FinalVer - SY - COMP - Syllabus - SVU-July - 2022

Syllabus
B. Tech Computer Engineering

(Second Year Semester III and IV)
From
Academic Year 2021-22
(Revision-1)
Approved by FOET 08/05/2021 and AC 28/06/2021
SY B. Tech COMP Revision 1.0
K J Somaiya College of Engineering, Mumbai-77

(A Constituent College of Somaiya Vidyavihar University)
It is notified for information of all concerned that the Board of Studies at its meeting held on
April 28, 2021; and the subsequent meeting of Faculty of Engineering & Technology held on
May 08, 2021 and the Academic Council held on June 28, 2021 amended the syllabus of S.Y.
B. Tech Computer Engineering and same be brought in to force from Academic Year 2021-22
with immediate effect.
SVU 2020 - R 1.0 S Y B. Tech COMP. AC 28/06/2021 Page 2 of 52

Preamble
KJSCE as a constituent college of Somaiya Vidyavihar University has the academic flexibility
to develop and implement its own curriculum KJSCE-SVU-2020 with features such as
inclusion of choice based Open Elective Courses, Audit Courses, Add on Credit Courses,
Exposure Courses, etc. Distinct assessment and evaluation methods are also designed based on
focus of individual courses. The outcome of this entire exercises; either by way of student
placements or the feedback received from all stakeholders is quite encouraging.
At present, Industry is moving towards Industrial revolution 4.0. Knowing very well that every
country's education system forms the basis of its progress and the groundwork for its future,
we need to be making engineering graduates equipped to take industrial challenges. A common
feature in successful education systems is the balance between tradition and the capacity to be
flexible and able to adapt to current social trends. To achieve this, Somaiya Vidyavihar
University allows for the undergraduate courses to have a focus on the changing industrial
scenario.
Our new revision in syllabus KJSCE-SVU-2020, introduced from the academic year 2020-21,
has been designed based on the revised guidelines from various accrediting bodies.
The said syllabus is a result of expert advice from members of Board of Studies, Faculty of
Engineering & Technology and Academic Council; both having due representation from
academia as well as appropriate industries. Subsequently faculty members of the college have
put in efforts to document it in the form which has been presented here.
Some of the highlights of the KJSCE-SVU-2020 syllabus are: Introduction of wide choice for
branch specific electives, more number of open or interdisciplinary electives, opportunity for
internships, etc. Courses like Object Oriented Programming Methodology, Full Stack
Development and Digital Design are designed as laboratory oriented courses and pay more
attention to hands-on learning.
Focus of academic processes in KJSCE is such that, by the time student completes the
requirements of the degree, he/ she will be able to acquire attributes required for profession as
an engineer. Outcomes are defined to acquire these attributes which lead to development of
curriculum, pedagogy and assessment tools. These tools need to be updated based on
experiences of teacher and learner. Hence teaching -learning -evaluation paradigm is going to
be a mix of traditional as well as use of ICT tools. Role of the faculty member changes from
tutor to trainer / instructor/ facilitator / mentor based on the outcomes targeted.
For measuring learning outcomes of students, traditional methods like tests, laboratory work
and End Semester Examinations (ESE) are implemented. Continuous Assessment (CA) is
carried out through tests and internal assessment (IA) like quizzes, case studies, mini projects
etc. These IA tools enable the students to develop competencies through solutions discussed,
improvisations suggested, feedbacks given by faculty members. Through these assessment
methods students get opportunity for reading research papers, presenting ideas and working in
a team.

Since the assessments are distributed throughout the term the learning process is continuously
monitored and graded.
The Department of Computer Engineering courses focus on thrust areas of Department. These
areas are Intelligent System and Data Processing, Network System and Security, Image
Analysis and Interpretation and System & Software Engineering.
College promotes co-curricular, extra-curricular activities as well as sports; making life outside
classroom exciting and rewarding. What makes these activities very effective is the fact that
these do not focus only on winning trophies but try to nurture generic skills such as leadership,
effective communication, teamwork etc. which are essential skills for a bright professional
career.
Along with my colleagues, I welcome you to Department of Computer Engineering and look
forward to lead you towards professional career.
Dr. Deepak Sharma

Head
Department of Computer Engineering
Dr. Shubha Pandit

Principal and Dean
Faculty of Engineering and Technology

Vision
To become a center of excellence in discipline of Computer Engineering for developing technically

adept professionals with ethical and leadership qualities in service of society.
Mission
 Provide sound technical foundation in Computer Engineering through comprehensive
curriculum and application oriented learning.
 Provide ambience for professional growth and lifelong learning for adapting to challenges in
rapidly changing technology
 Inculcate social and ethical values and leadership qualities
Program Educational Outcomes (PEO)
A graduate of Computer Engineering will
PEO1. Solve problems in diverse fields using knowledge of Computer Engineering.

PEO2. Excel in professional career, exhibit leadership qualities with ethics &soft skills.
PEO3. Pursue higher education, research or entrepreneurship, engage in professional development,
adapt to emerging technologies.
Program Outcomes (PO)

After successful completion of the program Computer Engineering Graduate will be able to:
PO1. Engineering knowledge: Apply the knowledge of mathematics, science, engineering

fundamentals, and an engineering specialization to the solution of complex engineering
problems.
PO2. Problem analysis: Identify, formulate, review research literature, and analyze complex
engineering problems reaching substantiated conclusions using first principles of
mathematics, natural sciences, and engineering sciences.
PO3. Design/development of solutions: Design solutions for complex engineering problems and
design system components or processes that meet the specified needs with appropriate
consideration for the public health and safety, and the cultural, societal, and environmental
considerations.
PO4. Conduct investigations of complex problems: Use research-based knowledge and research
methods including design of experiments, analysis and interpretation of data, and synthesis of
the information to provide valid conclusions.
PO5. Modern tool usage: Create, select, and apply appropriate techniques, resources, and modern
engineering and IT tools including prediction and modeling to complex engineering activities
with an understanding of the limitations.
PO6. The engineer and society: Apply reasoning informed by the contextual knowledge to assess
societal, cultural, environmental, health, safety and legal issues relevant to the professional
engineering practice; understanding the need of sustainable development
PO7. Multidisciplinary Competence: Recognize/ study/ analyze/ provide solutions to real-life
problems of multidisciplinary nature from diverse fields

PO8. Ethics: Apply ethical principles and commit to professional ethics and responsibilities and
norms of the engineering practice.
PO9. Individual and team work: Function effectively as an individual, and as a member or leader
in diverse teams, and in multidisciplinary settings.
PO10. Communication: Communicate effectively on complex engineering activities with the
engineering community and with society at large, such as, being able to comprehend and write
effective reports and design documentation, make effective presentations, and give and
receive clear instructions.
PO11. Project management and finance: Demonstrate knowledge and understanding of the
engineering and management principles and apply these to one’s own work, as a member and
leader in a team, to manage projects and in multidisciplinary environments.
PO12. Life-long learning: Recognize the need for, and have the preparation and ability to engage in
independent and life-long learning in the broadest context of technological change.
Program Specific Outcomes (PSO)
PSO1: Design, construct and implement hardware and software based modern Computing /
Information systems with varying complexities
PSO2: Demonstrate competence in designing, implementation and maintenance of computer based
applications, computer-controlled equipment and networks of intelligent devices.

Acronym for category of courses Acronyms used in syllabus document
Acronym Definition Acronym Definition
BS Basic Science Courses CA Continuous Assessment
ES Engineering Science ESE End Semester Exam
HS Humanities and Social Sciences IA Internal Assessment
including Management Courses
PC Professional Core Courses O Oral
PE Professional Elective courses P Practical
OE Open Elective Courses P&O Practical and Oral
LC Laboratory Courses TH Theory
PR Project TUT Tutorial
AC Audit Course TW Term work
AOCC Add on Credit Course ISE In- Semester Examination
AOAC Add on Audit Course CO Course Outcome
AVAC Add on Value Audit Course PO Program Outcome
EX Exposure Course PSO Program specific Outcome
I Interdisciplinary courses
Acronyms used for type of Course
Acronym used Definition

C Core Course
E Elective Course
O Open Elective Technical
H Open Elective Humanities/Management/SWAYAM-NPTEL
P Project
L Laboratory Course
T Tutorial
X Exposure course
A Audit course
Acronyms used in Eight Digit Course code e.g. 116U06C101
Acronym Definition
Serially as per code
1 SVU 2000 First revision
16 College code
U Alphabet code for type of programme
06 Programme code
C Type of course
1 Semester I – semester number
01 First course of semester – course serial number
It will be XX for the elective/choice based courses

Semester III
Credit Scheme
Teaching Scheme Credits
Total
(Hrs.) Assigned Total Course
Course Code Course Name (Hrs.)
Per Week TH – P – Credits Category
Per week
TH – P – TUT TUT
Integral Transform and
116U01C301 3– 0– 1 4 3–0– 1 4 BS
Vector Calculus
$
116U01C302 Data Structures 3– 0– 0 3 3–0– 0 3 PC
Computer Organization &
116U01C303 3– 0– 0 3 3– 0– 0 3 PC
Architecture
Object Oriented
116U01C304 Programming 1– 0– 2 3 1– 0– 2 3 PC
Methodology
116U01C305 Discrete Mathematics 3– 0– 1 4 3– 0– 1 4 PC
116U01L301 Digital Design Laboratory 1– 2– 0 3 1– 1– 0 2 PC
116U01L302 Data Structures Laboratory 0– 2– 0 2 0– 1– 0 1 PC
116U01L303 0– 2– 0 2 0– 1– 0 1 PC
Architecture Laboratory
Object Oriented
116U01L304 Programming 0– 2– 0 2 0– 1– 0 1 PC
Methodology Laboratory
Total 14– 8 – 4 26 14 – 4 – 4 22
$- Common with IT Branch
Examination Scheme
Examination Scheme
Marks
Course Code Course Name
CA
ISE IA ESE TW O% P P&O# Total
Integral Transform and
116U01C301 30 20 50 25 - - 125
Vector Calculus
$
116U01C302 Data Structures 30 20 50 - - - - 100
116U01C303 30 20 50 - - - - 100
Architecture
Object Oriented
116U01C304 Programming 30 20 50 - - - - 100
Methodology
116U01C305 Discrete Mathematics 30 20 50 25 - - - 125
116U01L301 Digital Design Laboratory - - - 50 25 - - 75
116U01L302 Data Structures Laboratory - - - 25 - - 25 50
116U01L303 - - - 25 25 - - 50
Architecture Laboratory
Object Oriented
116U01L304 Programming - - - 25 - - 25 50
Methodology Laboratory
Total 150 100 250 175 50 - 50 775
% Oral examination based on entire theory syllabus of corresponding theory course,
# based on practical & Syllabus of the corresponding theory course.

Semester IV
Credit and Examination Scheme

Semester IV
Credit Scheme
Teaching
Total Credits
Scheme (Hrs.) Total Course
Course Code Course Name (Hrs.) Assigned
Per Week Credits Category
Per week TH – P – TUT
TH – P – TUT
Probability, Statistics and
116U01C401 3–0–1 4 3–0–1 4 BS
Optimization Techniques$
116U01C402 Analysis of Algorithms 3–0–0 3 3–0– 0 3 PC
Relational Database
116U01C403 3–0–0 3 3– 0– 0 3 PC
Management Systems
Theory of Automata with
116U01C404 3–0–1 4 3– 0– 1 4 PC
Compiler Design
Web Programming
116U01L401 0-4- 0 4 0– 2– 0 2 PC
Laboratory
Analysis of Algorithms
116U01L402 0–2–0 2 0– 1– 0 1 PC
Laboratory
Relational Database
116U01L403 Management Systems 0–2–0 2 0– 1– 0 1 PC
Laboratory
116U01P401 Mini Project 1 –2 – 0 3 0–3– 0 3 PR
Total 13 – 10 – 2 25 12 – 7 – 2 21
Examination Scheme
Examination Scheme
Course Code Marks
Course Name
CA ESE TW O% P P&O# Total
ISE IA
116U01C401 30 20 50 25 - - - 125
116U01C402 Analysis of Algorithms 30 20 50 - - - - 100
Relational Database
116U01C403 30 20 50 - - - - 100
Management Systems
116U01C404 30 20 50 25 - - - 125
Compiler Design
Web Programming
116U01L401 - - - 50 - - 50 100
Laboratory
116U01L402 - - - 25 - - 25 50
Laboratory
Relational Database
116U01L403 Management Systems - - - 25 - - 25 50
Laboratory
116U01P401 Mini Project - - - 50 - - 50^ 100
Total 120 80 200 200 - - 150 750
% Oral examination based on entire theory syllabus, # based on practical & the corresponding theory
Syllabus $- Common with IT Branch ^Demo based on mini project and viva based on implementation

Course Code Course Title
116U01C301 Integral Transform and Vector Calculus
TH P TUT Total
Teaching
03 -- 01* 04
Scheme(Hrs.)
Credits Assigned 03 -- 01 04
Marks
Examination CA
ESE TW O P P&O Total
Scheme ISE IA
30 20 50 25 -- -- -- 125
* Batch wise Tutorial
Course prerequisites:
● Applied Mathematics-I
● Applied Mathematics –II
● Basics of Vector Algebra
Course Objectives
The objective of this course is to introduce different methods of finding Laplace Transform
and Inverse Laplace transform of given function. The course also familiarizes students with
the concepts of Fourier series, Fourier Integral and Fourier Transform of a given function.
The course also disseminates methods to find Z- Transform and Inverse Z- transform of a
function. Concepts of Differentiation and Integration of Vector functions with their
applications are also explained in this course. Using these methods it will be possible to
analyze and interpret a given real life situation and think of possible solutions.
Course Outcomes
At the end of successful completion of the course the student will be able to
CO1. Apply Different methods to find Laplace Transform and Inverse Laplace Transform of a
function
CO2. Find Fourier series, Fourier Integral and Fourier Transform of functions.
CO3. Apply Different methods to find Z-Transform and Inverse Z- Transform of a function.
CO4. Apply concepts of Gradient, curl and Divergence of a vector function to solve problems.
CO5. Apply concepts of Vector Integration to solve related problems.

Module Unit Details Hrs. CO
No. No.
1 Laplace Transform 12 CO 1
1.1 Definition of Laplace Transform, Laplace Transform of
sin(at), cos(at),sinh(at), cosh(at), erf(t), Heavi-side unit
step, dirac-delta function, Laplace Transform of periodic
function
1.2 Properties of Laplace Transform (without proof):
Linearity, first shifting theorem, second shifting theorem,
multiplication by t , division by t, Laplace Transform of
derivatives and integrals, change of scale.
1.3 Inverse Laplace Transform: Partial fraction method,
convolution theorem, Application of Laplace Transform:
Solution of ordinary differential equations
2 Fourier Series 12 CO2
2.1 Introduction: Definition, Dirichlet’s conditions, Euler’s

formulae, Fourier Series of Functions: Exponential,
trigonometric functions, even and odd functions, half
range sine and cosine series
2.2 Complex form of Fourier series
2.3 Fourier Integral, Fourier Transform and Inverse Fourier
Transform
3 Z-Transform 4 CO 3
3.1 Z-transform of standard functions
3.2 Properties of Z-transform (without proof): Linearity,
change of scale, shifting property, Multiplication by K,
Initial and Final value, Convolution theorem
3.3 Inverse Z- transform: Binomial expansion and Method of
Partial fraction
4 Vector Differentiation 8 CO 4
4.1 Scalar and vector product of three and four vectors and
their properties.
4.2 Gradient of scalar point function, divergence and curl of
vector point function.
4.3 Solenoidal and irrational vector fields.

5 Vector Integration 9 CO 5
5.1 Vector Integral: Line integral, Properties of line integral,
Surface integral, Volume integrals.
5.2 Green’s theorem in a plane (without proof) and related
problems
5.3 Gauss divergence theorem (without proof), Stokes
theorem (without proof) and related problems
Total 45
Recommended Books:
Sr. Name/s of Author/s Title of Book Name of Edition and
No. Publisher with Year of
country Publication
1. B. S. Grewal Higher Engineering Khanna 43rd Edition
Mathematics Publications,
2014
India
2. Erwin Kreyszig Advanced Engineering Wiley Eastern 10th Edition
Mathematics Limited, India
2015
3. N.P. Bali and Manish A Textbook of Engineering Laxmi 9th Edition
Goyal Mathematics Publications 2016
LTD, India
4. P. N. Wartikar and A text book of Applied Pune Vidyarthi 6th Edition
Mathematics Vol I & II Gruha, India
J. N. Wartikar 2012
Term-Work will consist of Tutorials covering the entire syllabus. Students will be graded
based on continuous assessment of their term work. At least 2 tutorials will be conducted
with the help of Mathematical and Statistical software in the Laboratory.

116U04C302 Data Structures
TH P TUT Total
Teaching
03 - - 03
Scheme(Hrs.)
Credits Assigned 03 - - 03
Marks
Examination CA
ESE TW O P&O Total
Scheme ISE IA
30 20 50 -- -- -- 100
Course prerequisites: Programming Language
Course Objectives:
The objective of this course is to introduce different types of data structure and how user can
use data structure in software development. The course also familiarizes students with the
concepts of advanced data structures such as balanced search trees, hash tables, priority
queues, sorting and searching. Students will be master in the implementation of linked data
structures such as linked lists and binary trees using any preferable language. Course mainly
focuses on choosing the appropriate data structure for a specified application.
Course Outcomes
CO1. Explain the different data structures used in problem solving.
CO2. Apply linear and non-linear data structure in application development.
CO3: Describe concepts of advance data structures like set, map & dictionary.
CO4. Demonstrate sorting and searching methods.
Module Unit No. Details Hrs. CO

No.
1 Introduction to Data Structures 04 CO 1
1.1 Defining Data structure, Types of Data Structures,
Abstract Data Type (ADT), Static and Dynamic
Implementations
1.2 Applications of data structures.
2 Linear data structures: Linked List, Stack and Queue 16 CO 2
2.1 Introduction and Representation of Linked List, Linked
List v/s Array, Implementation of Linked List, Circular
Linked List, Doubly Linked List, Application –
Polynomial Representation and Addition, Other additional
applications/Case study.
#Self-learning - Sparse matrix addition
2.2 The Stack as an ADT, Stack operations, Array
Representation of Stack, Linked Representation of Stack,

Application of stack – Polish Notation, Recursion and
other applications/Case study.
#Self-learning - Application of stack in evaluation of
postfix and prefix expression.
2.3 The Queue as an ADT, Queue operation, Array
Representation of Queue, Linked Representation of
Queue, Circular Queue, Priority Queue, and Double ended
queue, Application of Queues – Simulation and other
applications/Case study.
#Self-learning - Application of queue in Josephus’s
Problem.
3 Non-linear data structures: Tree and Graph 10 CO 2
3.1 Basic tree terminologies, Types of trees, Binary tree
representation, Binary tree operation, Binary tree
traversal, Binary search tree implementation, Threaded
binary trees. Different Search Trees -AVL tree, Multiway
Search Tree, B Tree, B+ Tree, and Trie,
Applications/Case study of trees.
#Self-learning Learning – Red-Black and Splay
Trees.
3.2 Introduction to graph as a data structure, Terminologies,
Representation, Traversals – Depth First Search (DFS)
and Breadth First Search (BFS). Applications/Case study
of Graphs.
4 Set, Map and Dictionary 7 CO 3
4.1 Set ADT, Set Implementation, and Partitions with Union-
Find operations, Tree based partition implementation.
4.2 Map ADT, Implementation, Hash Tables Application of
Maps
4.3 Dictionary ADT, Implementation, Application of
Dictionaries
#Self-earning - Exploring case studies on use of set, map and
dictionary
5 Searching and Sorting 8 CO 4
5.1 Sort Concept, Sort Stability , Bubble Sort, Insertion Sort,
Counting Sort
#Self-learning - Bucket and Radix sort
5.2 Search concept, Linear Search, Binary Search, Hashed
List Search, Comparison of searching Techniques
Total 45
# Self-learning topics will be evaluated through IA and/or Lab.

Recommended Books:
country Publication
1. Ellis Horowitz, Fundamentals Of Data University Second
Sartaj Sahni, Susan Structures In C Press Edition 2008
Anderson-Freed
2. Michael T Goodrich Data Structure and Algorithm Wiley Second

Roberto Tamassia in C++ Edition 2011
David Mount
3. Richard F. Gilberg Data Structures A CENGAGE Second
& Behrouz A. Pseudocode Approach with C Learning Edition 2005
Forouzan
4. Aaron M Data structure Using C Pearson Twelfth
Tanenbaum Impression
Yedidyah Langsam 2013
Moshe J Augentstein
5. Jean Paul Tremblay, An introduction to data Tata McGraw- Second
Paul G. Sorenson structures with applications Hill Education Edition 1984

116U01C303 Computer Organization and Architecture
TH P TUT Total
Teaching
03 02 -- 05
Scheme(Hrs.)
Credits Assigned 03 01 -- 04
Marks
Examination Scheme CA
ISE IA
30 20 50 25 25 -- -- 150
Course prerequisites: Students should be familiar with basic concepts of computers and their
applications.
Course Objectives:
Students will try to:
1. Conceptualize the basics of organization and architecture of a digital computer and the
detailed working of the ALU
2. Learn the function of each element of a memory hierarchy and detailed working of the
control unit
3. Study various input output techniques and their applications.
Course Outcomes:
After completing this course, students will be able to:
CO1- Describe and define the structure of a computer with buses structure and detail working
of the arithmetic logic unit and its sub modules
CO2- Understand the Central processing unit with addressing modes and working of control
unit in depth
CO3- Learn and evaluate memory organization and cache structure
CO4- Summarize Input output techniques and multiprocessor configurations

Module
Unit No. Details of Topic Hrs. CO
No.
Structure of a Computer System
Introduction of computer system and its sub modules,
1.0 1.1 Basic organization of computer and block level 04 CO1
description of the functional units. Von Neumann model
1.2 Introduction to buses, bus types, and connection I/O
devices to CPU and memory, PCI and SCSI
Arithmetic and Logic Unit
Introduction to Arithmetic and Logical unit, Computer
2.1 Arithmetic: Fixed and Floating point numbers, Signed
numbers, Integer Arithmetic, 2’s Complement
arithmetic
Booth’s Recoding and Booth’s algorithm for signed
2.0 2.2 11 CO1
multiplication, Restoring division and non-restoring
division algorithms
IEEE floating point number representation and
operations: Addition. Subtraction, Multiplication and
2.3 Division. IEEE standards for Floating point
representations :Single Precision and Double precision
Format
Central Processing Unit
CPU architecture, Register organization, Instruction
formats and addressing modes (Intel processor), Basic
instruction cycle.
3.1 Control unit Operation ,Micro operations : Fetch,
3.0 Indirect, Interrupt , Execute cycle Control of the 10 CO2
processor, Functioning of micro programmed control
unit, Micro instruction Execution and Sequencing,
Applications of Micro programming
RISC v/s CISC processors, RISC and CISC
3.2
Architecture, RISC pipelining, Case study on SPARC
Memory Organization.
Characteristics of memory system and hierarchy, Main
4.1 memory, Cache memory principles , Elements of Cache
Design
4.2 ROM, Types of ROM, RAM, SRAM, DRAM, Flash
4.0 11 CO3
memory, High speed memories
Cache Memory Organization: Address mapping,
Replacement Algorithms, Cache Coherence, MESI
4.3 protocol, Interleaved and associative memories, Virtual
memory, Main memory allocation, Segmentation
,Paging, Secondary storage, RAID levels

I/O Organization
5.0 5.1 External Devices, I/ O Modules 03 CO4
5.2 Programmed I/O, Interrupt driven I/O, DMA
Multiprocessor Configurations
6.1 Flynn’s classification, Parallel processing systems and
concepts
6.2 Introduction to pipeline processing and pipeline hazards
6.0 6.3 Design issues of pipeline architecture, Instruction 06 CO4
pipelining: Six Stage instruction pipeline.
6.4 8086 Instruction (Arithmetic Instructions, Logical
Instructions, Data transfer instructions)
Self-Learning; Pin Diagram of 8086, Minimum Mode and Maximum
mode with timing diagram
Total 45
Recommended Books:

country Publication
1. W.Stallings William Computer Organization Pearson 7th Edition
and Architecture: Prentice Hall
Designing for Publication
Performance
2. Hamacher, V. Computer Organization Tata McGraw 5th Edition
Zvonko, S. Zaky Hill
Publication
3. Hwang and Briggs Computer Architecture Tata McGraw
and Parallel Processing Hill
Publication
4. A. Tanenbaum Structured Computer Prentice Hall 4th Edition.
Organization Publication
5. John Uffenbeck 8086/8088 families: Pearson
Design Programming and Education
Interfacing
6. Douglas Hall Microprocessor and TMH
Interfacing Publication

116U01C304 Object Oriented Programming Methodology
TH P TUT Total
Teaching Scheme
01 02 02* 05
(Hrs./Week)
Credits Assigned 01 01 02 04
Marks
Examination CA
Scheme ESE TW O P P&O Total
ISE IA
30 20 50 25 -- -- 25 150
● Basics of Programming concepts
Course Objectives:
This course will provide the concept of object oriented designing and programming using
JAVA and C++. These courses also provide differences in Object oriented programming
approach in Java and C++. Students will learn about exception handling, Interfaces, file
handling, Inheritance and Multithreading.
Course Outcomes:
CO1. Understand the features of object oriented programming compared with procedural
approach with C++ and Java
CO2. Explore arrays, vectors, classes and objects in C++ and Java.
CO3. Implement scenarios using object oriented concepts (Drawing class diagram,
relationship between classes).
CO4. Explore the interface, exceptions, multithreading, packages
SVU 2020 R 1.0 KJSCE S Y B. Tech Syllabus Page 20 of 52

No. No.
1 Fundamentals of Object oriented Programming 04 CO1
1.1 Introduction, Procedural Programming Approach,
Structured Programming Approach, Modular
Programming Approach, OOP Approach
1.2 Objects and classes, Data abstraction and
Encapsulation, Inheritance and Polymorphism, Runtime
polymorphism, Static and Dynamic Binding,
Exceptions, Reuse, Coupling and Cohesion, Object
Oriented Features of Java and C++. Comparing Object
Oriented Concepts with Java and C++
2 Class, Object, Method and Constructor CO1,
08
CO2
2.1 Class Object and Method: member, method, Modifier,
Selector, iterator, State of an object, instanceof
operator, Memory allocation of object using new
operator.
2.2 Method overloading & overriding, constructor,
destructor, Types of constructor (Default,
Parameterized, copy constructor with object),
Constructor overloading, this, final, super keyword,
Garbage collection.
3 Arrays String and vectors 09 CO2
3.1 Arrays: Arrays: 1D, 2D, Variable Length array, for-
each with Array, Array of objects, Vectors: Vector,
ArrayList, Wrapper class. Command line Arguments.
3.2 Immutable string ,Methods of String class, String
comparison, concatenation, substring, toString method
3.3 String-Buffer class, StringBuilder class
4 Inheritance and Interface CO1,

08
CO4
4.1 Inheritance Types of Inheritance, Final class, abstract

class with constructor, abstract and non-abstract
methods, super keyword, Method Overriding.
4.2 Interface, final keyword Implementing interfaces,
extending interfaces
Difference between an Abstract class and an Interface

5 Class Diagram CO1,
06
CO 3
5.1 Class Diagram
5.2 Implementing Aggregation and Association
,composition ,multiplicity ,Generalization
6 Exception Handling & Packages, Multithreading 10 CO4
6.1 Packages: Creating Packages, Using Packages, Access
Protection, Predefined packages
6.2 Exception handling: Exception as objects, Exception
hierarchy, Try catch finally Throw, throws
6 .3 Multithreading: Thread life cycle, Multithreading
advantages and issues, Simple thread program, Thread
synchronization.
Total 45
Recommended Books:

country Publication
1. Herbert schildt The complete Reference Tata McGraw- 7th Edition
JAVA7 Hill 2017
2. Kathy Sierra Sun Certified Programmer McGraw-Hill 6th Edition,
for JAVA Edition 2013
3. Sachin Programming in JAVA Oxford 2nd Edition,
Malhotra,Saurabh University 2013
Chaudhary
4. E Balagurusamy Object Oriented Tata McGraw 5th Edition,
Programming in C++ Hill 2011
5. Grady Booch,James Unified Modeling Pearson
Rumbaugh,Ivar Language Education 3rd Edition
Jacobson
6. Yashwant Kanetkar Let us C++ BPB 16th Edition,
publications 2020
7. Ralph Bravaco,Shai Java Programming from Tata McGraw- McGraw-Hill
simoson the Group up Hill Edition

116U01C305 Discrete Mathematics
Teaching Scheme TH P TUT Total
(Hrs./Week) 03 -- 01* 04
Marks
Examination CA
Scheme ISE IA
30 20 50 25 -- -- -- 125
Course prerequisites
Basic Mathematics
Course Objectives
The objective of this course is to enable students to think logically and mathematically. It
will help them to solve the problems with mathematical reasoning, algorithmic thinking,
and modeling.
Course Outcomes
CO1: Use various mathematical notations, apply various proof techniques to solve real
world problems
CO2: Learn and apply core ideas of Set Theory, Relations & Functions
CO3: Use graphs and their types, to solve the practical examples
CO4: Understand the use of Algebraic Structures and lattice, to solve the problems

Module
No. Unit No. Details of Topic Hrs. CO
1 Set Theory 03 CO1
1.1 Sets, Venn diagrams, Operations on Sets
1.2 Laws of set theory, Power set and Products
1.3 Partitions of sets, The Principle of Inclusion and Exclusion
2 Logic 04 CO1
2.1 Propositions and logical operations, Truth tables
2.2 Equivalence, Implications
2.3 Laws of logic, Normal Forms
2.4 Predicates and Quantifiers
2.5 Mathematical Induction
3 Relations, Digraphs 09 CO2
3.1 Relations, Paths and Digraphs
3.2 Properties and types of binary relations
3.3 Manipulation of relations, Closures, Warshall’s algorithm
3.4 Equivalence relations
4 Posets and Lattice 09 CO2
4.1 Partial ordered relations (Posets) ,Hasse diagram
4.2 Lattice, sublattice
4.3 Types of Lattice ,Boolean Algebra
5 Functions and Pigeon Hole Principle 03 CO3
5.1 Definition and types of functions: Injective, Surjective and
Bijective
5.2 Composition, Identity and Inverse
5.3 Pigeon-hole principle, Extended Pigeon-hole principle
6 Graphs and Subgraphs 04 CO4
6.1 Definitions, Paths and circuits, Types of Graphs , Eulerian and
Hamiltonian
6.2 Planer graphs
6.3 Isomorphism of graphs
6.4 Subgraph
7 Algebraic Structures 13 CO4

7.1 Algebraic structures with one binary operation: semigroup,
monoids and groups
7.2 Cyclic groups, Normal subgroups,
7.3 Hamming Code ,Minimum Distance
7.4 Group codes ,encoding-decoding techniques

7.5 Parity check Matrix ,Maximum Likelihood
Mathematics of Cryptography - Modular Arithmetic,

7.6 Matrices, Linear Congruence, Ring ,GF Fields
#
Self Learning Topic – Function Generators
Total 45
# Students should prepare all Self Learning topics on their own. Self-learning topics will enable
students to gain extended knowledge of the topic. Assessment of these topics may be included in IA and
Laboratory Experiments.
Recommended Books:

country Publication
th
1 Kenneth H. Rosen Discrete Mathematics and Tata McGraw 7 Edition, 2017
its applications Hill
th
2 Bernard Kolman, Discrete Mathematical Pearson 6 Edition, 2017
Robert C. Busby Structures
th
3 C. L. Liu, Elements of Discrete Tata McGraw 4 Edition, 2012
D. P. Mohapatra Mathematics West Hill.
nd
4 Douglas West Graph Theory Pearson 2 Edition, 2017

116U01L301 Digital Design Laboratory
TH P TUT Total
Teaching Scheme
1 2 - 3
(Hrs./Week)
Credits Assigned 1 1 - 2
Marks
Examination CA
T-1 T-2 IA
- - - - 50 25 - - 75
Basics of Digital Electronics
Course Objectives:
The course introduces the students to the concepts of the design and implementation of digital
circuits. Laboratory experiments will be used to reinforce the theoretical concepts discussed in
lectures. The student will acquire knowledge of gates, flip flops, registers , counters K-maps.
The course also includes use of VHDL in the design, simulation, and testing of digital circuits.
Course Outcomes
CO1. Recall basic gates & logic families and binary, octal & hexadecimal calculations and
conversions.
CO2. Use different minimization techniques and solve combinational circuits.
CO3. Design synchronous and asynchronous sequential circuits.
CO4. Implement digital networks using VHDL.
*Term work will consist of practical’s covering the entire syllabus of Digital Design.
*Students will be graded based on continuous assessment of their term work

No. No.
Digital Design Lab
1 Binary Arithmetic and Codes: 2 CO1
1.1 Binary Addition and Subtraction (1’s and 2’s

complement method)
1.2 Gray Code, BCD Code, Excess-3 code, ASCII Code
2 Basic Digital Circuits & Minimization: 4 CO2
2.1 NOT, AND, OR, NAND, NOR, EX-OR, EX-NOR

Gates, NAND-NOR Realization.
2.2 Solving problems using theorems and properties of

Boolean Algebra
2.3 Standard SOP and POS form
2.4 Reduction of Boolean functions using Algebraic

method, K-map method (2,3,4 Variable)
3 Combinational Logic Design: 3 CO2
3.1 Half and Full Adder, Half and Full Subtractor, Four Bit
Binary Adder, Four Bit Binary Subtractor (1’s and 2’s
complement method)
3.3 Multiplexers and Demultiplexers, Decoders,
3.4 One bit, Two-bit ,4-bit Magnitude Comparator
4 Sequential Logic Design 4 CO3
4.1 Flip Flops: SR, D, JK and T Flip Flop, Truth Tables and
Excitation Tables, Flip-flop conversion
4.2 Counters: Design of Asynchronous and Synchronous

Counters, UP- DOWN counter.
4.3 Shift Registers: SISO, SIPO, PIPO, PISO
5 Introduction to VHDL 2 CO4
5.1 Introduction to VHDL, Syntax and Programming to be

done only during practical sessions
Total 15

Recommended Books:
Sr. Name/s of Author/s Title of Book Name of Publisher Edition and

No. with country Year of
Publication
1. R. P. Jain Modern Digital Tata McGraw Hill 4th Edition
Electronics 2009
2. J. Bhasker VHDL Primer Pearson Education 3rd Edition
2009
3. M. Morris Mano Digital Logic and PHI 1st Edition
computer Design 2008
4. Yarbrough John M Digital Logic Cengage Learning 1st Edition

Applications and 2006
Design
5. Douglas L. Perry VHDL Programming by Tata McGraw Hill 4th Edition
Example 2002

116U01L302 Data Structures Laboratory
TH P TUT Total
Teaching
-- 02 -- 02
Scheme(Hrs.)
Credits Assigned -- 01 -- 01
Marks
Examination CA
Scheme ISE IA
--
-- -- 25 -- -- 25 50
 Term-Work will consist of practical covering entire syllabus of “Data Structures”

116U01C302. Students will be graded based on continuous assessment of their term work.
 Practical and Oral Examination will consist of practical covering entire syllabus of “Data
Structures” 116U01L302.
Note: The faculty should conduct 8-10 experiments or mini project or case studies based on the above
syllabus. The programs should be implemented in any programming Language.

116U01L303 Computer Organization & Architecture Laboratory
TH P TUT Total
Teaching
02 -- 02
Scheme(Hrs.)
Credits Assigned 01 -- 01
Marks
CA
Examination ESE TW O P P&O Total
Scheme ISE IA
--
-- -- 25 25 -- -- 50
Term work will consist of experiments based on syllabus of ‘Computer Organization &
Architecture’ (116U01C303). Students will be graded based on continuous assessment of their term
work. Oral examination will be based on syllabus of ‘Computer Organization & Architecture’ and
laboratory experiments.
Note: The faculty should conduct around 10 experiments including case studies/self-study based on
the above syllabus. The programs should be implemented in C/C++ programming Language.

116U01L304 Object Oriented Programming and Methodology

TH P TUT Total
Teaching
-- 02 -- 02
Scheme(Hrs.)
Marks
CA
Scheme ISE IA
--
-- -- 25 -- -- 25 50
Term work will consist of experiments based on the syllabus of ‘Object Oriented Programming and
Methodology’ (116U01C304). Students will be graded based on continuous assessment of their term
work. Oral examination will be based on the syllabus of ‘Object Oriented Programming and
Methodology ‘and laboratory experiments.
Note: Program should be implemented in C++ and Java programming language.

Semester IV
Credit and Examination Scheme

Credit Scheme
Teaching
Total Credits
Scheme (Hrs.) Total Course
Course Code Course Name (Hrs.) Assigned
Per Week Credits Category
Per week TH – P – TUT
TH – P – TUT
116U01C401 3–0–1 4 3–0–1 4 BS
116U01C402 Analysis of Algorithms 3–0–0 3 3–0– 0 3 PC
Relational Database
116U01C403 3–0–0 3 3– 0– 0 3 PC
Management Systems
116U01C404 3–0–1 4 3– 0– 1 4 PC
Compiler Design
Web Programming
116U01L401 0-4- 0 4 0– 2– 0 2 PC
Laboratory
116U01L402 0–2–0 2 0– 1– 0 1 PC
Laboratory
Relational Database
116U01L403 Management Systems 0–2–0 2 0– 1– 0 1 PC
Laboratory
116U01P401 Mini Project 1 –2 – 0 3 0–3– 0 3 PR
Total 13 – 10 – 2 25 12 – 7– 2 21
116U01A401 Audit Course 2-0–0 2 -- -- AC
Examination Scheme
Examination Scheme
Course Code Marks
Course Name
CA ESE TW O% P P&O# Total
ISE IA
116U01C401 30 20 50 25 - - - 125
116U01C402 Analysis of Algorithms 30 20 50 - - - - 100
Relational Database
116U01C403 30 20 50 - - - - 100
Management Systems
116U01C404 30 20 50 25 - - - 125
Compiler Design
Web Programming
116U01L401 - - - 50 - - 50 100
Laboratory
116U01L402 - - - 25 - - 25 50
Laboratory
Relational Database
116U01L403 Management Systems - - - 25 - - 25 50
Laboratory
116U01P401 Mini Project - - - 50 - - 50^ 100
Total 120 80 200 200 - 150 750
116U01A401 Audit Course - - - - - - - -
% Oral examination based on entire theory syllabus, # based on practical & the corresponding theory Syllabus $-
Common with IT Branch ^Demo based on mini project and viva based on implementation

116U01C401 Probability, Statistics and Optimization Techniques
TH P TUT Total
Teaching
03 -- 01* 04
Scheme(Hrs.)
Marks
Examination CA
Scheme ISE IA
30 20 50 25 -- -- -- 125
● Basics of Statistics and Probability
● Introductory Linear programming problems
Course Objectives
This course Exposes students to the concepts of Correlation, Regression for given bivariate
data. Students are made familiar with different discrete and continuous probability
distributions. The course acquaints students with concepts of Large sample test, Small
sample test and Chi – Square test. The course familiarizes students with different methods
of solving Linear and Nonlinear Programming problems. Some basic queuing theory
models are also discussed in the course. Using these methods it will be possible to analyze
and interpret a given real life situation and think of possible solutions.
Course Outcomes
CO1. Apply concepts of correlation, regression for given bivariate data.
CO2. Apply concepts of Binomial, Poisson, Exponential and Normal distribution to solve
Engineering problems.
CO3. Apply Large sample test and small sample test to analyze collected data.
CO4. Apply concepts of Linear and Nonlinear programming methods to solve problems.
CO5. Apply the methods of single server limited queue and single server unlimited queue
models to analyze and interpret the data.

No. No.
1 Probability and Probability Distribution 12 CO 1
1.1 Conditional Probability, Bayes’ theorem, Joint
Probability
1.2 Discrete and Continuous Probability Distribution
1.3 Binomial Distribution, Poisson Distribution
1.4 Uniform Distribution, Normal Distribution, Exponential
Distribution
2 Correlation and Regression 06 CO 2
2.1 Correlation, Co-variance, Karl Pearson Coefficient of
Correlation & Spearman’s Rank Correlation Coefficient.
2.2 Regression Coefficients, lines of regression & logistic
regression
3 Sampling Theory 07 CO 3
3.1 Sampling distribution. Test of Hypothesis. Level of
significance, critical region. One tailed and two tailed
tests. Interval Estimation of population parameters.
Large and small samples.
3.2 Difference between sample mean and population means
for large samples, Test for significance of the difference
between the means of two large samples.
3.3 Student’s t-distribution: Test for significance of the
difference between sample mean and population means,
Test for significance of the difference between the means
of two Samples, paired t-test.
3.4 Chi-square distribution as a Test of Independence, Test
of the Goodness of fit and Yate’s correction.
3.5 Fisher’s z-test
4 Optimization Techniques 13 CO 4
4.1 Types of solution, Standard and Canonical form of LPP,
Basic and feasible solutions, simplex method.
4.2 Artificial variables, Big –M method (method of penalty).
4.3 Duality and Dual Simplex method

4.4 Unconstrained optimization, problems of two or three
variables with one equality constraint using Lagrange’s
Multiplier method.
4.5 Problems of two or three variables with one inequality
constraint using Kuhn-Tucker conditions
5 Queuing Theory 07 CO5
5.1 Introduction, Features of Queuing, solution of Queuing

models. M/M/1(Singal Server, Unlimited Queue Model)
5.2 M/M/1 Singal Server, limited Queue Model
Total 45
Recommended Books:
country Publication
1. B. S. Grewal Higher Engineering Khanna 43rd Edition
Mathematics Publications,
2014
India
2. Erwin Kreyszig Advanced Engineering Wiley Eastern 10th Edition
Mathematics Limited, India
2015
3. J. K. Sharma Operation research: Laxmi 6th Edition
Theory and Applications Publications, 2017
India
4. S.C.Gupta and Sultan Chand & 11th Edition
Fundamentals of Sons
V.K.Kapoor
Mathematical Statistics 2009
5. Ronald E. Walipole, Probabilities & Statistics Pearson 9th Edition
Raymond H.Myers for Engineers & Scientists Education
2010
6. P. N. Wartikar and A textbook of Applied Pune 6th Edition
Mathematics Vol I & II Vidyarthigruha,
J. N. Wartikar 2012
India
Term-Work will consist of Tutorials covering the entire syllabus. Students will be graded based on continuous
assessment of their term work. At least 2 tutorials will be conducted with the help of Mathematical and Statistical
software in the Laboratory.

116U01C402 Analysis of Algorithms
TH P TUT Total
Teaching Scheme
03 02 -- 05
(Hrs./Week)
Marks
Examination CA
ISE IA
30 20 50 25 -- -- 25 150
Data structure and Discrete Structures.
Course Objectives:
The objective of the course is to teach various techniques for effective problem solving in
computing. The different algorithm paradigms for problem solving will be used to illustrate
efficient methods to solve problems. The analysis of the algorithm will be demonstrated to
show the efficiency of the algorithm. The complexity theory of the problems is introduced
to students for further analysis of algorithms.
Course Outcomes
CO1: Analyze the asymptotic running time and space complexity of algorithms.
CO2: Describe various algorithm design strategies to solve different problems and analyze
complexity.
CO3: Develop string matching techniques

CO4: Describe the classes P, NP, and NP-Complete

Module Details Hrs. CO
No.
Introduction to analysis of algorithm

Performance analysis, space and time complexity, Growth of function-Big-
1 Oh; Omega; Theta Notation, Analysis of insertion sort, Introduction to 06 CO1
randomized algorithm, Solving Recurrence Problems by Substitution
Method, Recursion Tree Method, Masters Method
Algorithm Design Techniques
Divide and Conquer Technique

General method, Finding minimum and maximum algorithm and analysis,
06 CO2
Analysis of Merge sort, Analysis of Quick sort, Fast Fourier Transform,
Pattern matching using divide and conquer
Greedy Technique
General method, Knapsack problem, Minimum cost spanning trees-Kruskal’s 06 CO2
and Prims algorithm, Single source shortest path
2
Dynamic Programming Technique
General method, Multistage graphs, 0/1 knapsack, Travelling salesman
07 CO2
problem, Single source shortest path, All pairs shortest path, Matrix chain
multiplication
Backtracking Technique
04 CO2
General method, Sum of subsets, 8 queens problem, Graph coloring
Branch and Bound

04 CO2
General method, 0/1 Knapsack, 15 Puzzle Problem
String Matching Algorithms 06 CO3

3 The naïve string-matching Algorithms, String matching with finite automata,
The Knuth-Morris-Pratt algorithm, Longest common subsequence
Non-deterministic Polynomial Algorithms 06 CO4

Polynomial time, Polynomial time verification, NP Completeness and
4
reducibility, NP Completeness proof: Vertex Cover Problem, Clique
Problem
#Self-Learning Topic- Rod cutting algorithm, randomization algorithms
TOTAL 45
# Students should prepare all Self Learning topics on their own. Self-learning topics will enable students to gain
extended knowledge of the topic. Assessment of these topics may be included in IA.

Recommended Books:

country Publication
1 T. H. Coreman, C.E. “Introduction to PHI Publication 2nd Edition,
Leiserson, R.L. Algorithms”, 2005
Rivest, and C. Stein
2 Ellis Horowitz, Sartaj “Fundamentals of University 2nd Edition,
Sahni, S. Rajsekaran, Computer Algorithms” Press 2008
3 Alfred V. Aho, John “Data Structures and Pearson 4th Impression
E. Hopcroft, Jeffrey Algorithm” education 2009
D. Ullman
4 Michael Gooddrich & “Algorithm Design Wiley Student 2nd Edition.
Roberto Tammassia Foundation, Analysis and Edition
Internet Examples”

116U01C403 Relational Database Management Systems
TH P TUT Total
Teaching Scheme
03 02 -- 05
(Hrs./Week)
Marks
Examination CA
ISE IA
30 20 50 25 - - 25 150
Data Structure and programming knowledge
Course Objectives:
The objective of the course is to design and program database systems. It covers ER
(Entity-Relationship) approach to data modeling, the relational model of Database
systems (DBMS) and efficient database design using normalization. It covers Relational
Algebra and use of Query Languages such as SQL. This course also introduces
Transaction Management, Concurrency Control and Recovery Techniques. The course
achieves balance between firm theoretical foundation to designing moderate size
databases and creating, querying and implementing realistic databases.
Course Outcomes:
CO1: Understand the features of Relational database management systems.

CO2: Develop relational database design using the designed Entity-Relationship model.
CO3: Use SQL for Relational database creation, maintenance and query processing
CO4: Understand and analyze indexing, hashing, Query processing, query optimization, and
Normalization of relational database.
CO5: Apply the transaction, concurrency and recovery techniques

Module Unit
Details of Topic Hrs. CO
No. No.
Introduction 05 CO1
Introduction, Characteristics of databases, Comparison of
1.1 File system and Database approach, Users of Database
1 system, Concerns when using an enterprise database
Data Independence, DBMS system architecture,
1.2 Database Administrator
Data Modeling: Enhanced-Entity-Relationship Model and

10 CO2
Relational Data Model
Introduction, Benefits of Data Modeling, Types of Models,
2.1 Phases of Database Modeling, The Entity-Relationship (ER)
Model
2 Enhanced -Entity-Relationship (EER)- Model
2.2
Generalization, Specialization and Aggregation
Relational Model:Introduction, Data Manipulation, Data
2.3
Integrity, Advantages of the Relational Model
2.4 Mapping EER Model to Relational Model
Relational Algebra and Structured Query Language (SQL), 08 CO2
3.1 Relational Algebra, Relational Algebra Queries
Overview of SQL, Data Definition Commands, Domain
3.2 Constraints, Referential integrity
Set operations, aggregate function, null values, Data

3.
3.3 Manipulation
Commands
Data Control commands, Views in SQL, Nested and complex
3.4 queries, Assertions, Trigger, Security and authorization in
SQL
Query Processing and optimization 08 CO4
Indexing: Basic concepts, ordered indices: dense and sparse,
4.1
multilevel indices, secondary indices
Hashing: Static hashing, dynamic hashing, comparison of
4.2
ordered indexing and hashing
Query processing: Steps involved in query processing,
4 4.3 measures of query cost, algorithms for SELECT and
PROJECT operations.
Optimization: Overview, Transformation of relational
4.4
expressions, Estimating statistics, Choice of evaluation plan

Relational–Database Design 07 CO4
5.1 First Normal Form, Pitfalls in Relational-Database designs
5.2 Function Dependencies, Armstrong Axioms
5 5.3 2nd, 3rd, BCNF and 4th normal form
5.4 Decomposition, desirable properties of decomposition
5.5 Overall database design process
Transaction Management, Concurrency control and Recovery
07 CO5
protocols
6.1 Transaction concept, Transaction states, ACID properties
Characterizing schedule based on recoverability and
6.2
serializability
6 Concurrency Control: Two-Phase Lock-based
,Timestamp-based, Multi-version Concurrency Control,
6.3
Validation-based protocols, Deadlock Handling-Wait for
graph
Recovery System: Recovery concept, Log based recovery,
6.4
Shadow paging
Total 45
Recommended Books:

No. Publisher Year of
with country Publication
1 Elmasri and Navathe “Fundamentals of Pearson 6th Edition
Database Systems” Education
2 Korth, Silberchatz, “Database System McGraw Hill 6th Edition
Sudarshan Concepts”
3 Raghu “Database Management McGraw Hill 6th Edition
Ramakrishnan, Systems”
Johannes Gerhke
4 G. K. Gupta “Database Management McGraw Hill. 6th Edition
Systems”

116U01C404 Theory of Automata with Compiler Design
TH P TUT Total
Teaching Scheme
03 -- 01 04
(Hrs./Week)
Marks
Examination CA
ISE IA
30 20 50 25 -- -- -- 125
Students should be familiar with concepts of discrete structures.
Course Objectives
Aims to build concepts regarding the fundamental principles of Grammars, Automata Theory,
Turing Machines, PushDown Automata, Un-decidability and Intractable Problems. It aims to
understand the design of computing machines that can perform complex computation.
Course Outcomes
CO1: Describe regular languages using Regular Expressions, Finite Automata,

Nondeterministic Finite Automata, Mealy Machines, Moore Machines and its
applications
CO2: Write, simplify and normalize context free grammars and describe context free
languages using context free grammar and push down automata
CO3: Design Turing Machines for various problems and its applications
CO4: Understand the concept of Un-decidability and Recursively Enumerable
Languages

No. No.
1 Finite Automata 08 CO1
1.1 Introduction: Alphabets, String, Language, Basic Operations
on language, Concatenation, Kleene Star
Introduction to different phases of compiler.
1.2 Finite. Automata (FA) -its behavior; DFA -Formal definition,
state transition diagram, transition table, Language of a DFA.
NFA -Formal definition, state transition diagram, transition
table Language of an NFA. FA with epsilon-transitions,
Eliminating epsilon-transitions, Equivalence of DFAs and
NFAs, Conversion from NFA to DFA. Moore machine and
Mealy Machine- Formal definition, state transition diagram,
transition table, Conversion from Mealy to Moore machine
and Moore to Mealy machine. Application of Finite Automata
for Lexical Analysis and Lex tools
2 Regular Languages 09 CO1
2.1 Chomsky hierarchy, Regular sets, Regular Expression, Some
closure properties of Regular languages -Closure under
Boolean operations, reversal, homomorphism, inverse
homomorphism, etc..
2.2 FA and Regular Expressions, equivalence between FA and
regular expressions
2.3 Pumping lemma for Regular languages, Equivalence and
minimization of Finite Automata, Myhill-Nerode Theorem
2.4 Application of finite automata and regular expression in
lexical analysis
3 Context Free Grammars 07 CO2
3.1 Context-free Grammars (CFGs) -Formal definition, sentential
forms, leftmost and rightmost derivations, the language of a
CFG. Derivation tree or Parse tree-Definition, Simplification
of CFGs -Removing useless symbols, epsilon-Productions,
and unit productions
3.2 Relationship between parse trees and derivations. Parsing and

ambiguity, Application of CFGs, Ambiguity in grammars and
Languages.
3.3 Normal forms -CNF and GNF. Proving that some languages
are not context free -Pumping lemma for CFLs, applications.
Some closure properties of CFLs -Closure under union,
concatenation, Kleene closure, substitution, Inverse
homomorphism, reversal, intersection with regular set, etc.
Some more decision properties of CFLs

4 Push Down Automata 07 CO2
4.1 Pushdown Automata (PDA) -Formal definition, behavior and
graphical notation, Instantaneous descriptions (Ids),
4.2 The language of PDA (acceptance by final state and empty
stack). Equivalence of acceptance by final state and empty
stack, Equivalence of PDAs and CFGs,
4.3 Conversion: CFG to PDA, PDA to CFG.
4.4 DPDAs -Definition, DPDAs, Multi-stack DPDAs & NPDAs
and CFLs, Languages of DPDAs, NPDAs
5 Turing Machine 10 CO3
5.1 Turing Machines TM -Formal definition and behavior,
Transition diagrams, Language of a TM, TM as accepters
deciders and generators. TM as a computer of integer
functions, Design of TMs, Programming techniques for TMs -
Storage in state, multiple tracks, subroutines, etc.
5.2 Universal TMs, Variants of TMs –Multi-tape TMs,
Nondeterministic TMs. TMs with semi-infinite tapes, Multi-
stack machines, Simulating TM by computer, Simulating a
Computer by a TM
5.3 Equivalence of the various variants with the basic model.
5.4 Introduction to parsers, types of parser
5.5 Application of CFG, PDA, TM in parsing
6 Un-decidability and Recursively Enumerable Languages: 04 CO4
6.1 Recursive and Recursively Enumerable Languages.
Properties of Recursive and Recursively Enumerable
Languages.
6.2 Decidability and Undecidability, Halting Problem, Rice’s
Theorem, Greibach’s Theorem, Post Correspondence
Problem, Context Sensitivity and Linear Bound Automata.
TOTAL 45

Recommended Books:
Sr. Name/s of Title of Book Name of Edition and

No. Author/s Publisher with Year of
country Publication
1. John E. Hopcroft, “Introduction to Automata Pearson Third Edition,
Rajeev Motwani, Theory, Languages and Education 2006
Jeffrey D. Ullman Computation”
2. J.C.Martin, “Introduction to languages TMH Fourth
and the Theory of Edition, 2010
Computation”
3. Michael Sipser “Theory of Computation” Cengage Third Edition,
Learning 2012
4. O.G.Kakde “Theory of Computation” Laxmi First Edition,

Publications 2007
Term-Work will consist of Tutorials covering the entire syllabus. Students will be graded based on
Continuous Assessment of their term work.

116U01L401 Web Programming Laboratory
TH P TUT Total
Teaching -- 04 -- 04
Scheme(Hrs.)
Marks
Examination CA
Scheme ISE IA
-- -- -- 50 -- -- 50 100
Basic Programming skills.
Course Objectives
Objective of this course is to provide students an overview of the concepts required for
development of application based on Web Technologies.
Course Outcomes
CO1. Design dynamic web pages using various HTML tags.

CO2. Use CSS to prepare the layout of web pages.
CO3. Apply JavaScript for validation in client side programming.
CO4. Integrate server side pages using php.
CO5. Apply database operations by integrating SQL queries and session variables.

No. No.
1 HTML,DHTML
1.1 Designing of effective web site, Introduction to
different Web Technologies.
HTML Tag Reference, Global Attributes, Event
Handlers, Document Structure Tags, Formatting Tags,
4 1
Text Level formatting, Block Level formatting, List
Tags, Hyperlink tags, Image and Image maps,
1.2 Table tags, Form Tags, Frame Tags, Executable
content tags.
2 CSS and Bootstrap 4 2
2.1 What are style sheets?, Why are style sheets valuable?
Different approaches to style sheets, Using Multiple
approaches, Linking to style information in separate
file, Setting up style information, Using the <LINK>
tag, embedded style information, Using <STYLE>
tag, Inline style formation
2.2 Introduction to Bootstrap, Bootstrap grids, layouts,
bootstrap components like iconography, dropdowns,
input groups, navigation, alerts. and plugins
3 JavaScript 6 3
3.1 Introduction to JavaScript, Data Types, Operators,
Control Flow, Arrays, and Functions
3.2 Making Decisions / Repeating Code; Debugging and
Error Handling; Working with DOM and DHTML
3.3 Enhancing and Validating Forms
4 PHP Programming 7 4
PHP : Why PHP and MySQL?, Server-side web
scripting, Installing PHP, Adding PHP to HTML,
4.2 Syntax and Variables, Passing information between
pages, Strings, Arrays and Array Functions, Numbers,
Handling basic PHP errors / problems.
5 PHP and MySQL 9 5
5.1 PHP/MySQL Functions, Displaying queries in tables,
Building Forms from queries, String and Regular
Expressions, Sessions, Cookies, Integration of
complete web application and deployment.
5.2 #self learning topic: Study of Laravel framework
Total 30

Recommended Books:

No. Publisher Year of
with country Publication
1. Thomas Powell Web Design The complete Tata 5th edition
Reference McGrawHill 2010
2. Thomas Powell HTML and XHTML The Tata 5th edition
complete Reference McGrawHill 2010
3. Thomas Powell and JavaScript 2.0 : The Tata 3rd

Fritz Schneider Complete Reference, McGrawHill Edition,2013
4. Steven Holzner PHP : The Complete Tata 2nd edition
Reference McGrawHill 2008
Term-Work consists of programming assignments covering entire syllabus. Students will be

graded based on continuous assessment of their term work.

116U01P401 Mini Project
TH P TUT Total
Teaching Scheme
01 02 -- 03
(Hrs.)
Marks
Examination Scheme CA
ISE IA
- - - 50 - - 50 100
Course Objectives:
The objective of Mini Project is to address a real-world problem, find, implement and demonstrate
the solution for the same through the courses learned in earlier semesters. Identify various
hardware and software requirements for problem solution. It will also inculcate qualities such as
meeting deadlines, making and following work plan. The Mini Project may be beyond the scope of
courses learnt and interdisciplinary in nature.
Course Outcomes:
After successful completion of the course student should be able to
CO1. Understand the requirements for problem definition and scope.

CO2. Identify the various hardware and software, usage of Tools needed to meet the desired specification.
CO3. Describe the design in the form of algorithm/flowchart/block diagram.
CO4. Implement and test the design as per mentioned specifications.
CO5. Prepare a technical report based on Mini project.
CO6. Present technical seminar based on the Mini project work carried out.

Course delivery guidelines related to Software Engineering principles:
In Theory class teacher is expected to discuss and demonstrate the SDLC process based on
waterfall Model.

No. No.
1 Understand the problem definition (Analysis phase) 03 CO1
1.1 Identify users (different users/stakeholders )
1.2 Identify role of every user. who and for what ? (view
user, transaction user, admin user etc.)
1.3 Is there any parallel system? (survey concept.)
1.4 Demonstrate the systems known to students like LMS,
MS-Teams, Somaiya web site, with which students are
familiar, or the demos prepared/compiled by faculty
1.5 Identify need of the project
1.6 Prepare problem definition, including background, and
scope of the work
2 Identification of data and hardware software Requirement 02 CO2
(Analysis phase)
2.1 Identify data
2.2 Identify software , and hardware needed
2.3 Prepare E-R schema or equivalent description of data
2.4 Identify usage of Tools, library functions, APIs/packages,
applicable to solve the problem
3 Identify Design description (Design phase ) 03 CO3
3.1 Identify Design description
3.2 Represent the definition in the form of block diagram,
flowchart, use case diagram
3.3 Identify flow of information
3.4 Represent information flow in the graphical diagram (web
graph, flowchart etc.)
3.5 Design user interfaces as per role defined in 1.3 (UI with
HTML or equivalent Tool
3.6 Describe data in details (e g E-R to Relational Schema)
3.7 Define functions as per mentioned scope
4 Implementation phase (Coding phase) 03 CO4
4.1 Coding of different modules as defined in the scope
4.2 Integration of modules (as per 3.3)
4.3 Identify test cases related to different modules.
4.4 Prepare test cases
4.5 Prepare responsibility chart (Test case writer, Tester) in
the form of table

5 Testing phase 01 CO4
5.1 Test the modules as per written test case
5.2 Complete the test case chart (4.5 - responsibility chart)
6 Deployment Phase 01 CO4
6.1 Describe need of deployment
6.2 Study of GitHub
6.3 Upload the project on GitHub
7 Preparation of Report 02 CO5
7.1 Understand the different sections of Report template.
7.2 Write report specifying points 1 to 6 in corresponding
section
7.3 Describe contents of Presentation
8 Preparation of Presentation for final demonstration as per CO5
report sequence
Total 15
Term Work and Practical / Oral:

The mini project is a group project. Interdisciplinary projects are also permitted. Each project will be
assigned to one faculty member as a supervisor.
There will be continuous assessment and progress report of the project that needs to be maintained by
student(s). The final oral will be a presentation based on a demonstration of the project in front of a
committee of examiners.

116U01L402 Analysis of Algorithms Laboratory

TH P TUT Total
Teaching
-- 02 -- 02
Scheme(Hrs.)
Marks
CA
Scheme ISE IA
--
-- -- 25 -- -- 25 50
Term work will consist of experiments based on the syllabus of “Analysis of Algorithm
(116U01C402)”. Students will be graded based on the continuous assessment of their term work.
Practical and Oral examination will based on the syllabus of “Analysis of Algorithms” and
laboratory experiments.
116U01L403 Relational Database Management Systems Laboratory
TH P TUT Total
Teaching
-- 02 -- 02
Scheme(Hrs.)
Marks
CA
Scheme ISE IA
--
-- -- 25 -- -- 25 50
Term work will consist of experiments based on syllabus of ‘Relational Database Management
System’ (116U01C403). Students will be graded based on continuous assessment of their term
work. Oral and Practical examination will be based on syllabus of ‘Relational Database
Management System’ and laboratory experiments.

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Syllabus

B. Tech Computer Engineering

K J Somaiya College of Engineering, Mumbai-77

SVU 2020 - R 1.0 S Y B. Tech COMP. AC 28/06/2021 Page 2 of 52

SVU 2020 - R 1.0 S Y B. Tech COMP. AC 28/06/2021 Page 3 of 52

Dr. Deepak Sharma

Dr. Shubha Pandit

SVU 2020 - R 1.0 S Y B. Tech COMP. AC 28/06/2021 Page 4 of 52

To become a center of excellence in discipline of Computer Engineering for developing technically

Program Educational Outcomes (PEO)

A graduate of Computer Engineering will

PEO1. Solve problems in diverse fields using knowledge of Computer Engineering.

Program Outcomes (PO)

PO1. Engineering knowledge: Apply the knowledge of mathematics, science, engineering

SVU 2020 - R 1.0 S Y B. Tech COMP. AC 28/06/2021 Page 5 of 52

Program Specific Outcomes (PSO)

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Acronyms used for type of Course

Acronym used Definition

Acronyms used in Eight Digit Course code e.g. 116U06C101

SVU 2020 - R 1.0 S Y B. Tech COMP. AC 28/06/2021 Page 7 of 52

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2.1 Introduction: Definition, Dirichlet’s conditions, Euler’s

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Course prerequisites: Programming Language

Module Unit No. Details Hrs. CO

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2. Michael T Goodrich Data Structure and Algorithm Wiley Second

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Sr. Name/s of Author/s Title of Book Name of Edition and

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SVU 2020 R 1.0 KJSCE S Y B. Tech Syllabus Page 20 of 52

4 Inheritance and Interface CO1,

4.1 Inheritance Types of Inheritance, Final class, abstract

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Sr. Name/s of Author/s Title of Book Name of Edition and

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7 Algebraic Structures 13 CO4

7.3 Hamming Code ,Minimum Distance

7.4 Group codes ,encoding-decoding techniques

SVU 2020 R 1.0 KJSCE S Y B. Tech Syllabus Page 24 of 52

Mathematics of Cryptography - Modular Arithmetic,

Sr. Name/s of Author/s Title of Book Name of Edition and

SVU 2020 R 1.0 KJSCE S Y B. Tech Syllabus Page 25 of 52

Basics of Digital Electronics

SVU 2020 R 1.0 KJSCE S Y B. Tech Syllabus Page 26 of 52

1 Binary Arithmetic and Codes: 2 CO1

1.1 Binary Addition and Subtraction (1’s and 2’s

1.2 Gray Code, BCD Code, Excess-3 code, ASCII Code

2 Basic Digital Circuits & Minimization: 4 CO2

2.1 NOT, AND, OR, NAND, NOR, EX-OR, EX-NOR