WAP to implement BFS algorithm of a given graph (similarly as described for DFS, instead of back-edges count cross-edges).
3. Textbooks
1. Fundamentals of Data Structures of C, Ellis Horowitz, Sartaj Sahni, Susan Anderson-freed.
2. Data Structures in C, Aaron M. Tenenbaum.
3. Data Structures, S. Lipschutz.
4. Introduction to Algorithms, Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein.
4. Reference Books
1. Data Structures and Program Design In C, 2/E, Robert L. Kruse, Bruce P. Leung.

Course Name: Software Tools Lab

0 0 3 3 1.5
1. Course Outcomes
CSEN2152.1. Propose a software life cycle model for the given requirements and compile software requirement specifications
as per IEEE guidelines.
CSEN2152.2. Develop function-oriented design and/or object-oriented design for software systems using industry standard
techniques.
CSEN2152.3. Apply the knowledge of different coding standards and/or guidelines and propose test cases for sample software
system modules in different testing methods.
CSEN2152.4. Compare and contrast among different types of software maintenance and to decide on the maintenance models
to be employed depending on the situation.
CSEN2152.5. Apply different project management strategies for project planning such as to estimate the project size, duration
and cost.
CSEN2152.6. Apply the ideas of different project monitoring and control techniques such as WBS, Activity Network, PERT
chart, Critical path etc. to efficiently monitor and control the project. They will be able to identify different
software project risks and determine their mitigation approaches.
1. CodeLite IDE [Code::Blocks]: Learn to use CodeLite IDE for writing C/C++ programming languages.
2. Compiling with gcc: Learn all the command line options for compiling C programs in the Unix environment using
gcc.
3. Static and Dynamic Library: Understand the linking phase of a C program by creating and using static and dynamic
libraries.
4. Debugging with gdb: gdb is the standard C/C++ debugger to debug your code. Learn to interact with gdb directly via a
shell, or use a graphical interface provided by CodeLite IDE.
5. Memory profiling with valgrind: Learn to use valgrind which is a critical tool for helping one to find memory leaks in
the program: malloc without free, accessing an array outside its bounds, etc.
6. Code coverage testing with gcov: Learn about good testing using gcov to make sure the tests are exercising all the
branches in the code.
7. Runtime profiling with gprof: Learn about using gprof which is a very useful profiling tool for speeding up execution
speed of a program: it will show where your program is spending most of its time, so one can know about the most
important code to optimize
8. Makefile: Learn how to use makefile on Unix to properly build an executable.
9. Git for sharing files and version control: Learn to setup a repository so that it can sync your local with that on the
server. Learn to use CVS for version controlling.
3. Textbooks
1. The Definitive Guide to GCC, William von Hagen, 2nd Edition, 2006, Apress.
2. Linux Debugging and Performance Tuning: Tips and Techniques, Steve Best, Pearson Education,1st Edition, 2006.
4. Reference Books
1. Version control with Git, Jon Loeliger,1st Edition,2009, O'Reilly.
2. The Art of Debugging with GDB, DDD, and Eclipse, Norman Matloff, Peter Jay Salzman, 2008.
Course Name: Digital Logic Lab

0 0 2 2 1
1. Course Outcomes
ECEN2154.1. Use the concept of Boolean algebra to minimize logic expressions by the algebraic method, K-map method etc.
ECEN2154.2. Construct different Combinational circuits like Adder, Subtractor, Multiplexer, De-Multiplexer, Decoder,
Encoder, etc.
ECEN2154.3. Design various types of Registers and Counters Circuits using Flip-Flops (Synchronous, Asynchronous,
Irregular, Cascaded, Ring, Johnson).
ECEN2154.4. Realize different logic circuits using ICs built with various logic families.
Choose any ten experiments out of the twelve suggested next:
1. Realization of basic gates using Universal logic gates.
2. Four-bit parity generator and comparator circuits.
3. Code conversion circuits BCD to Excess-3 & vice-versa.
4. Construction of simple 3-to-8 Decoder circuit by 2-to-4 Decoders using logic gates.
5. Design a 4-to-1 Multiplexer using logic gates and use it as a Universal logic module.
6. Realization of SR (Set Reset), JK, and D flip-flops using Universal logic gates.
7. Construction of simple arithmetic logic circuits-Adder, Subtractor.
8. Realization of Asynchronous Up/Down Counter (Count up to 7) using logic gates.
9. Realization of Synchronous Up/Down Counter (Count up to 7) using logic gates.
10. Realization of Shift Registers using logic gates (Serial in Serial out and Parallel in Serial out).
11. Construction of Serial adder circuit using a D Flip-Flop and a Full adder.
12. Design a combinational circuit for BCD to Decimal conversion to drive 7-Segment display using logic gates.
C. HONORS COURSES
Course Name: Probability and Statistical Methods

4 0 0 4 4
1. Course Outcomes
MATH2111.1. Articulate the axioms (laws) of probability.
MATH2111.2. Compare and contrast different interpretations of probability theory and take a stance on which might be
preferred.
MATH2111.3. Formulate predictive models to tackle situations where deterministic algorithms are intractable.
MATH2111.4. Summarize data visually and numerically.
MATH2111.5. Assess data-based models.
MATH2111.6. Apply tools of formal inference.
Module 1 [10L]
Probability-I (Single variable probability distributions): Review of basic probability: Axiomatic definition, Addition and
Multiplication law, Conditional probability and Bayes’ Theorem, Expectation and Variance of single variable discrete and
continuous distributions, Normal approximation to Binomial and Poisson Distribution, Exponential and Multinomial
distribution, Moment generating and characteristic functions, Limit theorems: Markov’s inequality and Chebyshev’s inequality
with examples.
Module 2 [10L]
Probability-II (Joint Distribution and Markov Chains): Joint distribution using joint probability mass/density function,
Finding marginal pmf/pdf from joint distribution, Multiplicative property of joint pmf/pdf in case of independent random
variables, Markov Chains: Introduction, Chapman-Kolmogorov equations, Classification of states, Some applications:
Gambler’s Ruin Problem.
Module 3 [10L]
Statistics-I: Moments, Skewness and Kurtosis, Binomial, Poisson and Normal - evaluation of statistical parameters for these
three distributions, Covariance, Correlation and Regression, Spearman’s Rank Correlation coefficient, Curve fitting: Straight
line and parabolas.
Module 4 [10L]
Statistics-II: Population and Samples, The sampling distribution of mean (standard deviation known), The sampling
distribution of mean (standard deviation unknown), Point and Interval estimation, Tests of Hypotheses, Null Hypotheses and
Tests of Hypotheses with examples.
3. Textbooks
1. Probability and Statistics for Engineers, Richard A Johnson, Pearson Education.
2. Groundwork of Mathematical Probability and Statistics, Amritava Gupta, Academic Publishers .

4. Reference Books
1. Introduction to Probability Models, S.M. Ross, Elsevier.
2. Fundamentals of Mathematical Statistics, S.C. Gupta and V.K. Kapoor, Sultan Chand and Sons.
3. An Introduction to Probability theory and its applications Vol-I, W. Feller, John Wiley and Sons.


SYLLABUS OF 4th SEMESTER

A. THEORY COURSES
Course Name: Design & Analysis of Algorithms

4 0 0 4 4
1. Course Outcomes
CSEN2201.1. Remember time complexities of various existing algorithms in different situations.
CSEN2201.2. Understand the basic principles of different paradigms of designing algorithms.
CSEN2201.3. Apply mathematical principles to solve various problems.
CSEN2201.4. Analyze the complexities of various algorithms in worst case, best case and average case.
CSEN2201.5. Assess the computational hardness of a problem and learn how some of the well-known problems are proved to
be NP-hard and also design approximation algorithms for some of them.
CSEN2201.6. Create/ Design a good algorithm for a new problem given to him/ her.
Module 1 [10L]
Algorithm Analysis: Time and space complexity. Asymptotic Notations and their significance. Asymptotic Analysis. Finding
time complexity of well-known algorithms like-insertion sort, heapsort, Asymptotic solution to recurrences, Substitution
Method, Recursion Tree, Master Theorem.
Divide-and-Conquer Method: Basic Principle, Binary Search – Worst-case and Average Case Analysis, Merge Sort – Time
Complexity Analysis, quicksort – Worst-case and Average Case Analysis, Concept of Randomized Quicksort.
Medians and Order Statistics
Lower Bound Theory: Bounds on sorting and searching techniques.
Module 2 [16L]
Greedy Method: Elements of the greedy strategy. Fractional Knapsack Problem, Huffman codes.
Dynamic Programming: Basic method, use, Examples: 0-1 Knapsack Problem, Matrix-chain multiplication, LCS Problem.
Graph Algorithms: Minimum cost spanning trees: Prim's and Kruskal's algorithms and their correctness proofs (Greedy
Method). Shortest Path Algorithm: Dijkstra’s with correctness proof. (Greedy method), Bellman Ford with correctness proof,
All pair shortest path (Floyd-Warshall Algorithm) (Dynamic Programming).
Module 3 [10L]
Amortized Analysis: Aggregate, Accounting and Potential methods.
String matching algorithms: Different techniques – Naive algorithm, string matching using finite automata, and Knuth ,
Morris , Pratt ( KMP ) algorithm with their complexities
Randomized Algorithm: Skip List.
Module 4 [10L]
Disjoint Set Manipulation: UNION-FIND with union by rank, Path compression.
Network Flow: Ford Fulkerson algorithm, Max - Flow Min - Cut theorem (Statement and Illustration)
NP-completeness: P class, NP-hard class, NP-complete class. Relative hardness of problems and polynomial time reductions.
Satisfiability problem, Vertex Cover Problem, Independent Sets, Clique Decision Problem.
Approximation algorithms: Necessity of approximation scheme, performance guarantee. Approximation algorithms for 0/1
knapsack, vertex cover, TSP. Polynomial time approximation schemes: 0/1 knapsack problem .
3. Textbooks
1. Introduction to Algorithms by Cormen, Leiserson, Rivest and Stein. Third Edition, 2009. Prentice Hall.
2. Algorithm Design by Jon Kleinberg and Eva Tardos. Addison Wesley, 2005.
4. Reference Books
1. Computer Algorithms: Introduction to Design and Analysis by Sarah Basee and Allen van Gelder. 3rd Edition, Addison
Wesley.

Course Name: Computer Organization and Architecture

4 0 0 4 4
1. Course Outcomes
CSEN2202.1. Understand the basic organization of computer and different instruction formats and addressing modes.
CSEN2202.2. Analyze the concept of pipelining, segment registers and pin diagram of CPU.
CSEN2202.3. Understand and analyze various issues related to memory hierarchy.
CSEN2202.4. Understand various modes of data transfer between CPU and I/O devices.
CSEN2202.5. Examine various inter connection structures of multi-processor.
CSEN2202.6. Design architecture with all the required properties to solve state-of-the-art problems.
Module 1 [10L]
Basics of Computer Organization: Basic organization of the stored program computer and operation sequence for execution
of a program, Von Neumann & Harvard Architecture. RISC vs. CISC based architecture.
Fetch, decode and execute cycle, Concept of registers and storage, Instruction format, Instruction sets and addressing modes.
Basics of Control Unit Design - hardwired and micro programmed control, Horizontal and Vertical micro instruction.
Module 2 [11L]
Memory and I/O Organization: Memory system overview, Cache memory organizations, Techniques for reducing cache
misses, Hierarchical memory technology: Inclusion, Coherence and locality properties, Virtual Memory, Memory mapped IO.
Introduction to I/O interfaces. Interrupts, Interrupt hardware, Enabling and Disabling interrupts, Concept of handshaking,
Polled I/O, Priorities, Daisy Chaining. Vectored interrupts; Direct memory access, DMA control.
Module 3 [10L]
Pipelined Architecture: Brief Introduction, Performance Measures - speed up, Efficiency, performance - cost ratio etc.
Static pipelines - reservation tables, scheduling of static pipelines, definitions - minimum average latency, minimum achievable
latency, greedy strategy etc. Theoretical results on latency bounds without proof.
Vector Processing: Vector registers; Vector Functional Units; Vector Load / Store; Vectorization; Vector operations: gather /
scatter; Masking; Vector chaining.
Module 4 [9L]
SIMD Architectures: Brief introduction, various concepts illustrated by studying detailed SIMD algorithms, viz., Matrix
multiplication, Sorting on Linear array.
Interconnection Networks: Detailed study of Interconnection Network - Boolean cube, Mesh, Shuffle-exchange, Banyan,
Omega, Butterfly, Generalized Hypercube, Delta etc.
3. Textbooks
1. Computer Organization, 5th Edition, Carl Hamacher, Zvonko Vranesic, Safwat Zaky, MGH.
2. Computer System Architecture, 3rd Edition, Morris M. Mano, Pearson.
3. Computer Organization and Design: The Hardware/Software interface, David A. Patterson and John L. Hennessy, 3rd
Edition, Elsevier, 2005.
4. Advanced Computer Architecture and Parallel processing, Hwang & Briggs, MH.
5. Advanced Computer Architecture: Parallelism, Scalability, Programmability, Kai Hwang, McGraw-Hill.
4. Reference Books
1. Onur Mutlu’s lecture materials on Computer Architecture from CMU web site: https://users.ece.cmu.edu/~omutlu/.
2. NPTEL materials on Computer Organization.

Course Name: Operating Systems

3 0 0 3 3
1. Course Outcomes
CSEN2203.1. Develop knowledge about the importance of computer system resources and the role of operating system in
their management policies and algorithms.
CSEN2203.2. Understand processes and its management policies and scheduling of processes by CPU.
CSEN2203.3. Acquire an understanding of the need of process synchronization, evaluate the requirement for process
synchronization and coordination handled by operating system.
CSEN2203.4. Analyze the memory management and its allocation policies and compare different memory management
approaches.
CSEN2203.5. Understand the impact and co-relation of different scheduling algorithm in secondary storage and different
structure of file system and able to design the system with improved performance.
CSEN2203.6. Identify the different activities and impact of threat, virus, worm and able to protect system from them.
Module 1 [9L]
Introduction: Operating system functions, OS Architecture (Monolithic, Microkernel, Layered, Hybrid), Different types of
O.S. (batch, multi-programmed, time-sharing, real-time, distributed, parallel), Evaluation of OS.
System Structure: Computer system operation, I/O structure, storage structure, storage hierarchy, different types of
protections, Operating system structure (simple, layered, virtual machine), O/S services, System calls.
Protection & Security: Goals of protection, Domain of protection, Access matrix and its representation, Threats and system
security.
Processes and Threads: 7 state process model, Process scheduling, Operations on processes, Inter-process communication,
Threads overview, Benefits of threads, User and kernel threads.
Module 2 [11L]
CPU Scheduling: Scheduling criteria, Preemptive & non-preemptive scheduling, Scheduling algorithms (FCFS, SJF, RR,
Priority, Multi-level queue, Multi-level feedback queue), Comparative study of the algorithms, Multi-processor scheduling.
Process Synchronization: Background, Critical section problem, Software solution – Peterson and Bakery algorithm,
Synchronization hardware, Semaphores, Classical problems of synchronization.
Deadlocks: System model, Deadlock characterization, Methods for handling deadlocks, Deadlock prevention, Deadlock
avoidance, Deadlock detection, Recovery from deadlock.
Module 3 [9L]
Primary Memory: Background, Physical address, Logical address, Virtual address, Contiguous memory allocation (Fixed and
Variable partition), Non-contiguous memory allocation techniques (Paging, Segmentation, Segmentation with Paging), Virtual
memory, Demand Paging, Performance, Page replacement algorithms (FCFS, LRU, optimal), Thrashing.
Module 4 [7L]
Secondary Storage: Disk structure, Disk performance, Disk scheduling (FCFS, SSTF, SCAN, C-SCAN), Boot block, Bad
blocks.
File Systems: File concept, Access methods, Directory structure, File system structure, Allocation methods (Contiguous,
Linked, Indexed), Free-space management (Bit vector, Linked list, Grouping), Directory Implementation (Linear list, Hash
table), Efficiency and Performance.
I/O Management: PC Bus Structure, I/O connections, Data transfer techniques (Programmed, Interrupt driven, DMA), Bus
arbitration (Daisy chain, Polling, Independent request), Blocking and non-blocking I/O, Kernel I/O subsystem (Scheduling,
Buffering, Caching, Spooling and device reservation, Error handling).
3. Textbooks
1. Operating System Concepts, 10E, Silberschatz A., Galvin P. B., Gagne G., Wiley Publications.
2. Operating Systems Internals and Design Principles, 9E, Stalling W., Pearson Education.
4. Reference Books
1. Operating System: Concept & Design, Milenkovie M., McGraw Hill.
2. Operating System Design & Implementation, Tanenbaum A.S., Prentice Hall NJ.
3. Operating System Concepts, Silberschatz A., Peterson J. L., Wiley Publications.
4. Operating Systems A Concept Based Approach, Dhamdhere D.M., McGraw Hill.

Course Name: Algebraic Structures

4 0 0 4 4
1. Course Outcomes
MATH2201.1. Describe the basic foundation of computer related concepts like sets, Posets, lattice and Boolean Algebra.
MATH2201.2. Analyse sets with binary operations and identify their structures of algebraic nature such as groups, rings and
fields.
MATH2201.3. Give examples of groups, rings, subgroups, cyclic groups, homomorphism and isomorphism, integral domains,
skew-fields and fields.
MATH2201.4. Compare even permutations and odd permutations, abelian and non-abelian groups, normal and non-normal
subgroups and units and zero divisors in rings.
MATH2201.5. Adapt algebraic thinking to design programming languages.
MATH2201.6. Identify the application of finite group theory in cryptography and coding theory.
Module 1 [10L]
Sets, Relations and Functions: Basic operations on sets, Venn diagrams. Binary relations defined on sets, equivalence
relations and equivalence classes, order, relation and lattices, partially ordered sets, Hasse diagrams, maximal, minimal, greatest
and least elements in a partially ordered set, lattices and their properties, principle of duality, distributive and complemented
lattices.
Module 2 [10L]
Group Theory I: Cartesian product, Binary operation, Composition Table. Group, Elementary theorems on groups, Quasi-
group and Klein’s 4 group. Permutations, Product of permutations, Group property of permutations, Cyclic permutation,
Transposition, Even and Odd permutations, Proposition regarding permutations, Alternating Groups.
Module 3 [10L]
Group Theory II: Order of an element of a group, Properties of the order of an element of a group , Subgroups, some basic
theorems on subgroups, Cyclic group, Cosets, Lagrange’s theorem, Fermat’s Little Theorem(statement only). Normal
subgroup, some basic theorems on Normal subgroup.
Module 4 [6L]
Morphisms, Rings and Fields: Homomorphism and Isomorphism of groups, some basic theorems. Rings, some elementary
properties of a ring, Ring with unity, Characteristic of a ring, Ring with zero divisors, Sub-ring, Integral domain, Field, Division
Ring or Skew Field. (Emphasis should be given on examples and elementary properties).
3. Textbooks
1. Higher Algebra, S.K.Mapa, Sarat Book Distributors.
2. Advanced Higher Algebra, J.G.Chakravorty and P.R.Ghosh, U.N. Dhur and Sons.
4. Reference Books
1. A First course in Abstract Algebra, J.B.Fraleigh, Narosa.
2. Algebra, M. Artin, Pearson.
Course Name: Microprocessors & Microcontrollers

Course Code: AEIE2205
2 0 0 2 2
1. Course Outcomes
AEIE2205.1. Understand the architecture of 8-bit microprocessor (8085A).
AEIE2205.2. Develop the skill in program writing of 8-bit microprocessor (8085A).
AEIE2205.3. Understand the architecture and develop the skill in program writing of 16-bit microprocessor (8086).
AEIE2205.4. Understand the architecture and develop the skill in program writing of microprocessor 8051 and PIC16F877.
AEIE2205.5. Understand the architecture and operation of programmable peripheral device 8255A.

Module 1 [6L]
Introduction to 8-bit microprocessor: 8085 microprocessor internal architecture, 8085 pin configuration, Software
instruction set, timing diagram of the instructions.
Module 2 [7L]
Addressing modes and Assembly language programming: Interrupts of 8085 processor: classification of interrupts,
Programming using interrupts. Counter and Time delay, Support IC chips 8255- Block diagram, pin configuration, mode of
operation, control word(s) format and Interfacing with Microprocessors.
Module 3 [7L]
Introduction to 8086/8088 Architecture: Architecture, memory segmentation, pin configuration, clock generator, instruction
set, addressing modes and assembly language programming of 8086/8088, interrupts.
Module 4 [6L]
Introduction to microcontrollers: Intel MCS-51 family features, 8051 architecture, pin configuration, I/O ports and memory
organization; Instruction set and basic assembly language programming, interrupts and returns; Interrupts, timer/counter and
serial communication.
Brief introduction to PIC microcontroller (16F877): Architecture, pin details, memory layout etc.
3. Textbooks
1. Microprocessor architecture, programming and applications with 8085/8085A, Ramesh S. Gaonkar, Wiley eastern Ltd.
2. Fundamental of Microprocessor and Microcontrollers, B. Ram, Dhanpat Rai Publications.
3. Microprocessors and Microcontrollers, N. Senthil Kumar, M. Saravanan, S. Jeevanathan, Oxford Publications.
4. 8085 Microprocessor and its Applications, A. Nagoor Kani, Third Edition, TMH Education Pvt. Ltd.
4. Reference Books
1. The 8051 Microcontroller and Embedded. Systems. Using Assembly and C. Muhammad Ali Mazidi, Janice Gillispie
Mazidi. Rolin D. McKinlay, Second Edition, Pearson Publication.
2. Advanced Microprocessors and Peripherals, A.K.Ray, K.Bhurchandi, TMH Education Pvt. Ltd.
3. PIC Microcontroller and Embedded. Systems. Using Assembly and C. Muhammad Ali Mazidi, Janice Gillispie Mazidi.
Rolin D. McKinlay, Pearson Publication.
4. Design with PIC Microcontroller, John Peatman, Pearson Publication.
Course Name: Environmental Sciences (Mandatory)

Course Code: EVSC2016
2 0 0 2 0
1. Course Outcomes
EVSC2016.1. Understand the natural environment and its relationships with human activities.
EVSC2016.2. Characterize and analyze human impacts on the environment.
EVSC2016.3. Integrate facts, concepts, and methods from multiple disciplines and apply to environmental problems.
EVSC2016.4. Educate engineers who can work in a multi-disciplinary environment to anticipate and address evolving
challenges of the 21st century.
EVSC2016.5. Understand and implement scientific research strategies, including collection, management, evaluation, and
interpretation of environmental data.
EVSC2016.6. Design and evaluate strategies, technologies, and methods for sustainable management of environmental systems
and for the remediation or restoration of degraded environments.
Module 1 [6L]
Socio Environmental Impact: Basic ideas of environment and its component
Population growth: exponential and logistic; resources; sustainable development.
Concept of green chemistry: green catalyst, green solvents
Environmental disaster and social issue: environmental impact assessment, environmental audit, environmental laws and
protection act of India.

Module 2 [6L]
Air Pollution: Structures of the atmosphere, global temperature models, Greenhouse effect, global warming; acid rain: causes,
effects and control. Lapse rate and atmospheric stability; pollutants and contaminants; smog; depletion of ozone layer; standards
and control measures of air pollution.
Module 3 [6L]
Water Pollution: Hydrosphere; pollutants of water: origin and effects; oxygen demanding waste; thermal pollution; pesticides;
salts. Biochemical effects of heavy metals; eutrophication: source, effect and control. Water quality parameters: DO, BOD,
COD. Water treatment: surface water and wastewater.
Module 4 [6L]
Land Pollution: Land pollution: sources and control; solid waste: classification, recovery, recycling, treatment and disposal.
Noise Pollution: Noise: definition and classification; noise frequency, noise pressure, noise intensity, loudness of noise, noise
threshold limit value; noise pollution effects and control.
3. Textbooks
1. Basic Environmental Engineering and Elementary Biology, Gour Krishna Das Mahapatra, Vikas Publishing House P. Ltd.
2. Environmental Chemistry, A. K. De, New Age International.
3. Environmental Chemistry with Green Chemistry, A. K. Das, Books and Allied P. Ltd.
4. Reference Books
1. Environmental Science, S. C. Santra, New Central Book Agency P. Ltd.
2. Fundamentals of Environment & Ecology, D. De, D. De, S. Chand & Company Ltd.
Course Name: Design & Analysis of Algorithms Lab

0 0 3 3 1.5
1. Course Outcomes
CSEN2251.1. Understand and Apply different types of algorithm designing paradigms like divide and conquer, greedy,
dynamic programming etc.
CSEN2251.2. Realize and Apply underlying mathematical principles of algorithms in the corresponding implemented program.
CSEN2251.3. Analyse and Evaluate the performance of various algorithms by observing the actual running time and main
memory consumption of the corresponding implemented programs for best case, worst case and average case
input data.
CSEN2251.4. Create / Design a good algorithm for solving real life computing problems, by using various design techniques
and data structures, learnt in this course.
A tentative list (non-exhaustive) of the practical topics is given below:
1. Divide and Conquer: Implement Quick Sort and randomized version of quick sort using Divide and Conquer approach.
Check the running time for each of the n! combinations or input sequences of a particular set of integers to observe the
best, worst and average cases.
2. Divide and Conquer: Implement Merge Sort using Divide and Conquer approach. Check the running time for each of the
n! combinations or input sequences of a particular set of integers to observe the best, worst and average cases.
3. Implement Heapsort algorithm. Check the running time for each of the n! combination or input sequences of a particular
set of integers to observe the best, worst and average cases.
4. Dynamic Programming: Find the minimum number of scalar multiplications needed for chain of Matrices.
5. Dynamic Programming: Implement Bellman Ford Algorithm to solve Single Source shortest Path problem of a graph.
6. Dynamic Programming: Implement Floyd-Warshall Algorithm to solve all pair shortest path for a graph.
7. Dynamic Programming: Solve 0/1 Knapsack problem using dynamic problem.
8. Dynamic Programming: Solve Longest Common Subsequence problem using dynamic problem.
9. Greedy method: Implement Dijkstra’s algorithm to find Minimum Spanning Tree of a graph by using minimum priority
Queue or minimum heap data structure.
10. Greedy method: Implement Prim’s algorithm to find Minimum Spanning Tree of a graph by using minimum priority
Queue or minimum heap data structure.

11. Greedy method: Implement Kruskal’s algorithm to find Minimum Spanning Tree of a graph by implementing and using
various operations of Disjoint-set Forest data structure.
12. Greedy method: Implement Huffman coding using greedy approach.
13. Realization of Amortized Analysis: Implement a Queue using Stacks.
14. Implement KMP algorithm for string matching
15. Implement Ford-Fulkerson algorithm to get maximum flow in a given flow network.
16. Randomized Algorithm: Implement Skip-List).
3. Textbooks
1. Introduction to Algorithms, Cormen, Leiserson, Rivest and Stein. Third Edition, 2009. Prentice Hall.
2. Algorithm Design, Jon Kleinberg and Eva Tardos. Addison Wesley, 2005.
4. Reference Books
1. Computer Algorithms: Introduction to Design and Analysis, Sarah Basee and Allen van Gelder. 3rd Edition, Addison
Wesley.
Course Name: Computer Architecture Lab

0 0 2 2 1
1. Course Outcomes
CSEN2252.1. Understand the role of simulator environment in digital hardware design
CSEN2252.2. Design various combinational and sequential logic circuits using simulator
CSEN2252.3. Realize / Implement simulator-based circuits performing various arithmetic and logic operations
CSEN2252.4. Interpret informal system requirements into formal testbenches
CSEN2252.5. Realize various types of memory / storage elements via simulator
CSEN2252.6. Create / Design a standard open-source CPU in RTL and / or in microarchitecture using logic building blocks.
Programming using VHDL
1. All Logic Gates (Data flow and Behavioral model)
2. Half adder and half subtractor (Data flow and Behavioral Model)
3. Combinatorial Designs (Data flow and Behavioral Model)
a. 2:1 Multiplexer
b. 4:1 Multiplexer
c. 3:8 Decoder
d. Comparator
4. Full adder and full subtractor (Data flow, Behavioral and Structural Model)
5. Sequential design of flip flops (SR, JK, D, T)
6. ALU design
7. Ripple carry adder (Structural Model)
8. Adder subtractor composite unit (Structural Model)
9. 4 bit synchronous and asynchronous counters.
10. Small projects like stepper motor.
3. Textbooks
1. VHDL: Programming by Example, Douglas L. Perry, Fourth Edition, McGraw Hill.
4. Reference Books
1. Introduction to Logic Circuits & Logic Design with VHDL, LaMeres, Brock J, Springer.

Course Name: Operating Systems Lab

0 0 3 3 1.5
1. Course Outcomes
CSEN2253.1. Understand and implement basic services and functionalities of the operating system using system calls.
CSEN2253.2. Will be able to describe and create user defined processes.
CSEN2253.3. Understand the benefits of thread over process and implement them.
CSEN2253.4. Synchronization programs using multithreading concepts.
CSEN2253.5. Use modern operating system calls and synchronization libraries in software to implement process
synchronization.
CSEN2253.6. Implementation of Inter-process communication using PIPE.
1. Shell programming: Creating a script, making a script executable, shell syntax (variables, Conditions, control structures,
functions and commands).
2. Process: starting new process, replacing a process image, duplicating a process image, waiting for a process, zombie
process.
3. Signal: signal handling, sending signals, signal interface, signal sets.
4. Semaphore: programming with semaphores (use functions semctl, semget, semop, set_semvalue, del_semvalue,
semaphore_p, semaphore_v).
5. POSIX Threads: programming with pthread functions (viz. pthread_create, pthread_join, pthread_exit, pthread_attr_init,
pthread_cancel)
6. Inter-process communication: pipes (use functions pipe, popen, pclose), named pipes (FIFOs, accessing FIFO).
3. Textbooks
1. Your Unix: The Ultimate Guide, Sumitabha Das, MH
4. Reference Books
1. Beginning Linux Programming, Neil Matthew, Richard Stones, Wrox.
Course Name: Microprocessors & Microcontroller Lab

0 0 2 2 1
1. Course Outcomes
AEIE2255.1. Understand and apply different instructions of 8085 microprocessor.
AEIE2255.2. Understand and apply different instructions of 8086 microprocessor.
AEIE2255.3. Understand and apply different instructions of 8051 microcontroller.
AEIE2255.4. Interface 8085A microprocessor with different input and output devices (e.g., LEDs, seven segments displays
ADC, DAC, and stepper motor etc.).
AEIE2255.5. Interface 8086A microprocessor/ 8051 microcontroller with different input and output devices (e.g., LEDs,
seven segments displays ADC, DAC, and stepper motor etc).
1. Familiarization with 8085A trainer kit components with the process of storing and viewing the contents of memory as well
as registers. Repeat the above all using 8085A Simulator.
2. Study of programs using basic instruction set (data transfer, load/store, arithmetic, logical) of 8085A microprocessor.
3. Programming using 8085A trainer kit/simulator for:
a) Copying and Shifting block of memory
b) Packing and unpacking of BCD numbers
c) Addition/Subtraction of two 8-bit Hex numbers
d) Addition of 16-bit Hex numbers.
e) BCD Addition
f) Binary to ASCII conversion
g) String Matching and Sorting.

4. Familiarization of 8086 microprocessor trainer kit/simulator using data transfer, load/store, arithmetic and logical
instructions.
5. Write assembly language programs (ALP) using 8086 microprocessor trainer kit /simulator on the following:
a) Finding the largest/ smallest number from an array
b) Arranging numbers in ascending/descending order
c) Shifting a block of data from one memory location to another
d) Addition of a series of BCD numbers
e) String matching
6. Interfacing of 8085A through 8255A PPI/ 8051 Microcontroller with switches and LEDs to perform
a) Display operation
b) Blinking operation and
c) Scrolling operation
7. Interfacing with seven segment displays through 8-bit latch (e.g., 74LS373) using- a) 8085A trainer kit, b) 8086A trainer
kit through 8255A PPI.
8. Interfacing of ADC, DAC, and Stepper motor with 8085A/8086 microprocessor trainer kit.
3. Textbooks and References

Assignment Sets to be provided.



A. THEORY COURSES
Course Name: Database Management Systems

4 0 0 4 4
1. Course Outcomes
CSEN3101.1. Identify the basic concepts and various data model used in database design. Be able to model an application’s
data requirements using conceptual modeling tools like ER diagrams and design database schemas based on the
conceptual model.
CSEN3101.2. Formulate relational algebra expression for queries and evaluate it using the concept of query processing and
optimization.
CSEN3101.3. Create RDBMS schema mapping various business validations and formulate queries based on that schema using
SQL to satisfy business requirements.
CSEN3101.4. Apply normalization and various types of dependencies for evaluating a relational database design.
CSEN3101.5. Apply and relate the concept of transaction, concurrency control and recovery in database.
CSEN3101.6. Understand with basic database storage structures and access techniques: file and page organizations, indexing
methods including B tree, and hashing.
Module 1 [10L]
Introduction: An overview of database management system, Database system Vs file system, ACID properties, Advantage of
database, Data Independency, Integrity constraints, Evolution of DBMS, Different types of databases, Database Languages,
Three-schema architecture of a database, Different users of Database, Role of DBA.
Relational Database Design using ER Model: Data modeling concepts, Notations for ER diagram ( entity, different types of
attributes, relationship, cardinality and degree of relationship, weak entity), Concepts of Super Key candidate key and primary
key, Mapping Constraints (Mapping Cardinality constraint, Participation Constraints, Key Constraints), Design Issues,
Generalization, aggregation, Extended E-R features (Generalization & Specialization, Aggregation, Attribute Inheritance ),
Examples of Drawing ER diagram, Convert ER diagrams into tables.
Relational Data Model: Concept of relations, Relational Algebra Operators: Selection, Projection, Union, Intersection, Set
difference, Cross product, Rename, Assignment, Various types of joins, Division.
Module 2 [10L]
Introduction to SQL: DDL, DML, DCL, TCL, Data definition in SQL, Table, Primary key and foreign key definitions, DDL
syntax and semantics – Create/Alter/Drop/Truncate, Implementing various constraints in DDL (Data Types, Null, Primary Key,
Unique Key, Referential Integrity Constraints using foreign key, Complex business rules using trigger and assertions), Creating
and using views, Creating Index.
Data manipulation in SQL: Insert, Edit, Delete and Basic select- from- where block and its semantics, Update behaviors,
Complex Querying using inner and outer join, Nested queries - correlated and uncorrelated, Aggregate functions group by and
having clauses, Unions, Intersection, Minus.
Cursors, Trigger, Procedures and Functions in SQL/PL SQL, Using JSON functions in Oracle.
Dependency theory: (functional dependencies, Armstrong's axioms for FDs, Closure of a set of FDs, Minimal covers:
irreducible set of Functional Dependencies or Canonical Cover), Attribute Closure, Determine candidate Keys of a relation.
Module 3 [10L]
Data Base Design & Normalization: Different anomalies in designing a Database, Normalization and different Normal Forms,
Definitions of 1NF, 2NF, 3NF and BCNF and using various normal form during design, Decompositions and desirable
properties of them, Lossy and Loss-less join decompositions, Dependency preservation, Normalization using multi-valued
dependencies and 4NF, Join dependency, Definition of 5NF.
Module 4 [13L]
Concurrency control and Recovery Management: Transaction Fundamentals: OLTP environments, Concurrency issues,
Need for transactions, Necessary properties of transactions (ACID properties), and Transaction states.
Concurrency control schemes (Pessimistic scheme, Optimistic scheme, pros and cons), Scheduling Transactions for concurrent
execution, Anomalies with Interleaved Execution, Various schedules (Serial, Conflict serializability, View serializability),
Testing of conflict serializability.
Recoverability and recoverability of Schedule (Irrecoverable schedule, Recoverable with cascading rollback), Lock-Based
Concurrency Control, Lock Based Protocols, Two Phase Locking and how it works, Deadlock in DBMS, Wait-for graph,
Detecting deadlocks using wait-for graphs, Schemes of Deadlock prevention (explain with example Wait-Die Scheme, Wound
wait scheme). Transaction Support in SQL.

File Organization & Index Structures: File Organization: Fixed-Length and Variable-Length Records Organization of
Records in Files (Sequential File Organization, Clustering File Organization.
Index: Basic Concept, Various types (Ordered, Hash), Ordered Indexing Methods (Primary Index - Dense index, Sparse index),
Multilevel and Secondary Indices, Using B-trees as dynamic multi-level indexes, Introduction to B+ tree index and various
operation in B+ tree index. Creating Indexes using SQL - Function-Based Index, Bitmap Indexing.
Query Processing and Optimization: Different steps of processing a high-level query, Notation for Query Trees and Query
Graphs, Translating SQL into relational algebra, Query Optimizer Concepts, Measures of Query Cost, Different Query
Algorithms used (no details), Concepts of Materialization and Pipelining, Heuristic Optimization of Query Trees, Statistical
Information for Cost Estimation, Steps used for Cost-Based Optimization.
3. Textbooks
1. Database System Concepts, Henry F. Korth and Silberschatz Abraham, Mc.Graw Hill.
2. Fundamentals of Database Systems, Elmasri Ramez and Navathe Shamkant, Benjamin Cummings Publishing Company.
3. Database Management System, Ramakrishnan, McGraw-Hill.
4. Transaction Processing: Concepts and Techniques, Gray Jim and Reuter Address, Moragan Kauffman Publishers.
5. Advanced Database Management System, Jain, CyberTech.
6. Introduction to Database Management, Vol. I, II, III, Date C. J., Addison Wesley.
7. Principles of Database Systems, Ullman JD., Galgottia Publication.
4. Reference Books
1. Principles of Database Management Systems, James Martin, 1985, Prentice Hall of India, New Delhi.
2. Database Management Systems, Arun K.Majumdar, Pritimay Bhattacharya, Tata McGraw Hill.
Course Name: Formal Language & Automata Theory

4 0 0 4 4
1. Course Outcomes
CSEN3102.1. Recall the basic characteristics of various types of machines, languages and grammars.
CSEN3102.2. Compare different computational models, languages and grammars based on their properties and behaviors.
CSEN3102.3. Apply formal mathematical methods to prove properties of languages, grammars, and automata.
CSEN3102.4. Apply the knowledge of theory of computation to an engineering application (e.g., designing the compilers).
CSEN3102.5. Classify formal languages and evaluate whether a language/grammar belongs to a given type or not.
CSEN3102.6. Design automata for given languages/grammars. Generate languages/grammars for a given automaton and
Construct grammars for languages and vice versa.
Module 1 [11L]
Fundamentals: Basic definition of sequential circuit, block diagram, mathematical representation, concept of transition table
and transition diagram, Design of sequence detector (Application of concept of Automata to sequential circuit design),
Introduction to finite state model.
Finite state machine: Definitions, capability & state equivalence, kth- equivalence concept. Minimization of FSM,
Equivalence between two FSM’s, Limitations of FSM; Moore & Mealy machine and their conversion.
Finite Automata: Deterministic finite automaton (DFA) and non-deterministic finite automaton (NFA). Transition diagrams
and Language recognizers; Application of finite automata, NFA with ϵ transitions - Significance, acceptance of languages.
Design of DFA/ NFA for given languages.
Conversions and Equivalence: Equivalence between NFA with and without ϵ transitions. NFA to DFA conversion.
Module 2 [12L]
Introduction to Formal Languages and Grammars: Chomsky Classification of grammar: unrestricted, context sensitive,
context free and regular grammar. Grammar Formalism: Right linear and left linear grammars, Regular grammar, Regular
Languages, Regular sets. Regular expressions, identity rules, Problems on Regular expressions. Arden’s theorem statement,
proof and applications. Constructing finite Automata for a given regular expressions, Regular string accepted by NFA/DFA.
Pumping lemma of regular sets. Closure properties of regular sets (proofs not required). Equivalence between regular grammar
and FA.

Module 3 [13L]
Context free grammar: Introduction to Context free grammars, Derivation/ parse trees, Sentential forms, Right most and
leftmost derivation of strings, ambiguity in context free grammars, various problems on CFG. Minimization of Context Free
Grammars: Removal of useless, null and unit productions. Chomsky normal form and Greibach normal form. Pumping Lemma
for Context Free Languages. Enumeration of properties of CFL (proofs omitted). Closure property of CFL, Ogden’s lemma &
its applications.
Push Down Automata: Push down automata, Definition and design of PDA. Acceptance of CFL, Acceptance by final state
and acceptance by empty state and its equivalence. Equivalence of CFL and PDA, conversion from one to another. (Proofs not
required). Introduction to DCFL and DPDA.
Module 4 [12L]
Turing Machine: Introduction to Turing Machine, Definition, Model. Design of TM for different languages, TM as language
accepter. TM as transducers. Computable functions. Languages accepted by a TM, recursively enumerable and recursive
languages. Diagonalization method. Church’s hypothesis, counter machine. Types of Turing machines (proofs not required).
Universal Turing Machine. Decidability, Undecidability, Various Undecidable problems like Post's Correspondence Problem
(PCP), Turing Machine Halting Problem, Ambiguity of Context Free Grammars etc.
3. Textbooks
1. Introduction to Automata Theory Language and Computation, Hopcroft H.E. and Ullman J. D., Pearson Education.
2. An Introduction to Formal Languages and Automata, Peter Linz, Jones and Bartlett Publishers.
3. Introduction to the Theory of Computation, Sipser Michael. Cengage Learning.
4. Theory of Computer Science, Automata Languages and computation”, Mishra and Chandrashekaran, 2nd edition, PHI.
4. Reference Books
1. Switching & Finite Automata, ZVI Kohavi, 2nd Ed., Tata McGraw Hill.
2. Introduction to Computer Theory, Daniel I.A. Cohen, John Wiley.
3. Introduction to languages and the Theory of Computation, John C Martin, TMH.
4. Elements of Theory of Computation, Lewis H.P. & Papadimitrou C.H. Pearson.
Course Name: Object Oriented Programming

4 0 0 4 4
1. Course Outcomes
CSEN3103.1. Understand the principles of object-oriented programming.
CSEN3103.2. Compare the relative merits of C++ and Java as object-oriented programming languages.
CSEN3103.3. Understand the importance of error management and incorporate exception-handling in object-oriented
programs.
CSEN3103.4. Apply multithreading techniques to improve performance.
CSEN3103.5. Apply the features of C++ and Java supporting object-oriented programming to develop modular applications.
CSEN3103.6. Analyse problems and estimate when object-oriented programming is an appropriate methodology to design and
develop object-oriented software using C++ and Java.
Module 1 [10L]
Overview of Object-Oriented Programming Concepts: Difference between OOP and procedural programming – advantages
& disadvantages. class, object, message passing, inheritance, encapsulation, polymorphism.
OOP with C++: Basic Programming Concepts: Data Types, Operators, Control Statements & Loops, Functions & Parameters
Arrays, Pointers & References. Class & Object, Abstraction / Encapsulation, Access Specifier. Static Member, Friend Function.
Constructor and Destructor.
Module 2 [10L]
OOP with C++: Function and Operator Overloading. Inheritance and Derived Class: Abstract Class, Runtime Polymorphism,
Virtual Base Class, Overriding. Exception Handling. Namespaces, Class Template and Function Template.
Module 3 [10L]
OOP with Java: Features of Java, Byte Code & JVM, Concepts of Java Application and Applet. Basic Programming Concepts:
Data Types, Operators, Control Statements & Loops, Functions & Parameters, Array. String Handling Concepts & related
Functions, Command Line Arguments. User Input through Scanner. Class & Object, Access Specifier, Static Members,

Constructor, Garbage Collector, Nested & Inner Class: Function Overloading, Inheritance, Runtime Polymorphism, Abstract
Class.
Module 4 [11L]
Package and Interface. Exception Handling: Types of Exception Classes, Use of Try & Catch with Throw, User-defined
Exceptions Classes. Threads, Communication and Synchronization of Threads: Multithreading, Thread Lifecycle, Thread
Priorities, Inter-thread Communication. Applet Programming (using Swing): Applet Lifecycle, Application & Applet,
Parameter Passing, Event Model & Listener, I/O.
3. Textbooks
1. The C++ Programming Language, Stroustrup, Adisson Wesley.
2. Object Oriented Programming in C++, R. Lafore, SAMS.
3. Java 2.0 Complete Reference, H. Schildt, McGrawHill.
4. Reference Books
1. JAVA How to Program, Deitel and Deitel, Prentice Hall.
2. Programming with Java: A Primer, E. Balagurusamy, 3rd Ed. – TMH.
Course Name: Electronic Design Automation

2 0 0 2 2
1. Course Outcomes
ECEN3106.1. Getting exposure to VLSI Design Cycle, Process nodes and Design Challenges.
ECEN3106.2. Designing of Industry Standard CMOS Combinational Digital Gates.
ECEN3106.3. Designing of Industry Standard TG based Sequential Digital Gates.
ECEN3106.4. Learning High Level Synthesis in EDA flow.
ECEN3106.5. Learning Logic Synthesis in EDA flow and Verilog RTL.
ECEN3106.6. Learning Physical Place and Route in EDA flow.
Module 1 [8L]
VLSI Circuits & Physical Layout: MOS Transistor Characteristics, MOS as Digital Switch, NMOS Logic Family, CMOS
Logic Family, CMOS Inverter Characteristics, Delay & Noise, CMOS NAND, NOR and Combinational Logic Circuits, Pass
Transistor Logic & Transmission Gate, CMOS Sequential Circuits, CMOS D-Latch and D-Flip-Flop, Setup and Hold Time.
CMOS Cross Section, Layout and Mask layers, Inverter Layout, Lambda Rule vs Micron Rule, Std Cell Layout Topology,
Stick Diagram, Euler Path Algorithm.
Module 2 [4L]
VLSI Design Methodology: Moore’s Law, Scale of Integration (SSI, MSI, LSI, VLSI, ULSI, GSI), Technology growth and
process Node.
VLSI Design Cycle, Full Custom Design, Std Cell based Semi Custom Design, Gate Array Design, PLD, FPGA: CLB, LUT.
Module 3 [6L]
EDA: High level Synthesis and Logic Synthesis: High level Synthesis EDA Flow, Control and Data Flow Graph, Scheduling,
Allocation, Binding, Verilog RTL.
Combinational Logic Optimization: Binary Decision Diagram (BDD), OBDD, ROBDD, Technology Mapping: Pattern DAG,
Subject DAG, Sequential Logic Optimization.
Module 4 [6L]
EDA: Physical Design Automation: Physical Layout Automation EDA Flow, Partitioning: KL Algorithm, Floor-planning
cost function, Floor plans Placement, Global Routing: Steiner Tree, Maze Routing. Detailed Routing: Channel Routing,
Horizontal Constraint Graph, Vertical Constraint Graph, Cyclic Constraint, Left-edge Algorithm.
3. Textbooks
1. Principles of CMOS VLSI Design, A Systems Perspective, Neil Weste, Kamran Eshraghian, Addison Wesley, 2nd
Edition, 2000.
2. Algorithms for VLSI Physical Design Automation, N. Sherwani, Kluwer Academic Publishers (3rd edition).

4. Reference Books
1. CMOS Digital Integrated Circuits, Analysis and Design, Sung-Mo Kang, Yusuf Leblebici, Tata McGraw Hill (3rd Edition).
2. CMOS VLSI Design, A Circuits and Systems Perspective (3rd Edition), Neil Weste, David Harris, Ayan Banerjee. Pearson.
3. Digital Integrated Circuit, Design Perspective, M. Rabaey, Prentice-Hall.
4. VLSI Design and EDA TOOLS, Angsuman Sarkar, Swapnadip De, Chandan Kumar Sarkar, Scitech Publications (India)
Pvt. Ltd., 2011.
5. Algorithms for VLSI Design Automation, Gerez, Wiley, 2011.
LIST OF COURSES FOR PROFESSIONAL ELECTIVE – I
Paper Code Paper Name

CSEN3131 Computer Graphics and Multimedia
CSEN3132 Data Mining and Knowledge Discovery
CSEN3133 Web Technologies
CSEN3134 Graph Algorithms
CSEN3135 Introduction to Data Analysis with Python and R
Course Name: Computer Graphics & Multimedia

3 0 0 3 3
1. Course Outcomes
CSEN3131.1. Compare and study effectiveness of different line and circle drawing algorithms on Raster scan display.
CSEN3131.2. Design two-dimensional graphics and apply two dimensional transformations.
CSEN3131.3. Design three-dimensional graphics and apply three dimensional transformations.
CSEN3131.4. Apply Illumination and color models and apply clipping techniques to graphics.
CSEN3131.5. Demonstrate activities and applications of device dependent and independent color models, image
representation techniques (raster and random graphics).
CSEN3131.6. Understood Different types of Multimedia File Format and demonstrate image, video, text analysis tools and
techniques.
Module 1 [10L]
Introduction to computer graphics & graphics systems: Overview of computer graphics, representing pictures, preparing,
presenting & interacting with pictures for presentations; Visualization & image processing; RGB color model, direct coding,
lookup table; storage tube graphics display, Raster scan display, 3D viewing devices, Plotters, printers, digitizers, Light pens
etc.; Active & Passive graphics devices; Computer graphics software.
Scan Conversion: Points & lines, Line drawing algorithms; DDA algorithm, Bresenham’s line algorithm, Circle generation
algorithm; Ellipse generating algorithm; scan line polygon, fill algorithm, boundary fill algorithm, flood fill algorithm.
Module 2 [9L]
2D transformation & viewing: Basic transformations: translation, rotation, scaling; Matrix representations & homogeneous
coordinates, transformations between coordinate systems; reflection shear; Transformation of points, lines, parallel lines,
intersecting lines, Viewing pipeline, Window to view port co-ordinate transformation, clipping operations, point clipping, line
clipping, clipping circles, polygons & ellipse. Cohen and Sutherland line clipping, Sutherland-Hodgeman Polygon clipping,
Cyrus-beck clipping method.
Overview of 3D Transformation and Viewing: 3D transformations: translation, rotation, scaling & other transformations.
rotation about an arbitrary axis in space, reflection through an arbitrary plane; general parallel projection transformation;
clipping, viewport clipping, 3D viewing.
Module 3 [8L]
Curves: Curve representation, surfaces, designs, Bezier curves, B-spline curves, end conditions for periodic B-spline curves,
rational B-spline curves.
Hidden surfaces: Depth comparison, Z-buffer algorithm, Back face detection, BSP tree method, the Painter’s algorithm, scan-
line algorithm; Hidden line elimination, wire frame methods, fractal -geometry.
Color & shading models: Light & color model; interpolative shading model; Texture.

Module 4 [9L]
Text: Different types of text representation, Hypertext, text representation formats.
Audio: Basic Sound Concepts, Types of Sound, Digitizing Sound, Computer Representation of Sound (Sampling Rate,
Sampling Size, Quantization), Audio Formats, Audio tools, MIDI.
Video: Analogue and Digital Video, Recording Formats and Standards (JPEG, MPEG, H.261) Transmission of Video Signals,
Video Capture.
Animation: Techniques of 2D & 3D animation, formats of Animation
Image and Video Database: Image representation, segmentation, similarity-based retrieval, image retrieval by color, shape
and texture; indexing- k-d trees, R-trees, quad trees.
3. Textbooks
1. Computer Graphics (C version 2nd Ed.), Hearn, Baker, Pearson education .
4. Reference Books
1. Schaum’s outlines Computer Graphics (2nd Ed.), Z. Xiang, R. Plastock, TMH.
2. Computer Graphics: Principles and Practice, 2nd Edition, Foley, Vandam, Feiner and Hughes, Pearson Education, 2003.
3. Mathematical Elements for Computer Graphics (2nd Ed.), D. F. Rogers, J. A. Adams, TMH.
4. Multimedia: Computing, Communications & Applications, Ralf Steinmetz and Klara Nahrstedt, Pearson Ed.
5. Multimedia Communications, Fred Halsall, Pearson Ed.
6. Multimedia Fundamentals: Vol. 1- Media Coding and Content Processing, Ralf Steinmetz and Klara Nahrstedt, PHI.
7. Principles of Multimedia, Ranjan Parekh, TMH.
8. Introduction to Computer Graphics and Multimedia, A Mukhopadhyay, A Chattopadhyay, Vikas Publication.
Course Name: Data Mining & Knowledge Discovery

3 0 0 3 3
1. Course Outcomes
CSEN3132.1. Learn and understand basic knowledge of data mining and related models.
CSEN3132.2. Understand and describe data mining algorithms.
CSEN3132.3. Understand and apply Data mining algorithms.
CSEN3132.4. Suggest appropriate solutions to data mining problems.
CSEN3132.5. Analyze data mining algorithms and techniques.
CSEN3132.6. Perform experiments in Data mining and knowledge discovery using real-world data.
Module 1 [9L]
Introduction and Rule-based Classification: What is Data Mining? Why do we need data mining? Data Mining System
- Architecture and Processes. Challenges in Data Mining.
Decision Tree: General approach for solving a classification problem, Decision Tree Induction, Overfitting Pruning.
Rule-based Classification: How a rule-based classifier work s, rule -ordering schem es , how to build a rule-based
classifier, direct and indirect methods for rule extraction.
Module 2 [9L]
Advanced Classification Techniques: Bayes’ Classifier: Bayes’ theorem, Naïve Bayes classifier.
Support Vector Machines (SVM): Maximum margin hyperplanes, Linear SVM: separable case, non-separable case, Non-linear
SVM.
Module 3 [9L]
Ensemble Methods, Association Rule Mining: Ensemble Methods: Bagging, Boosting, Random Forests
Association Rule Mining: Introduction, Frequent itemset generation, (Apriori principle, candidate generation and pruning),
Rule generation, Compact representation of frequent item sets, FP-growth algorithm, Sub-graph mining.
Module 4 [9L]
Cluster Analysis: Introduction: Motivations, objectives and applications of clustering. Different types of clustering.
Partitional Clustering: K-means, Bisecting K-means, PAM.
Hierarchical Clustering: Agglomerative, Divisive, MIN, MAX, dendrogram representation.
Density-based Clustering: DBSCAN. Cluster evaluation, further reading – OPTICS, DENCLUE, CHAMELEON, BIRCH,
CURE, ROCK.

3. Textbooks
1. Data Mining Concepts and Techniques, 3rd, Edition, J. Han and M. Kamber, Morgan Kaufmann Publishers, July 2011.
4. Reference Books
1. Introduction to Data Mining, P. N. Tan, M. Steinbach and V. Kumar, Pearson Publishers.
2. Pattern Recognition and Machine Learning, First Edition, C. Bishop, Springer, 2006.
3. Neural Networks and Learning Machines, Third Edition, S. Haykin, PHI Learning, 2009.
4. Pattern Classification, Second Edition, R. Duda, P. Hart and D. Stock, Wiley-Interscience, 2000.
Course Name: Web Technologies

3 0 0 3 3
1. Course Outcomes
CSEN3133.1. Understand the basic tags of HTML, CSS, java script and DHTML.
CSEN3133.2. Connect a server-side program using servlet and JSP to a DBMS and perform insert, update and delete operations
on DBMS table.
CSEN3133.3. Write a server-side program using servlet and JSP to store the data sent from client, process it and store it on
database.
CSEN3133.4. Prepare a well-formed / valid XML document, schema to store and transfer data.
CSEN3133.5. Understand various types of attacks and their characteristics.
CSEN3133.6. Get familiar with network security designs using available secure solutions (such as PGP, SSL, IPsec).
Module 1 [8L]
Introduction: Commonly used protocols: HTTP, HTTPs, TELNET, Electronic Mail-POP3, SMTP etc., WWW-Evolution
and its characteristics.
Basics of Web Technology: Static web page, Dynamic web page, Active web page.
HTML and CSS: Introduction, Editors, Elements, Attributes, Heading, Paragraph. Formatting, Link, Head, Table, List, Block,
Layout, CSS. Form, Iframe, Colors, Colorname, Colorvalue. Image Maps.
Module 2 [10L]
Web page scripting, server and client side: Java Script: Data types, variables, operators, conditional statements, array object,
date object, string object.
Extensible Markup Language (XML): Introduction, Tree, Syntax, Elements, Attributes, Validation, Viewing. XHTML in brief.
Java Servlet: Servlet environment and role, HTML support, Servlet API, The servlet life cycle, Cookies and Sessions.
Module 3 [10L]
Advanced Java Server Side Programming: JSP: JSP architecture, JSP servers, JSP tags, understanding the layout in JSP,
Declaring variables, methods in JSP, inserting java expression in JSP, processing request from user and generating dynamic
response for the user, using include and forward action, Creating ODBC data source name, introduction to JDBC, prepared
statement and callable statement. J2EE: An overview of J2EE web services.
Module 4 [8L]
Network Security: Threats: Malicious code-viruses, Trojan horses, worms; Active and Passive attacks: eavesdropping,
spoofing, modification, denial of service attacks.
Network security techniques: Password and Authentication; VPN, IP Security, security in electronic transaction, Secure
Socket Layer (SSL).
Firewall: Introduction, Packet filtering, Stateful, Application layer, Proxy.
3. Textbooks
1. Web Technologies: HTML, JAVASCRIPT, PHP, JAVA, JSP, ASP.NET, XML and Ajax, Dreamtech Press; first edition.
2. Web Technologies, Godbole and Kahate, Tata McGraw-Hill Education.
3. Web Technologies: A Computer Science Perspective, Jeffrey C. Jackson, Pearson,2011.
4. Reference Books
1. Web Technology: A Developer's Perspective, N.P.Gopalan and J. Akilandeswari, PHI Learning, Delhi, 2013.
2. Internetworking Technologies, An Engineering Perspective, Rahul Banerjee, PHI Learning, Delhi, 2011.
3. Java Servlets and JSP, Murach's.

4. Java for the Web with Servlets, JSP, and EJB, Budi. Kurniawan.
5. Cryptography and Network security, William Stallings.
6. Introduction to Web Technology, Pankaj Sharma, S K Kataria and Sons; Reprint 2013 edition.
Course Name: Graph Algorithms

3 0 0 3 3
1. Course Outcomes
CSEN3134.1. Learn the advanced concepts and key features of Graph algorithms.
CSEN3134.2. Understand the algorithmic approach to Graph related problems.
CSEN3134.3. Explain and analyze the major graph algorithms.
CSEN3134.4. Employ graphs to model engineering problems, when appropriate.
CSEN3134.5. Defend and argue the application of the specific algorithm to solve a given problem.
CSEN3134.6. Synthesize new algorithms that employ graph computations as key components, and analyze them.
Module 1 [8L]
Connected components and transportation related graph problems: Representation of graphs, Sub graphs, Degree
Sequences, Connectivity, Cut-Vertices and Bridges, Digraphs; Depth First Search. DFS for undirected graphs, non-separable
components and directed graphs. Topological Sorting. Strongly connected components, Tarjan's algorithm for strongly
connected components; Eulerian tours, Characterization. De Bruijn Sequences. Eulerian Digraphs ; Hamiltonian graphs and
travelling salesman problem. Exponential-time dynamic programming for the TSP, approximation algorithms and the
approximation ratio, MST-doubling heuristic, Christofides' heuristic.
Module 2 [10L]
Flow networks and Bipartite graphs: Max flow min cut theorem, max flow algorithms and their applications; Min cost max
flow algorithm, their applications; Bipartite graphs, formulating bipartite maximum matching as a flow problem.
Matching and covering related graph problems: Matchings, stable marriage problem, Gale-Shapley algorithm for stable
marriage problem; Hopcroft–Karp algorithm. Using matchings to find vertex covers and independent sets.
Module 3 [10L]
Graph Coloring, Planarity and longest path: Graph coloring, greedy coloring, Maximal clique; Brooks theorem, the greedy
algorithm, the Welsh-Powell bound, critical graphs, chromatic polynomials, girth and chromatic number, Vizing's theorem.
Introduction to planarity of the graph, duality of the planar graph and max cut of the planar graph. Euler's formula, Kuratowski's
theorem, toroidal graphs, 2-cell embeddings, graphs on other surfaces; Longest path Problem, hardness and heuristic for
solution.
Module 4 [8L]
Random graphs and Selected topics: Random graphs and probabilistic methods; Dominating sets, the reconstruction problem,
intersection graphs, interval graphs, perfect graphs, Chordal graphs; Maximum Clique-Minimum coloring problem in interval
graph; Algorithms for independent set, clique and vertex coloring in Chordal graphs.
3. Textbooks
1. Graph Algorithms, Shimon Even and Guy Even, Cambridge University Press, 2nd Edition 2012.
2. Introduction to Graph Theory, Douglas B. west, Prentice Hall, 2001.
3. Graph Theory and Its Applications, Jonathan L. Gross and Jay Yellen.
4. Advanced graph algorithms, T. Kloks.
4. Reference Books
1. Graph Theory, R. Diestel, Springer-Verlag, 2nd edition, 2000.
2. Modern Graph Theory, Bela Bollobas, Springer, 1998.
3. Algorithm Design, Jon Kleinberg and Eva Tardos.

Course Name: Introduction to Data Analysis with Python and R

3 0 0 3 3
1. Course Outcomes
CSEN3135.1. Understand the basics of data science and of the Python Programming Language and its data structures viz.
Lists, Tuples, Dictionaries and Sets.
CSEN3135.2. Develop Python codes using iterations, recursion, function, input/output with files and using exception
handling.
CSEN3135.3. Learn how to manipulate strings, use regular expression, object-oriented features of Python and to write good
and efficient codes in Python.
CSEN3135.4. Apply NumPy library and develop codes using Pandas data structures (Series and Data Frames) and other
features of Pandas.
CSEN3135.5. Learn and understand the basics of the R Programming Language and its data structures to develop R codes
using Selection, Modification, Logical sub-setting, conditionals, loops and other advanced features of R
language and debug the code.
CSEN3135.6. Apply Python and R in building solutions to basic data analysis problems.
Module 1 [9L]
Data Science Introduction: Facets of data. The Big Data Ecosystem and Data Science. The Data Science Process. Retrieval,
cleansing, integrating and transforming data. Exploratory Data Analysis. Data Visualization.
Introduction to Python: History of Python. Setting up the development environment. Variables, Expressions, Statements.
Functions, Conditionals, Recursion, Iteration.
Data Organization: Files and Exceptions. Classes, objects, inheritances, Object Oriented Programming in Python.
Module 2 [9L]
Manipulating Strings: Regular Expressions in Python.
Python Data Structures: Lists, Tuples, Dictionaries, Sets.
Effective Python: Pythonic Thinking and Writing Better Pythonic Code.
Module 3 [9L]
Processing with NumPy: The Basics of NumPy Arrays. Array Indexing: Accessing Single Elements. Array Slicing: Accessing
Subarrays. Reshaping of Arrays. Array Concatenation and Splitting. Computation on NumPy Arrays: Universal Functions. The
Slowness of Loops. Aggregations: Min, Max, Summing the Values in an Array. Computation on Arrays: Broadcasting. Rules
of Broadcasting.Comparisons, Masks, and Boolean Logic. Working with Boolean Arrays. Boolean Arrays as Masks. Fancy
Indexing.
Data Manipulation with pandas: Introduction to pandas data structures. Series, Data frames, Index objects. Re-indexing,
Selection, Filtering, Axis Indices, Summarizing, Handling missing data, Hierarchical Indexing.
Module 4 [9L]
R Programming Introduction: R UI, RStudio, Functions, Arguments, Scripts.
R Data Structures: Vectors, Attributes, Matrices, Arrays, Classes, Factors, Lists, Data Frames.
Computing with R: Using R Operations: Selection, Modification, Logical sub-setting.
Handling Missing Information, Conditionals, Scoping rules, Assignment, Evaluation, Loops: For, While, Repeat, Efficiency
Issues. R Code: Debugging, Profiling, Simulations with R code.
3. Textbooks
1. Introduction to Programming Using Python, Y. Daniel Liang. Pearson, 2017.
2. Python for Data Analysis, Wes McKinney, O’Reilly, 2017.
3. Hands on Programming in R, Garrett Grolemund, O'Reilly.
4. Reference Books
1. Python for Everybody, Charles Severance, 2016.
2. Advanced R, Hadley Wickham. CRC Press, 2015.
3. R for Data Science, Hadley Wickham and Garrett Grolemund, 2017.
4. Introducing Data Science, D. Cielen, A. Meysman, M.Ali, Manning Publishers, 2018.
5. Effective Python, Brett Slatkin, Pearson, 2015.

B. PRACTICAL COURSES
Course Name: Database Management Systems Lab

0 0 3 3 1.5
1. Course Outcomes
CSEN3151.1. Learn to use Entity Relationship Diagram (ERD) model as a blueprint to develop the corresponding relational
model in a RDBMS system like Oracle DBMS.
CSEN3151.2. Apply DDL component of Structured query language (SQL) to create a relational database from scratch through
implementation of various constraints in Oracle RDBMS system.
CSEN3151.3. Apply DML component of Structured query language (SQL) for storing and modification of data in Oracle
RDBMS system.
CSEN3151.4. Apply DQL component of Structured query language (SQL) to construct complex queries for efficient retrieval
of data from existing database as per the user requirement specifications.
CSEN3151.5. Conceptualize and apply various P/L SQL concepts like cursor, trigger in creating database programs.
CSEN3151.6. Develop a fully-fledged database backend system using SQL and P/L SQL programming to establish overall
integrity of the database system.
Creation of a database using a given ERD Model as blueprint:
SQL Data Definition Language - Create (and Alter) table structure, Apply (and Alter) constraints on columns/tables viz.,
primary key, foreign key, unique, not null, check. Verify/ Review the table structure (along with applied constraints) using
appropriate data dictionary tables like user_constraints, user_cons_columns, etc. Create view, materialized view using one or
more table.
SQL Data Manipulation Language - Insert into rows (once at a time/ and in bulk) from a table, Update existing rows of a table,
Delete rows (a few or all rows) from a table.
Data Query Language (DQL):

Basic select-from-where structure - Usage of Top, Distinct, Null keywords in query, Using String and Arithmetic Expressions,
Exploring Where Clause with various Operators and logical combination of various conditions, Sorting data using Order By
clause. Usage of IN, LIKE, ALL keywords.
Introduction to Joins -Natural Joins, equi-join, non-equi-join, Self-Join, Inner Join, Outer (left, right) Join.
Set operations- Unions, Intersect, minus set operations on table data using SQL.
Using single row functions in Queries - NVL function (to handle ambiguity of null data), upper, lower, to_date, to_char
functions, etc.
Using group/multiple row functions in Queries like Count, Sum, Min, Max, Avg, etc, using Group By and Having Clause,
Using Group By with Rollup and Cube.
Sub-query - Working with various nested structure of Sub Queries - use in from or where clause with more than one level of
nesting, correlated sub-query- Ranking table data using correlated sub-query.
P/L SQL:
Stored Procedures and Functions- Basic programming constructs of PL / SQL like if, else, else-if, loop, while, for structure
Populate stored procedure variables with the data fetched from table using SQL command.
Working with Cursors - Creating Cursors, parameterized cursor, Locks on cursors, Exploring advantages of cursors.
Introduction to triggers - Constraints vs Triggers, Creating, Altering, Dropping triggers, use of for/ after/ instead of triggers,
Using trigger to validate/ rollback a Transaction, Automatically populate integer data based primary key columns (e.g., Id.)
using trigger.
3. Textbooks
2. Fundamentals of Database Systems, Elmasri Ramez and Novathe Shamkant, Benjamin Cummings Publishing Company.
4. Reference Books
1. SQL, PL/SQL: The Programming Language of Oracle (With CD-ROM) (English) 4th Revised Edition, Ivan Bayross,
BPB Publications.

Course Name: Object Oriented Programming Lab

0 0 3 3 1.5
1. Course Outcomes
CSEN3153.1. Apply object-oriented principles or features in software design process to develop C++ and Java programs for
real life applications.
CSEN3153.2. Reduce the complexity of procedural language by employing operator overloading, inheritance and exception
handling techniques for developing robust and reusable software.
CSEN3153.3. Develop programs using stream classes for various I/O operations and design concurrent programs using threads
to maximize the use of processing power.
CSEN3153.4. Design applications for text processing using String class and develop user interactive applications using event
handling.
CSEN3153.5. Analyse the difference between two object-oriented programming languages C++ and Java.
Assignments on C++:
Day 1
1.Introduction to OOPs concepts, Difference between Structure and Class 2. Use of Constructor and Destructor
Day 2
1. Function overloading, Friend Function, Friend Class
Day 3
1. Operator Overloading without using friend function 2. Operator Overloading with using friend function
Day 4
1. Inheritance: Single, Multilevel, Multiple, Hybrid
Day 5
1. Virtual Base class, Virtual Function, Abstract Class
Day 6
1. Exception Handling 2. Templates and namespace
Assignments on Java:
Day 7
1. Understanding Java platform, compilation, and execution of a java program.
2. Implement class, object, constructor, methods, and other OOP features.
Day 8
1. Inheritance Basics, more uses of constructor, method overriding, use of final.
Day 9
1. Object class, practical use of abstract class.
2. Using Interface for achieving multiple inheritance, implementation of package.
Day 10
1. Exception handing fundamentals, java built-in exceptions, Use of Scanner class for console input, use of own Exception
subclass.
Day 11
1. Java thread life cycle model and implementation approach, thread priority, implementation of synchronization.
2. I/O Basics, byte stream and character streams, reading and writing files.
Day 12
1. Applet life cycle implementation, text processing using Java predefined String, StringBuilder and StringBuffer classes.
Day 13
1. GUI basics and Window fundamentals, working with different Component, Container and Layout Managers.
Day 14
1. Event handling for interactive GUI application.
3. Textbooks
1. The C++ Programming Language, Stroustrup, Adisson Wesley.
2. Object Oriented Programming in C++, R. Lafore, SAMS.
3. Java 2.0 Complete Reference, H. Schildt, McGrawHill.
4. Reference Books
1. JAVA How to Program, Deitel and Deitel, Prentice Hall.
2. Programming with Java: A Primer, E. Balagurusamy – 3rd Ed. – TMH.
Course Name: Electronic Design Automation Lab

0 0 2 2 1
1. Course Outcomes
ECEN3156.1. Learning Industry Standard Frontend and Synthesis CAD Tool (Xilinx Vivado).
ECEN3156.2. Learning Industry Standard Verilog RTL Behavioral and Structural Design.
ECEN3156.3. Learning Logic Synthesis and Place and Route using FPGA Flow.
ECEN3156.4. Learning Industry Standard Backend CAD Tool (Mentor Graphics).
ECEN3156.5. Designing CMOS Combinational Digital Gates
ECEN3156.6. Designing CMOS/TG Sequential Digital Gates.
List of Experiments:
1. Familiarities with Xilinx Vivado Front end and Synthesis CAD Tool
2. Verilog RTL Design and Testing of Digital Gates (INV, NAND, NOR, MUX, AOI, OAI …)
3. Verilog RTL Design and Testing of Functional Blocks (Adder, Decoder, ALU …)
4. Verilog RTL Design and Testing of Sequential Gates (Latch, Flop …)
5. Verilog RTL Structural Design and Testing of Functional Blocks
6. Verilog RTL Design and Testing for Finite State Machine (Mealy, Moore)
7. Logic Synthesis and P & R using Vivado for FPGA
8. Familiarity with Mentor Graphics Back end CAD Tool
9. CMOS Inverter, NAND, NOR Delay, VTC, Noise Analysis
10. CMOS/TG Sequential Design and Analysis
3. Textbooks
1. Principles of CMOS VLSI Design, A Systems Perspective, Neil Weste, Kamran Eshraghian, Addison Wesley, 2nd Edition.
2. Algorithms for VLSI Physical Design Automation, N. Sherwani, Kluwer Academic Publishers (3rd edition).
4. Reference Books
1. CMOS Digital Integrated Circuits, Analysis and Design, Sung-Mo Kang, Yusuf Leblebici, Tata McGraw Hill (3rd Edition).
2. CMOS VLSI Design, A Circuits and Systems Perspective (3rd Edition), Neil Weste, David Harris, Ayan Banerjee. Pearson.
4. VLSI Design and EDA TOOLS, Angsuman Sarkar, Swapnadip De, Chandan Kumar Sarkar, Scitech Publications (India)
Pvt. Ltd., 2011.
5. Algorithms for VLSI Design Automation, Gerez, Wiley, 2011.
C. HONORS COURSES
Course Name: Artificial Intelligence

3 0 0 3 3
1. Course Outcomes
CSEN3111.1. Remember and understand the basic principles of state-space representation of any given problem, various
searching and learning algorithms, game playing techniques, planning algorithms, logic theorem proving,
natural language processing etc.
CSEN3111.2. Comprehend the importance of knowledge as far as intelligence is concerned and the fundamentals of
knowledge representation and inference techniques.
CSEN3111.3. Use the knowledge gained so far to logically infer new knowledges in both certain and uncertain environment.

CSEN3111.4. Illustrate various AI searching algorithms, like state-space search algorithm, adversarial search algorithm,
constraint satisfaction search algorithm and different learning models and planning techniques as and when
required.
CSEN3111.5. Apply the working knowledge of Prolog/ Lisp in order to write simple Prolog/ Lisp programs and to explore
more sophisticated Prolog/ Lisp code on their own.
CSEN3111.6. Design and evaluate the performance of various heuristics applied to real- world situations.
Module 1 [9L]
Introduction: Definition of AI, Intelligent Behavior, Turing Test, Typical AI Problems, Various AI Approaches, Limits of AI.
Introduction to Intelligent Agents: Agents & environment, Agent Architecture, Agent Performance, Rational Agent, Nature
of Environment, Simple Reflex Agent, Goal Based Agent, Utility Based Agent.
Knowledge Representation & Propositional Logic: Knowledge representation issues, Approaches to knowledge
representation, Propositional Logic – its syntax & semantics, Inference rules, Resolution for propositions, Limitation of
Propositional Logic.
Problem Solving using Single Agent Search: Introduction to State-space search, state-space search notation, search problem,
Formulation of some classical AI problems as a state space search problem, Explicit Vs. Implicit State space.
Uninformed Search Techniques: Basic Principles, Evaluating parameters, BFS, DFS, Depth Limited Search, Iterative
Deepening DFS, Uniform Cost Search & Bidirectional Search, Properties of various search methods & their comparative
studies.
Module 2 [9L]
Informed Search Methods: Basic Principles, Heuristics, A* Search and its properties, Admissible & Consistent heuristic,
Iterative deepening A* (IDA*) and AO* search, Local Search Techniques – Hill climbing & Simulated Annealing, Comparison
with other methods
Problem Solving using Two Agent Search: Adversarial Search – Game Tree, MINIMAX Algorithm, Alpha-Beta Pruning,
Performance Analysis.
Constraint Satisfaction Problem: Definition of CSP, Representation of CSP, Formulation of Various popular problems as
CSP, Solution methods of CSP – Backtracking & Forward Checking.
Module 3 [9L]
Knowledge Representation & Predicate Logic: Syntax & Semantics of FOPL, Representation of facts using FOPL, Clauses,
Resolution, Unification methods of inference, Default & Non-Monotonic reasoning.
Knowledge Representation using Rules: Rule based system, Horn clauses, Procedural vs. declarative knowledge, forward &
backward reasoning, Introduction of logic programming using PROLOG/ LISP.
Probabilistic reasoning: Representing knowledge in an uncertain domain, probabilistic inference rules, Bayesian networks –
representation & syntax, semantics of Bayesian net, Brief discussion on Fuzzy sets & fuzzy logic.
Other Representational Formalism: Inheritable knowledge, Semantic network, Inference in Semantic network, Extending
Semantic Network, Frames, Slots as objects.
Module 4 [9L]
Planning: Introduction, Simple planning agent, Problem solving vs. planning, Logic based planning, Goal Stack planning,
Planning as a search, Total-order vs. partial order planning.
Learning: Overview, Taxonomy of learning system, various learning models, learning rules, Naïve Bayes classifier and
Decision tree based learning, Brief idea about learning using Neural Network & Genetic Algorithm.
Natural Language Processing: Introduction, Syntactic processing, semantic analysis, discourse & pragmatic processing.
Expert Systems: Representing and using domain knowledge, expert system shells, and knowledge acquisition.
3. Textbooks
1. Artificial Intelligence: A Modern Approach, Stuart Russell & Peter Norvig, Pearson Education.
2. Artificial Intelligence, Rich & Knight, TMH.
4. Reference Books
1. Artificial Intelligence & Intelligent Systems, N.P Padhy, Oxford University Press.
2. Introduction to Artificial Intelligence & Expert Systems, Dan W. Patterson, PHI.
3. Artificial Intelligence: A new Synthesis, Nils J. Nilsson, Morgan Kaufmann Publishers, Inc.
4. PROLOG Programming for Artificial Intelligence, Ivan Bratko, Pearson India.

Course Name: Artificial Intelligence Lab

0 0 2 2 1
1. Course Outcomes
CSEN3161.1. Remember and understand the working principles of PROLOG/ LISP
CSEN3161.2. Apply LIST structure of PROLOG as and when required
CSEN3161.3. Make use of CUT to the programs as and when required
CSEN3161.4. Solve the problems by using accumulator
CSEN3161.5. Apply the principles of reasoning and inference to real world problems
CSEN3161.6. Design programs to solve various puzzles.
In this laboratory students will be familiarized with PROLOG/ LISP language. A tentative outline is given below:
1. Introduction to PROLOG facts & rules with the help of a simple family tree; how the goals are given in PROLOG; some
simple queries on the family tree
2. Formation of recursive definition; how PROLOG executes the goals; simple assignments
3. How PROLOG deals with problems with numbers – integers, real; with some examples
4. Introduction to LIST structure; how PROLOG implements LIST; some simple assignments on LIST.
5. Some more complex assignments on LIST; Introduction of Accumulators – simple assignments
6. Introduction to CUT with simple assignments; implementation of Sorting algorithms
7. PROLOG clauses for file operation – with simple assignments
8. Implementation of Graph Search algorithms like DFS, BFS; Some application of DFS & BFS
9. Implementation of some well-known puzzles, like 8-queens problem, Towers-of-Hanoi problem, Missionaries &
Cannibals problem etc.
10. Introduction to LISP
11. Some simple assignments on LISP.
3. Textbooks
1. PROLOG Programming for Artificial Intelligence, Ivan Bratko, Pearson India.
4. Reference Books
1. Logic and Prolog Programming, Saroj Kaushik, New Age International Publishers.



A. THEORY COURSES
Course Name: Software Engineering

4 0 0 4 4
1. Course Outcomes
CSEN3201.1. Propose a software life cycle model for the given requirements and compile software requirement specifications
as per IEEE guidelines.
CSEN3201.2. Develop function-oriented design and/or object-oriented design for software systems using industry standard
techniques.
CSEN3201.3. Apply the knowledge of different coding standards and/or guidelines and propose test cases for sample software
system modules in different testing methods.
CSEN3201.4. Compare and contrast among different types of software maintenance and to decide on the maintenance models
to be employed depending on the situation.
CSEN3201.5. Apply different project management strategies for project planning such as to estimate the project size, duration
and cost.
CSEN3201.6. Apply the ideas of different project monitoring and control techniques such as WBS, Activity Network, PERT
chart, Critical path etc. to efficiently monitor and control the project. They will be able to identify different
software project risks and determine their mitigation approaches.
Module 1 [12L]
Introduction to Software Engineering: Software Engineering – objectives and definitions, Software Life Cycle – different
phases, Lifecycle Models - Waterfall, Relaxed Waterfall, RAD, Prototyping, Incremental, Spiral.
Modern Software Engineering practices: Agile: Values and Principles, Philosophy, Agile vs. Waterfall, Methods and
Practices of Agile, Pitfalls of Agile methodology, Scrum: Roles, Workflow: Sprint, Daily Scrum, Sprint review etc., Limitations
of scrum, Extreme Programming: Principles, Guidelines, Activities, Values, Practices, Introduction to DevOps and SEMAT.
Requirements Analysis and Specification Phase: Requirements Collection and Analysis, Requirement Specifications –
General Structure of Software Requirement Specifications (SRS), Functional and Non-functional Requirements, Representing
Requirements as Use Cases with examples.
Structured Analysis Modeling Techniques: Process Model using Context Diagrams (CD) and Data Flow Diagram (DFD)
with examples, Data Dictionary, Decision Tree, Decision Table with examples, Data Model using Entity Relationship Diagram
(ERD) with examples.
Module 2 [12L]
Design Phase: Overview – Comparison between Requirement Analysis and Design, Attributes of Good Design, Design
Approaches – Functional and Object-Oriented Design approaches, Design Aspects – Top-Down and Bottom-Up, Structured
Design – Module Design (or High-Level Design), Detail Design (or Low-Level Design), Functional Decomposition –
Abstraction, Structure Chart, Structured English, Design Issues – Cohesion, Coupling.
Object Oriented Analysis and Design: OOAD Basic Concepts, Unified Modeling Language (UML) – different types of
diagrams for different views of system, User View – Use Case Diagram with examples, Structural Views – Class Diagram with
examples, Behavioral View – Sequence, Collaboration, Activity and State Chart Diagrams with examples.
Module 3 [12L]
Coding or Programming: Programming Principles and Guidelines – Structured Programming, Code Re-use, Coding
Standards / Guidelines, Coding Process – Incremental Coding, Test Driven Development, Pair Programming / Extreme
Programming Source Code Version Control, Build, Code Refactoring.
Review and Testing: Self Review / Peer Review, Testing Overview-- Objective, Definition, Static and Dynamic Testing,
Functional vs. Non-functional Testing, Testing Artifacts – Test Cases and Test Suites, Traceability Matrix, Test Data, Stub and
Driver, Testing Process – Test Case Design, Test Case Execution, Test Result, Defect Logging and Tracking, Testing Methods
-- White Box Testing with Test Coverage using Control Flow Graph (CFG) and Cyclomatic Complexity, Black Box Testing
with Equivalence Class Partitioning and Boundary Value Analysis, Testing Level – Unit Testing, Integration Testing, System
Testing, (User) Acceptance Testing, Regression Testing, Performance Testing, Usability Testing, Non-functional Testing.
Module 4 [12L]
Software Maintenance: Types of Maintenance – Corrective, Preventive, Adaptive Change Management and Maintenance
Process models, Estimation of maintenance cost.
Software Estimation: Overview of Software Estimation – Size, Effort, Duration and Cost

Size Estimation Methods – Lines of Code (LOC) and Function Points (FP) Estimation of Effort and Duration based on Size
and Productivity, Constructive Cost Model (COCOMO) – Basic COCOMO, Intermediate COCOMO (COCOMO 81), Detailed
COCOMO (COCOMO II).
Project Management: Project Management Overview - Planning, Staffing, Execution, Monitoring and Control
Responsibilities of Project Manager, Project Scheduling – Work Breakdown Structure (WBS) and Activity network, Gantt
Charts, PERT chart, Determining the Critical Path.
Configuration Management: Overview of Configuration Management, Software Configuration Management tasks:
Identification, Change Control, Version Control, Auditing, Concept of Baseline, Versioning of Configurable Items (CI).
3. Textbooks
1. Software Engineering: A Practitioners Approach, 5th Ed, R. S. Pressman, McGraw-Hill, 2001.
2. Software Engineering, 7th Ed, Sommerville, Addison-Wesley, 2005.
4. Reference Books
1. Software Engineering: A Precise Approach, 3rd Edition, Pankaj Jalote, 2013.
2. Fundamentals of Software Engineering, 3rd Edition, Rajib Mall, 2013.
3. Fundamentals of Software Engineering, 2nd Ed, C. Ghezzi, M. Jazayeri and D. Mandrioli, Prentice Hall of India, 2003.
Course Name: Computer Networks

4 0 0 4 4
1. Course Outcomes
CSEN3202.1. Learn the terminology and concepts of the OSI reference model, TCP‐IP reference model and the need for the
layered architecture.
CSEN3202.2. Understand the concepts of protocols, network interfaces, and design/performance issues in local area
networks and wide area networks
CSEN3202.3. Analyze the requirements for a given organizational structure and select the most appropriate networking
architecture and technologies
CSEN3202.4. Demonstrate various types of routing techniques
CSEN3202.5. Defend and argue the various quality of service measures to improve network throughput.
CSEN3202.6. Synthesize the strength and shortcomings of the underlying protocols, and then go on to hypothesize new and
better application layer protocols.
Module 1 [10L]
Introduction: Direction of data flow (simplex, half duplex, full duplex), Network topology, categories of network (LAN,
MAN, WAN).
Protocols and standards: Reference models: OSI reference model, TCP/IP reference model, their comparative study
Physical Layer: Digital signal coding, Modulation (Digital and Analog), Multiplexing, Switching, Telephone Networks,
Transmission Media and its properties.
Module 2 [13L]
Data link layer: Framing / Stuffing, Error detection and correction.
Flow Control Protocols: Stop-and-Wait / Go-Back-N / Selective Repeat; HDLC, PPP.
MAC sub-layer: Ethernet (IEEE 802.3): ALOHA / CSMA-CD / Collision Resolution, Controlled Access and Channelization
methods.
Devices: Transparent Bridges / Source-Route Bridges / Ethernet Switches; Backward Learning Algorithm; Construction of
Spanning Trees; Routers.
Module 3 [10L]
IPv4: Packet format; Classful addressing / sub-netting / subnet mask; CIDR / super-netting / masks.
IPv6: address format / packet format / differences with IP (v4).
Protocols: IP, ICMP, ARP.
Routing algorithm: concept of static and dynamic routing, Distance vector / Link state algorithm.
Protocols: OSPF, BGP.
Module 4 [10L]
Transport Layer: Process to process delivery / multiplexing and other services of transport layer.

Transport Layer protocols: TCP: Three-way handshaking, Window management, Flow and congestion control with slow
start, additive increase, multiplicative decrease; UDP; Difference between UDP and TCP.
General Congestion control algorithm: open and closed loop; Techniques to improve: QoS Leaky bucket / Token bucket.
Modern Topics: Introduction to wireless LAN and Bluetooth, Mobile IP, Mobile TCP.
3. Textbooks
1. Computer Networks, Andrew S. Tanenbaum, Pearson Education, Fourth edition.
2. Data and Computer Communication, William Stallings, Prentice Hall, Seventh edition.
3. High speed Networks and Internets, William Stallings, Pearson education, Second edition.
4. Reference Books
1. Cryptography and Network security, William Stallings, PHI, Third edition.
2. ISDN and Broadband ISDN with Frame Relay and ATM, William Stallings.
3. Computer Networking: A Top-Down Approach, 5th Ed., Kurose & Ross.
Course Name: Economics for Engineers

3 0 0 3 3
1. Course Outcomes
HMTS3201.1. Evaluate a project and estimate the total cost of the project
HMTS3201.2. Apply financial analytical methodologies to prepare a report regarding the financial performance of an
organization
HMTS3201.3. Participate actively in an organization’s capital budgeting process
HMTS3201.4. Provide vital inputs regarding the pricing of a product
HMTS3201.5. Apply the knowledge of the interplay of various economic variables and indicators in workplace
HMTS3201.6. Provide insight about different accounting concepts and apply broader concepts like costs, revenues, assets,
liabilities, capital, profit, investment and interest.
Module 1 [8L]
Market: Meaning of Market, Types of Market, Perfect Competition, Monopoly, Monopolistic and Oligopoly market.
The basic concept of economics – needs, wants, utility.
National Income-GDP, GNP. Demand & Supply, Law of demand, Role of demand and supply in price determination,
Price Elasticity.
Inflation: meaning, reasons, etc.
Module 2 [8L]
Business: Types of business, Proprietorship, Partnership, Joint-stock company, and cooperative society – their characteristics.
Banking: role of commercial banks; credit and its importance in industrial functioning.
Role of central bank: Reserve Bank of India.
International Business or Trade Environment.
Module 3 [12L]
Financial Accounting-Journals. Ledgers, Trial Balance, Profit & Loss Account, Balance Sheet.
Financial Statement Analysis (Ratio and Cash Flow analysis).
Cost Accounting- Terminology, Fixed, Variable and Semi-variable costs.
Break Even Analysis. Cost Sheet. Budgeting and Variance Analysis.
Marginal Cost based decisions.
Module 4 [8L]
Time Value of Money: Present and Future Value, Annuity, Perpetuity.
Equity and Debt, Cost of Capital.
Capital Budgeting: Methods of project appraisal - average rate of return - payback
period - discounted cash flow method: net present value, benefit cost ratio, internal rate of return.
Depreciation and its types, Replacement Analysis, Sensitivity Analysis.
3. Reference Books
1. Financial Accounting- A Managerial Perspective, R. Narayanswami, Prentice-Hall of India Private Limited. New Delhi

2. Fundamentals of Financial Management, Horne, James C Van, Prentice-Hall of India Private Limited, New Delhi
3. Modern Economic Theory, H. L. Ahuja., S. Chand. New Delhi.
4. Engineering Economic Analysis, Newman, Donald G., Eschenbach, Ted G., and Lavelle, Jerome P., New York: Oxford
University Press. 2012.
LIST OF COURSES FOR PROFESSIONAL ELECTIVE - II

CSEN3231 Advanced Operating System
CSEN3232 Enterprise Application in Java EE
CSEN3233 Machine Learning
CSEN3234 Computational Geometry
CSEN3235 Cloud Computing
CSEN3235 Big Data
Course Name: Advanced Operating System

3 0 0 3 3
1. Course Outcomes
CSEN3231.1. Describe operating system structures and communication protocols.
CSEN3231.2. Understand key mechanisms and models for distributed systems including logical clocks, causality, vector
timestamps, distributed hash tables, consistent global states, election algorithms, distributed mutual exclusion,
consistency, replication, fault tolerance, distributed deadlocks, recovery, agreement protocols
CSEN3231.3. Learn how to design and implement distributed algorithms.
CSEN3231.4. Understand the high-level structure distributed file systems.
CSEN3231.5. Design various areas of research in distributed systems.
CSEN3231.6. Understand the basic concepts of real time operating system.
Module 1 [9L]
Introduction to Distributed System: Introduction, Examples of distributed system, Resource sharing, Challenges
Operating System Structures: Review of structures: monolithic kernel, layered systems, virtual machines. Process based
models and client server architecture; The micro-kernel based client-server approach.
Communication: Inter-process communication, Remote Procedure Call, Remote Object Invocation, Tasks and Threads.
Examples from LINUX, Solaris 2 and Windows NT.
Module 2 [9L]
Theoretical Foundations: Introduction. Inherent Limitations of distributed Systems. Lamport's Logical clock. Global State:
Chandy, Lamport's Global State Recording Algorithm.
Distributed Mutual Exclusion: Classification of distributed mutual exclusion algorithm. Non-Token based Algorithm:
Lamport's algorithm, Ricart-Agrawala algorithm. Token based Algorithm: Suzuki-Kasami's broadcast algorithm. A
comparative performance analysis of different algorithms w.r.t Response time, Synchronization delay, Message traffic,
Universal performance bound.
Distributed Deadlock Detection: Deadlock handling strategies in distributed systems. Control organizations for distributed
deadlock detection. Centralized and Distributed deadlock detection algorithms: Completely Centralized algorithms, path
pushing, edge chasing, global state detection algorithm.
Module 3 [9L]
Distributed file systems: Issues in the design of distributed file systems: naming, writing policy, Cache consistency,
Availability, Scalability and Semantics. Use of the Virtual File System layer. Case Studies: Sun NFS, The Sprite File System,
CODA, The x-Kernel Logical File System.
Distributed Shared Memory: Architecture and motivations. Algorithms for implementing DSM: The Central-Server
Algorithm, The Migration Algorithm, The Read-Replication Algorithm, The Full-Replication Algorithm. Memory Coherence.
Case Studies: IVY, Clouds.

Distributed Scheduling: Issues in Load Distributing: Load, Classification of Load Distribution, Load Balancing vs Load
Sharing, Preemptive vs Non-preemptive; Components of a load distribution; Stability.
Module 4 [9L]
Real Time operating Systems: Operating system basics, Tasks, Process and Threads, Multiprocessing and multitasking, task
communication, task synchronization, Definition and types of RTOS; A reference model of Real Time System- Processors,
Resources, Temporal parameters, Periodic Task; Aperiodic Task, Sporadic Task; Commonly used approaches to Real Time
Scheduling - Clock driven, event driven, Priority based scheduling- Inter-process communication mechanisms – Evaluating
operating system performance- power optimization strategies for processes – Example Real time operating systems-POSIX-
Windows CE.
3. Textbooks
1. Advanced Concepts in Operating Systems, Singhal Mukesh & Shivaratri N. G., TMH.
2. Distributed Operating Systems, Tanenbaum, A. S., Prentice Hall India.
3. Distributed Operating Systems Concepts and Design, Pradeep K. Sinha, Prentice Hall India.
4. Real-Time Systems, Jane W. S. Liu, Pearson Education.
4. Reference Books
1. Distributed Systems Principles and Paradigms, Andrew S. Tanenbaum and Maarten Van Steen, PHI.
2. Modern Operating Systems, 2ndEdition Tanenbaum, A. S., Prentice Hall 2001.
3. Concurrent Systems, 2nd Edition, Bacon, J., Addison Wesley 1998.
4. Applied Operating Systems Concepts, 1st Edition, Silberschatz, A., Galvin, P. and Gagne, G., Wiley 2000.
5. Distributed Systems: Concepts and Design, 3rd Edition, Coulouris, G. et al, Addison Wesley 2001.
Course Name: Enterprise Application in Java EE

3 0 0 3 3
1. Course Outcomes
CSEN3232.1. Identify the basic needs and application of server-based technology like JEE.
CSEN3232.2. Understand the various components available in JEE and their applicability in MVC pattern.
CSEN3232.3. Handling RDBMS using JDBC and JPA in JEE.
CSEN3232.4. Understand various data interchange formats and using XML for data exchange.
CSEN3232.5. Understand and using JEE components in distributed environment.
CSEN3232.6. Developing an enterprise-wide web application using components of JEE.
Module 1 [8L]
Client & server-side programming.
Enterprise architecture styles: Single tier, 2-tier, 3-tier, n-tier; Relative comparison of the different layers of architectures.
MVC Architecture: Explanation, Need, Drawbacks.
Overview of JEE, Different components & containers.
Overview of Java servlets, Servlet process flow/ Architecture, Understanding Servlet life cycle, Other important objects and
methods in Servlet API, Servlet Vs CGI, Developing servlet using IDE.
Short introduction of JSP, Comparison between JSP & servlet.
XML Overview, Different types of XML Parsing, XML Schema, How To use XSD, Namespace, Declaring and Applying
Namespaces.
Module 2 [10L]
Java Server Faces: Introduction, Benefits of Java Server Faces, Design goals and Features of JSF, JSF Application Structure,
Understanding the JSF Request Processing Lifecycle.
Getting started JSF application using IDE (NetBeans), The key pieces of the JSF pie, Managed beans, JSF User Interface
Component Model, Usages of JSF UI Components/tags (form, outputText, inputText, commandButton, inputSecret,
commandLink, graphicImage, message, messages, dataTable, column, panelGrid, panelGroup, selectOneListbox,
selectBooleanCheckbox, selectOneRadio etc.), Exploring the JSF expression language, Standard and custom validation and
converter , Value Binding, Method Binding, FacesContext, FacesMessage, Event Handling.
Navigation model example, Introduction to Facelets, Creating Facelets Views and Mapping Faces Servlet, Facelets Templates,
JSF Composite Components, JSF Web Resources, Using HTML5-Friendly Markup in JSF.

Module 3 [10L]
EJB: Introduction, Comparison of EJB & Java Beans, Applications, Drawbacks, Different types of enterprise beans, Services
provided by EJB container.
JDBC: Introduction, Database driver, Different approaches to connect an application to a database server,
Establishing a database connection and executing SQL statements, JDBC prepared statements, JDBC data Sources, Developing
CRUD operation in JSF application and plain Java Application using JDBC.
Module 4 [8L]
Java Persistence API: Overview, Important terms and annotations related to JPA, Java Persistence Query Language, Creating
Queries in JPQL, JPQL Static and Dynamic Query Example, JPA JPQL Bulk Data Operations, Using JPA from Web
application using JSF, Using JPA in a Java application.
Introduction to Web Services and RESTful Web Services, Differences, Advantages and Disadvantages, RESTful Key
Elements, Important annotation of JAX-RS, Developing RESTful Web Services with JAX-RS, Database access using JPA and
RESTful Service, Accessing RESTful Service from Java application and Web application using JSF.
3. Textbooks
1. Professional JAVA Server Programming, Allamaraju and Buest, SPD Publication.
2. Beginning J2EE 1.4 Ivor Horton, SPD Publication.
3. Advanced Programming for JAVA 2 Platform Austin and Pawlan, Pearson.
4. Reference Books
1. Internet & Java Programming, Krishnamoorthy & S. Prabhu, New Age Publication.
2. Online resources from reputed sites like Oracle Doc, TutorialsPoint, Guru 99, Java Code.
3. Geek, Vogella.com etc.
Course Name: Machine Learning

3 0 0 3 3
1. Course Outcomes
CSEN3233.1. Learn and understand the basics of machine learning approaches and paradigm.
CSEN3233.2. Understand and describe various machine learning algorithms.
CSEN3233.3. Understand complexity of Machine Learning algorithms and their limitations.
CSEN3233.4. Mathematically analyze various machine learning approaches and paradigms
CSEN3233.5. Analyze various machine learning techniques to get an insight of when to apply a particular machine learning
approach.
CSEN3233.6. Apply common Machine Learning algorithms in practice and implementing their own using real-world data.
Module 1 [9L]
The learning Problem: Example of learning, Components of learning, A simple model, Types of learning;
The Linear Model I: Input Representation, Linear Classification, Linear and Logistic Regression, Nonlinear Transformation.
Module 2 [9L]
Error and Noise; Training vs Testing: From Training to Testing, Dichotomies, Growth Function, key notion: Break Points;
The VC Dimension: The definition, VC Dimension of Perceptrons, Interpreting the VC Dimension, Utility of VC Dimension.
Bias-Variance Tradeoff: Bias and Variance, Learning Curves.
Module 3 [10L]
The linear Model II: Logistic Regression, Nonlinear Transformation, Likelihood measure, Gradient Descent;
Neural Networks: Neural Network Model, Backpropagation algorithm; Introduction to Radial Basis Function, Recurrent
Neural Network, Convolution Neural Network and Deep Neural Network.
Module 4 [9L]
Support Vector Machines (SVM): The Margin, Maximizing the Margin, The solution, Support Vectors, Nonlinear
Transform; Kernel Methods: The Kernel methods, Soft-margin SVM; Overfitting: What is overfitting? Dealing with
overfitting; Regularization: Regularization - informal, Regularization – formal, Weight decay, Choosing a regularizer.
3. Textbooks
1. Learning from Data - A short Course, Y. S. Abu-Mostafa, M. Magdon-Ismail, H. T. Lin, AMLbook.com.

2. Computational Intelligence Principles, Techniques and Applications, Konar, Springer, 2012.
3. Machine Learning, First Edition, T. Mitchell, McGraw-Hill, 1997.
4. Reference Books
1. Neural Networks and Learning Machines, Third Edition, S. Haykin, PHI Learning, 2009.
2. Pattern Recognition and Machine Learning, Christopher M. Bishop, Springer, 2010.
3. Deep Learning, Ian Goodfellow, Yoshua Bengio, Aaron Courville, Francis Bach, MIT Press, 2017.
Course Name: Computational Geometry

3 0 0 3 3
1. Course Outcomes
CSEN3234.1. Learn and understand the common algorithms for solving well-known geometric problems.
CSEN3234.2. Learn and understand the common data structures for efficient storage and querying of geometric data.
CSEN3234.3. Identify problems where algorithms for existing geometric problems can be useful.
CSEN3234.4. Learn standard techniques for designing algorithms and data structures for geometric problems.
CSEN3234.5. Develop algorithms and data structures for simple geometric problems.
CSEN3234.6. Implement geometric algorithms.
Module 1 [9L]
Preliminaries: Introduction, Applications, Plane Sweep paradigm and applications. Line Segment Intersection, Intersections
amongst orthogonal segments, Bentley-Ottoman algorithm, Red-Blue segment intersections. Finding Maximal Points.
Convex Hull: Different Paradigms: Convex Hulls. Properties. Graham's Scan, Jarvis' March (Gift Wrapping), Quick Hull,
Divide and Conquer algorithm (Preparata-Hong), Chan's Algorithm, Randomized Incremental Construction.
Module 2 [10L]
Point Location and Triangulation: Planar Point Location, Polygon Partitioning and Triangulation, Kirkpatrick's method,
Trapezoidal Decompositions, Persistent Data Structures.
Voronoi Diagram and Delaunay Triangulation: Closest Pairs. Bi-chromatic Closest Pairs, Fortune’s sweep Algorithm,
Delaunay triangulations.
Module 3 [9L]
Range Searching: Introduction, Orthogonal Range searching, Priority Search Trees, kd-trees, Range Trees, Interval Trees,
Segment Trees.
Non - Orthogonal Range Searching, Half - Plane Range Searching, Simplex Range Searching, Partition Trees, Cuttings, Adding
Range Restrictions. Colored Range Searching.
Visibility Problems: Visibility Problems, Polygons and Art Gallery Theorems.
Module 4 [9L]
Arrangements and Duality: Arrangements. Construction. Complexity. Zone Theorem. Levels in an Arrangement. Davenport
Schinzel sequences and geometric applications. Complexity of lower and upper envelopes. Duality transformations.
Geometric Optimization: Parametric search and application to geometric optimization.
Geometric Approximation: Dudley's theorem and applications, well-separated pair decompositions and geometric spanners,
VC dimension, epsilon-nets and epsilon-approximations.
3. Textbooks
1. Computational Geometry: Algorithms and Applications (2nd Edition), M. de Berg, M. van Kreveld, M. Overmars, O.
Schwarzkopf, Springer-Verlag, 2000.
2. Computational Geometry, F. Preparata and M. Shamos, Springer-Verlag, 1985
3. Computational Geometry: An Introduction Trough Randomized Algorithms, K. Mulmuley, Prentice-Hall, 1994.
4. Reference Books
1. Discrete and Computational Geometry, S. L. Devadoss and J. O’Rourke, 2011
2. Computational Geometry Lecture Notes, David M. Mount, Department of Computer Science, University of Maryland.

Course Name: Cloud Computing

3 0 0 3 3
1. Course Outcomes
CSEN3235.1. Appreciate the benefits and limitations of cloud-based computing environments.
CSEN3235.2. Understand the underlying principles of cloud virtualization, cloud storage, cloud security.
CSEN3235.3. Analyze the suitability and/or applicability of various cloud computing models, platforms, services, solution
offerings and tools from some industry leaders.
CSEN3235.4. Gain insight into various distributed computing issues (like performance, scalability, availability, reliability) in
light of distributed file systems (such as HDFS, GFS).
CSEN3235.5. Identify security and privacy issues in cloud computing.
CSEN3235.6. Apply Knowledge to provide solution for real life problems.
Module 1 [7L]
Basics of Cloud Computing: Defining a Cloud, Cloud Types – NIST Cloud Reference Model, Cloud Cube Model,
Deployment Models – Public, Private, Hybrid, and Community Clouds, Service Models – Infrastructure as a Service (IaaS),
Platform as a Service (PaaS), and Software as a Service (SaaS), Characteristics of Cloud Computing, Benefits and Limitations
of Cloud Computing.
Module 2 [10L]
Cloud Services and/or Applications: IaaS – Basic Concept and Characteristics, Virtual Machine Instances / Images, examples
of IaaS solutions, PaaS – Basic Concept and Characteristics, Tools and Development Environment with examples, SaaS –
Basic Concept and Characteristics, Open SaaS and SOA, examples of SaaS solutions, Identity as a Service (IDaaS).
Module 3 [10L]
Cloud Solution Offerings: Concepts of Abstraction and Virtualization; Virtualization: Taxonomy of Virtualization
Techniques; Hypervisors: Machine Reference Model for Virtualization.
Solution Offerings from Industry Leaders; Amazon: some AWS Components and Services – Compute (EC2),
Storage [Simple Storage Service (S3), Elastic Block Store (EBS), Simple Queue Service (SQS)], Database
(Relational, NoSQL, SimpleDB), Content Distribution (CloudFront), Deployment (Elastic Beanstalk)
Google: quick look at Google Applications Portfolio – AdWords, Analytics, overview of GWT, a few Google APIs,
some key services of GAE.
Module 4 [9L]
Cloud Storage and Security: Cloud-based Storage: Block Devices and File Devices, Managed Storage and
Unmanaged Storage, File Systems – GFS and HDFS.
Cloud Security: Security Concerns, Security Boundary, Security Service Boundary, Security Mapping Overview,
Data Security – Storage Access, Storage Location, Tenancy, Encryption, Auditing, Compliance, Identity
Management (awareness of Identity Protocol Standards).
3. Textbooks
1. Cloud Computing Bible, Barrie Sosinsky, Wiley India Pvt. Ltd, 2012.
2. Mastering Cloud Computing, Rajkumar Buyya, Christian Vecchiola, S. Thamarai Selvi, McGraw Hill, 2013.
3. Cloud Computing: Theory and Practice, Dan Marinescu, Morgan Kaufmann, 2014.
4. Cloud Computing: A Hands-on Approach, A Bahga and V Madisetti, 2014.
5. Cloud Computing: A Practical Approach for Learning and Implementation, A Srinivasan and J Suresh, Pearson, 2014.
6. Cloud Computing, U S Pande and Kavita Choudhary, S Chand, 2014.
7. Cloud Computing for Dummies, J Hurwitz, M Kaufman, F Halper, R Bloor, John Wiley & Sons, 2014.
8. Cloud Computing, Kris Jamsa, Jones & Bartlett Learning, 2015.
4. Reference Books
1. The NIST Definition of Cloud Computing: Recommendations of the National Institute of Standards and Technology, Peter
Mell and Timothy Grance, National Institute of Standards and Technology Special Publication 800-145, 2011.
2. Introduction to Cloud Computing Architecture: White Paper (1st Edition), Sun Microsystems Inc., 2009.
3. A Survey on Open-source Cloud Computing Solutions, Patrícia Takako Endo, Glauco Estácio Gonçalves, Judith Kelner,
Djamel Sadok, VIII Workshop on Clouds, Grids and Applications at UFPE, Brazil.
4. GFS: Evolution on Fast-Forward – Kirk McKusick (BSD/BFFs) interviews Sean Quinlan (former GFS Tech Leader),
CACM, 2009-2010.
5. The Google File System (GFS), Sanjay Ghemawat, Howard Gobioff, Shun-Tak Leung, 2011.
6. The Hadoop Distributed File System: Architecture and Design, Dhruba Borthakur, Apache Software Foundation, 2007.
Course Name: Big Data

3 0 0 3 3
1. Course Outcomes
CSEN3236.1. Develop understanding of the MapReduce paradigm.
CSEN3236.2. Solve Matrix-Vector problems using the MapReduce paradigm.
CSEN3236.3. Solve Relational Algebra operations using the MapReduce paradigm.
CSEN3236.4. Solve basic algorithmic problems in Graph Theory using the MapReduce paradigm.
CSEN3236.5. Solve problems in Text Processing using the MapReduce paradigm.
CSEN3236.6. Implement MapReduce solutions using the Hadoop framework.
Module 1 [9L]
Introduction: Big Data Analysis. The new software stack. Distributed file system. Physical organization of compute nodes.
Large-scale file system organization; Introduction to the MapReduce paradigm. Map tasks. Grouping by keys. Reduce tasks.
Combiners. Details of MapReduce execution. Coping with node failures; Basic MapReduce Algorithm Design Local
Aggregation. Pairs and Stripes. Computing Relative Frequencies. Secondary Sorting.
Module 2 [9L]
Matrix and Relational Algebra Operations Using MapReduce: Matrix-Vector Multiplication by MapReduce. Case of large
vectors. Matrix Multiplication using cascade of two MapReduce operations. Single pass matrix multiplication; Relational-
Algebra Operations. Computing Selections by MapReduce. Computing Projections by MapReduce. Union, Intersection, and
Difference by MapReduce. Computing Natural Join by MapReduce. Grouping and Aggregation by MapReduce.
Module 3 [9L]
Advanced Processing using MapReduce: Graph Algorithms using MapReduce: Shortest Paths, Friends-of-Friends.
PageRank computation in MapReduce. Parallel Breadth First Search. Issues in Graph Processing; Text Processing Using
MapReduce. EM Algorithms. Hidden Markov Models. Viterbi, Forward and Backward Algorithms. HMM Training in
MapReduce. Word Alignment with MapReduce; Design Patterns using MapReduce. Summarization patterns, Filtering
patterns, Data organization patterns, Join Patterns, Meta patterns, Input output patterns.
Module 4 [9L]
Big Data Solution Frameworks: Starting Hadoop. Components of Hadoop. HDFS. Working with files in HDFS. MapReduce
using Hadoop. Streaming in Hadoop. Advanced MapReduce: Chaining MapReduce jobs, Joining data from different sources.
MapReduce programs in local mode and pseudo-distributed mode. Moving data into and out of Hadoop. Applying MapReduce
patterns to Big Data. Streamlining HDFS for Big Data.
The Hadoop Ecosystem. Pig, Hive, HBase, Sqoop, Zookeeper, Flume, Oozie, Avro.
Fast Big Data Processing with Apache Spark.
3. Textbooks
1. Mining of Massive Datasets, Jure Leskovec, Anand Rajaraman, Jeff Ullman. Cambridge University Press. 2011.
2. Hadoop – The Definitive Guide, Tom White. 4th Edition, 2015.
3. Data-Intensive Text Processing with MapReduce, Jimmy Lin and Chris Dyer. Morgan and Claypool Publishers. 2010.
4. Reference Books
1. Hadoop in Action, Chuck Lam. Manning Publishers. 2011.
2. Hadoop in Practice, Alex Holmes. Manning Publishers. 2012.
MapReduce Design Patterns, Donald Miner and Adam Shook. O’Reilly, 2012.
LIST OF COURSES FOR OPEN ELECTIVE - I

AEIE3221 Fundamentals of Sensors and Transducers
CHEN3221 Water and Liquid Waste Management
ECEN3221 Artificial Intelligence in Radio Communication
ECEN3222 Designing with Processors and Controllers
ECEN3223 Analog and Digital Communication
MATH3221 Computational Mathematics
MATH3223 Scientific Computing
Course Name: Fundamentals of Sensors and Transducers

3 0 0 3 3
1. Course Outcomes
AEIE3221.1. Use different methods for converting a physical parameter into an electrical quantity
AEIE3221.2. Select the best fit transducers, including those for measurement of temperature, strain, motion, position and light
intensity
AEIE3221.3. Choose proper sensor comparing different standards and guidelines to make sensitive measurements of physical
parameters like displacement stress, force, acceleration flow, etc.
AEIE3221.4. Acquire knowledge on high temperature sensing systems used in steel, aluminum, copper plants.
AEIE3221.5. Acquire knowledge on Smart sensors.
AEIE3221.6. Identify different type of sensors used in real life applications and paraphrase their importance.
Module 1 [10L]
Definition, principle of sensing & transduction, classification of transducers.
Resistive Transducers: Potentiometric transducer; Construction, symbol, materials, Loading effect, error calculations,
sensitivity. Strain gauge; Theory, type, materials, gauge factor, temperature compensation and dummy gauge, adhesive,
Inductive sensor: Principle, common types, Reluctance change type, Mutual inductance change type, transformer action type
LVDT: Construction, material, I/O characteristics curve offset, discussion.
Module 2 [6L]
Capacitive sensors: Variable distance-parallel plate type, variable area- parallel plate, variable dielectric constant type,
calculation of sensitivity.
Piezoelectric transducers: piezoelectric effect, charge and voltage co-efficient and relationships, crystal model, materials,
natural & synthetic type, charge amplifier, ultrasonic sensors: Liquid velocity and level measurements, Microphone, response
characteristics.
Module 3 [12L]
Thermal sensors: Resistance Temperature Detector (RTD): materials, temperature range, R-T characteristics configurations,
applications
Thermistors: materials, shape, R-T characteristics, ranges and accuracy specification.
Thermocouple: Thermo laws, types, temperature ranges, series and parallel configurations, cold junction compensation,
compensating cables.
Thermal Radiation sensors: types, constructions and comparison. Semiconductor type IC and PTAT type.
Module 4 [8L]

Radiation sensors: LDR, Photovoltaic cells, photodiodes, photo emissive cell types, materials, construction, response,
applications. Geiger counters, Scintillation detectors, Introduction to smart sensors.
3. Textbooks
1. Sensor and transducers, D. Patranabis, 2nd edition, PHI
2. Transducers and Instrumentation, D.V.S Murty, 2nd edition, PHI.
4. Reference Books
1. Instrument transducers, H.K.P. Neubert, Oxford University press.
2. Measurement systems: application & design, E.A.Doebelin, Mc Graw Hill.
Course Name: Water and Liquid Waste Management

Course Code: CHEN3221
3 0 0 3 3
1. Course Outcomes
CHEN3221.1. Identify the importance of Legislative orders prevalent in India concerning Water and Liquid Waste
Management
CHEN3221.2. Describe the methodology of Establishing and Operating Water and Liquid Waste intensive processes.
CHEN3221.3. Use the principles of Water Management in order to conserve water and solve water-shortage problems
prevalent in India.
CHEN3221.4. Design the Water Treatment and Wastewater Treatment plants following the standard code of practice.
Module 1 [9L]
Introduction to Water Quality and its Storage. Methodology of Water flow measurement. Classification and various Water and
Wastewater Standards prevalent in India. Legislative aspects including Water Act. 1974 and its revisions. Consent to Establish
and Consent to operate water intensive industries. Water conservation methodologies in 1) Process industry, 2) Construction
industry and 3) Service industry. Rainwater Harvesting and various recharge techniques. Principles of Water Audit.
Module 2 [9L]
Water pollution: Sources, sampling and classification of water pollutants, determination of basic parameters and computations
associated with: BOD, COD, TS, TDS, SS; Waste water treatment: primary, secondary, tertiary and advanced; aerobic
treatment with special reference to activated sludge, trickling filter, RBDC and RBRC, EA; non-conventional: WSP, anaerobic
treatment with special reference to AFFR, UASB, numerical problems associated with all topics sited here.
Module 3 [9L]
Preliminaries of Water treatment processes, Basic design consideration: Pre-design, Raw water intake, Screening and aeration,
Water conveyance, Coagulation, Flocculation and Precipitation, Sedimentation, filtration, color, taste and odor control,
Disinfections and fluoridation, Water quality -- Physico Chemical and Bacteriological quality. Water Treatment Plant with
design criteria: Slow sand bed and Rapid sand bed filter, layout, Process control, Non-conventional water treatment processes
and its design, numerical problems associated with all topics sited here.
Module 4 [9L]
Liquid Waste Management in selected process industries – fertilizer, refineries and petrochemical units, pulp and paper
industries, Tanneries, Sugar industries, Dairy, Alcohol industries, Electroplating and metal finishing industries, Root Zone and
Reed Bed Treatment for Effluents of small-scale industries, Ranking of wastewater treatment alternatives. Case Studies.
3. Textbooks
1. Introduction to Environmental Engineering and Science, Wendell P. Ela, Gilbert M. Masters, PHI, Ed 3rd Edition.
2. Wastewater Engineering, Metcalf & Eddy, Tata Mc-Graw Hill – 2002.
3. Wastewater treatment for pollution control, S.J. Arceivala, TMH, 2nd Edition .
4. Water Treatment Principles and Design, J.M. Montogomery, John Willey and Sons.
4. Reference Books
1. Pollution Control in Process Industries, S.P. Mahajan, Tata Mc Graw Hill, 2008.
2. Introduction to Environmental Engineering, M. Davis, D. Cornwell, Tata Mc GrawHill, 2012.
3. Standard Methods for Examination of Water and Wastewater, APHA / AWWA, 20th Edition.
4. Manual of Water Supply and Treatment: CPHEEO, Ministry of Urban Development, Govt. of India, 1999.
5. Water Treatment Plant Design, 5th Edition: ASCE and AWWA, 1912.
6. Design of Water treatment Plant - Part I, A G Bhole, Indian Water Works Association.
Course Name: Artificial Intelligence in Radio Communication

3 0 0 3 3
1. Course Outcomes
ECEN3221.1. Understand difference between passive radios and cognitive radios.
ECEN3221.2. Explain difference between SDR and cognitive Radios
ECEN3221.3. Apply in AI in radios.
ECEN3221.4. Analyze weakness on cognitive radios
ECEN3221.5. Develop radios based on Genetic Algorithm (GA).
ECEN3221.6. Evaluate radio performance.
Module 1 [10L]
SDR- history, concept (reconfigurable radios), SDR- benefits, problems, GNU radio design. Cognitive Radios- brief history,
basic concept; Cognitive Radio Design, Cognitive Engine Design. AI in wireless communication; AI techniques in radios.
Module 2 [10L]
Optimization of Radio Resources,
Multi-objective optimization- BER, Transmit Power, Bandwidth, Spectral Efficiency, Interference, SINR, dependence .
Module 3 [8L]
Genetic Algorithms for Radio optimization,
Review, simple example, multi-objective GA, Wireless system- GA, simple CBDT example.
Module 4 [8L]
Cognitive Radio Network, Distributed AI,
Example- Cognitive Engine, System Design, Interference, Over-the-air results.
3. Textbooks
1. Artificial Intelligence in Wireless Communications, Thomas W Rondeau, Charles W Bostian, Artech House, 2009.
2. Cognitive Radio Techniques: Spectrum Sensing, Interference Mitigation and Localization, Kandeepan Sithamparanathan,
Andrea Giorgetti, Artech House, 2012.
4. Reference Books
1. Cognitive Radio Technology, Martin Bates, Bruce A Fettee, Elsevier Science & Technology.
Course Name: Designing with Processors and Controllers

3 0 0 3 3
1. Course Outcomes
ECEN3222.1. Understand microprocessors and microcontrollers – their operation and programming.
ECEN3222.2. Identify RISC processors from CISC processors and apply them in circuits.
ECEN3222.3. Analyse operations of different serial and parallel buses and interrupts.

ECEN3222.4. Evaluate different hardware designs and memory configurations.
ECEN3222.5. Write RTOS for complex processor-based designs.
ECEN3222.6. Design processor and controller based intelligent systems for real life problems.
Module 1 [8L]
Designing with microprocessors and microcontrollers- the issues and solutions, Embedded systems VS General computing
systems, Purpose of Embedded systems, optimizing design metrics, prominent processor and controller technology, RISC vs
CISC.
Module 2 [10L]
Devices and Communication Buses: I/O types, serial and parallel communication devices, wireless communication devices,
timer and counting devices, watchdog timer, real time clock, serial bus communication protocols UART RS232/RS85, parallel
communication network using ISA, PCI, PCT-X, Internet embedded system network protocols, USB, Bluetooth. Different
types of I/O devices and interfacing: Keypad, LCD, VGA. Introduction to I/O interfaces: Interrupts, Interrupt hardware,
Enabling and disabling interrupts, Concepts of handshaking, Polled I/O, Memory mapped I/O, Priorities, Stack and Queues.
Vectored interrupts, Direct memory access, few types of Sensors and actuators .
Module 3 [10L]
Memory: SRAM, DRAM, EEPROM, FLASH, CACHE memory organizations, (direct, associative, set associative mapping),
Virtual memory, organization, mapping and management techniques, Fundamental issues in Hardware software co-design,
Unified Modeling Language (UML), Hardware Software trade-offs DFG model, state machine programming model, model for
multiprocessor system. Introduction to ARM architecture, Processor design, ARM organization and implementation.
Module 4 [8L]
Real Time Operating Systems: Operating system basics, Tasks, Process and Threads, Multiprocessing and multitasking, task
communication, task synchronization, qualities of good RTOS. Resource Management/scheduling paradigms: static priorities,
static schedules, dynamic scheduling, best effort current best practice in scheduling (e.g. Rate Monotonic vs. static schedules),
Real-world issues: blocking, unpredictability, interrupts, caching, Examples of OSs for embedded systems - RT Linux, VRTX,
Mobile phones, RFID.
3. Textbooks
1. The Art of Designing Embedded Systems, Jack Ganssle, (Newnes), 1999.
2. An Embedded Software Primer, David Simon, (Addison Wesley), 2000.
3. Embedded microcontroller and processor design: G. Osborn (Pearson).
4. Embedded System design: S. Heath (Elsevier).
5. ARM System-on-Chip Architecture, Steve Furber, (Pearson).
4. Reference Books
1. RTS: Real-Time Systems, C.M. Krishna and Kang G. Shin, McGraw-Hill, 1997.
2. Advances in Hard Real-Time Systems,J. A. Stankovic and K. Ramamritham, IEEE Computer Society Press, Washington
DC, September 1993.
3. Introduction to Embedded Systems: Shibu K. V. (TMH).
4. Embedded System Design – A unified hardware and software introduction: Frank Vahid, Tony Givargis, (John Wiley)
5. Embedded Systems: Rajkamal (TMH).
6. Embedded Systems: L. B. Das (Pearson).
7. Selected papers and references.
Course Name: Analog and Digital Communication

3 0 0 3 3
1. Course Outcomes
ECEN3223.1. Understand & apply the concepts of various types of signals, techniques for signal transmission and signal
modulation from the knowledge gathered earlier.
ECEN3223.2. Identify various parameters associated with Amplitude and frequency Modulation, time and frequency domain
representations, side band frequencies etc. and apply these knowledges to solve numerical problems.

ECEN3223.3. Apply sampling theorem to sample analog signal properly and differentiate among pulse modulation &
demodulation techniques and understand PCM, DPCM.
ECEN3223.4. Analyze performance of various digital modulation & demodulation techniques and understand concept of
OFDM and Spread Spectrum Modulation system.
ECEN3223.5. Analyze various multiplexing and Multiple access techniques and compare modern multiple access schemes,
explain the concept of frequency reuse, channel assignment strategies and make use of wireless communication
tools
ECEN3223.6. Compare and analyze different communication systems.
Module 1 [10L]
Introduction: Signal Analysis and Transmission: Overview of communication: base-band transmission, various types of
signals, analog signal, digital signal, fundamental limitations in communication system - noise, power and bandwidth. Fourier
series and Fourier Transformation representations, Modulation and its need and types; Time domain and frequency domain
analysis. AMPLITUDE MODULATION: Modulation principle and definitions, spectrum and power considerations, DSB,
SSB, VSB and AM principles. Different type of modulator circuits.
DEMODULATOR Basic principle of coherent detections, envelope detectors.
FREQUENCY AND PHASE MODULATION Principles and definitions, Relationship between frequency and phase
modulations. Phase and frequency deviations, Spectrum of FM signal, bandwidth considerations. Effect of modulation index
on bandwidth, Narrow band and sideband FM and PM principles, RADIO RECEIVER Basic block diagram of TRF,
Superheterodyne principle.
Module 2 [10L]
Digital Transmission: Sampling theorem, sampling rate, aliasing and aperture effect; analog pulse modulation -PAM (ideal,
natural & flat topped sampling),PWM, PPM; basic concept of pulse code modulation, block diagram of PCM; quantizer; non-
uniform quantizer, conceptual idea of A-law &-law compounding; encoding, coding efficiency, source, line coding channel
coding & properties, NRZ & RZ, AMI, Manchester coding PCM, DPCM, Delta modulation, adaptive delta modulation (basic
concept and applications); baseband pulse transmission, matched filter (its importance and basic concept), error rate due to
noise;, Nyquist criterion for distortion-less transmission.
Module 3 [8L]
Digital Modulation Techniques: Types of Digital Modulation, coherent and non-coherent Binary Modulation Techniques,
Bit rate, baud rate; information capacity generation and detection, digital carrier modulation techniques: ASK, PSK and FSK,
DPSK. Concept of QAM and M-ary Communication, M-ary phase shift keying, (QPSK), Generation, detection, , Offset
Quadrature Phase shift Queuing (OQPSK), Minimum Shift Keying (MSK), Basic Concept of OFDM and Spread Spectrum
Modulation.
Module 4 [8L]
Multiplexing: -TDM, FDM. Multiple Access Techniques and Radio Communication: Multiple access techniques, TDMA,
FDMA and CDMA in wireless communication systems, advanced mobile phone system (AMPS), global system for mobile
communications (GSM), cellular concept and frequency reuse, channel assignment and handoff, Bluetooth, introduction to
satellite communication.
3. Textbooks
1. Principles of Communication Systems, 2nd ed., Taub and Schilling, Mc-Graw Hill
2. Communication Systems,B.P.Lathi , BS Publications
3. Analog Communication, V Chandra Sekar, Oxford University Press
4. Reference Books
1. Communication System,4/e, Carlson, Mc-Graw Hill.
2. Fundamentals of Communication Systems, Proakis&Salehi, Pearson.
3. Communication Systems: 2/e, Singh &Sapre, TMH.
4. Principles of Electrical Communications, P K Ghosh University Press.
5. Digital and Analog Communication Systems, 2/e, L.W.Couch Ii, Macmillan Publishing.
6. Electronic Communication Systems, Blake, Cengage Learning.
7. Analog Communication Systems, S Sharma, Katson Books.

Course Name: Computational Mathematics

3 0 0 3 3
1. Course Outcomes
MATH3221.1. Identify patterns in data in the form of recurrences and using the latter to evaluate finite and infinite sums.
MATH3221.2. Explain combinatorial phenomena by using binomial coefficients, generating functions and special numbers.
MATH3221.3. Solve computational problems by applying number theoretic concepts such as primality, congruences, residues
etc.
MATH3221.4. Analyze the properties of networks by invoking graph theoretic concepts such as connectivity, matchings,
coloring etc.
MATH3221.5. Combine the concepts of recurrences, sums, combinatorics, arithmetic and graph theory in order to
comprehend computational methods.
MATH3221.6. Interpret mathematically the algorithmic features of computational situations.
Module 1 [9L]
Sums: Sums and recurrences, manipulation of sums, multiple sums, general methods, finite and infinite calculus, infinite sums.
Module 2 [9L]
Binomial coefficients and special numbers: Basic identities involving binomial coefficients. Bernoulli numbers, Euler
numbers, harmonic numbers, Fibonacci numbers, recurrence relations for these numbers .
Module 3 [9L]
Integer functions and arithmetic: Floors and ceilings, the binary operation ‘mod’, divisibility, primes, relative primality, the
congruence relation ‘mod’, residues, Euler phi function, Fermat’s Little Theorem, Wilson Theorem, primitive roots, the law of
quadratic reciprocity, (Statement only).
Module 4 [9L]
Generating functions: Basic maneuvers, well-known sequences and their generating functions, using generating functions to
solve recurrences, generating functions for special numbers.
3. Textbooks
1. Concrete Mathematics, Ronald Graham, Donald Knuth, Oren Patashnik, Addison-Wesley.
4. Reference Books
1. Elementary Number Theory, David Burton, McGraw Hill.
Course Name: Scientific Computing

3 0 0 3 3
1. Course Outcomes
MATH3223.1. Analyze certain algorithms, numerical techniques and iterative methods that are used for solving system of
linear equations.

MATH3223.2. Implement appropriate numerical methods for solving advanced engineering problems dealing with
interpolation, integration and differentiation.
MATH3223.3. Apply the knowledge of matrices for calculating eigenvalues and eigenvectors and their stability for reducing
problems involving Science and Engineering
MATH3223.4. Develop an understanding to reduce a matrix to its constituent parts in order to make certain subsequent
calculations simpler.
MATH3223.5. Develop the concept of predictor-corrector methods in solving Initial Value Problems numerically.
MATH3223.6. Apply numerical techniques in solving Boundary Value Problems where the analytical methods fail.
Module 1 [9L]
System of Linear Equations: Linear systems, solving linear systems; Gauss elimination, pivoting and scaling, Gauss-Jordan
method; Symmetric positive define systems and indefinite systems, Cholesky factorization; Iterative method: Gauss Jacobi and
Gauss Seidel, Error prediction and acceleration.
Module 2 [9L]
Eigen Value problems: QR algorithm; Power Method; Linear least square data fitting; Singular Value Decomposition .
Module 3 [9L]
Interpolation, Integration & Differentiation: Purpose of interpolation, Choice of interpolating function, Polynomial
interpolation, Piecewise polynomial interpolation: cubic spline interpolation, General form of quadrature rule; Newton-Cotes
rule, Gaussian quadrature rule, Numerical Differentiation: Methods Based on Finite Difference approximations.
Module 4 [9L]
Initial Value & Boundary Value Problem: Multistep method to solve Initial Value Problem and its stability, Predictor-
corrector method: Adam Moulton method, Milne’s Method, Solving Boundary Value Problems: Finite Difference Method,
Shooting Method.
3. Textbooks
1. Numerical Linear Algebra, Trefethen L. N. and Bau D., SIAM.
2. Fundamentals of Matrix Computation, Watkins D. S., Wiley.
3. Numerical Solutions to Partial Differential Equations, Smith G. D., Oxford University Press.
4. Numerical methods for scientific and engineering computation, Jain M. K. and Iyengar S.R.K.
5. Elementary Numerical Analysis - An Algorithmic Approach, Conte S. D. and Boor C. D., McGraw Hill.
6. Introduction to Numerical Analysis, Atkinson K. E., John Wiley.
4. Reference Books
1. Matrix Computation, Golub G. H. and Van Loan C.F., John Hopkins U. Press, Baltimore.
2. Introduction to Matrix Computations, Stewart G. W., Academic Press.
3. Applied numerical linear algebra, Demmel J.W., SIAM, Philadelphia.
4. Numerical Solutions of Differential Equations, Jain M.K.
5. Numerical solutions of partial Differential Equations (Finite difference methods), Smith.
6. Scientific Computing: An Introductory Survey, Heath M. T., McGraw Hill.
7. Numerical Methods for Engineers and Scientists, Joe D. Hoffman, McGraw Hill.
8. Numerical Linear Algebra, W. Layton and M. Sussman.
Course Name: Indian Constitution and Civil Society

Course Code: INCO3016
2 0 0 2 0
1. Course Outcomes
INCO3016.1. Analyze the historical, political and philosophical context behind the Indian Constitution-making process
INCO3016.2. Appreciate the important principles characterizing the Indian Constitution and institute comparisons with other
constitutions
INCO3016.3. Understand the contemporaneity and application of the Indian Constitution in present times
INCO3016.4. Critique the contexts for constitutional amendments in consonance with changing times and society

INCO3016.5. Establish the relationship between the Indian Constitution and civil society at the collective as well as the
individual levels
INCO3016.6. Consciously exercise the rights and the duties emanating from the Indian Constitution to one’s own life and
work.
Module 1 [6L]
Introduction to the Constitution of India-Historical Background; Making of Indian Constitution -the process of framing the
constitution, the constituent assembly.
Module 2 [6L]
Salient Features of the Indian constitution; Comparison with the constitutions of other countries.
Module 3 [6L]
Relevance of the Constitution of India; Constitution and Governance; Constitution and Judiciary; Constitution and Parliament-
Constitutional amendments.
Module 4 [6L]
Constitution and Society- democracy, secularism, justice; Constitution and the individual citizen- Fundamental Rights,
Directive Principles of state policy and Fundamental Duties.
3. Reference Books
1. Civil Society and Democracy, C.M.Elliot, (ed.), OUP, Oxford, 20012.
2. The Idea of the Modern State, David Held et.al (ed), Open Univ. Press, Bristol, 1993.
3. State and Civil Society, Neera Chandoke, Sage, Delhi, 1995.
Course Name: Software Engineering Lab

0 0 3 3 1.5
1. Course Outcomes
CSEN3251.1. Prepare SRS document for sample application system as per IEEE guidelines.
CSEN3251.2. Design sample software application problem using various UML diagrams (e.g. Use Case Diagram, Class
Diagram, Sequence Diagram etc.) using tools like Microsoft Visio.
CSEN3251.3. Design test cases for sample application module(s).
CSEN3251.4. Estimate the project size, duration and cost for sample application system using industry standard method like
FPA.
CSEN3251.5. Prepare project schedule.
CSEN3251.6. Plan the staffing for sample application system.
Exercises and Assignments on:
1. Preparation of Software Requirement Specification for sample application system(s) as per IEEE guidelines.
2. Designing a system using UML Diagrams for sample application problems: Use Case Diagrams, Class Diagrams and
Sequence Diagrams using tools.
3. Designing Test Cases for sample application module(s).
4. Estimation of Project Size for sample application system(s) – Function Point Analysis (FPA).
5. Preparation of Project Schedule and Staffing Plan for sample software project(s).
3. Textbooks
1. Uml: A Beginner's Guide, Jason T. Roff, McGraw-Hill, 2002.
2. Applying UML and Patterns: An Introduction to Object-Oriented Analysis and Design and Iterative Development, 3rd
Edition, Craig Larman, 2004.
4. Reference Books

1. The IFPUG Guide to IT and Software Measurement edited by IFPUG, CRC Press, 2012.
Course Name: Computer Networks Lab

0 0 3 3 1.5
1. Course Outcomes
CSEN3252.1. Learn the terminology and concepts of network management in Linux platform by understanding shell
commands and implementing the same.
CSEN3252.2. Understand the concepts of protocols, network interfaces, and design/performance issues through programs.
CSEN3252.3. Understanding the need of dividing stream of data into smaller units and implementing program to send such
data units across a network.
CSEN3252.4. Demonstrate various types of protocols to transfer packets of data from a source to destination machine.
CSEN3252.5. Understand the need of different types of Transport Layer Protocols and implement them through socket
programming.
CSEN3252.6. Learn how to synthesize the learning gathered from different network layers to build useful, relevant and user-
friendly applications with the objective to solve real life problems.
1. Implement Simple TCP Client Server Application.
2. Implement TCP Echo Server Client Application.
3. Implement TCP Chat Server Client Application.
4. Implement a File Server Client application.
5. Implement UDP Echo Server Client Application.
6. Implement UDP Time Server Client Application.
7. Implement multithreaded chat program.
8. Implement Web based protocol (looking up URLs, retrieving & examining content, posting a form etc.).
9. Implement Multicasting / Broadcasting socket I/O.
10. Implement Sliding Window Protocol using Non-Blocking I/O (try the Selective Repeat).
11. Implement Secured TCP echo protocol.
12. Experimenting on cross-platform network-based communication issues.
3. Textbooks
1. Computer Networks, Andrew S. Tanenbaum, Pearson Education, Fourth edition.
2. Data and Computer Communication, William Stallings, Prentice hall, Seventh edition.
3. High speed Networks and Internets, William Stallings, Pearson education, Second edition.
4. Reference Books
1. Cryptography and Network security, William Stallings, PHI, Third edition.
2. ISDN and Broadband ISDN with Frame Relay and ATM, William Stallings.
3. Computer Networking: A Top-Down Approach, 5th Ed., Kurose & Ross.
C. SESSIONAL COURSES
Course Name: Term Paper and Seminar

0 0 4 4 2

1. Course Outcomes
CSEN3293.1. Students will demonstrate the ability to prepare appropriately to participate effectively in class discussion.
CSEN3293.2. Students will demonstrate the ability to follow discussions, oral arguments, and presentations, noting main
points or evidence and tracking threads through different comments.
CSEN3293.3. Further, students will be able to challenge and offer substantive replies to others' arguments, comments, and
questions, while remaining sensitive to the original speaker and the classroom audience.
CSEN3293.4. Students will learn to prepare materials on a topic relevant to the course and demonstrate critical faculties with
the text discussed.
Discussion and presentation on various technical topics.
Open Elective - I course(s) to be offered by CSE Department
Course Name: Fundamentals of RDBMS

3 0 0 3 3
1. Course Outcomes
CSEN3221.1. Identify the basic concepts and various data model used in database design. Be able to model an application’s
data requirements using conceptual modeling tools like ER diagrams and design database schemas based on the
conceptual model.
CSEN3221.2. Formulate relational algebra expression for queries and evaluate it using the concept of query processing and
optimization.
CSEN3221.3. Create RDBMS schema mapping various business validations and formulate queries based on that schema using
SQL to satisfy business requirements.
CSEN3221.4. Apply normalization and various types of dependencies for evaluating a relational database design.
CSEN3221.5. Apply and relate the concept of transaction, concurrency control and recovery in database.
CSEN3221.6. Understand with basic database storage structures and access techniques: file and page organizations, indexing
methods including B tree, and hashing.
Module 1 [8L]
Introduction: Concept & Overview of DBMS, Data Models, Database Languages, Role of database administrator and database
Users, Three Tier architecture of DBMS.
Entity-Relationship Model: Basic concepts, Design Issues, Mapping Constraints, Keys, Entity-Relationship Diagram, Weak
Entity Sets, Extended E-R features.
Module 2 [10L]
Relational Model:
Structure of relational Databases, Relational Algebra, Extended Relational Algebra Operations, Views, Modifications of
the Database.
Relational Database Design: Functional Dependency, Different anomalies in designing a Database., Normalization using
functional dependencies, Decomposition, Boyce-Codd Normal Form, 3NF, Normalization using multi-valued dependencies.
Module 3 [8L]
SQL and Integrity Constraints: Concept of DDL, DML, DCL. Basic Structure, Set operations, Aggregate Functions, Null
Values, Domain Constraints, Referential Integrity Constraints, views, Nested Subqueries, Stored procedures and triggers.
Module 4 [10L]
Internals of RDBMS: Transaction processing, Concurrency control and Recovery Management: transaction model properties,
state serializability, lock base protocols, two phase locking.
File Organization & Index Structures: File & Record Concept, Placing file records on Disk, Fixed and Variable sized
Records, Types of Single-Level Index (primary, secondary, clustering), Multilevel Indexes, Dynamic Multilevel Indexes using
B tree and B+ tree.

3. Textbooks
2. Fundamentals of Database Systems, Elmasri Ramez and Novathe Shamkant, Benjamin Cummings Publishing Company.
3. Database Management System, Ramakrishnan, McGraw-Hill.
4. Transaction Processing: Concepts and Techniques, Gray Jim and Reuter Address, Moragan Kauffman Publishers.
5. Advanced Database Management System, Jain, CyberTech.
6. Introduction to Database Management, Vol. I, II, III, Date C. J., Addison Wesley.
7. Principles of Database Systems, Ullman JD., Galgottia Publication.
4. Reference Books
1. Principles of Database Management Systems, James Martin, 1985, Prentice Hall of India, New Delhi.
2. Fundamentals of Database Systems, Ramez Elmasri, Shamkant B.Navathe, Addison Wesley Publishing Edition.
3. Database Management Systems, Arun K.Majumdar, Pritimay Bhattacharya, Tata McGraw Hill.



A. THEORY COURSES
Course Name: Principles of Management

3 0 0 3 3
1. Course Outcomes
HMTS4101.1. Study the evolution of Management.
HMTS4101.2. Understand various management functions and have some basic knowledge on different aspects of
management.
HMTS4101.3. Understand the planning process in an organization.
HMTS4101.4. Understand the concept of organizational structure.
HMTS4101.5. Demonstrate the ability to direct, lead and communicate effectively.
HMTS4101.6. Analyze and isolate issues and formulate best control methods.
Module 1 [8L]
Management: Definition, nature, purpose and scope of management. Skills and roles of a Manager, functions, principles;
Evolution of Management Thought: Taylor Scientific Management, Behavioural Management, Administrative Management,
Fayol’s Principles of Management, Hawthorne Studies.
Types of Business organization -Sole proprietorship, partnership, company-public and private sector enterprises
-Organization culture and Environment –Current trends and issues in Management.
Module 2 [8L]
Planning: Types of plans, planning process, Characteristics of planning, Traditional objective setting, Strategic Management,
premising and forecasting.
Organizing: Nature and Purpose-Formal and informal, organizational chart, organization structure-types-line and staff
authority, departmentalization, delegation of authority, centralization and decentralization.
Controlling: Concept, planning-control relationship, process of control, Types of Control, Control Techniques
Human Resource Management-HR Planning, Recruitment, Selection, Training and Development, Performance
Management, Career planning and management.
Module 3 [8L]
Directing: Foundations of individual and group behaviour –motivation –motivation theories –motivational-Techniques –job
satisfaction –job enrichment –leadership –types and theories of leadership –Communication –process of communication
–barrier in communication –effective communication –communication and IT
Decision-Making: Process, Simon’s model of decision making, creative problem solving, group decision-making.
Coordinating: Concepts, issues and techniques.
Module 4 [8L]
Leading: Managing Communication: Nature & function of communication, methods of interpersonal communication, barriers
of effective communication, direction of communication flow, role of technology in managerial communication
Motivating Employees: Define motivation, compare and contrast early theories of motivation, compare and contrast
contemporary theories of motivation & current issues.
Being an Effective Leader Define leader/ leadership, compare and contrast early theories of leadership, understand three
contingency theories, understand modern views on leadership. Motivation, Leadership, Communication, Teams and
Teamwork.
Management by Objectives (MBO): Management by exception; Styles of management: (American, Japanese and Indian),
McKinsey’s 7-S Approach, Self-Management.
3. Reference Books
1. Stephen P. Robbins and Mary Coulter, “Management”, Pearson Education, 2017, 13 th edition.
2. Koontz H. and Weihrich H., "Essentials of Management", Mcgraw Hill Int. Ed., 2015,10th edition.
3. Bhat And Kumar A. “Management: Principles, Processes & Practices”, Oxford University Press, 2016, 2 nd edition.
4. Robbins, Coulter, and DeCenzo, “Fundamentals of Management”, Pearson Education, 2016, 9 th edition.
5. Richard L. Daft, "Management", Cengage Learning, 10 th edition.

LIST OF COURSES FOR PROFESSIONAL ELECTIVE – III

CSEN4131 Soft Computing
CSEN4132 Cryptography & Network Security
CSEN4133 Image Processing
CSEN4134 Approximation Algorithms
CSEN4135 Information Retrieval
CSEN4136 NoSQL Database with MongoDB
Course Name: Soft Computing

3 0 0 3 3
1. Course Outcomes
CSEN4131.1. Learn about soft computing techniques and their applications.
CSEN4131.2. Understand Local and Global optimal solutions for complex optimization problems.
CSEN4131.3. Analyze various neural network architectures.
CSEN4131.4. Understand the concepts of fuzzy sets, knowledge representation using fuzzy rules, approximate reasoning,
fuzzy inference systems, and fuzzy logic.
CSEN4131.5. Understand the genetic algorithm concepts for real life problems.
CSEN4131.6. Identify and apply a suitable Soft Computing technology to solve the problem; construct a solution and
implement a Soft Computing solution.
Module 1 [9L]
Introduction: Introduction to Soft Computing, Different tools and Techniques, Usefulness and applications.
Fuzzy sets and Fuzzy logic: Definition, Fuzzy sets versus crisp sets, Fuzzy Membership Functions, Fuzzification & De-
Fuzzification, Fuzzy set theoretic operations, Fuzzy Arithmetic, Extension Principle, Fuzzy numbers, Linguistic variables,
Fuzzy logic, Linguistic hedges, Fuzzy rules and fuzzy reasoning, Fuzzy inference systems, Introduction to Rough Set.
Module 2 [9L]
Artificial Neural Network: Introduction, Supervised & Unsupervised Learning, basic models, Hebb's learning,
Perceptron, Multilayer feed forward network, Back propagation algorithm, Competitive learning, Self-Organizing Feature
Maps, Introduction to Convolution and Recurrent neural network.
Module 3 [9L]
Evolutionary Algorithms Introduction to Genetic Algorithm (GA), GA operators, different types of encoding, selection rules,
elitist model, Schema theorem and convergence of Genetic Algorithm, Introduction to MOOA, Pareto optimal front, Multi-
Objective Genetic Algorithm (MOGA). VEGA, NSGA, NSGA-II.
Module 4 [9L]
Swarm Intelligence Techniques: Introduction, Key Principles of Swarm, Overview of Ant Colony Optimization (ACO),
Particle Swarm Optimization (PSO), Artificial Bee Colony Optimization (ABC) techniques with Applications, Introduction to
Granular Computing.
Advance Neural Network Systems: Genetic Algorithm for Neural Network Design and Learning, Basic idea of 3rd generation
Neural networks, Spike Neural networks.
3. Reference Books
1. Davis E. Goldberg, GeneticAlgorithms: Search, Optimization and Machine Learning, Addison Wesley.
2. Cin- Teng- Lin, C. S. George Lee, Prentice Hall, Neural Fuzzy Systems: A neuro Fuzzy Synergism to intelligent Systems.
3. B.Yegnanarayana, Artificial Neural Networks, PHI.
4. S. Rajasekaranand G.A.VijaylakshmiPai. Neural Networks FuzzyLogic, and Genetic Algorithms, PHI.
5. TimothyJ.Ross, Fuzzy Logic with Engineering Applications, McGraw-Hill.
6. K.H.Lee. First Course on Fuzzy Theory and Applications, Springer-Verlag.

Course Name: Cryptography & Network Security

3 0 0 3 3
1. Course Outcomes
CSEN4132.1. Understand the concepts of Cryptography and Network Security including Private and Public key cryptography
and various protocols to protect computing system against potential threats.
CSEN4132.2. Explore Mathematical techniques for supporting the cryptographic mechanisms.
CSEN4132.3. Analyze and compare various cryptographic techniques.
CSEN4132.4. Evaluate security mechanisms using rigorous approaches by key ciphers, message authentication and Hash
functions.
CSEN4132.5. Investigate various network security applications, IPsec, Firewall, IDS, Web Security, Email Security and
Malicious software etc.
CSEN4132.6. Design a secure network after analyzing the vulnerabilities in any computing system.
Module 1 [9L]
Introduction Need for Security, Security approaches, Principles of Security, Types of attack, Plain text & Cipher text,
Substitution Techniques, Transposition Techniques, Encryption & Decryption, Symmetric & Asymmetric key Cryptography,
Key Range & Key Size.
Brief Introduction of Number Theory - Euclidean algorithm, Euler‘s totient function, Fermat‘s theorem and Euler‘s
generalization, Chinese Remainder Theorem, primitive roots and discrete logarithms, Quadratic residues, Legendre and Jacobi
symbols.
Module 2 [9L]
Symmetric Key Cryptography Overview, Block Cipher, DES algorithm, AES algorithm, IDEA algorithm, Blowfish, RC5
algorithm.
Asymmetric Key Cryptography Overview, RSA, Key Management – Key Distribution, Diffie-Hellman Key Exchange
Algorithm, Elliptic Curve Arithmetic, Elliptic Curve Cryptography.
Module 3 [9L]
Authentication Methods Message Digest, Kerberos
Digital Signatures Algorithms (DSA, ElGamal signature, ECDSA), Digital Signature Standard, Authentication Protocols.
Module 4 [9L]
Email Security PGP, MIME, S/MIME.
Protocols IP Sec-Architecture, AH protocol, Encapsulating Security Payload (ESP) Protocol, ISAKMP Protocol, Oakley Key
Determination Protocol, VPN.
Web Security SSL, Firewalls.
3. Reference Books
1. Cryptography and Network Security: Principles and Practice, 7/E, William Stallings, Pearson.
2. Cryptography and Network Security, 3rd Edition, Atul Kahate, McGraw Hill Education (India) Private Limited.
3. Cryptography and Information Security, 2nd Edition, V. K. Pachghare, PHI Learning Private Limited.
Course Name: Image Processing

3 0 0 3 3
1. Course Outcomes
CSEN4133.1. Understand the general terminology, basic concepts and applications of digital image processing.
CSEN4133.2. Implement two dimensional ﬁlters in both spatial and frequency domain for image enhancement.
CSEN4133.3. Analyze and develop various image restoration techniques.
CSEN4133.4. Evaluate the methodologies for image segmentation, compression etc.
CSEN4133.5. Implement various morphological algorithms.
CSEN4133.6. Apply image processing algorithms in practical applications.

Module 1 [8L]
Introduction: Background, Digital Image Representation, Fundamental steps in Image Processing, Elements of Digital Image
Processing - Image Acquisition, Storage, Processing, Communication, Display.
Digital Image Formation: A Simple Image Model, Geometric Model- Basic Transformation (Translation, Scaling, Rotation),
Perspective Projection, Sampling & Quantization - Uniform & Non uniform.
Mathematical Preliminaries: Neighbor of pixels, Connectivity, Relations, Equivalence & Transitive Closure; Distance
Measures, Arithmetic/Logic Operations, Fourier Transformation, Properties of the two-dimensional Fourier Transform,
Discrete Fourier Transform, Discrete Cosine & Sine Transform.
Module 2 [8L]
Image Enhancement: Spatial Domain Method, Frequency Domain Method, Contrast Enhancement -Linear & Nonlinear
Stretching, Histogram Processing; Smoothing - Image Averaging, Mean Filter, Low-pass Filtering; Image Sharpening. High-
pass Filtering, High-boost Filtering, Derivative Filtering, Homomorphic Filtering; Enhancement in the frequency domain -
Low pass filtering, High pass filtering.
Digital Image Transforms: Basis for transformation, Introduction to Fourier Transform, DFT, FFT, Properties of Fourier
Transform, DCT, Walsh Transform, Hadamard Transform, Haar Transform.
Module 3 [7L]
Image Restoration: Degradation Model, Discrete Formulation, Algebraic Approach to Restoration - Unconstrained &
Constrained; Constrained Least Square Restoration, Restoration by Homomorphic Filtering, Geometric Transformation -
Spatial Transformation, Gray Level Interpolation.
Image Compression: Encoder-Decoder model, Types of redundancies, Lossy and Lossless compression, Entropy of an
information source, Huffman Coding, Arithmetic Coding, LZW coding, Transform Coding, Sub-image size selection, Run
length coding, Bit-plane encoding, Bit-allocation, JPEG, Lossless predictive coding, Lossy predictive coding.
Module 4 [10L]
Morphological Image Processing: Basics, SE, Erosion, Dilation, Opening, Closing, Hit-or-Miss Transform, Boundary
Detection, Hole filling, Connected components, convex hull, thinning, thickening, skeletons, pruning, Reconstruction by
dilation and erosion.
Image Segmentation: Point Detection, Line Detection, Edge detection, Combined detection, Edge Linking & Boundary
Detection - Local Processing, Global Processing via The Hough Transform; Thresholding – Iterative thresholding, Otsu‘s
method, multivariable thresholding, Region Oriented Segmentation - Basic Formulation, Region Growing by Pixel
Aggregation, Region Splitting & Merging, Watershed algorithm.
3. Reference Books
1. Digital Image Processing, Gonzalves, Pearson
2. Digital Image Processing, Jahne, Springer India
3. Digital Image Processing & Analysis, Chanda &Majumder, PHI
4. Fundamentals of Digital Image Processing, Jain, PHI
Course Name: Approximation Algorithms

3 0 0 3 3
1. Course Outcomes
CSEN4134.1. Understand the general terminology, basic concepts and applications of digital image processing.
CSEN4134.2. Implement two dimensional ﬁlters in both spatial and frequency domain for image enhancement.
CSEN4134.3. Analyze and develop various image restoration techniques.
CSEN4134.4. Evaluate the methodologies for image segmentation, compression etc.
CSEN4134.5. Implement various morphological algorithms.
CSEN4134.6. Apply image processing algorithms in practical applications.
Module 1 [7L]

Introduction: P vs NP, NP Optimization problems, Approximation Ratio, Additive vs. Multiplicative. Techniques: Greedy and
combinatorial methods, Local search.
Module 2 [7L]
Techniques: Dynamic programming and approximation schemes.
Module 3 [10L]
Linear programming rounding methods (randomized, primal-dual, dual-fitting, iterated rounding), Semi-definite program-
based rounding.
Module 4 [8L]
Metric methods: inapproximability, Hardness of approximation: simple proofs, approximation preserving reductions, some
known results. Problems that can be discussed -
Tour Problem: TSP; Scheduling; Connectivity & Network Design: Steiner tree, Steiner forests, Survival network;
Covering Problems: Vertex cover, Set cover; Constraint Satisfaction: MaxSAT proble; Cut Problems: Sparsest cut, Multi
cut, Multiway cut.
3. Textbooks
1. The Design of Approximation Algorithms, David P. Williamson and David B. Shmoys, Cambridge University Press.
4. Reference Books
1. Approximation Algorithms by Vijay Vazirani, Springer-Verlag, 2004.
Course Name: Information Retrieval

3 0 0 3 3
1. Course Outcomes
CSEN4135.1. Identify basic theories and analysis tools as they apply to information retrieval.
CSEN4135.2. Develop understanding of problems and potentials of current IR systems.
CSEN4135.3. Learn and appreciate different retrieval algorithms and systems.
CSEN4135.4. Apply various indexing, matching, organizing, and evaluating methods to IR problems.
CSEN4135.5. Be aware of current experimental and theoretical IR research.
CSEN4135.6. Analyze and design solutions for some practical problems.
Module 1 [9L]
Information retrieval model, Information retrieval evaluation; Document Representation – Boolean Model, Posting Lists,
Inverted Indices, Skip Lists; Query languages and query operation – proximity search, Phrase Queries Meta- data search;
Tolerant Retrieval – B-Trees, Permuterm Index, Edit Distance – Different variations.
Module 2 [9L]
Indexing Construction and Searching – BSBI, SPIMI, Heap’s Law Zipf’s Law; Scoring and ranking feature vectors, tf-idf
various schemes; Evaluation and computations of scores and ranked retrieval; Relevance feedback.
Module 3 [9L]
Language Models – Query Likelihood Models; Text Classification and Naïve Bayes – Bernoulli model, feature selection;
Vector Space Classification – kNN, Rocchio Classification.
Module 4 [9L]
Flat Clustering – K means, K medoids, Evaluation of clustering, Models for clustering; Hierarchical Clustering – Single Link,
Complete Link, Group Average and Centroid, Inversion Points, Divisive Clustering – Basics; Latent Sematic Analysis – SVD,
Low Rank Approximations;
Web Search Basics, Link Analysis – Page Rank, HITS.
3. Textbooks
1. C. D. Manning, P. Raghavan and H. Schütze, Introduction to Information Retrieval, Cambridge University Press, 2008.
2. R. Baeza-Yates, B. Ribeiro-Neto, Modern Information Retrieval, Addison-Wesley, 2011 (2ndEdition).

4. Reference Books
1. Chakrabarti, S., Mining the web: Mining the Web: Discovering knowledge from hypertext data 2002.
2. B. Croft, D. Metzler, T. Strohman, Search Engines: Information Retrieval in Practice, Addison Wesley, 2009.
Course Name: NoSQL Database with MongoDB
3 0 0 3 3
1. Course Outcomes
CSEN4136.1. Identify the basic needs of migrating to NoSQL database like MongoDB.
CSEN4136.2. Understand the concepts of documents and various features in MongoDB
CSEN4136.3. Understand the data model used for MongoDB and design document-based database.
CSEN4136.4. Handling CRUD operations of MongoDB using various tools (Compass, Mongo Shell)
CSEN4136.5. Understand the concept of ODM/ORM tool like Mongoose and using its methods.
CSEN4136.6. Developing REST API using Express application for CRUD operations using Mongoose/Native driver of
MongoDB.
Module 1 [9L]
Overview NoSQL and MongoDB: Introduction to NoSQL Databases, Types of NoSQL database, Introduction to MongoDB
and key features, Advantages of MongoDB over RDBMS and its limitation, MongoDB Atlas vs MongoDB Compass, Hadoop
Vs. MongoDB.
Installing MongoDB community Edition in local server and getting started with MongoDB Server, Creating and Working with
Database via MongoDB Compass, Introduction to Mongo Shell.
MongoDB CRUD methods: Insert, Find, Update and delete documents. Comparison between different databases such as
Hadoop and MongoDB, Casandra Vs MongoDB.
Module 2 [9L]
Data Modeling in MongoDB: Document Structure (Embedded Documents, Reference Documents) and relationship, Example
of data modeling in MongoDB, Some considerations while designing schema in MongoDB, Data Modeling Concepts, Data
Model Examples and Patterns. Example of data modeling Some MongoDB Shell Collection Methods, Views, Replication and
Sharding.
Module 3 [9L]
Aggregation Pipeline: aggregate() Method, Pipeline Concept and stages, Pipeline Expressions, Pipeline Operators and Indexes,
Create and apply aggregation pipeline using Aggregation Pipeline Builder Index and Query Optimization : Types of indexes,
Index creation , Role of Index in query performance, Query optimizer processes, Query plan, Distributed query
Mongoose: Introduction to access database using Express app, Mongoose vs MongoDB native driver, Terminology of
Mongoose, Key Methods and Properties of Mongoose
Set up of Express application based on node and running the server, Develop simple CRUD operations using Mongoose in
Express application.
Module 4 [9L]
MongoDB Atlas cloud Setup, Accessing MongoDB Atlas using Mongoose, CRUD using MongoDB native driver in Express
application. Transactions, Migration from RDBMS.
3. Textbooks
1. MongoDB: The Definitive Guide - Powerful and Scalable Data Storage by Kristina Chodorow - O’REILLY
2. MongoDB Simply In-Depth Paperback – November 19, 2019 by Ajit Singh, Sultan Ahmad
3. Mastering MongoDB 4.x: Expert techniques to run high-volume and fault-tolerant database solutions using MongoDB
4.x, 2nd Edition Paperback –by Alex Giamas – Packt.
4. Reference Books
1. MongoDB: The Definitive Guide 3e: Powerful and Scalable Data Storage by Shannon Bradshaw, Eoin Brazil, Kristina
Chodorow - O’REILLY
2. Mastering MongoDB 3.x - An expert's guide to building fault-tolerant MongoDB by Alex Giamas, Free pdf download
available.

LIST OF COURSES FOR OPEN ELECTIVE – II

AEIE4121 Instrumentation and Telemetry
BIOT4124 Biosensor
ECEN4121 Software Defined Radio
MATH4121 Methods in Optimization
MECH4124 Engineering Computational Techniques
Course Name: Instrumentation and Telemetry

3 0 0 3 3
1. Course Outcomes
AEIE4121.1. Understand different blocks of generalized measurement system.
AEIE4121.2. Clarify operation of indigenous sensors and transducers.
AEIE4121.3. Gain knowledge of measurement system for industrial parameters like pressure, flow, level and temperature.
AEIE4121.4. Design various signal conditioning circuits for sensors.
AEIE4121.5. Select telemetry system required for a given application.
AEIE4121.6. Justify the need of process data multiplexing and de-multiplexing in telemetry.
Module 1 [8L]
Generalized measurement system.
Introduction to telemetry principles: Basic systems, classifications, non-electrical telemetry systems, voltage and current
telemetry systems.
Sensors and transducers: resistive, capacitive, inductive, magneto strictive, piezoelectric, hall sensor, optical, and applications.
Module 2 [10L]
Measurement of pressure and vacuum: Introduction, diaphragm, capsule, bellows, bourdon tube, DP transmitters – capacitive,
Mcleod gauge.
Flow rate measurement: head type flow meters – orifice, pitot tube, venturimeter; electromagnetic flow meters; ultrasonic flow
meters.
Level measurement: float and displacers type instruments, resistive and capacitive type level instrument; D/P type sensors;
ultrasonic level instruments.
Temperature measurement: thermocouple, RTD, thermistors.
Module 3 [10L]
Data handling system: signal conditioning circuits, instrumentation amplifiers, ADC, DAC.
Basic classification of telemetry systems: voltage, current, position, frequency and time, components of telemetry and remote-
control systems, sampling theorem, sample and hold, quantization error, data conversion, coding, introduction to fiber optic
communication system.
Module 4 [8L]
Multiplexing; time division multiplexers and de-multiplexer theory, scanning procedures, frequency division multiplexers with
constant and proportional bandwidth, de-multiplexers. Fundamentals of radio-telemetry system, RF link system design,
pipeline telemetry; power system telemetry, PSK, QPSK , FSK,
IEEE 802.11.
3. Reference Books
1. D. Patranabis, Telemetry principles, TMH, New Delhi
2. E. L. Gruenberg, Handbook of Telemetry and Remote control, Mc Graw Hill
3. B. P. Lathi, Modern Digital and Analog Communication Systems, Oxford University Press
4. Ginz Beng “Fundamentals of Automation and Remote Control”.
5. Feng Zhao and Leonidas. J. Guibas, Wireless Sensor Networks: An Information Processing Approach, Morgan Kaufmann.
6. David Hanes, Gonzalo Salgueiro, Patrick Grossetete, Rob Barton, Jerome Henry, IoT Fundamentals: Networking
Technologies, Protocols, and Use Cases for the Internet of Things, Cisco Press.

Course Name: Biosensor

Course Code: BIOT4124
3 0 0 3 3
1. Course Outcomes
BIOT4124.1. State types of bio-recognition elements and describe the fundamental components required to make a viable
biosensor.
BIOT4124.2. Illustrate types of enzyme immobilization methods used to make a biosensor and immobilize it to a transducer
for the construction of biosensor.
BIOT4124.3. Describe each type of biosensing element in relation to their uses in biosensors.
BIOT4124.4. Understand the classification, construction and working principle of various transducers.
BIOT4124.5. Understand the concepts, types, working principles and practical applications of important biosensors.
BIOT4124.6. Explain the working principle of different types of inhibition-based biosensors.
Module 1 [9L]
Introduction to biological system and Biosensors Biosensor: principle, general characteristics; Proteins and enzymes: basic
properties, denaturation and renaturation, immobilization of enzymes; Advantages and limitations of biosensors; Classification
of biosensors based on bioreceptor; Immobilization and coupling of bioreceptors.
Module 2 [9L]
Bio-recognition based sensors Principle, operation and limitation of: Microbial sensor, Immunological sensor, Nucleic acid
sensor. Other bioreceptors (e.g., animal, plant tissue); Different types of inhibitors: principles, operations, applications and
limitations.
Module 3 [9L]
Biosensor based on transducer Classification of biosensor based on transducer; Calorimetric, Electrochemical
(potentiometric, amperometric), Optical, Piezoelectric, Semiconductor biosensor: principle, construction, calibration and
limitations.
Module 4 [8L]
Application of biosensor Clinical and diagnostics sector, Industrial sector: Food, Environmental, defense sector; commercially
available biosensor.
3. Reference Books
1. Biosensors by Tran Minh Canh. London. Chapman and Hall, 1993.
2. Biosensors Fundamentals and applications, Turner, A.P.F, Karube. I. and Wilson, G.S, Oxford Univ. Press.
3. Engineering Biosensors, kinetics and design applications by Ajit Sadana. San Diego, Academic Press, 2002.
4. D. Thomas and J.M. Laval – Enzyme Technology in concepts in Biotechnology by Balasubramaniam et al, Univ. Press.
Course Name: Software Defined Radio

3 0 0 3 3
1. Course Outcomes
ECEN4121.1. Understand the technological differences between families of radios.
ECEN4121.2. Explain the function of reconfigurable hardware.
ECEN4121.3. Analyze the processing techniques required for software defined radio.
ECEN4121.4. Evaluate the effects of probability in communication reliability.
ECEN4121.5. Analyze the synchronization requirements in SDR and SDR based networks.
ECEN4121.6. Analyze functioning of different families of radios.
Module 1 [10L]
Introduction to SDR, Brief history of development of SDR, RF architectures applied in SDR, Processing architectures suitable
for SDR, Software environment for SDR. SDR- benefits, problems, GNU radio design.

Module 2 [12L]
Signals and Systems in relation to SDR, Probability in Communications- the effects on reliability, Understanding SDR
hardware, Timing and Carrier synchronization, Frame synchronization, Channel coding.
Receive techniques for SDR, Transmit Power, Bandwidth, Spectral Efficiency, Interference.
Module 3 [8L]
OFDM, introduction and implementation of the general model, Channel estimation, Equalization, Power allocation techniques
for bits.
Module 4 [6L]
SDR – some applications, future directions.
SDR-3000 series Software Defined Radio Transceiver Systems Smart Antenna API for SDR
Networking and SDR- some case histories, Vehicular networking.
3. Reference Books
1. Software Defined Radio for Engineers by T.Collins, Robin Getz, Di Pu, and Alexander M. Wyglinski, Artech House, 2015
2. Cognitive Radio Techniques: Spectrum Sensing, Interference Mitigation and Localization- By Sithamparanathan,
Kandeepan, Giorgetti, Andrea, Artech House, 2012
3. Cognitive Radio Technology- By Bates, Martin; Fettee, Bruce A, Elsevier Science & Technology
4. Software Defined Radios: From Smart(er) to Cognitive by Liesbet Van Der Perre, Michael Timmers and SofiePollin,
Springer.
Course Name: Methods in Optimization

3 0 0 3 3
1. Course Outcomes
MATH4121.1. Describe the way of writing mathematical model for real-world optimization problems.
MATH4121.2. Identify Linear Programming Problems and their solution techniques.
MATH4121.3. Categorize Transportation and Assignment problems.
MATH4121.4. Apply the way in which Game theoretic models can be useful to a variety of real-world scenarios in economics
and in other areas.
MATH4121.5. Apply various optimization methods for solving realistic engineering problems and compare their accuracy and
efficiency.
MATH4121.6. Convert practical situations into non-linear programming problems and solve unconstrained and constrained
programming problems using analytical techniques.
Module 1 [9L]
Linear Programming Problem (LPP) - I: Formulation of an LPP; Graphical Method of solution of an LPP; Convex
Combination and Convex Set; Convex Hull and Convex Polyhedron; Canonical and Standard form of an LPP; Basic Solution
of a system of linear equations; Simplex Method; Big-M Method; Concept of Duality; Mathematical formulation of duals.
Module 2 [9L]
Linear Programming Problem (LPP) - II and Game Theory: Transportation Problems (TP); Representation of
Transportation Problems as LPP; Methods of finding initial basic feasible solution of TP: North-West Corner Rule, Matrix
Minima Method, Vogel’s Approximation Method; Optimality test of the basic feasible solution; Assignment Problems;
Hungarian Method.
Strategies; The Minimax and Maximin Criterion; Existence of Saddle Point; Games without a Saddle Point; Mixed Strategies;
Symmetric Games; Dominance Principle; Two-Person Zero-Sum Game; Graphical Method of Solution; Algebraic Method of
Solution.
Module 3 [9L]
Non-Linear Programming Problem (NLPP) - I: Single-variable Optimization; Multivariate Optimization with no
constraints: Semidefinite Case, Saddle Point; Multivariate Optimization with Equality Constraints: Method of Lagrange
Multipliers; Multivariable Optimization with inequality constraints: Kuhn-Tucker Conditions.

Module 4 [9L]
Non-Linear Programming Problem (NLPP) – II: Unimodal Function; Elimination Methods: Interval Halving Method,
Fibonacci Method, Golden Section Method; Interpolation Methods: Quadratic Interpolation Methods; Cubic Interpolation
Method, Newton Method, Quasi- Newton Method, Secant Method.
3. Textbooks
1. Linear Programming and Game Theory by J. G. Chakraborty and P. R. Ghosh, Moulik Library.
2. Operations Research by Kanti Swarup, P. K. Gupta and Man Mohan, S. Chand and Sons.
3. Engineering Optimization by S. S. Rao, New Age Techno Press.
4. Reference Books
1. Algorithms for Minimization without Derivative by R. P. Brent, Prentice Hall.
2. Operations Research: Theory and Applications by J. K. Sharma, Laxmi Publications.
3. Operations Research by T. Veerarajan, The Orient Blackswan.
Course Name: Engineering Computational Techniques

3 0 0 3 3
1. Course Outcomes
MECH4124.1. Formulate mathematical models and classify different types of error.
MECH4124.2. Solve a system of linear algebraic equations by different methods and find out the roots.
MECH4124.3. Apply the regression and interpolation methods for curve fitting and construct different types of optimization
problems.
MECH4124.4. Apply different numerical integration methods to calculate anti-derivatives.
MECH4124.5. Solve ordinary differential equations by different methods.
MECH4124.6. Apply the Finite Difference Method and the Finite Element Method to solve one-dimensional and two-
dimensional problems in partial differential equations.
Module 1 [10L]
Simple Mathematical model of engineering problem, Approximations– Significant figures, Accuracy, Precision & Error;
definition and formulations. Round-off and truncation errors, error propagation, total numerical error. Formulation and solution
of linear algebraic equations, Gauss elimination, LU decomposition. Solution of linear algebraic equations through iteration
methods Roots of Equation: Newton-Raphson method, Secant Method, roots of polynomial: Muller’s method, Bairstow’s
method.
Module 2 [10L]
Linear and polynomial regression, Multiple linear regression, general linear least squares. Interpolation methods: Newton’s
divided difference interpolation of polynomials, Lagrange interpolation of polynomials. Optimization: one dimensional
unconstraint problem, Golden-section search, multi dimension unconstraint problem, Gradient method.
Module 3 [11L]
Numerical Integration: The Trapezoidal rule, Simpson’s rule, Gauss quadrature two points and three points. NUMERICAL
SOLUTION OF ORDINARY DIFFERENTIAL EQUATIONS. Euler’s Method, Improvements of Euler’s Method, Solved
Examples. Runge-Kutta Methods, Systems of Equations, Solved Examples. General Methods for Boundary-Value Problems,
Eigenvalue Problems, Solved Examples.
Module 4 [9L]
NUMERICAL SOLUTION OF PARTIAL DIFFERENTIAL EQUATIONS. Finite Difference: Elliptic Equations. The Laplace
Equation, Solution Technique, Boundary Conditions, The Control Volume Approach, Solved Examples. Finite Difference:
Parabolic Equations. The Heat-Conduction Equation, Explicit Methods, A Simple Implicit Method, The Crank-Nicolson
Method, Parabolic Equations in Two Spatial Dimensions, Solved Examples. Finite-Element Method. The General Approach,
Finite-Element Application in One Dimension, Two Dimensional Problems, Solved Examples.
3. Textbooks
1. Numerical Methods for engineers, Steven C Chapra & Raymond P. Canale, McGraw- Hill.
2. Numerical Analysis, P Sivaramakrishna Das and C Vijaykumari, Pearson Education.
3. Computational Methods in Engineering, S.P. Venkateshan and Prasanna Swaminathan, Academic Press.

4. Reference Books
1. Numerical Methods for engineers, Steven C Chapra & Raymond P. Canale, McGraw- Hill.
2. Numerical Analysis, P Sivaramakrishna Das and C Vijaykumari, Pearson Education.
LIST OF COURSES FOR OPEN ELECTIVE – III

AEIE4127 Introduction to Embedded System
BIOT4126 Biopolymer
ECEN4127 Ad-Hoc Wireless Networks
MATH4126 Linear Algebra
MECH4130 Ecology and Environmental Engineering
Course Name: Introduction to Embedded System

3 0 0 3 3
1. Course Outcomes
AEIE4127.1. Explain the definitions, components and requirements of the Embedded System.
AEIE4127.2. Acquire knowledge in the area of embedded system using AVR microcontroller.
AEIE4127.3. Develop the interfacing and communication techniques of the Embedded System.
AEIE4127.4. Learn the basic concept of RTOS.
AEIE4127.5. Understand the message passing technique, task synchronization techniques.
AEIE4127.6. Develop algorithms for real time applications of Embedded System.
Module 1 [10L]
Introduction to an embedded system: Definition of Embedded Systems, Embedded System V/S General Computing System,
Challenges in Embedded System Design, Design Process, Requirements, Examples of Embedded Systems.
Embedded System Architecture: Harvard Vs Princeton, CISC Vs RISC. Introduction to AVR, PIC, ARM and Arduino based
systems.
Module 2 [10L]
Overview of AVR microcontroller: Introduction to AVR (ATmega328p-pu) microcontroller, pin layout, architecture,
program memory, Data Direction register (DDRx), Port Registers (PORTx), PWM registers (8-bit), ADC registers, interrupts,
basics of communication, overview and interfacing I/O devices with I2C Bus, UART and Serial Peripheral Interchange (SPI)
bus.
Module 3 [8L]
Embedded operating systems: Operating system basics, types of operating systems, tasks, process and threads,
multiprocessing and multitasking, task scheduling; task communication: shared memory, message passing, remote procedure
call and sockets, task synchronization: task communication/synchronization issues, task synchronization techniques, device
drivers, how to choose an RTOS.
Module 4 [8L]
Hardware Interfacing and Programming with ATmega 328p: Interfacing of LCD, interfacing with analog sensors (i.e
LM35, ADXL 335 accelerometer), interfacing of stepper motor, interfacing with a keyboard and MPU6050 (MEMS
Accelerometer and Gyroscope) using I2C bus.
3. Reference Books
1. Raj Kamal, “Embedded System-Architecture, Programming, Design”, Mc Graw Hill, 2013.
2. Shibu K.V, “Introduction to Embedded Systems”, Tata McGraw Hill, 2009.
3. Elliot Williams, AVR Programming: Learning to Write Software for Hardware, Maker Media, Incorporated, 2014.
4. Muhammad Ali Mazidi, Sarmad Naimi, SepehrNaimi, “The AVR Microcontroller and Embedded Systems: Using
Assembly and C”; Pearson, 2014.
5. Dhananjay Gadre, “Programming and Customizing the AVR Microcontroller”; McGraw Hill Education, 2014.
6. Silberschatz Galvin Gagne, “Operating System Concepts”, WILEY, 2014.

Course Name: Biopolymer

3 0 0 3 3
1. Course Outcomes
BIOT4126.1. Students will acquire basic knowledge of biopolymer and can classify biopolymer according to their
composition.
BIOT4126.2. Students will get familiar with the structures, properties and applications of different protein-based biomaterial.
BIOT4126.3. Students will be able to explain the structures, properties and applications of different carbohydrate-based
biomaterial.
BIOT4126.4. Students will comprehend the knowledge of different type and applications of bioplastics.
BIOT4126.5. Students will learn about the different composite material that can be used as biomaterial. They will be familiar
with the applications, advantages and disadvantages of bioplastics and composite materials.
BIOT4126.6. Students will classify biodegradable polymer and will analyze the biodegradation techniques.
Module 1 [9L]
Introduction to biopolymers and protein biopolymers Classification of Biopolymers; Collagen, Keratin and Fibroin:
Structure, production (conventional and cloning method), properties and its use (Tissue regeneration scaffolds and others).
Module 2 [9L]
Carbohydrates as Biomaterials Carbohydrate (Starch, Alginate, Chitin, Agarose) and modified carbohydrates (modified
starch, polydextrose, chitosan etc.): Structure, production, properties and applications.
Module 3 [9L]
Application of Bioplastics and composite materials Definition of bioplastics, Types of bioplastics such as starch-based,
cellulose-based plastics and some aliphatic polyesters (PLA, PHB), polyamides, bio-based composites from soybean oil and
chicken feathers, bio-derived polyethylene and genetically modified bioplastics. Composite theory of fiber reinforcement (short
and long fibers, fibers pull out); applications and limitations of bioplastics and composite materials.
Module 4 [8L]
Polymer biodegradation: Classification of biodegradable polymers (Natural, Synthetic and modified naturally modified);
Techniques for analysis of biodegradation of polymers- Enzyme assays, Plate test, Respiratory test, Gas evolution test (CO2 &
CH4), Field trial.
3. Reference Books
1. Ratledge C and Kristiansen B, Basic Biotechnology, Cambridge University Press, 2nd Edition, 2001.
2. Doi Y, Microbial Polyesters, VCH Weinheim, 1990.
Course Name: Ad Hoc Wireless Networks

3 0 0 3 3
1. Course Outcomes
ECEN4127.1. Understand the under lying technologies of wireless communication networks.
ECEN4127.2. Analyze the various design issues and challenges of Ad hoc Networks.
ECEN4127.3. Understand the different routing protocols and their operations.
ECEN4127.4. Learn about the contention in MAC layer and ways to solve them.
ECEN4127.5. Understand the network design strategies to assure adequate QoS.
ECEN4127.6. Apply their knowledge to develop new and improved applications.
Module 1 [10L]
Introduction Ad hoc wireless networks, Applications of Ad hoc wireless networks. Issues in Ad hoc wireless networks, Static
and mobile Ad hoc network, Indoor Outdoor network model.

MAC Protocols Issues in designing a MAC protocol for Ad hoc wireless Networks, Hidden and Exposed terminal problem,
Contention based protocols with reservation mechanisms and scheduling mechanisms, MAC protocols using directional
antennas, IEEE802.11 in Ad hoc mode.
Module 2 [8L]
Routing Protocols Issues in designing a routing protocol for Ad hoc wireless Networks, Classification of routing protocols,
Proactive & Reactive routing protocol, Unicast & Multicast routing algorithm. Location aided routing, Link reversal routing,
Hybrid routing algorithm, Energy aware routing algorithm, Hierarchical routing, QoS aware routing.
Module 3 [6L]
Transport Layer Protocols Issues in designing a transport layer protocol for Ad hoc wireless Networks, Design goals of a
transport layer protocol for Ad hoc wireless Networks, Classification of transport layer solutions, TCP over Ad hoc wireless
Networks.
Module 4 [12L]
QoS in Ad hoc wireless network Issues and challenges in providing QoS in Ad hoc wireless networks, Classification of QoS
solutions, QoS in wireless ad hoc network – analysis of degradation of receiver sensitivity, practical solutions.
Energy Management Schemes Battery management, transmission power management, System power management schemes.
3. Reference Books
1. “Ad Hoc Mobile Wireless Networks – Protocols and Systems” - Chai K. Toh – Prentice Hall.
2. “Ad hoc wireless Networking”, Xiuzhen Cheng, Xiao Hung, DingZhu Du, Kluwer Academic publishers.
3. “Mobile Ad Hoc Networking” – Stefano Basagni, Marco Conti, Silvia Giardano, Ivan Stojmenovic –Wiley India.
Course Name: Linear Algebra

3 0 0 3 3
1. Course Outcomes
MATH4126.1. Explain concepts of diagonalization, orthogonal diagonalization and Singular Value Decomposition (SVD).
MATH4126.2. Discuss basis, dimension and spanning sets.
MATH4126.3. Design Gram-Schmidt Orthogonalization Process and QR decomposition using concepts of inner product
spaces.
MATH4126.4. Analyze Least squares solutions to find the closest line by understanding projections.
MATH4126.5. Define linear transformations and change of basis.
MATH4126.6. Illustrate applications of SVD such as, Image processing and EOF analysis, applications of Linear algebra in
engineering with graphs and networks, Markov matrices, Fourier matrix, Fast Fourier Transform and linear
programming.
Module 1 [9L]
Characteristic equations, Eigen Values and Eigen vectors, Diagonalization, Applications to differential equations, Symmetric
matrices, Positive definite matrices, similar matrices, Singular Value Decomposition, Generalized Inverses.
Module 2 [9L]
Definition of Field, Vector Spaces, Elementary Properties in Vector Spaces, Subspaces, Linear Sum of Subspaces, Spanning
Sets, Linear Dependence and Independence, Basis and Dimension. Application to matrices and system of linear equations.
Module 3 [9L]
Inner Product Spaces, Concept of Norms, Orthogonality, Projections and subspaces, Orthogonal Complementary Subspaces,
Orthogonal Projections, Gram-Schmidt Orthogonalization Process, Least square approximations, QR decomposition.
Module 4 [9L]
Linear Transformations, kernels and images, The Rank-Nullity-Dimension Theorem. Matrix representation of a Linear
Transformation, Change of Basis, Linear space of linear mappings.
3. Textbooks
1. Linear Algebra and its Applications: Gilbert Strang (Thomson Brooks/Cole Cengage Learning)

4. Reference Books
1. Matrix Computations: Gene H. Golub, Charles F. Van Loan (JHU Press)
2. Linear Algebra: Kenneth M. Hoffman, Ray Kunze (Prentice-Hall)
3. Linear Algebra: A Geometric Approach: S. Kumaresan (PHI)
Course Name: Ecology and Environmental Engineering

3 0 0 3 3
1. Course Outcomes
MECH4130.1. Identify the current and emerging environmental engineering issues
MECH4130.2. Learn ethical and societal responsibilities and to act accordingly
MECH4130.3. Assess the impact of human activities on the environment
MECH4130.4. Interpret the various types of pollutants and its probable remedies
MECH4130.5. Formulate and construct solutions to minimize and mitigate environmental impacts
MECH4130.6. Analyze and practice the profession of environmental engineering in the public and /or private sector.
Module 1 [9L]
Introduction: Components of environment, basic ideas of ecology and environment, concepts related to environmental
perspective: man, society, environment and their inter relationship.
Population growth and associated problems, definition of resource; renewable, non-renewable, potentially renewable; effect of
excessive use vis-a-vis population growth, definition of pollutant and contaminant; EIA (Environmental Impact Assessment).
Environmental degradation: acid rain, toxic element; primary and secondary pollutants: emission standard, criteria pollutant,
oxides of carbon, nitrogen and Sulphur, particulates; overall methods for pollution prevention; environmental problems and
sustainable development.
Ecological concepts and natural resources: Introduction to ecological perspective, the value of environment, levels of
organization in the biotic component of the environment, ecosystem processes, the human dimension, environmental gradients,
tolerance and adaptation, environmental changes and threats to the environment.
Module 2 [9L]
Air Pollution and Control: Atmospheric composition- troposphere, stratosphere, mesosphere, thermosphere; Energy Balance:
conductive and convective heat transfer, radiation heat transfer, simple global temperature modal.
Green –house effects: Definition, impact of greenhouse gases on the global climate; climate, weather: Difference between
climate and weather; Global weather and its consequences.
Depletion of ozone layer: CFC, destruction of ozone layer by CFC, impact of other greenhouse gases, effect of ozone
modification.
Standards and control measures: Industrial, commercial and residential air quality standard.
Emission controls: Emission controls for coal fired power plants; Emission controls for Highway Vehicles.
Air pollution & Biosphere: Meteorology and air pollution, adiabatic lapse rate, atmospheric stability, temperature inversions.
Module 3 [9L]
Water Pollution: Water resources-unusual properties of water, the hydrologic cycle; organic pollutants, inorganic pollutants,
sediments, radioactive materials.
Thermal pollutants, ground water pollution/ arsenic contamination.
Surface water quality: Rivers & Streams, Bio chemical oxygen demand (BOD); water quality in lakes and reservoirs. Water
pollution control & water recycling.
Noise Pollution: Sound and Human Acoustics, Noise Measurement Units.
Noise classification: Transport noise, Road traffic noise, occupational noise, Neighborhood noise, noise pollution hazards,
permissible noise levels, Noise control.
Module 4 [12L]
Hazardous substances and risk analysis: Definition of Hazardous substances, legislation, Risk Assessment, Hazard Identification.
Environmental Engineering Technologies: Water treatment, Waste water treatment, solid waste treatment, Hazardous waste
treatment.
Environmental Management Systems (EMS): Meanings, Goals & Objectives, Implementation, EMS Model, ISO 14001-
Certification, Importance, usefulness.

3. Textbooks
1. Introduction to Environmental Engineering & Science, G. M. Masters, Prentice Hall India.
2. Environmental Management, Dey & Dey, New Age International (P) Ltd.
4. Reference Books
1. Environmental Engineering, Gerard Kiely, Mcgraw Hill Education.
B. SESSIONAL COURSES
Course Name: Project-I

0 0 8 8 4
1. Course Outcomes
CSEN4195.1. Demonstrate a sound technical knowledge of their selected project topic.
CSEN4195.2. Understand the problems from the related domain, formulate them formally, analyze the complexity of the
problem and apply their knowledge to solve it.
CSEN4195.3. Design engineering solutions to complex problems utilizing a systematic approach.
CSEN4195.4. Communicate effectively with their peer groups and the community at large in written as well as in oral form.
CSEN4195.5. Demonstrate their knowledge, skills, and techniques to solve various real-life problems related to the
engineering domain.
C. HONORS COURSES
Course Name: Compiler Design

3 0 0 3 3
1. Course Outcomes
CSEN4111.1. To identify the key concept areas of language translation and compiler design.
CSEN4111.2. To be familiar with compiler architecture.
CSEN4111.3. To understand the working principles of various phases of a compiler and their interdependencies.
CSEN4111.4. To apply the theory of compilation, in particular, lexical analysis, syntax and semantic analysis, code
generation, and optimization phases of compilation for solving real life examples.
CSEN4111.5. To analyze the similarities and differences among various parsing techniques and grammar transformation
techniques.
CSEN4111.6. To persuade different state of the art register allocation and code optimization strategies.
Module 1 [9L]
Introduction to compiler: Analysis of a source program; Different phases of compilation; Cousins of a compiler.
A simple one-pass Compiler
Lexical Analysis: Role of a lexical analyzer, Tokens, Patterns, Lexemes. Input buffering, Specifications of a token,
Recognition of tokens. A language for specifying lexical analyzer: Design of a lexical analyzer generator (Lex / Flex).
Module 2 [12L]
Syntax Analysis: Role of a parser, Context free grammars. Top-down Parsing, Non-recursive Predictive parsing (LL(1)).
Bottom-up parsing, Handles, Viable prefix, Various forms of LR parsers: SLR(1), LR(0), LR(1). Construction of LALR(1)
parsing table using/avoiding LR(1) parsing tables. Parser generators (YACC / Bison).
Type Checking: Type systems; Specification of a simple type checker, Equivalence of type expressions, Type conversions.
Run-Time Environment Source Language Issues: Procedures, Activation Trees, Control stacks, Scope of variable
declarations. Storage Organization: Sub-division of run-time memory, Activation Records.
Storage Allocation strategies: Static allocation, stack allocation, heap allocation.
Scope: Blocks; with and without Nested Procedures, Access Links, Displays, Parameter passing.
Symbol tables: organization; data structures used.
Module 3 [7L]
Syntax Directed Translation: Syntax directed definitions: Synthesized attributes, Inherited attributes. Construction of Syntax
trees: Expressions, DAG for Expressions. Bottom-up evaluation of S-Attributed definitions: Synthesized attributes on a parser
stack. L-Attributed definitions: Translation schemes. Top-down Translation: Elimination of left recursion. Bottom-up
Evaluation of Inherited Attributes: Removing embedding actions, inheriting attributes, simulating the evaluation of inherited
attributes, Replacing inherited attributes by synthesized attributes.
Intermediate Code Generation: Intermediate Languages: Graphical representation, Three-address code: different types.
Translation into three-address code, Quadruples / Triples / Indirect Triples, their comparisons. Translation of declarations
statements: Procedures, Records, Assignment statements. Addressing array elements, Boolean expressions, Control statements.
Back patching. Procedure calls.
Module 4 [8L]
Code generation: Issues in the design of a code generator: Memory management; Instruction selection; The target machine.
Run-time storage management. Basic blocks and flow graphs: Transformations on basic blocks; Flow graphs; Loops; A simple
code generator: Algorithm; Conditional statements. Register allocation and assignment. The DAG representation of basic blocks.
Code optimization: Principal source of optimization: Common sub-expression, Copy propagation, Dead code elimination, Loop
optimization, Code motion, Induction variables. Loops in flow graphs: Dominators, Natural loops, Inner loops. Peephole
optimization.
3. Reference Books
1. Aho, Sethi, Ullman: Compilers: Principles, Techniques and Tools: 2 nd Edition, Pearson Education.
2. Holub - “Compiler Design in C” – PHI
3. Tremblay and Sorenson Compiler Writing-McgrawHill International.
4. Chattopadhyay, S- Compiler Design (PHI)
Course Name: Compiler Design Lab

0 0 2 2 2
1. Course Outcomes
CSEN4161.1. Learn the different Phases of a compiler using various available tools.
CSEN4161.2. Optimize a given program.
CSEN4161.3. Learn to generate an assembly language program equivalent to a source language program.
CSEN4161.4. Understand how to design solutions for complex engineering problems and to design system components or
processes that meet the specified needs with appropriate consideration.
In this lab, a given mini–Language MNL will be considered. This language is a simple procedural high-level language, only
operating on integer data, with a syntax looking remotely similar to a simple C language syntax. The syntax of the language
MNL will be defined by a BNF grammar.
[The detailed BNF notation for MNL will be notified to students later]
Group A:
These experiments are to be implemented using the C language.
1. Develop a lexical analyzer to recognize a few patterns in MNL. (Ex. identifiers, constants, comments, operators etc.).
2. Implement Stack storage allocation strategies.
3. Design Predictive parser for the given language.
4. Design LALR bottom up parser for the above language.
Group B:
These experiments are to be implemented using Flex and Bison tool.
5. Implementation of Lexical Analyzer using Flex Tool.
6. Generate Bison specification for a few syntactic categories.
a) Program to recognize a valid arithmetic expression that uses operator +, – , * and /.
b) Implementation of a simple Calculator.
7. Convert the BNF rules of MNL into Bison form.
Group C:
8. Implementation of Simple Code Optimization Techniques.
9. Explore Code Optimization options implemented in the gcc compiler.

Open Elective – II and Open Elective - III course(s) to be offered by CSE Department
Course Name: Fundamentals of Operating Systems

3 0 0 3 3
1. Course Outcomes
CSEN4121.1. Apply knowledge of mathematics, science and engineering in the areas of process management, memory
management and storage management.
CSEN4121.2. Understand the underlying technologies and features of memory management and storage management.
CSEN4121.3. Understand the various design issues in process management.
CSEN4121.4. Learn operating system operation, structures.
CSEN4121.5. Be familiar with various types of operating systems.
CSEN4121.6. Identify the concepts learned here which are used in their own field of work.
Module 1 [8L]
Introduction of General Operating System: Introduction: What do OS do? Computer System Organization, Interrupt Driven
System, Storage Structure, I/O Structure, Operating System Functions, OS Services, Dual Mode Operations, Kernel, System
Calls, Types of System Calls
Types of Operating Systems: Computer System Architecture (Monolithic, Microkernel, Layered, Hybrid), Different types of
O.S. (Batch, Multi-programmed, Time-sharing, Real-time, Distributed, Parallel, for Mobile Unit, Single Processor System,
Multiprocessor Systems), Virtual Machines, System Boot.
Module 2 [10L]
Process Concept: What is process, Operations on Process (Process States), Process Control Block, Process Scheduling,
Scheduling Queues,
Cooperating Process: Co-operating Processes, Inter-process Communication. IPC, Examples in IPC, Communication in Client-
Server Systems
Threads: Threads, Benefits of Threads, User and Kernel Threads.
CPU Scheduling: Scheduling Criteria, Pre-emptive & Non-pre-emptive Scheduling, Scheduling Algorithms (FCFS, SJF, RR,
priority).
Module 3 [11L]
Process Synchronization: Critical Section Problem, Critical Region, Synchronization Hardware. Petersons Solution, Classical
Problems of Synchronization, Semaphores, Monitors, Synchronization examples, Atomic Transactions.
Deadlock: Deadlocks: System model, Deadlock characterization, Method of handling Deadlock, Deadlock Prevention,
Avoidance, Detection, Recovery from deadlock.
Module 4 [11L]
Memory Management Strategies: Contiguous Memory Allocation, Paging, Structure of Page Table, Segmentation, Demand
Paging, Copy-on-Write, Swapping, Page Replacement, Allocation of Frames, Thrashing, Memory Mapped Files, Allocating
Kernel Memory, Operating System examples.
File Management: File System: File Concept, Access Methods, Directory Structure, File System Mounting, File Sharing,
Protection.
3. Textbooks
1. Silberschatz, P B Galvin, G Gagne, Operating systems, 9th edition/10th edition, John Wiley and sons.
4. Reference Books:
1. William Stalling, "Operating Systems: Internals and Design Principles", Pearson Education, 1st Edition, 2018.
2. Andrew S Tanenbaum, Herbert BOS, "Modern Operating Systems", Pearson Education, 4th Edition, 2016.
Course Name: Intelligent Web and Big Data

3 0 0 3 3
1. Course Outcomes
CSEN4126.1. Understand the basic concepts related to Web Intelligence and Big Data.
CSEN4126.2. Explain the terms data mining, neural networks, support vector machine, text analytics, text mining, web
mining etc.
CSEN4126.3.
CSEN4126.4. Learn how to use and deploy various web/social/mobile analytics platforms.
CSEN4126.5. Understand the importance of Web intelligent as the art of customizing items in response to the needs of the
users.
CSEN4126.6.
CSEN4126.7. Learn the concepts of Hadoop and MapReduce.
CSEN4126.8.
CSEN4126.9. Apply big data technologies in business intelligence using geospatial data, location-based analytics, social
networking, reality mining, and cloud computing.
Module 1
Intelligent Information Retrieval Learning from User Interactions – Rating and Voting, E-Mailing and Link Forwarding,
Bookmarking, Purchasing Items, Customer Reviews Extracting Intelligence from Tags – Tag-related Meta-data, Tag
Generation; Leveraging Tags: Dynamic Navigation, using Tag Clouds, Targeted Search, Recommendations based on Tags
Extracting Intelligence from Contents – Blogs, Wikis, Message Boards.
Module 2
Recommendations, Clustering and Classification Creating Suggestions and Recommendations – Concepts of Distance and
Similarity, Recommendations based on Similar Users, Recommendations based on Similar Items; Recommendations based on
Contents Clustering – Overview of Clustering Algorithms Classification – Need for Classification; Overview, Automatic
Categorization of E-Mails and Spam Filtering; Classification and Fraud Detection, Combining Classifiers.
Module 3
Introduction to Hadoop Starting Hadoop; Components of Hadoop: HDFS, working with files in HDFS; Introduction to
MapReduce; Streaming in Hadoop; Advanced MapReduce: Chaining MapReduce Jobs, Joining Data from Different Sources;
Developing MapReduce Programs in Local Mode and Pseudo-distributed Mode; Moving Data into and out of Hadoop; Data
Input and Output in MapReduce; Applying MapReduce Patterns to Big Data; Streamlining HDFS for Big Data.
Module 4
Algorithms Using MapReduce Matrix-Vector Multiplication by MapReduce, Relational-Algebra Operations, Computing
Selections by MapReduce, Computing Projections by MapReduce, Union, Intersection, and Difference by MapReduce,
Computing Natural Join by MapReduce, Grouping and Aggregation by MapReduce, Matrix Multiplication.
3. Textbooks
1. Algorithms of the Intelligent Web by H Marmanis and D Babenko from Manning Publishers, 2009
2. Collective Intelligence in Action by S Alag from Manning Publishers, 2009
3. Hadoop in Action by Chuck Lam from Manning Publishers, 2011
4. Hadoop in Practice by Alex Holmes from Manning Publishers, 2012
5. Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, Jeff Ullman from Cambridge University Press, 2011.
4. Reference Books:
1. Mining the Web: Discovering Knowledge from Hypertext Data by Chakrabarti from Morgan Kaufmann Publishers, 2002.
2. Recommender Systems Handbook by Francesco Ricci, Lior Rokach, Bracha Shapira, Paul B. Kantor from Springer, 2011



Syllabus of 8th Semester

A. THEORY COURSES
LIST OF COURSES FOR PROFESSIONAL ELECTIVE – IV

CSEN4231 Distributed Algorithms
CSEN4232 Mobile Computing
CSEN4233 Pattern Recognition
CSEN4234 Computational Complexity
CSEN4235 Social Network Analysis
CSEN4236 Robotics
CSEN4237 Web Development with Node and Express
Course Name: Distributed Algorithms

3 0 0 3 3
1. Course Outcomes
CSEN4231.1. Learn the basics of distributed algorithms, designed to run on multiple processors, without any tight centralized
control
CSEN4231.2. Understand various kinds of distributed computing environments, including shared-memory and network-
based environments
CSEN4231.3. Identify problems solvable in distributed computing environments and also identify certain tasks that cannot
be carried out in certain kinds of distributed settings
CSEN4231.4. Design distributed algorithms and analyze the correctness, performance, and fault-tolerance of their algorithms;
also, will be able to prove lower bounds and other impossibility results in distributed settings.
CSEN4231.5. Learn the applications of distributed algorithms in many practical systems ranging from large computer
networks to multiprocessor shared-memory systems, including problems of communication, data management,
resource management, synchronization, and distributed agreement.
Module 1 [8L]
Synchronous networks: Model – Leader election (symmetry-breaking) – Network searching, Broadcast and converge-cast.
Shortest paths, spanning trees – Processor failures: Stopping and Byzantine – Fault-tolerant consensus: Algorithms and lower
bounds – Other problems: Commit, k-agreement, Approximate agreement. Distributed commit.
Module 2 [8L]
Asynchronous model – Interaction State Machines (I/O automata), Proving Correctness of Distributed algorithms.
Asynchronous networks, no failures: Model – Leader election, network searching, spanning trees, revisited. – Synchronizers
(used to run synchronous algorithms in asynchronous networks) – Logical time, replicated state machines. – Stable property
detection (termination, deadlock, snapshots).
Module 3 [10L]
Asynchronous shared-memory systems, no failures: Model – Mutual exclusion algorithms and lower bounds – Practical mutual
exclusion algorithms – Resource allocation, Dining Philosophers Problem. Asynchronous
shared-memory, with failures – Impossibility of consensus – Atomic (linearizable) objects, atomic read/write objects, atomic
snapshots – Wait-free computability; wait-free consensus; wait-free vs. f-fault-tolerant objects.
Module 4 [10L]
Shared-memory multiprocessor programming – Contention, caching, locality – Reader/writer locks – List algorithms: locking
algorithms, optimistic algorithms, lock-free algorithms – Transactional memory Asynchronous networks, with failures –
Asynchronous networks vs. asynchronous shared-memory – Impossibility of consensus, revisited – Failure detectors and
consensus – Paxos consensus algorithm, Self-stabilizing algorithm, Partially-synchronous systems – Models – Timing-based
Mutual exclusion, consensus – Clock synchronization
3. Textbooks
1. Distributed Algorithms, (The Morgan Kaufmann Series in Data Management Systems), Nancy A. Lynch
4. Reference Books
1. Introduction to Reliable and Secure Distributed Programming, Christian Cachin, Rachid Guerraoui, Luís Rodrigues.
2. Distributed Algorithms - An Intuitive Approach, Wan Fokkink.
3. Introduction to Distributed Algorithms, Gerard Tel.
Course Name: Mobile Computing

3 0 0 3 3
1. Course Outcomes
CSEN4232.1. To learn the wireless and mobile networking fundamentals.
CSEN4232.2. To learn the evolution of different generations of mobile networks.
CSEN4232.3. To analyze different inter-networking challenges and solutions in wireless mobile networks.
CSEN4232.4. To analyze the modifications necessary in normal IP and TCP protocols to make them mobility enabled.
CSEN4232.5. To understand the basics of MANET, WAN, LAN and PAN.
CSEN4232.6. To learn WAP basics.
Module 1 [10L]
Introduction to Mobile Communication: Introduction to mobile wireless communication and systems, Description of cellular
system. Channel interferences. Channel assignment schemes. Concept of 1G. Multiple Access Technologies in cellular
communication: Time division multiple access (TDMA), Frequency division multiple access (FDMA), Code Division Multiple
Access (CDMA). Second generation (2G) Network: Global system for mobile communication (GSM). 2.5G Wireless
Networks-GPRS, CDMA (IS 95), Third Generation 3G Wireless Networks-UMTS, Fourth Generation 4G Wireless Networks-
LTE Advanced.
Module 2 [10L]
Mobile Network and Transport Layer: Wireless LAN – IEEE 802.11. PAN-Bluetooth- Piconet, Scatternet, Connection
Establishment, Protocol Stack. Recap of Mobile IP. Traditional TCP, Indirect TCP, Snooping TCP, Mobile TCP, ATCP,
Transmission / Timeout Freezing Selective Retransmission, Transaction oriented TCP.
Module 3 [8L]
Mobile Routing and Application Protocols: Mobile Ad hoc Networks (MANETs): Overview, Properties of a MANET,
routing and various routing algorithms- DSR, WRP, DSDV, AODV, ZRP. Multicast Routing Algorithms: MAODV, ODMR
Wireless Application Protocol (WAP): The Mobile Internet standard, WAP Gateway and Protocols, wireless markup
Languages (WML).
Module 4 [10L]
Advanced Issues in Mobile Network: Wireless Sensor Network. Fifth Generation (5G) Wireless Networks: MIMO System
Design and Channel Allocation schemes; Convex Optimization based treatment. Cognitive Radio and Internet of Things. SDN.
3. Textbooks
1. Wireless Networks: Applications and Protocols, T.S. Rappaport, Pearson Education
2. Wireless Communications, A. Goldsmith, Cambridge University Press.
3. Wireless Communication: Stallings, Pearson.
4. Mobile Communications, Jochen Schiller, 2nd Edition, Pearson Education, India.
5. NPTEL Materials from the course of Convex Optimization offered by Aditya P. Jagannatham.
6. Prototyping and Load Balancing the Service Based Architecture of 5G Core using NFV by Vamshi Kiran Buyakar, Harsh
Agarwal, Bheemarjuna Reddy Tamma, and Antony Franklin (Indian Institute of Technology Hyderabad), published in
IEEE NETSOFT 2019.

Course Name: Pattern Recognition

3 0 0 3 3
1. Course Outcomes
CSEN4233.1. Learn and understand feature, pattern and the problem of pattern recognition.
CSEN4233.2. Understand and describe the difference between supervised and unsupervised learning.
CSEN4233.3. Understand and apply pattern recognition algorithm that utilizes supervised learning.
CSEN4233.4. Understand and apply pattern recognition algorithm that utilizes unsupervised learning.
CSEN4233.5. Analyze pattern recognition algorithms and techniques.
CSEN4233.6. Design simple pattern recognition systems.
Module 1 [9L]
Introduction – Definitions, Representations of Patterns and Classes, overview of different approaches, Metric and non-metric
measures. Feature selection criteria and algorithms; Minimum distance classifiers, k-NN rule, Discriminant functions (linear
and non-linear), parametric and nonparametric learning.
Module 2 [9L]
Decision Trees, Bayesian classification, Decision Boundaries, training and test sets, Neural network models for pattern
recognition - Perceptron, Multi-layer Perceptron, some applications.
Module 3 [9L]
Clustering techniques – Unsupervised learning, basic hierarchical and non-hierarchical clustering algorithms, c-means, fuzzy
c-means, DBSCAN, Concepts of hierarchical clustering, Clustering Large datasets.
Module 4 [9L]
Dimensionality reduction, principal components analysis, independent component analysis, some applications, some advanced
topics with applications, (e.g., neuro-fuzzy approach, genetic algorithms, data mining).
3. Reference Books
1. Devi V.S.; Murty, M.N., Pattern Recognition: An Introduction, Universities Press, Hyderabad, 2011.
2. R. O. Duda, P. E. Hart and D. G. Stork: Pattern Classification and Scene Analysis, 2nd ed., Wiley, New York, 2000.
3. 2. J. T. Tou and R. C. Gonzalez: Pattern Recognition Principles, Addison-Wesley, London, 1974.
4. Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
5. K. Fukunaga: Introduction to Statistical Pattern Recognition, 2nd eel, Academic Press, New York, 1990.
6. A. K. Jain and R. C. Dubes: Algorithms for Clustering Data, Prentice Hall, Englewood Cliffs, 1988.
7. Neural Networks and Learning Machines, Simon Haykin, Third Edition, PHI Learning, 2009.
Course Name: Computational Complexity

3 0 0 3 3
1. Course Outcomes
CSEN4234.1.By the end of the course, a student should have a broad understanding of the various notions in computational
complexity theory to classify computational problems.
CSEN4234.2.One should become familiar with the important complexity classes, how they are related to each other, typical
problems in those classes, and some of the fundamental open problems in the field.
CSEN4234.3.After completion of the course, a student should have the ability to follow the proofs.
CSEN4234.4.Students should be able to develop a concept of the techniques used in analyzing computational complexity.
CSEN4234.5.The course will enable a student to understand how one problem can be reduced to another. In addition, they will
learn to construct reductions for simple examples.
CSEN4234.6.The course will also briefly introduce applications of complexity theory to different domains.

Module 1 [10L]
Computational Models; Problems, Computability, Algorithms, and Complexity; Introduction to P and NP; Turing machines
(time and space bounds, nondeterminism); Turing machines Logic (Boolean logic, circuits).
Module 2 [10L]
P, NP, coNP, and NP-Completeness; P vs. NP, NP vs. coNP; NP-completeness of SAT and other problems; Complexity classes
(hierarchy theorem, P, NP, Co-NP); Reduction and completeness; Interactive proof systems; Polynomial hierarchy.
Module 3 [8L]
Randomized computation: Basic concept, Definitions and relation among the randomized classes RP, coRP, PP, BPP; Relation
of BPP to the polynomial hierarchy and non-uniform computation; Approximability.
Module 4 [8L]
Nondeterministic Space Classes: Logarithmic space; Polynomial space, Savitch’s Theorem; Exponential time and space. A
PSPACE complete problem- quantified Boolean formula problem (QBF). Derandomization; Pseudorandom constructions:
expanders and extractors. Proofs of PCP theorems and the Fourier transform technique.
3. Textbooks
1. Christos H. Papadimitriou: Computational Complexity, Addison-Wesley Longman.
2. Sanjeev Arora and Boaz Barak: Computational Complexity: A Modern Approach. Cambridge University Press, 2009.
4. Reference Books
1. Michael Garey and David S. Johnson: Computers and Intractability: A Guide to the Theory of NP-Completeness. New
York: W. H. Freeman & Co., 1979.
2. Michael Sipser: Introduction to the Theory of Computation, PWS Publishing.
3. John E. Hopcroft and Jeffrey D. Ullman, Introduction to Automata, Languages and Computation, Addison-Wesley, 1979.
4. J. Balcazar, J. Diaz, and J. Gabarro, Structural Complexity, Volumes I and II, Springer.
Course Name: Social Network Analysis

3 0 0 3 3
1. Course Outcomes
CSEN4235.1. Learn the basic knowledge related to social network and its application.
CSEN4235.2. Understand the use of various measures and metrics in social network analysis.
CSEN4235.3. Identify the computational aspect of various problems in social network analysis
CSEN4235.4. Design and analyze algorithms of social network analysis
CSEN4235.5. Evaluate and apply the working principle of various computational models in social network analysis.
Module 1 [9L]
Introduction: Motivating challenges in analyzing social networks. Measures and Metrics: Degree centrality, Eigen vector
centrality, Katz centrality, Page Rank, hubs and authorities (HITS), closeness centrality, betweenness centrality, groups of
vertices, transitivity, reciprocity, signed edges and structural balance, similarity, homophily and assortative mixing. Large
Scale Structure of Networks: Components, shortest paths and the small world effect, degree distributions, power laws and
scale-free networks, distributions of centrality measures, clustering coefficients.
Module 2 [9L]
Random Networks Understanding mean number of edges, mean degree, degree distribution, clustering coefficient, giant
component, small components, and average path lengths for the following models- Erdos-Renyi Network, Small-world
networks and Watts-Strogatz model, Preferential attachment and Barabasi-Albert model.
Module 3 [8L]
Propagation of Information Networks Contagion Models: Models of disease spread –SI, SIS, SIR, SIRS and related literature.
Outbreak detection. Influence Maximization: Influence spread models -independent cascade model, linear threshold model.
Maximizing propagation of influence under different setups –greedy approximation algorithm by Kempe et.al. and related
literature.

Module 4 [10L]
Community Detection What is a community? Notion of disjoint and overlapping communities. Goodness measures –modularity.
Benchmarks and comparing with the benchmarks (F-measure, NMI, Omega index),
Strength of weak ties and related models. Clique Percolation model. Modularity maximization, Clauset-Newman-Moore (CNM)
method, Louvain Method. Label propagation algorithm and its variants. Random walks, Entropy-based method: Infomap.
Community preserving sparsification of social networks.
3. Textbooks
1. Networks: An Introduction, Mark Newman, Oxford University Press.
2. Social Network Analysis: Methods and Applications, S.Wasserman, K. Faust., Cambridge University Press, 1994.
4. Reference Books
1. Networks, Crowds and Markets: Reasoning About a Highly Connected World, David Easley, Jon Kleinberg, Cambridge
University Press 2010.
Course Name: Robotics

3 0 0 3 3
1. Course Outcomes
CSEN4236.1. To understand the concept of Robot mechanical structure, modelling and control
CSEN4236.2. To understand the Kinematics of Robotic structure
CSEN4236.3. To realise and remember the concept of sensors, actuators and motion control
CSEN4236.4. To apply the knowledge of programming in developing an automated system
CSEN4236.5. To understand the concepts of Force Control and Visual Servoing
CSEN4236.6. To develop algorithms for robotic motion planning.
Module 1 [10L]
Introduction: Robot Mechanical Structure; Industrial Robotics; Advanced Robotics; Robot Modelling, Planning and Control
Kinematics: Pose of a Rigid Body; Rotation Matrix; Composition of Rotation Matrices; Euler Angles; Angle and Axis; Unit
Quaternion; Homogeneous Transformations; Direct Kinematics; Kinematics of Typical Manipulator Structure; Joint Space
and Operational Space; Kinematic Calibration; Inverse Kinematics Problem.
Module 2 [10L]
Trajectory Planning: Path and Trajectory; Joint Space Trajectories; Operational Space Trajectories. Actuators and Sensors:
Joint Actuating System; Drives; Proprioceptive Sensors; Exteroceptive Sensors. Control Architecture: Functional Architecture;
Programming Environment; Hardware Architecture. Motion Control: The Control Problem; Joint Space Control; Decentralized
Control; Computed Torque Feed Forward Control; Centralized Control; Operational Space Control; Comparison Among
Various Control Schemes.
Module 3 [8L]
Force Control: Manipulator Interaction with Environment; Compliance Control; Impedance Control; Force Control;
Constrained Motion; Natural and Artificial Constraints; Hybrid Force/Motion Control. Visual Servoing: Vision for Control;
Image Processing; Pose Estimation; Stereo Vision; Camera Calibration; The Visual Servoing Problem; Position-based Visual
Servoing; Image-based Visual Servoing; Comparison Among Various Control Schemes; Hybrid Visual Servoing.
Module 4 [8L]
Mobile Robots: Nonholonomic Constraints; Kinematic Model; Chained Form; Dynamic Model; Planning; Path and Timing Law;
Motion Control; Odometric Localization. Motion Planning: The Canonical Problem; Configuration Space; Planning via
Retraction; Planning via Cell Decomposition; Probabilistic Planning; Planning via Artificial Potentials; The Robot Manipulator
Case.
3. Reference Books
1. Robotics Modelling, Planning and Control, Bruno Siciliano, Lorenzo Sciavicco, Luigi Villani, Giuseppe Oriolo, Springer
Publications.
2. Principles of Robot Motion, Theory, Algorithms and Implementation, Howie Choset, Kevin Lynch, Seth Hutchinson,
George Kantor, Wolfram Burgard, Lydia Kavraki, and Sebastian Thrun, The MIT Press, Cambridge, Massachusetts,
London, England.

Course Name: Web Development with Node and Express

3 0 0 3 3
1. Course Outcomes
CSEN4237.1. Understand the JavaScript and technical concepts behind Node JS
CSEN4237.2. Structure a Node application in modules and use npm and manage node packages
CSEN4237.3. Understand and use the various core module
CSEN4237.4. Build a Web Server in Node and understand how it really works
CSEN4237.5. Build a web application and API more easily using Express
CSEN4237.6. Develop commonly used functionalities in web application using various third-party packages available in
Node environment and REST API to access database like MySql MongoDB.
Module 1 [12L]
Prerequisite: Prototype in JavaScript, Inheritance in JavaScript, Understanding Classes in JavaScript (ES6), Function in
Javascript, (Arrow function, Synchronous and Async function), Async callbacks, Promise, Callback vs Promise, Async/Await.
(5L) Overview NodeJS: Brief History, What is Node.js, Features of Node.js, Elements that compose Node.js, (Node, V8, Libuv
and C++), Node.js Process Model, Understanding some important terminologies in JavaScript/Node (Blocking, Synchronous
and Asynchronous operation, Callback Functions, Call Stack, Thread pool etc.), How call stack help execution of a program.
Setup Node.js Development Environment: Installation, Node.js Console – REPL, Create Node.js Web Server, Handle HTTP
Request, Node Package Manager (NPM) Module: Concepts, Types of Modules, Import/Export module, How the codes inside
a module run, Steps to process require function.
Module 2 [10L]
Node.js Basics features: Variables and Buffer, Core modules of Node, Using Process Module, Request and Response Objects,
Rendering Content, Using Event Emitter, Node.js File System, Data Access from MySql and MongoDB. Overview of Express
Js : Web framework, What are middlewares in Node.Js and How they work, Introduction to Express Js and its advantages,
Creation of simple web application using Express.js, Application-level middleware, Error-handling middleware, Built-in
middleware in Express, Using Middleware, Creating Your Own Express.js Middleware, Routes and Controller.
Module 3 [10L]
Event Loop: Architecture of Event Loop, Understanding the Node.js event loop phases and how it executes the JavaScript
code. Template Engine: Various template engines for Node, Advantage, Using EJS (Embedded JavaScript Template), EJS
partial, Introduction to Jade Template Engine. Working with Form: Form handling process, Validation and sanitization, Server-
Side Form Validation using Express-Validator, Body-Parser and EJS.
Module 4 [12L]
Developing Commonly used Functionalities: Understanding REST APIs, MVC Pattern, Developing REST based CRUD API
using Node Js and Express following MVC pattern, Sending Email, File Uploading, TCP client sever communication using net
module, Saving Geolocation data with Google Maps API and MongoDB.
3. Textbooks
1. Paperback by Sandro Pasquali
2. Node.Js Web Development: Create real-time server-side applications with this practical, step-bystep guide Paperback, by
David Herron.
4. Reference Books
1. Online resources from reputed sites like Nodejs.org, TutorialPoint, WSchools.com, Guru 99, Javatpoint
2. Node.js Notes for professionals – GoalKicker.com, Free Programming Book.

LIST OF COURSES FOR PROFESSIONAL ELECTIVE – V

CSEN4241 Distributed Databases
CSEN4242 Natural Language Processing
CSEN4243 Parallel Algorithms
CSEN4244 Real Time & Embedded System
CSEN4245 Quantum Computing
CSEN4246 Computer Vision
Course Name: Distributed Databases

3 0 0 3 3
1. Course Outcomes
CSEN4241.1. Understand the basic concepts of database, communication network, and distributed database.
CSEN4241.2. Identify the concepts of creating and maintaining databases in a distributed environment Understand and use
the various core module
CSEN4241.3. Learn to design a distributed database using horizontal and vertical fragmentation.
CSEN4241.4. Learn to manage distributed transactions and concurrency control.
CSEN4241.5. Design all types of distributed queries using query optimization techniques.
Module 1 [6L]
Definition of Distributed database (DDB), DDB features, comparison with centralized databases, Distributed Database
Management Systems (DDBMSs), Review of Relational algebra and SQL, Review of basic concepts of computer networks.
Reference architecture of DDB, Distribution Transparency (for Read-only and Update applications), Integrity constraints in
DDB.
Module 2 [10L]
A Framework for Distributed Database Design, Types of Data Fragmentation, Design of Database Fragmentation, Allocation
of Fragments. Equivalence Transformations for Queries, Operator Graph, Transforming Global Queries into Fragment Queries,
Distributed Grouping, and Aggregate Function Evaluation.
Module 3 [14L]
A Framework for Transaction Management, Supporting Atomicity of Distributed Transactions, Concurrency Control for
Distributed Transactions, Commit protocols. Foundations of Distributed Concurrency Control, Distributed Deadlocks. Basic
concepts of Reliability, Non-blocking Commitment protocols, Reliability, and Concurrency control.
Module 4 [6L]
A framework for Distributed Query Processing, A framework for Distributed Database administration – Catalog Management,
Authorization, and Protection. The basic concept of Parallel Databases.
3. Textbooks
1. Stefano Ceri and Giuseppe Pelagatti: Distributed Databases – Principles and Systems, 1st Edition, Tata McGraw-Hill.
2. M Tamer Ozsu and Patrick Valduriez, ―Principles of Distributed Database Systems, 2nd Edition, Pearson Education.
4. Reference Books
1. Silberschatz, Korth and Sudarshan: Database System Concepts, TMH.
2. Ramakrishnan and Gehrke: Database Management Systems, TMH.
3. Elmasri and Navathe: Fundamentals of Database Systems, Pearson.

Course Name: Natural Language Processing

3 0 0 3 3
1. Course Outcomes
CSEN4242.1. Learn various models, methods, and algorithms of Natural Language Processing (NLP) to build an automation
tool to solve like, speech recognition, machine translation, spam filtering, text classification, spell checking etc.
CSEN4242.2. Understand and estimate the parameters of the probabilistic models.
CSEN4242.3. Identify problems solvable in language automation environments and also identify certain tasks that are
challenging to carry out with traditionally existing statistical models.
CSEN4242.4. Understand the linguistic phenomena and will explore the linguistic features relevant to each NLP task.
CSEN4242.5. Identify the opportunities for research await and prepare to conduct research in NLP or related fields.
Module 1 [9L]
Introduction to NLP: Natural language processing issues and strategies. Tools of NLP, Linguistic organization of NLP, NLP
as an Application domain.
Word Classes: Regular Expressions: Chomsky hierarchy, CFG and different parsing techniques. Morphology: Inflectional,
derivational, parsing and parsing with FST, Combinational Rules. Joint and conditional probability. Probabilistic Language
modeling and it’s Applications.
Module 2 [11L]
Language Modeling and Naïve Bayes: Markov models, N- grams. Estimating the probability of a word and smoothing.
Counting words in Corpora, simple N-grams, smoothing (Add One, Written-Bell, Good-Turing).
Part of Speech Tagging and Hidden Markov Models: Part of Speech tagging, Indian Language on focus Morphology
Analysis, Accuracy Measure and Probability, HMM, Viterbi algorithm for finding most likely HMM Path. HMM tagging,
transformation-based tagging. Probabilistic Context Free Grammars: Weighted context free grammars.
Module 3 [8L]
Semantics: Representing Meaning: Unambiguous representation, canonical form, expressiveness, meaning structure of
language. Semantic Analysis: NLP and IR, How NLP has used IR Towards Latent Semantic. Lexical Semantics: Lexemes
(synonymy, hyponymy etc), WordNet, metonymy and their computational approaches Supervised and Unsupervised methods.
Word Sense Disambiguation: Selectional restriction based, machine learning based and dictionary-based approaches.
Module 4 [8L]
Pragmatics: Information Theory: Entropy, Cross-entropy, information gain. Reference resolution and phenomena, syntactic and
semantic constraints. Pronoun resolution algorithm, text coherence, and discourse structure. Natural Language Generation:
Introduction to language generation, architecture, discourse planning (text schemata, rhetorical relations). Resource Constrained
WSD, Parsing Algorithms, Parsing Ambiguous Sentences, Probabilistic Parsing Algorithms.
3. Textbooks
1. D. Jurafsky., J. H. Martin., Speech and Language Processing – An introduction to Language processing, Computational
Linguistics, and Speech Recognition, Pearson Education.
4. Reference Books
1. Allen, James, Natural Language Understanding, Benjamin/Cummings, 2ed.
2. Bharathi, A., Vineet Chaitanya and Rajeev Sangal., Natural Language Processing- “A Pananian Perspective”, Prentice
Hall India, Eastern Economy Edition.
3. Eugene Cherniak, Statistical Language Learning, MIT Press, 1993.
4. Manning, Christopher and Heinrich Schutze., Foundations of Statistical Natural Language Processing, MIT Press.
5. Cognitively Inspired Natural Language Processing, Abhijit Mishra, Pushpak Bhattacharyya, Springer.

Course Name: Parallel Algorithms

3 0 0 3 3
1. Course Outcomes
CSEN4243.1. Learn the basics of parallel algorithms and how a sequential version of an algorithm can be converted to a
parallel version.
CSEN4243.2. Identify problems solvable in parallel computing environments and analyze the performance of parallel
algorithms.
CSEN4243.3. Explain the architecture of large-scale parallel systems and how massive parallelism is implemented in
accelerator architectures.
CSEN4243.4. Design parallel programs for large-scale parallel systems, shared address space platforms, and heterogeneous
platforms.
CSEN4243.5. Learn some parallel programming techniques and use the same for implementing and testing some of the
parallel algorithms learnt in this course.
Module 1 [9L]
Architecture: Parallelism in uniprocessor system, memory-interleaving, pipelining and vector processing, parallel computer
structures, architectural classifications; Shared-Memory (SM) SIMD Computers – EREW / CREW / ERCW/ CRCW;
Programming MIMD Computers.
System interconnect architectures: Static interconnection networks: array, tree, mesh, hypercube, cube-connected-cycles,
butterfly, Cayley graphs; Dynamic interconnection networks: crossbar, Clos network, multistage interconnection networks,
blocking, non-blocking and rearrangeable operations, properties and routing.
Parallel computer models: PRAM models, program properties: conditions of parallelism, program partitioning and
scheduling, granularity and scalability.
Analyzing Algorithms: Running Time, Speedup, Number of Processors.
Module 2 [10L]
Selection: A sequential algorithm; Desirable Properties for Parallel Algorithms: Number of Processors, Running Time, Cost.
An Algorithm for Parallel Selection.
Basic Techniques: Balanced Trees; Divide & Conquer; Partitioning; Pipelining; Cascading.
Merging: A Network for Merging; Merging on the CREW & EREW Model; Finding the Median of Two Sorted Sequences.
Sorting: Sorting on a Linear Array, Sorting on the CRCW /CREW / EREW model; Sorting by Conflict-Free Merging, Sorting
by Selection.
Searching: Searching on a sorted sequence / random sequence / Trees / Mesh.
Module 3 [7L]
Lists & Trees: List ranking; Euler-Tour technique; Tree contraction.
Graphs: Connected components; Minimum Spanning Trees; All pairs shortest paths.
Strings: String Matching; Text Analysis.
Module 4 [10L]
Arithmetic Computation: Adding n integers; Multiplying two numbers; Prefix sum; Polynomial Multiplication & Division.
Matrix Operations: Transposition; Matrix multiplication.
Decision and Optimization problem: Computing Prefix Sums; Knapsack problem.
Fourier Transforms: Fast Fourier Transform; The DFT computation in parallel.
Networked computers as a multi-computer platform: Basics of message passing, computing using workstation clusters,
software tools, Message Passing Interface MPI, CUDA and General-Purpose GPU (GPGPU) programming.
3. Reference Books
1. S.G.Akl: Design and Analysis of Parallel Algorithms, Prentice Hall.
2. J. Ja Ja: Introduction to Parallel Algorithms, Addison Wesley, 1990.
3. M.G. Quinn: Design of Efficient Algorithms for Parallel Computers, McGraw Hill, 1988. 4. K. Hwang: Computer
Arithmetic: Principles, Architecture and Design, John Wiley.
4. Hwang & Briggs: Advanced Computer Architecture and Parallel processing, McGraw Hill.
5. Peter Pacheco: Parallel Programming with MPI
6. Jason Sanders, Edward Kandrot: CUDA by Example: An Introduction to General-Purpose GPU Programming.

7. T. Leighton: Introduction to Parallel Algorithms and Architectures: Arrays, Trees, Hypercubes, Morgan Kauffmann Pub.,
San Mateo, 1992.
Course Name: Real Time & Embedded System

3 0 0 3 3
1. Course Outcomes
CSEN4244.1. Identify the issues that are involved in designing an embedded system.
CSEN4244.2. Understand the interfacing protocols and handle the interfacing devices
CSEN4244.3. Do device-level programming.
CSEN4244.4. Know the design considerations of an RTOS and handle its interrupts.
CSEN4244.5. Critically appreciate system requirements in terms of hardware, drivers, Operating Systems binding, and
protocol stack implementation.
Module 1 [10L]
Examples of Embedded Systems, Classification of Embedded Systems, Skills required for an Embedded System designer.
Sensors and Actuators, Embedded Processors with examples. Memory Architecture revisited (Technologies, Hierarchy,
Models). Input and Output, Revisiting I/O Hardware, Interrupts, DMA, etc. Serial/Parallel devices, Sophisticated
interfacing features, Timers/Counters/Watchdog timers, Real-Time Clocks, Network Interface Cards, Wireless devices, etc.
Serial and Parallel interfacing protocols --- RS232/RS485, I2C, SPI, CAN, Field-bus (Profibus), USB (v2.0), Bluetooth, Zig-
Bee, Wireless sensor networks, Sigma-Delta, PCI, etc. Network protocols, Wireless and mobile system protocols.
Module 2 [7L]
Need of RTOS in embedded systems, Foreground/Background systems, multitasking. Review of Process management, Timer
functions and scheduling, Events, Memory management, Devices, File and I/O subsystem management. Multitasking Issues,
critical sections, Semaphores, Message Queues, Mailboxes, Pipes Functions, Sockets, File maps, etc. Handling of interrupts in
RTOS environment. Features of Real Time Operating Systems, Soft versus Hard RTOS-s, RTOS Task Scheduling Models,
Interrupt Latency, RTOS Security Issues.
Module 3 [7L]
Introduction to Device Drivers, Device Driver types and issues involved. Programmed-I/O, Interrupt Servicing, Multiple
Interrupts, Top and Bottom halves of ISR. Interrupt latency and Deadlines, Direct Memory Access, Device Driver
Programming.
Module 4 [8L]
Arduino Systems, Open-Source Hardware, Rapid Prototyping, Hands-on implementation. MSP430 family RISC CPU
architecture, Compiler-friendly features, Instruction set, Clock/Memory subsystems, etc. Study of one RTOS like RTLinux.
Writing a Linux/Windows Network Device Driver. Quantitative Analysis and Verification of Embedded Systems. Common Lisp
as an embedded extension language.
3. Reference Books
1. William Stallings, Computer Organization and Architecture, Pearson, 2016 (10th ed).
2. E. A. Lee and S. A. Seshia, Introduction to Embedded Systems - A Cyber-Physical Systems Approach, Second Edition,
MIT Press, 2017.
3. Ajoy Ray and K. Bhurchandi, Advanced Microprocessors and Peripherals, Tata Mcgraw Hill Education Private Limited,
2006.
4. Jonathan Corbet, Alessandro Rubini, Greg Kroah-Hartman, Linux Device DriversLinux Device Drivers, Third Edition,
O’Reilly Media, Inc., 2005.
5. Doug Abbott, Linux for Embedded and Real-time Applications, Elsevier, 2017.
4. Reference Books
1. Allen, James, Natural Language Understanding, Benjamin/Cummings, 2ed.
2. Bharathi, A., Vineet Chaitanya and Rajeev Sangal., Natural Language Processing- “A Pananian Perspective”, Prentice
Hall India, Eastern Economy Edition.
3. Eugene Cherniak, Statistical Language Learning, MIT Press, 1993.
4. Manning, Christopher and Heinrich Schutze., Foundations of Statistical Natural Language Processing, MIT Press.
Cognitively Inspired Natural Language Processing, Abhijit Mishra, Pushpak Bhattacharyya, Springer
Course Name: Quantum Computing

3 0 0 3 3
1. Course Outcomes
CSEN4245.1. Understand the major mathematical representations of quantum operations,
CSEN4245.2. Distinguish between classical and quantum computation
CSEN4245.3. Describe a few key applications of quantum computing
CSEN4245.4. Implement basic quantum algorithms,
CSEN4245.5. Understand and describe quantum information concepts
CSEN4245.6. Identify key aspects of quantum supremacy over conventional computation.
Module 1 [9L]
Introduction and Overview: Brief history and postulates of quantum theory; Heisenberg Uncertainty Principle; Recapitulation
of the basic principles of classical computation.
Quantum Mechanics: Cbits and Qbits; Reversible operations on Cbits and Qbits; Quantum measurements – Positive operator
valued measures and Projective measurements; General features and some simple examples.
Module 2 [9L]
Linear Algebra and Hilbert Spaces: Basis vectors- Orthogonal and Orthonormal vectors; Inner product spaces, Completeness
and Separable Hilbert spaces; Unitary operations and Projectors; Tensor Products.
Fundamental quantum notions: No-cloning theorem; Quantum entanglement; Quantum nonlocality – Bell’s inequality.
Module 3 [10L]
Quantum Circuits: Pauli and Hadamard gates; Prototype examples; Reversible computing.
Quantum Algorithms: Deutsch-Josza algorithm; Simon’s problem; Quantum Fourier transform; Shor’s period-finding
algorithm; Grover’s algorithm for searching.
Module 4 [8L]
Quantum Computers: Physical qubits; Noise and Decoherence.
Basic aspects of quantum information theory: Shannon and von-Neumann entropy; Conditional entropy, relative entropy and
Mutual information. Basics of Quantum Cryptography.
3. Reference Books
1. N. David Mermin, Quantum Computer Science – An Introduction, Cambridge University Press, 2007.
2. Michael A. Nielsen and Issac L. Chuang, “Quantum Computation and Information”, Cambridge (2002).
3. Riley Tipton Perry, “Quantum Computing from the Ground Up”, World Scientific Publishing Ltd (2012).
4. Lecture Notes
1. John Preskill’s lecture notes: http://www.theory.caltech.edu/people/preskill/ph229/
2. David Mermin’s lecture notes: http://people.ccmr.cornell.edu/_mermin/qcomp/CS483.html
Course Name: Computer Vision

3 0 0 3 3
1. Course Outcomes
CSEN4246.1. Understand the fundamental problems of computer vision.
CSEN4246.2. Gain knowledge on various techniques, mathematical concepts and algorithms used in computer vision to
facilitate further study in this area.
CSEN4246.3. Recognize objects and represent shapes.
CSEN4246.4. Analyze and track motions.
CSEN4246.5. Utilize the programming and scientific tools for implementation.

Module 1 [9L]
Introduction: overview of computer vision, related areas, and applications; overview of software tools; overview of course
objectives.; introduction to OpenCV.
Image formation and representation: imaging geometry, radiometry, digitization, cameras and projections, rigid and affine
transformations.
Filtering: convolution, smoothing, differencing, and scale space.
Module 2 [9L]
Feature detection: edge detection, corner detection, line and curve detection, active contours, SIFT and HOG descriptors, shape
context descriptors.
Object recognition and shape representation: alignment, appearance-based methods, invariants, image eigenspaces, data-based
techniques.
Model fitting: Hough transform, line fitting, ellipse and conic sections fitting, algebraic and Euclidean distance measures.
Module 3 [7L]
Camera calibration: camera models; intrinsic and extrinsic parameters; radial lens distortion; direct parameter calibration;
camera parameters from projection matrices; orthographic, weak perspective, affine, and perspective camera models.
Epipolar geometry: introduction to projective geometry; epipolar constraints; the essential and fundamental matrices;
estimation of the essential/fundamental matrix.
Model reconstruction: reconstruction by triangulation; Euclidean reconstruction; affine and projective reconstruction.
Module 4 [7L]
Motion analysis: the motion field of rigid objects; motion parallax; optical flow, the image brightness constancy equation, affine
flow; differential techniques; feature-based techniques; regularization and robust estimation; motion segmentation through EM.
Motion tracking: statistical filtering; iterated estimation; observability and linear systems; the Kalman filter; the extended Kalman
filter.
3. Reference Books
1. Computer Vision: Algorithms and Applications, R. Szeliski, Springer, 2011.
2. Computer Vision: A Modern Approach, D. Forsyth and J. Ponce, Prentice Hall, 2nd ed., 2011.
3. Introductory techniques for 3D computer vision, E. Trucco and A. Verri, Prentice Hall, 1998.
4. Lecture Notes
1. John Preskill’s lecture notes: http://www.theory.caltech.edu/people/preskill/ph229/
2. David Mermin’s lecture notes: http://people.ccmr.cornell.edu/_mermin/qcomp/CS483.html

LIST OF COURSES FOR OPEN ELECTIVE - IV

AEIE4221 Process Instrumentation
AEIE4222 Medical Instrumentation
BIOT4221 Computational Biology
BIOT4222 Non-conventional Energy
BIOT4223 Biology for Engineers
CHEN4222 Introduction to Solar and Wind Technology
ECEN4221 Low Power High Performance Digital VLSI Circuit
ECEN 4222 Cellular and Mobile Communication
ECEN4223 Optical Fiber Communication
HMTS4222 Introduction to French Language
Course Name: Process Instrumentation

3 0 0 3 3
1. Course Outcomes
AEIE4221.1 Acquire knowledge about the characteristics of different process instruments.
AEIE4221.2 Explain the working principle and functions of displacement, strain, pressure, temperature, flow and level
measuringinstruments.
AEIE4221.3 Formulate the mathematical equation of the linear processes and derive their response.
AEIE4221.4 Apply their knowledge of controllers and final control element in various control schemes for effective
process control.
AEIE4221.5 Gain knowledge of industrial signal transmission and transmitters.
AEIE4221.6 Choose proper automation system for specific application.
Module 1[9L]
Introduction to process and instrumentation, static and dynamic characteristics of instruments, active and passive transducers;
measurement methods and applications: displacement, strain, pressure, temperature, flow and level measurement.
Module 2 [9L]
Introduction to process control, open and closed loop process, mathematical model and transfer function, dynamic behavior
of first and second order processes; feedback controllers: on-off controllers, basic control modes, PID controllers.
Module 3 [9L]
Control system instrumentation: transducers and transmitters, two wire and four wire transmitters, smart transmitters, final
control elements; feedforward, ratio and cascade control; basic concept of stability.
Module 4 [9L]
Introduction to process automation, brief idea and application of PLC, DCS and SCADA; case study: boiler drum level
control/ distillation column control.
3. Reference Books
1. B. G. Liptak, Instrumentation Engineers Handbook (Measurement), Chilton Book Co.; 1994.
2. John P. Bentley, Principles of Measurement Systems, Third edition, Addison Wesley Longman Ltd., UK, 2000.
3. E.O. Doebelin, Measurement Systems - Application and Design, Fourth edition, McGraw-Hill International Edition, New
York, 1992.
4. U. A. Bakshi, A.V.Bakshi; Instrumentation Engineering; Technical Publications; 2009.
5. Harold E. Soisson; Instrumentation in Industry; John Wiley & Sons Canada, Limited, 1975.
6. B.E. Noltingk, Instrumentation Reference Book, 2nd Edition, Butterworth Heinemann, 1995.
7. L.D. Goettsche, Maintenance of Instruments and Systems – Practical guides for measurements and control, ISA, 1995.

Course Name: Medical Instrumentation

3 0 0 3 3
1. Course Outcomes
AEIE4221.1 Explain the fundamental principles and applications of different transducers used for body parameter
measurements.
AEIE4221.2 Understand the physiology of biomedical systems and different methods in the design of biomedical
instruments.
AEIE4221.3 Learn the different methods of medical imaging systems, concepts related to the operations and analysis of
biomedical instruments.
AEIE4221.4 Learn various therapeutic devices.
AEIE4221.5 Design various type bio-telemetry system.
AEIE4221.6 Aware of the importance of electrical safety and apply it in the design of different assisting.
Module 1[8L]
Transduction Principles: Transducers- Definition, principles of sensing and transduction, characteristics, classification,
concept of signal conditioning; Body Temperature transducers- thermoresistive, thermoelectric, semiconductor, chemical
thermometry and operating specifications; Blood Pressure transducer- Strain gauge type, variable capacitance type, LVDT and
operating specifications; Blood Flow transducers- based on piezoelectric effects, electromagnetic effects, operating
specifications; Acoustic Transducers- Heart sound.
Module 2 [10L]
Bio-potentials and electrodes: Bio-potentials- Origin and electrical activity of cells, resting and action potentials of cells;
Electrodes- Electrode theory and half-cell potential, Electrode-Electrolyte interface, types of electrodes: surface, needle and
micro electrodes and respective applications. Electrode impedance, electrode jellies and creams; Measurement of electrical
activities in Cardiovascular system- ECG, Einthoven’s triangle, electrodes, amplifiers, cardiac pace-maker, defibrillator,
Measurement of electrical activities in muscles and brain: EMG, EEG.
Module 3 [10L]
Biomedical imaging: ultrasound imaging, radiography, CT scan, MRI and applications, Plethysmography;
Assisting and therapeutic instruments: Pacemakers, defibrillators, Hearing aids. Ventilators, Heart-lung machine, Diathermy;
Module 4 [8L]
Philosophy of biotelemetry and patient safety: transmission and reception aspects of biological signals via long distances;
electrical safety of patients. Measurements of blood pH, pCO2, pO2.
3. Reference Books
1. Leslie Cromwell, Fred J. Weibell, Erich A. Pfeiffer, Biomedical Instrumentation and Measurements, Second edition,
Prentice-Hall India, 1997.
2. R.S. Khandpur, Handbook of Biomedical Instrumentation, 2 Edition, Tata McGraw Hill New Delhi, 1987.
3. John G. Webster, Medical Instrumentation application and design, Third edition, Wiley, 1997.
4. S. K. Venkata Ram, Biomedical Electronics and Instrumentation, Galgotia Publication Pvt. Ltd., New Delhi.
5. Geddes L.A and Baker L.E, Principles of Applied Biomedical Instrumentation, Third edition, Wiley-Interscience, 1989.
Course Name: Computational Biology

3 0 0 3 3
1. Course Outcomes
BIOT4221.1 Acquire basic understanding of structures and functions of different biomolecules.
BIOT4221.2 Obtain knowledge about the different metabolic pathways.
BIOT4221.3 Explain different biological data and biological databases.
BIOT4221.4 Understand classification of databases and how the biological data are stored in those databases.
BIOT4221.5 Obtain the knowledge of different algorithms and programming languages to manage biological data.
BIOT4221.6 Apply different tools and software for analysis of biological data.

Module 1[10L]
Introduction to Biomolecules: Introduction to biochemistry and molecular biology; Biomolecules: structure, function and
metabolic pathways.
Module 2[10L]
Scope of Computational Biology Definition of computational biology; origin and development of computational biology;
Nature and Types of biological data; Data Structures: Sequences (GENbank files), Secondary structures, Super-secondary
structures (Motifs), Tertiary structures (Pubchem and PDB structure files); Interaction Networks, Photographic Data:
Fingerprints (DNA and MS), Microarray data; Biological databases.
Module 3[10L]
Preferred Algorithms, Programming languages and Operating systems Principles of Pattern recognition: Use of Hidden Markov
Model and Artificial Neural Networks in computational biology; Significance of Python and C/C++; Operating system: Bio-
Linux (Selected Bioinformatics packages).
Module 4[10L]
Applications of Computational biology Molecular Modeling and Dynamics: introduction to Open MM library; GROMACS as
an example of GUI in the public domain; computer based drug design (public domain and proprietary); Mathematical modeling
of cell growth kinetics; Embedded systems for computational biology: High throughput data collection, processing and
analysis; LC-MS, DNA microarrays and other applications (e.g. mobile microscopy and high throughput micro-PCR); Systems
biology and Metabolic Engineering.
3. Text books:
1. Introduction to Bioinformatics, by Arthur M. Lesk (International Fourth Edition) (2014), Oxford University Press.
2. Essential Bioinformatics, by JinXiong, Cambridge University Press (2006).
4. Reference books:
1. Biochemistry: Jeremy M. Berg, John L. Tymoczko and LubertStryer, 7th edition, Academic Press.
2. Introduction to Bioinformatics: T K Attwood, D J Parry-Smith and S. Phukan (2008) Pearson.
3. Fundamentals of Database Systems, 5th Edition, R. Elmasri and S.B. Navathe (2009)
4. Bioinformatics-A Machine Learning Approach- By Baldi and Brunak, 2nd Edition (2006), John Wiley Inc.
5. Dynamics of Proteins and Nucleic Acids: J. Andrew McCammon and Stephen C. Harvey, Cambridge University Press
(1998).
6. Molecular Modelling: Principles and Applications-2nd Edition, Andrew R. Leach-Pearson (2016)
7. Molecular Modelling and Drug Design-K.Anand Solomon-1st edition (2011)-MJP Publishers.
Course Name: Non-conventional Energy

3 0 0 3 3
1. Course Outcomes
BIOT4222.1 Understand the concept and necessity of non-conventional energy as an alternative source of energy.
BIOT4222.2 Comprehend and apply the concepts of solar energy to design Photovoltaic cells and wind energy to design wind
turbine.
BIOT4222.3 Classify and design different biogas production processes.
BIOT4222.4 Design a production process for biodiesel.
BIOT4222.5 Understand the concept of hydrogen energy as a clean fuel and characterize the hydrogen production process.
BIOT4222.6 Comprehend the importance and classification of hydrogen fuel cells.
Module 1 [10L]
Non-conventional energy: Different forms Solar energy: Solar energy balance, production of electricity, photovoltaic systems.
Wind Energy: Wind energy conversion systems, power generation. Calculations on wind turbine. Hydro thermal energy: Basics
of hydro thermal energy. Energy from waves and tides.
Module 2 [10L]
Biogas Biomass as a renewable energy source; types of biomasses – forest, agricultural and animal residues, industrial and
domestic organic wastes. Classification of biogas production processes: combustion, pyrolysis, gasification and other thermo-
chemical processes. Production of alcohol and biogas from biomass. Biogas from anaerobic digestion.

Module 3[10L]
Bio-diesel: Fundamentals; Trans-esterification of vegetable oils for biodiesel production; Characterization of biodiesel;
Biodiesel from different sources; Economics, current trends and future prospects in usage of biodiesel.
Module 4[10L]
Hydrogen energy: Hydrogen energy system and analysis; Hydrogen infrastructure; Safety, codes and standards.
Hydrogen production: Electrolysis; Thermochemical; Hydrogen from fossil fuel, biomass and renewable sources of energy.
Problems on combustion of fuels. Hydrogen storage: Carbon storage materials; Metal hydrides and chemical hydrides;
Cryogenic hydrogen storage. Hydrogen fuel cells: Principle, importance and classification.
3. Textbooks:
1. J.E. Smith, Biotechnology, 3rd ed. Cambridge University Press.
2. S. Sarkar, Fuels and combustion, 2nd ed., University Press.
3. Donald L. Klass, Biomass for renewable energy, fuels and chemicals, Academic Press.
Course Name: Biology for Engineers

3 0 0 3 3
1. Course Outcomes
BIOT4223.1 Understand the basic structure and function of cells and cellular organelles.
BIOT4223.2 Understand the fundamental concepts of cellular reproduction and cell metabolism.
BIOT4223.3 Characterize the different types of proteins, lipids and carbohydrates.
BIOT4223.4 Analyze the mechanism of inheritance of characters through generations.
BIOT4223.5 Understand and implement the working principles of enzymes and their applications in biological systems and
industry.
BIOT4223.6 Design and evaluate different environmental engineering projects with respect to background knowledge about
bioresources, biosafety and bioremediation.
Module 1
Basic cell biology Prokaryotic and Eukaryotic cells, Cell theory; Cell structure and function, Cell organelles, Structure and
function of DNA and RNA, Central Dogma; Genetic code and protein synthesis, differences between eukaryotic and prokaryotic
protein synthesis.
Module 2
Biochemistry and cellular aspects of life Biochemistry of carbohydrates, proteins and lipids; Cell metabolism – Glycolysis,
TCA cycle, Fermentation; Cell cycle and cell death; Stem cells and their applications, Basics of Mendelian Genetics.
Module 3
Enzymes and industrial applications Enzymes – significance, co-factors and co-enzymes, classification of enzymes; Enzyme
kinetics, enzyme inhibition, models for enzyme action; Restriction enzymes; industrial applications of enzymes; enzymes in
human gene therapy and disease diagnostics.
Module 4
Biodiversity and bioengineering innovations Molecular motors, Basics of neural networks; Tissue Engineering; Basic concepts
of environmental biosafety, bioresources, biodiversity, bioprospecting, bioremediation, biosensors; recent advances in
engineering designs inspired by examples in biology.
3. Textbooks:
1. Wiley Editorial, “Biology for Engineers: As per Latest AICTE Curriculum,” Wiley-India, 2018.
2. S. ThyagaRajan, N. Selvamurugan, M. P. Rajesh, R. A. Nazeer, Richard W. Thilagaraj, S. Barathi, and M. K. Jaganathan,
“Biology for Engineers,” Tata McGraw-Hill, New Delhi, 2012.
4. Reference Books
1. Jeremy M. Berg, John L. Tymoczko and LubertStryer, “Biochemistry,” W.H. Freeman and Co. Ltd., 6th Ed., 2006.
2. Robert Weaver, “Molecular Biology,” MCGraw-Hill, 5th Edition, 2012.

3. Jon Cooper, “Biosensors A Practical Approach” Bellwether Books, 2004.
4. Martin Alexander, “Biodegradation and Bioremediation,” Academic Press, 1994.
5. Kenneth Murphy, “Janeway's Immunobiology,” Garland Science; 8th edition, 2011.
Course Name: Introduction to Solar and Wind Technology

Course Code: CHEN4222
3 0 0 3 3
1. Course Outcomes
CHEN4222.1. Understand different technologies used for solar collectors.
CHEN4222.2. Students will be able to evaluate the performance and efficiency of different devices that extract power from
solar energy.
CHEN4222.3. Students will be able to understand the main components of wind energy system and its functions.
CHEN4222.4. Understand the different types of wind turbines.
Module 1: [10L]
Introduction to Radiation heat transfer: Blackbody radiation, Stefan-Boltzmann Law, Wien’s Displacement Law,
emissivity, absorptivity, radiation view factor, radiation shield. Solar radiation: sun earth geometric relationship, solar angles,
sun’s trajectories in different seasons, zenith solar time, air mass, solar beam, total solar radiation & diffuse radiation, solar
radiation on different surfaces at different angles, extraterrestrial radiation. Attenuation of solar radiation by the atmosphere,
beam and diffuse components of hourly and daily radiation, clearness index.
Module 2: [10L]
Solar Thermal Collector: Flat plate collector, Unglazed, Single- a n d d o u b l e - g l a z e d solar collectors, Optical
losses and thermal losses, thermal analysis and performance characteristics. Concentrating solar collectors: General
description; concentrators, receivers, Orienting/tracking requirements, Paraboloid dish collectors, Scheffler dish, Linear
Fresnel Reflector Collector.
Introduction to Solar PV: Crystal structure, band theory, energy band diagrams, Fermi level, intrinsic and extrinsic
semiconductor, Standard solar cell structure, I-V characteristics, FF, Voc, Isc, Pmax, conversion efficiency, losses in solar cell,
Rs, Rsh, impact of radiation and temperature; Silicon wafer based solar PV technology, Single and poly crystalline silicon solar
cells; Thin film technology of solar cell, Merits and demerits of thin film technologies.
Module 3: [10 L]
Basics of Wind Energy Conversion: Power available in the wind spectra, Wind turbine power and torque, Classification of
wind turbines: Horizontal axis and Vertical axis, Characteristics of wind rotors, Aerodynamics of wind turbines (Airfoil,
Aerodynamic theories, Axial momentum theory, Blade element theory, Strip theory), Rotor design, Rotor performance.
Analysis of wind regimes: The wind (Local effects, Wind shear, Turbulence, Acceleration effect, Time variation),
Measurement of wind (Ecological indicators, Anemometers, Cup anemometer, Propeller anemometer, Pressure plate
anemometer, Pressure tube anemometers, Sonic anemometer, Wind direction), Analysis of wind data (Average wind speed,
Distribution of wind velocity, Statistical models for wind data analysis; Weibull distribution, Rayleigh distribution),Energy
estimation of wind regimes (Weibull based approach, Rayleigh based approach).
Module 4: [10L]
Wind energy conversion systems: Wind electric generators (Tower, Rotor, Gear box, Power regulation, Safety brakes,
Generator; Induction generator, Synchronous generator. Fixed and variable speed operations, Grid integration), Wind farms,
Offshore wind farms, Wind pumps (Wind powered piston pumps, Limitations of wind driven piston pumps; The hysteresis
effect, Mismatch between the rotor and pump characteristics, Dynamic loading of the pump’s lift rod, Double acting pump,
Wind driven roto-dynamic pumps, Wind electric pumps). Performance of wind energy conversion systems: Power curve of the
wind turbine, Energy generated by the wind turbine (Weibull based approach, Rayleigh based approach), Capacity 26 factor,
Matching the turbine with wind regime, Performance of wind powered pumping systems (Wind driven piston pumps,
Wind driven roto-dynamic pumps, Wind electric pumping systems)
3. Textbooks:
1. Sukhatme S. &Nayak J., Solar Energy: Principles of Thermal Collection and Storage, Third Edition, Tata McGraw Hill,
2008.
2. Solanki C.S.; Solar Photovoltaics – Fundamentals, Technologies and Applications; PHI Learning, 3rd edition, 2015.
3. Efstathios E. (Stathis) Michaelides, Renewable Energy Sources, Springer, 2012.
4. Sathyajith Mathew, Wind Energy: Fundamentals, Resource Analysis and Economics, Springer, 2006.

4. Reference Books:
1. Goswami D.Y., Kreith F. &Kreider J.F.; Principles of solar Engineering, Taylor and Francis, Philadelphia, 2000.
2. N.K. Bansal and M.K. Kleeman, Renewable Sources of Energy and Conversion Systems, Tata McGraw-Hill, 1984.
Course Name: Low Power High Performance Digital VLSI Circuit Design
3 0 0 3 3
1. Course Outcomes
ECEN4221.1. Understand different technologies used for solar collectors
ECEN4221.2. Learn the timing Verification flows.
ECEN4221.3. Learn and apply the Static Timing Analysis Method.
ECEN4221.4. Design interconnects.
ECEN4221.5. Know the Process Variation impact on design.
ECEN4221.6. Learn the various Dynamic Power Reduction Techniques.
ECEN4221.7. Learn the Standby Power Reduction Techniques.
Module 1 [10L]
VLSI Verification Flows and Timing Analysis: Unit1: VLSI Design and Verification Cycles: Logic, circuit and Layout design
and Verification, pre-layout simulation, parasitic Extraction and Back-annotation, post layout verification. Unit2: Timing
Analysis: Dynamic vs Static Timing Analysis. Types of Path for Timing Analysis: Data-path, Clock-path, Clock Gating Path,
Asynchronous Path. Flop based Design: Launch path, Capture Path, Longest Path, Shortest Path, Critical Path. Timing checks:
Setup (max) check, Hold (min) check, Gated Clock check, Process Variation study with PVT analysis, Clock Skew, Library
Cell characterization.
Module 2 [6L]
VLSI Interconnect Design Component of Interconnect, Interconnect Cross Section, Wire material, Interconnect Modelling,
Interconnect Design Issues and WirePlan: Capacitance, Delay, Lumped Model vs Distributed Model, RC Scaling, Repeater,
Interconnect Power, Interconnect Noise: Coupling, Cross Talk
Module 3 [12L]
Dynamic Power Reduction: Unit1: Definition of dynamic power, Transition probability, Signal probability, Transition
probability of basic gates, Glitch power, sources of switching capacitance. Unit2: Dynamic Power reduction with Vdd, Delay
vs Power Trade-off, Dual Vdd, Dynamic Voltage Scaling (DVS), Capacitance Scaling, Transistor sizing, Transition probability
reduction by clock gating, Logic restructuring, Input Reordering, Glitch reduction.
Module 4 [8L]
Standby Power Reduction: Unit1: Definition of Leakage power: Gate Leakage, Channel Leakage, Junction Leakage. Channel
leakage issue with Threshold Voltage Scaling. Unit2: Technology Solution of Gate Leakage reduction: High-K, FinFET,
Channel leakage reduction techniques: Multiple Threshold Voltage, Long Channel Transistor, Device Downsizing, Stacking,
Power Gating, Dual Vdd, Dynamic Body-Biasing, Technology Solution: FinFET.
3. Textbooks:
1. CMOS VLSI Design, A Circuits and Systems Perspective (4th Edition) Neil Weste, David Harris. Addison-Wesley, Pearson
2. Practical Low Power Digital VLSI Design, Author: Gary Yeap, KLUWER ACADEMIC PUBLISHERS, 2010.
4. Reference Books:
1. Low Power CMOS VLSI Circuit Design, Kuashik Roy and Sharat Prasad, John Wiley & Sons, Inc. 2009.

Course Name: Cellular and Mobile Communication

3 0 0 3 3
1. Course Outcomes
ECEN4222.1. Learn about the evolution of radio communication and fundamental design strategies of cellular network.
ECEN4222.2. Appreciate the challenges of rf communication.
ECEN4222.3. Understand the concepts of propagation over wireless channels.
ECEN4222.4. Learn about the both physical and networking of lte-4g systems.
ECEN4222.5. Understand the functioning of ip technology.
ECEN4222.6. Apply their knowledge for research work in communication domain.
Module 1 [11L]
Introduction Brief introduction to wireless communication and systems, Evolution of wireless/mobile standards - 1G, 2G, 3G
and 4G and related networks, Brief introduction to 5G network, Potential challenges. Cellular Networks: Design
Fundamentals Principle of cellular communication, Description of cellular system- Cellular Structure, Cell clustering, and
Capacity enhancement techniques for cellular networks, Frequency Reuse- Co-channel and Adjacent channel interferences,
Channel Assignment Strategy, Handoff Schemes, Mobility Management- Location, Radio Resource and Power management.
Module 2 [8L]
Multiple Access Techniques for Wireless Communications Introduction to multiple access techniques, Narrow band
channelized systems- Frequency Division Duplex and Time Division Duplex Systems, Frequency Division Multiple Access,
Time Division Multiple Access, Wideband Systems- Principles of WDM, Spread Spectrum Multiple Access, Space Division
Multiple Access, Orthogonal Frequency Division Multiple Access. GSM& GPRS: Architecture and Protocols- 2G & 2.5G
Introduction, GSM subsystems, GSM subsystems entities, GSM Air Interface, GSM frequency bands and allocation strategies,
GSM channel structure, GSM call set-up procedure, GPRS (2.5G) network architecture, GPRS Attachment and Detachment
procedure.
Module 3 [9L]
Overview of CDMA Systems- 2G CDMA Evolution-An overview, CDMA IS-95 systems, CDMA channel concept-Forward
and Reverse, Transmission power control- Near Far problem and Multipath Phenomenon, Handoff process. The Universal
Mobile Telecommunication System-3G UMTS Network architecture, Frequency allocation strategy, UMTS channels. LTE
4G Introduction to LTE network architecture, Uplink and Downlink frequency bands and allocation strategies, Channel
Structure of LTE, Channel dependent multiuser resource scheduling.
Module 4 [8L]
Key Enablers for LTE 4G Multicarrier concepts, Basics of OFDM, SC-FDE and SC-FDMA, OFDM in LTE, Timing and
Frequency synchronization, Multiple Access for OFDM systems, OFDMA and SC-FDMA in LTE, OFDMA system design
considerations. Mobile Internet Protocol Basic Mobile IP, Mobile IP Type-MIPv4 and MIPv6, Basic Entities of MIPv4,
MIPv4 Operations, Registration, Tunneling and Reverse Tunneling, Triangular Routing.
3. Textbooks:
1. Wireless Communications: Principles and Practice, T.S. Rappaport, Pearson Education
2. Wireless Communication and Networks: 3G and Beyond, I. Saha Misra, TMH Education.
3. Fundamentals of LTE, Arunabha Ghosh, Jan Zhang, Jefferey Andrews, Riaz Mohammed, Pearson Education.
4. Reference Books:
1. Wireless Digital Communications: Modulations and Spread Spectrum Applications, K. Feher, Prentice Hall.
2. Wireless Communications and Networking, J.W.Mark and W. Zhuang, PHI.

Course Name: Optical Fiber Communication

3 0 0 3 3
1. Course Outcomes
ECEN4223.1. Apply the basic idea of electronics, physics and solid state devices and explain the operation of different
components in an optical communication system.
ECEN4223.2. Understand the properties of optical fiber and categorize the transmission characteristics of a wave through the
optical fiber.
ECEN4223.3. Analyze the structure of various optical sources and can classify them according to the performance, efficiency
and application.
ECEN4223.4. Explain the operation of optical detectors and can analyze the performance parameters of a detector.
ECEN4223.5. Recognize the current optical technologies used for long distance communication and their application in
optical networks.
ECEN4223.6. Solve the problems related to optical fiber communication and can justify the physical significance of the
solutions.
Module 1 [8L]
Introduction to communication systems: Principles, Components; Different Forms of Communications, Advantages of Optical
Fiber Communication, Spectral Characteristics. Optical Fiber: Cylindrical Wave Guide Structure (qualitative discussions
only), Fabrication and Related Parameters, Single and Multimode Operation; Attenuation and losses, Material and Wave Guide
Dispersion. Fiber Splices, Fiber Optic Connectors, OTDR.
Module 2 [10L]
Optical Sources Light Emitting Diode: Principle, Structures, Power and Efficiency, Surface Emitting LED And Edge Emitting
LED, Super Luminescent Diode (SLD), Coupling of LEDs to Fibers. Laser diodes: Principle, Modes, Double Heterostructure,
Gain and Index Guiding, Distributed Lasers, Narrow Line Width Lasers.
Module 3 [12L]
Detectors & Other Network Components: Photo Detectors: Photo Diodes, Optical Detection Principles, Efficiency,
Responsively, Bandwidth. WDM System: Preamplifiers; Noise Sources, Wavelength Division Multiplexing: Building Blocks;
Multiplexing; Intensity Modulation/Direct Detection System; Principle of Regeneration. Optical amplifiers& Filters: EDFA,
SOA, Raman Amplifier, Fabry-Perot Filters.
Module 4 [6L]
Optical Network: Network Topologies: LAN, MAN, WAN; Topologies: Bus, Star, Ring; Ethernet; FDDI; Telecom
Networking: SDH/SONET, SONET/SDH Layers, SONET Frame Structure, SONET/SDH Physical Layer.
3. Textbooks:
1. Fiber Optics and Optoelectronics, R. P. Khare, Oxford University Press
2. Optical Fiber Communication: John M. Senior (Pearson)
3. Optical Networks – A Practical Perspective: Rajiv Ramaswami, K. N. Sivarajan, Galen H. Sasaki (Morgan-Kaufman)
4. Optical Communication Systems: John Gawar (PHI).
4. Reference Books:
1. Optical Fiber Communication: Gerd Kaiser (TMH).

Course Name: Introduction to French Language

3 0 0 3 3
1. Course Outcomes
HMTS4222.1. Write simple sentences
HMTS4222.2. Engage in interpersonal interaction in basic French.
HMTS4222.3. Communicate about day-to-day activities, participate in formal interactions.
HMTS4222.4. Learn the standard pronunciation Parisienne.
HMTS4222.5. Construct expressions using basic vocabularies and simple grammatical structures.
HMTS4222.6. Acquire the elementary language skills of speaking, reading and writing.
Module 1 [9L]
The French Alphabet, the vowels, pronunciation rules, stress and accents. Greetings, giving and requesting personal details.
The numbers, nationalities, professions. Gender. The three conjugations: -er,-ir , -re. The verbs Etre, Avoir and S’appeller.
Vocabulary Resources: the days of the week, the parts of the day, about habits. Expressing frequency. Asking and telling the
time.
Module 2 [9L]
The presente indicative. Some uses of à, de, en. The definite article: Le, La, Les. The indefinite article: Un, Une , Des. Reflexive
verbs. Expressing existence and location. Vocabulary Resources: leisure activities, the weather, geography, tourist attractions.
Speaking about physical appearance and character. Expressing and comparing likes, dislikes and interests, one’s profession.
Asking about likes and dislikes. Speaking about personal relationships, the family, daily activities. Adjectives to describe
character, music.
Module 3 [9L]
Adjectif Infterrogatif: Quand, Comment, Où, Pourquoi, Combien, Quel/Quelle. Identifying objects. Expressing needs. Shopping:
asking for items, asking about prices, etc. Talking about preferences. The numbers over 100. The colours, clothes, everyday
objects. Demonstratives: Ce, Cette, Ces.
Module 4 [9L]
The verb Aimer, Adorer, Préférer, Détester. Quantifiers (Beaucoup, un peu, bien). Possessives. Reflexive verbs. Direction
(Près, Loin, à côté de). Ordering and giving information about food. Speaking about different culinary habits. Describing
districts, towns and cities. Adjectives to describe a district.
3. Reference Books:
1. Cosmopolite 1 (Text Book, Work Book)
2. Webster’s French grammar and vocabularies.
3. Collin’s easy learning French Grammar and Practice)
B. SESSIONAL COURSES
Course Name: Project-II

0 0 16 16 8
1. Course Outcomes
CSEN4295.1. Demonstrate a sound technical knowledge of their selected project topic.
CSEN4295.2. Understand the problems from the related domain, formulate them formally, analyze the complexity of the
problem and apply their knowledge to solve it.
CSEN4295.3. Design engineering solutions to complex problems utilizing a systematic approach.
CSEN4295.4. Communicate effectively with their peer groups and the community at large in written as well as in oral form.
CSEN4295.5. Demonstrate their knowledge, skills, and techniques to solve various real-life problems related to the
engineering domain.

Course Name: Comprehensive Viva voce

- - - - 1
1. Course Outcomes
CSEN4297.1. Understand and demonstrate their overall technical knowledge in the program domain.
CSEN4297.2. Apply the fundamental knowledge of Computer Science Engineering in advanced problems.
CSEN4297.3. Present their ideas clearly and precisely.
CSEN4297.4. Analyze a situation and identify possible practical solutions to implement it.
CSEN4297.5. Communicate effectively and face interviews with confidence.
C. HONORS COURSES
Course Name: Disaster Response Services and Technologies

Course Code: HMTS4011*
4 0 0 4 4
1. Course Outcomes
HMTS4011.1. Recall the basic concepts and terminologies of disaster and disaster management.
HMTS4011.2. Understand disaster risk assessment, risk reduction and community preparedness plans.
HMTS4011.3. Interpret and characterize hazards, vulnerabilities and strategies for disaster mitigation.
HMTS4011.4. Examine techniques for post disaster situation awareness, damage and need assessment.
HMTS4011.5. Evaluate post disaster remedial measures and long-term recovery planning.
HMTS4011.6. Design emergency communication infrastructures, technologies and services.
Module 1 [10L]
Definition of disaster, types of disasters, phases of disasters, factors contributing to disaster impact and severity, disaster profile
of India, definition of disaster management, disaster management cycle, Disaster Management Act 2005, organizations
involved in disaster management.
Module 2 [10L]
Disaster Preparedness: Disaster risk assessment, disaster risk reduction, preparedness plans, community preparedness, and
emergency resource networks. Disaster Mitigation: Concepts of hazard, hazid, hazan and hazop as part of safety and risk
management; types of vulnerabilities, vulnerability assessment, strategies for disaster mitigation, structural mitigation and non-
structural mitigation, disaster mitigation initiatives in India.
Module 3 [10L]
Disaster Response: Need for coordinated disaster response, SPHERE standards in disaster response, role of government,
international agencies and NGOs, post disaster situation awareness, post disaster damage and need assessment. Disaster
Recovery and Reconstruction: Post disaster effects and remedial measures, creation of livelihood options, disaster resistant
house construction, sanitation and hygiene, education and awareness, dealing with victims’ psychology, long-term counter
disaster planning.
Module 4 [10L]
Emergency communication infrastructures; emerging technologies for disaster resilience - drones, VR/AR, social media
technologies, real-time mapping system; examples of disaster management information systems; examples of smartphone/
web-based applications for disaster management.
3. Reference Books:
1. R. Nishith, Singh AK, “Disaster Management in India: Perspectives, issues and strategies”, New Royal book Company.
2. Bhattacharjee Suman, Roy Siuli, Das Bit Sipra, "Post-disaster Navigation and Allied Services over Opportunistic Networks",
Springer Verlag, Singapore.
3. Basu Souvik, Roy Siuli, Das Bit Sipra, "Reliable Post Disaster Services over Smartphone Based DTN: An End-to-End
Framework", Springer, Singapore.
4. Sahni, Pardeepet.al. (Eds.),” Disaster Mitigation Experiences and Reflections”, Prentice Hall of India, New Delhi.
5. Goel S. L., "Disaster Administration and Management Text and Case Studies", Deep & Deep Publication Pvt. Ltd., New
Delhi.
6. Liu Zhi, Ota Kaoru, "Smart Technologies for Emergency Response and Disaster Management", IGI Global.
7. Rajib Shaw, "Disaster Risk Reduction - Methods, Approaches and Practices", Springer Verlag, Singapore.
Open Elective - IV course(s) to be offered by CSE Department
Course Name: Basics of Mobile Computing

3 0 0 3 3
1. Course Outcomes
CSEN4221.1. To understand the infrastructure to develop mobile communication system.
CSEN4221.2. To analyze the measures taken to increase the capacity in mobile systems as well as in the entire protocol
architecture.
CSEN4221.3. To describe different inter-networking challenges and solutions in wireless mobile networks.
CSEN4221.4. To analyze the modifications necessary in normal IP and TCP protocols to make them mobility enabled.
CSEN4221.5. To learn the basics of Ad Hoc Networking Protocols.
CSEN4221.6. To motivate the students to pursue research in the area of wireless communication and mobile computing field.
Module 1 [11L]
Introduction to Mobile Computing, Cellular Mobile Wireless Networks: -Systems and Design Fundamentals, Frequency Reuse,
Cochannel and Adjacent channel interference. First Generation Wireless Networks - AMPS, Second Generation (2G) Wireless
Cellular Networks – GSM, 2.5G Wireless Networks-GPRS, CDMA (IS 95), Third Generation 3G Wireless Networks-UMTS,
Fourth Generation 4G Wireless Networks-LTE Advanced. Fifth Generation 5G Wireless Networks: Architecture, Main
features. MIMO concept. Convex Optimization based treatment of channel allocation.
Module 2 [9L]
Wireless LAN – IEEE 802.11. PAN-Bluetooth- Piconet, Scatternet, Connection Establishment, Protocol Stack. WiMax – IEEE
802.16. Physical layer, Modulation: OFDM; MIMO; Duplexing; Protocol stack; MAC Layer; Network Architecture.
Module 3 [10L]
Basics of Computer Networking – Layering & OSI. Challenges from Mobile Environment. Mobile IP: Goals, Entities, Agent
Advertisement and Discovery, Registration. Tunnelling and Encapsulation, Reverse Tunnelling. Cognitive Radio and Internet
of Things. SDR: User and Control plane.
Module 4 [8L]
Mobile Ad hoc Networks (MANETs): Overview, Properties of a MANET, routing and various routing algorithms- DSR,
DSDV, AODV. Traditional TCP, Indirect TCP, Snooping TCP, Mobile TCP, ATCP, Transmission / Timeout Freezing
Selective Retransmission, Transaction oriented TCP.
3. Reference Books:
1. Mobile Communications, Jochen Schiller, 2nd Edition, Pearson Education, India.
2. Mobile Communication Systems, Krzysztof Wesolowski, Wiley
3. Wireless Communications: Principles & Practice, 2nd edition, Theodore S. Rappaport, Prentice Hall
4. Wireless Communication: Stallings, Pearson
5. NPTEL materials on 5G from Aditya P Jagannath’s lectures
6. A Survey of 5G Network: Architecture and Emerging Technologies by Akhil Gupta and Rakesh Kumar Jha, IEEE Access,
28 July, 2015.

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HERITAGE INSTITUTE OF TECHNOLOGY

(An Autonomous Institute Under MAKAUT)

B.Tech Course Structure

HERITAGE INSTITUTE OF TECHNOLOGY

Department of Computer Science & Engineering

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*N.B.: HMTS4011 Honors Course is for Lateral Entry Students Only

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Open Electives to be offered by Computer Science and Engineering department for

Credit Summary for B Tech Programme with effect from 2018-2019

Honors Course for B. Tech Computer Science & Engineering Students

Contact Hours / Credit

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Definition of Credit (as per AICTE):

Range of Credits (as per AICTE):

Swayam/MOOCs Courses recommended to the students of CSE department

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HERITAGE INSTITUTE OF TECHNOLOGY

SYLLABUS OF 1ST SEMESTER

Department of Computer Science & Engineering

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Course Name: Chemistry-I

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Course Name: Mathematics-I

Course Name: Basic Electrical Engineering

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Course Name: Chemistry–I Lab

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Course Name: Basic Electrical Engineering Lab

3. Textbooks and References

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Course Name: Communication for Professionals

Course Name: Professional Communication Lab

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HERITAGE INSTITUTE OF TECHNOLOGY

SYLLABUS OF 2nd SEMESTER

Department of Computer Science & Engineering

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Course Name: Physics-I

Course Name: Mathematics-II

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Course Name: Programming for Problem Solving

Course Name: Business English

Course Name: Physics-I Lab

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Course Code: PHYS1051

Module 2 Experiments in Optics:

Module 3 Electricity & Magnetism experiments:

Module 4 Quantum Physics Experiments: