B.tech 3 Cse Syllabus

KRUCET B.Tech.
R20 Regulations
ANNEXURE-I
KRISHNA UNIVERSITY COLLEGE OF ENGINEERING & TECHNOLOGY
B.TECH. - COMPUTER SCIENCE & ENGINEERING

Course Structure (R20) – III Year – V Semester & VI Semester
Semester–V
S.No. Course Code Course Name L T P Credits
1. Computer Networks 3 0 0 3
2. Artificial Intelligence 3 0 0 3
3. Formal Languages and Automata Theory 3 0 0 3
4. Professional Elective Course – I 3 0 0 3
Software Testing
Internet of Things
Computer Vision
5. Open Elective Course – I 3 0 0 3
6. Computer Networks Lab 0 0 3 1.5
7. Artificial Intelligence Lab 0 0 3 1.5
8. Skill oriented course – III 1 0 2 2
Mobile Application Development
9. Evaluation of Community Service Project 1.5
Total 21.5
Open Elective-I
S.No. Course Code Course Name Offered by the Dept.

1 Optical Communications ECE
2 Wireless Communication System ECE
3 Optimization Techniques Mathematics
4 Materials Characterization Techniques Physics
5 Chemistry of Energy Materials Chemistry
Note:
1. A student is permitted to register for Honours or a Minor in IV semester after the results of III
Semester are declared and students may be allowed to take maximum two subjects per semester
pertaining to their Minor from V Semester onwards.
2. A student shall not be permitted to take courses as Open Electives/Minor/Honours with content
substantially equivalent to the courses pursued in the student's primary major.
3. A student is permitted to select a Minor program only if the institution is already offering a Major
degree program in that discipline.
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KRUCET B.Tech. R20 Regulations
Semester–VI
S.No Course Code Course Name L T P Credits
1. Compiler Design 3 0 0 3
2. Machine Learning 3 0 0 3
3. Data Warehousing and Data Mining 3 0 0 3
4. Professional Elective Course– II 3 0 0 3
Advanced Computer Architecture
Computer Graphics
Natural Language Processing
5. Open Elective Course – II 3 0 0 3
6. Compiler Design Lab 0 0 3 1.5
7. Machine Learning Lab 0 0 3 1.5
8. Data Warehousing and Data Mining Lab 0 0 3 1.5
9. Skill oriented course – IV 1 0 2 2
Soft Skills
10. Mandatory Non-credit Course
Intellectual Property Rights & Patents 2 0 0 0
Total 21.5
Industry Internship (Mandatory) for 6 – 8 weeks duration during summer vacation
Open Elective-II
S.No Course Code Course Name Offered by the Dept.
1 Satellite Communications ECE

2 Embedded Systems ECE
3 Wavelet Transforms & its applications Mathematics
4 Physics Of Electronic Materials and Devices Physics
5 Chemistry of Polymers and its Applications Chemistry
OPEN ELECTIVES TO BE OFFERED BY CSE FOR OTHER BRANCHES
Open Elective-I Open Elective-II

Object Oriented Programming Through Artificial Intelligence
Java
Database Management System Mobile Computing
Computer Graphics Principles of Operating Systems
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KRISHNA UNIVERISTY COLLEGE OF ENGINEERING & TECHNOLOGY

B. Tech (CSE)– III- V Semester L T P C
3 0 0 3
COMPUTER NETWORKS
Course Objectives:
The course is designed to
 Understand the basic concepts of Computer Networks.
 Introduce the layered approach for design of computer networks
 Expose the network protocols used in Internet environment
 Explain the format of headers of IP, TCP and UDP
 Familiarize with the applications of Internet
 Elucidate the design issues for a computer network.
Course Outcomes:
After completion of the course, students will be able to
 Identify the software and hardware components of a computer network
 Design software for a computer network
 Develop new routing, and congestion control algorithms
 Assess critically the existing routing protocols
 Explain the functionality of each layer of a computer network
 Choose the appropriate transport protocol based on the application requirements
UNIT I Computer Networks and the Internet

What Is the Internet? The Network Edge, The Network Core, Delay, Loss, and Throughput in Packet-
Switched Networks(Textbook 2), Reference Models, Example Networks, Guided Transmission Media,
Wireless Transmission(Textbook 1)
UNIT II The Data Link Layer, Access Networks, and LANs

Data Link Layer Design Issues, Error Detection and Correction, Elementary Data Link Protocols,
Sliding Window Protocols (Textbook 1) Introduction to the Link Layer, Error-Detection and -
Correction Techniques, Multiple Access Links and Protocols, Switched Local Area Networks
Link Virtualization: A Network as a Link Layer, Data Center Networking, Retrospective: A Day in the
Life of a Web Page Request (Textbook 2)
UNIT III The Network Layer
Routing Algorithms, Internetworking, The Network Layer in The Internet (Textbook 1)

UNIT IV The Transport Layer
Connectionless Transport: UDP (Textbook 2), The Internet Transport Protocols: TCP, Congestion
Control (Textbook 1)
UNIT V Principles of Network Applications
Principles of Network Applications, The Web and HTTP, Electronic Mail in the Internet, DNS—The
Internet’s Directory Service, Peer-to-Peer Applications Video Streaming and Content Distribution
Networks (Textbook 2)
Textbooks:
1. Andrew S.Tanenbaum, David j.wetherall, Computer Networks, 5th Edition, PEARSON.
2. James F. Kurose, Keith W. Ross, “Computer Networking: A Top-Down Approach”, 6th edition,
Pearson, 2019.
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Reference Books:
1. Forouzan, Data communications and Networking, 5th Edition, McGraw Hill Publication.
2. Youlu Zheng, Shakil Akthar, “Networks for Computer Scientists and Engineers”, Oxford
Publishers, 2016.
Online Learning Resources:

https://nptel.ac.in/courses/106105183/25
http://www.nptelvideos.in/2012/11/computer-networks.html
https://nptel.ac.in/courses/106105183/3
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3 0 0 3
ARTIFICIAL INTELLIGENCE
Course Objectives:
This course is designed to:
 Introduce Artificial Intelligence
 Teach about the machine learning environment
 Present the searching Technique for Problem Solving
 Introduce Natural Language Processing and Robotics
Course Outcomes:
 Apply searching techniques for solving a problem
 Design Intelligent Agents
 Develop Natural Language Interface for Machines
 Design mini robots
 Summarize past, present and future of Artificial Intelligence
UNIT I Introduction
Introduction: What is AI, Foundations of AI, History of AI, The State of Art.
Intelligent Agents: Agents and Environments, Good Behaviour: The Concept of Rationality, The Nature of
Environments, The Structure of Agents.
UNIT II Solving Problems by searching
Problem Solving Agents, Example problems, Searching for Solutions, Uninformed Search Strategies,
Informed search strategies, Heuristic Functions, Beyond Classical Search: Local Search Algorithms and
Optimization Problems, Local Search in Continues Spaces, Searching with Nondeterministic Actions,
Searching with partial observations, online search agents and unknown environments.
UNIT III Reinforcement Learning & Natural Language Processing
Reinforcement Learning: Introduction, Passive Reinforcement Learning, Active Reinforcement Learning,
Generalization in Reinforcement Learning, Policy Search, applications of RL
Natural Language Processing: Language Models, Text Classification, Information Retrieval, Information
Extraction.
UNIT IV Natural Language for Communication
Natural Language for Communication: Phrase structure grammars, Syntactic Analysis, Augmented
Grammars and semantic Interpretation, Machine Translation, Speech Recognition
Perception: Image Formation, Early Image Processing Operations, Object Recognition by appearance,
Reconstructing the 3D World, Object Recognition from Structural information, Using Vision.
UNIT V Robotics
Robotics: Introduction, Robot Hardware, Robotic Perception, planning to move, planning uncertain
movements, Moving, Robotic software architectures, application domains
Philosophical foundations: Weak AI, Strong AI, Ethics and Risks of AI, Agent Components, Agent
Architectures, Are we going in the right direction, What if AI does succeed.
Textbooks:
1. Stuart J.Russell, Peter Norvig, “Artificial Intelligence A Modern Approach”, 3rd Edition, Pearson Education,
2019.
Reference Books:
1. Nilsson, Nils J., and Nils Johan Nilsson. Artificial intelligence: a new synthesis. Morgan Kaufmann, 1998.
2. Johnson, Benny G., Fred Phillips, and Linda G. Chase. "An intelligent tutoring system for the accounting cycle:
Enhancing textbook homework with artificial intelligence." Journal of Accounting Education 27.1 (2009): 30-
39.
http://peterindia.net/AILinks.html
http://nptel.ac.in/courses/106106139/
https://nptel.ac.in/courses/106/105/106105152/
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3 0 0 3
FORMAL LANGUAGES AND AUTOMATA THEORY
Course Objectives:
 Introduce languages, grammar, and computational models
 Explain the Context Free Grammars
 Enable the students to use Turing machines
 Demonstrate decidability and un-decidability for NP-Hard problems
Course Outcomes:
 List types of Turing Machines
 Design Turing Machine
 Formulate decidability and undesirability problems
UNIT I : The Theory of Automata: Description of Finite Automation, Mathematical Model of Computer
System, DFA, NFA, The equivalence of DFA and NFA, Melay and Moore machines, Minimization of Finite
Automata.
UNIT II: Regular Sets and Regular Grammars: Regular Expressions, Finite Automata and Regular
Expressions, Pumping Lemma for Regular sets, Applications of pumping lemma, Closure properties of
Regular sets and Grammar.
UNIT III: Context Free Languages: CFL and Derivative Trees, Ambiguity in CFG, Simplification of CFG,
Pumping lemma for CFL
Pushdown Automata: Definition, Pushdown Automata and CFL, Parsing and Pushdown Automata.
UNIT IV: Turing Machines: TM Model and Representation, Languages accepted by TM, Design of TM,
Universal Turing Machines.
UNIT V: Formal Languages: Chomsky classification of Languages, Operations on Languages, Operations

on Languages, Languages and Automata, Undecidability, NP-Hard and NP-Complete Problems.
Textbooks:
1. Introduction to Automata Theory, Languages and Computation, J.E.Hopcroft, R.Motwani and
J.D.Ullman, 3rd Edition, Pearson, 2008.
2. Theory of Computer Science-Automata, Languages and Computation, K.L.P.Mishra and
N.Chandrasekaran, 3rd Edition, PHI, 2007.
Reference Books:
1. Formal Language and Automata Theory, K.V.N.Sunitha and N.Kalyani, Pearson, 2015.
2. Introduction to Automata Theory, Formal Languages and Computation,
ShyamalenduKandar, Pearson, 2013.
3. Theory of Computation, V.Kulkarni, Oxford University Press, 2013.
4. Theory of Automata, Languages and Computation, Rajendra Kumar, McGraw Hill, 2014.

https://nptel.ac.in/courses/106106049/
https://nptel.ac.in/courses/106104028
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3 0 0 3
SOFTWARE TESTING
(Professional Elective Course-I)
Course Objectives:
 Introduce the fundamentals of various testing methodologies.
 Describe the principles and procedures for designing test cases.
 Teach debugging methods.
Course Outcomes :
 Understand the basic testing procedures.
 Develop reliable software
 Design test cases for testing different programming constructs
 Test the applications by applying different testing methods and automation tools
UNIT I
Introduction: Purpose of Testing, Dichotomies, Model for Testing, Consequences of Bugs,
Taxonomy of Bugs.
Flow graphs and Path testing: Basics Concepts of Path Testing, Predicates, Path Predicates and
Achievable Paths, Path Sensitizing, Path Instrumentation, Application of Path Testing.
UNIT II
Flow Testing Transaction Flow Testing: Transaction Flows, Transaction Flow Testing Techniques.
Dataflow testing: Basics of Dataflow Testing, Strategies in Dataflow Testing, Application of
Dataflow Testing.
UNIT III
Domain Testing: Domains and Paths, Nice & Ugly Domains, Domain testing, Domainsand
Interfaces Testing, Domain and Interface Testing, Domains and Testability.
UNIT IV
Logic Based Testing Paths, Path products and Regular expressions: Path Products & Path
Expression,Reduction Procedure, Applications, Regular Expressions & Flow Anomaly
Detection.Logic Based Testing: Overview, Decision Tables, Path Expressions, KV
Charts,Specifications.
UNIT V
Graph Matrices and Application State, State Graphs and Transition Testing: State Graphs,
Good & Bad State Graphs, State Testing, Testability Tips.
Graph Matrices and Application: Motivational Overview, Matrix of Graph, Relations, Power of a
Matrix, Node Reduction Algorithm, Building Tools.
Textbooks:
1. Boris Beizer, “Software testing techniques”, Dreamtech, second edition, 2002.
Reference Books:
1. Brian Marick, “The craft of software testing”, Pearson Education.
2. Yogesh Singh, “Software Testing”, Camebridge
3. P.C. Jorgensen, “Software Testing” 3rd edition, Aurbach Publications (Dist.by SPD).
4. N.Chauhan, “Software Testing”, Oxford University Press.
5. P.Ammann&J.Offutt, “Introduction to Software Testing” , Cambridge Univ. Press.
6. Perry, “Effective methods of Software Testing”, John Wiley, 2nd Edition, 1999.
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http://www.nptelvideos.in/2012/11/software-engineering.html
https://onlinecourses.nptel.ac.in/noc16_cs16/preview
https://nptel.ac.in/courses/117105135
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3 0 0 3
INTERNET OF THINGS
(Professional Elective Course – I)
Course Objectives:
 Understand the basics of Internet of Things and protocols.
 Discuss the requirement of IoT technology
 Introduce some of the application areas where IoT can be applied.
 Understand the vision of IoT from a global perspective, understand its applications,
determine its market perspective using gateways, devices and data management
Course Outcomes:
 Understand general concepts of Internet of Things.
 Apply design concept to IoT solutions
 Analyze various M2M and IoT architectures
 Evaluate design issues in IoT applications
 Create IoT solutions using sensors, actuators and Devices
UNIT I Introduction to IoT

Definition and Characteristics of IoT, physical design of IoT, IoT protocols, IoT communication
models, IoT Communication APIs, Communication protocols, Embedded Systems.
UNIT II Prototyping IoT Objects using Microprocessor/Microcontroller

Working principles of sensors and actuators, setting up the board – Programming for IoT, Reading
from Sensors, Communication: communication through Bluetooth, Wi-Fi.
UNIT III IoT Architecture and Protocols

Architecture Reference Model- Introduction, Reference Model and architecture, IoT reference
Model, Protocols- 6LowPAN, RPL, CoAP, MQTT, IoT frameworks- Thing Speak.
UNIT IV Device Discovery and Cloud Services for IoT

Device discovery capabilities- Registering a device, Deregister a device, Introduction to Cloud
Storage models and communication APIs Web-Server, Web server for IoT.
UNIT V UAV IoT

Introduction to Unmanned Aerial Vehicles/Drones, Drone Types, Applications: Defense, Civil,
Environmental Monitoring; UAV elements and sensors- Arms, motors, Electronic Speed
Controller(ESC), GPS, IMU, Ultra sonic sensors; UAV Software –Arudpilot, Mission Planner,
Internet of Drones(IoD)- Case study FlytBase.
Textbooks:
1. Vijay Madisetti and ArshdeepBahga, “ Internet of Things ( A Hands-on-Approach)”, 1st
Edition, VPT, 2014.
2. Handbook of unmanned aerial vehicles, K Valavanis; George J Vachtsevanos, New York,
Springer, Boston, Massachusetts : Credo Reference, 2014. 2016.
Reference Books:
1. Jan Holler, VlasiosTsiatsis, Catherine Mulligan, Stefan Avesand, Stamatis Karnouskos,
David Boyle, “ From Machine-to-Machine to the Internet of Things: Introduction to a New
Age of Intelligence”, 1st Edition, Academic Press, 2014.
2. ArshdeepBahga, Vijay Madisetti - Internet of Things: A Hands-On Approach, Universities
Page 9 of 65
Press, 2014.
3. The Internet of Things, Enabling technologies and use cases – Pethuru Raj, Anupama C.
Raman, CRC Press.
4. Francis daCosta, “Rethinking the Internet of Things: A Scalable Approach to Connecting
Everything”, 1st Edition, Apress Publications, 2013
5. Cuno Pfister, Getting Started with the Internet of Things, O‟Reilly Media, 2011, ISBN: 978-
1-4493- 9357-1
6. DGCA RPAS Guidance Manual, Revision 3 – 2020
7. Building Your Own Drones: A Beginners' Guide to Drones, UAVs, and ROVs,
John Baichtal

2. https://www.arduino.cc/
3. https://www.raspberrypi.org/
4. https://nptel.ac.in/courses/106105166/5
5. https://nptel.ac.in/courses/108108098/4
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3 0 0 3
COMPUTER VISION
(Professional Elective Course – I)
Course Objectives:
The objective of this course is to understand the basic issues in computer vision and major
approaches to address the methods to learn the Linear Filters, segmentation by clustering,
Edge detection, Texture.
Course Outcomes:
After completing the course, you will be able to:
 Identify basic concepts, terminology, theories, models and methods in the field of
computer vision,
 Describe known principles of human visual system,
 Describe basic methods of computer vision related to multi-scale representation, edge
detection and detection of other primitives, stereo, motion and object recognition,
 Suggest a design of a computer vision system for a specific problem
UNIT I LINEAR FILTERS
Introduction to Computer Vision, Linear Filters and Convolution, Shift Invariant Linear
Systems, Spatial Frequency and Fourier Transforms, Sampling and Aliasing Filters as
Templates, Technique: Normalized Correlation and Finding Patterns, Technique: Scale and
Image Pyramids.
UNIT II EDGE DETECTION
Noise- Additive Stationary Gaussian Noise, Why Finite Differences Respond to Noise,
Estimating Derivatives - Derivative of Gaussian Filters, Why Smoothing Helps, Choosing a
Smoothing Filter, Why Smooth with a Gaussian? Detecting Edges-Using the Laplacian to
Detect Edges, Gradient-Based Edge Detectors, Technique: Orientation Representations and
Corners.
UNIT III TEXTURE
Representing Texture –Extracting Image Structure with Filter Banks, Representing Texture
using the Statistics of Filter Outputs, Analysis (and Synthesis) Using Oriented Pyramids –The
Laplacian Pyramid, Filters in the Spatial Frequency Domain, Oriented Pyramids,
Application: Synthesizing Textures for Rendering, Homogeneity, Synthesis by Sampling
Local Models, Shape from Texture, Shape from Texture for Planes
UNIT IV SEGMENTATION BY CLUSTERING
What is Segmentation, Human Vision: Grouping and Gestalt, Applications: Shot Boundary
Detection and Background Subtraction. Image Segmentation by Clustering Pixels,
Segmentation by Graph-Theoretic Clustering. The Hough Transform, Fitting Lines, Fitting
Curves
UNIT V RECOGNIZATIONBYRELATIONSBETWEENTEMPLATES
Finding Objects by Voting on Relations between Templates, Relational Reasoning Using
Probabilistic Models and Search, Using Classifiers to Prune Search, Hidden Markov Models,
Application: HMM and Sign Language Understanding, Finding People with HMM.
Textbooks:
David A. Forsyth, Jean Ponce, Computer Vision – A modern Approach, PHI, 2003.
Reference Books:
1. Geometric Computing with Clifford Algebras: Theoretical Foundations and Applications in
Computer Vision and Robotics, Springer;1 edition,2001by Sommer.
2. Digital Image Processing and Computer Vision,1/e,bySonka.
3. Computer Vision and Applications: Concise Edition (WithCD) by Jack Academy Press,
2000.
Online Learning Resources:https://nptel.ac.in/courses/106105216https://nptel.ac.in/courses/108103174
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0 0 3 1.5
COMPUTER NETWORKS LAB

Course Objectives:
 To understand the different types of networks
 To discuss the software and hardware components of a network
 To enlighten the working of networking commands supported by operating system
 To impart knowledge of Network simulator 2/3
 To familiarize the use of networking functionality supported by JAVA
 To familiarize with computer networking tools.
Course Outcomes (CO):
 Design scripts for Wired network simulation
 Design scripts of static and mobile wireless networks simulation
 Analyze the data traffic using tools
 Design JAVA programs for client-server communication
 Construct a wired and wireless network using the real hardware
List of Experiments:
1. Implement the data link layer framing methods such as character, character stuffing and bit stuffing.
2. Implement on a data set of characters the three CRC polynomials – CRC 12, CRC 16 and CRC CCIP.
3. Implement Dijkstra‘s algorithm to compute the Shortest path through a graph.
4. Take an example subnet graph with weights indicating delay between nodes. Now obtain Routing table art
each node using distance vector routing algorithm.
5. Take an example subnet of hosts. Obtain broadcast tree for it.
6. Take a 64 bit playing text and encrypt the same using DES algorithm.
7. Write a program to break the above DES coding 8. Using RSA algorithm encrypt a text data and Decrypt
the same.
8. Install Network Simulator 2/3. Create a wired network using dumbbell topology. Attach agents, generate
both FTP and CBR traffic, and transmit the traffic. Vary the data rates and evaluate the performance using
metric throughput, delay, jitter and packet loss.
9. Create a static wireless network. Attach agents, generate both FTP and CBR traffic, and transmit the
traffic. Vary the data rates and evaluate the performance using metric throughput, delay, jitter and packet
loss.
10. Create a mobile wireless network. Attach agents, generate both FTP and CBR traffic, and transmit the
traffic. Vary the data rates and evaluate the performance using metric throughput, delay, jitter and packet
loss.
References:
1. Shivendra S.Panwar, Shiwen Mao, Jeong-dong Ryoo, and Yihan Li, “TCP/IP Essentials
A Lab-Based Approach”, Cambridge University Press, 2004.
2. Cisco Networking Academy, “CCNA1 and CCNA2 Companion Guide”, Cisco
Networking Academy Program, 3rd edition, 2003.
3. Elloitte Rusty Harold, “Java Network Programming”, 3rd edition, O’REILLY, 2011.
Online Learning Resources/Virtual Labs:

 https://www.netacad.com/courses/packet-tracer - Cisco Packet Tracer.
 Ns Manual, Available at: https://www.isi.edu/nsnam/ns/ns-documentation.html, 2011.
 https://www.wireshark.org/docs/wsug_html_chunked/ -Wireshark.
 https://nptel.ac.in/courses/106105183/25
 http://www.nptelvideos.in/2012/11/computer-networks.html
 https://nptel.ac.in/courses/106105183/3
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
0 0 3 1.5
ARTIFICIAL INTELLIGENCE LAB

Course Objectives
 To teach the methods of implementing algorithms using artificial intelligence techniques
 To illustrate search algorithms
To demonstrate the building of intelligent agents
Course Outcomes:
 Implement search algorithms
 Solve Artificial intelligence problems
 Design chat bot and virtual assistant
1. Write a program to implement DFS and BFS
2. Write a Program to find the solution for traveling salesman Problem
3. Write a program to implement Simulated Annealing Algorithm
4. Write a program to find the solution for the wumpus world problem
5. Write a program to implement 8 puzzle problem
6. Write a program to implement Towers of Hanoi problem
7. Write a program to implement A* Algorithm
8. Write a program to implement Hill Climbing Algorithm
9. Build a Chatbot using AWS Lex, Pandora bots.
10. Build a bot that provides all the information related to your college.
11. Build a virtual assistant for Wikipedia using Wolfram Alpha and Python
12. The following is a function that counts the number of times a string occurs in another string:
# Count the number of times string s1 is found in string s2
Def count substring(s1,s2):
count = 0
for i in range(0,len(s2)-len(s1)+1):
if s1 == s2[i:i+len(s1)]:
count += 1
return count
For instance, countsubstring(’ab’,’cabalaba’) returns 2.
Write a recursive version of the above function. To get the rest of a string (i.e. everything but the first
character).
13. Higher order functions. Write a higher-order function count that counts the number of elements in
a list that satisfy a given test. For instance: count (lambda x: x>2, [1, 2, 3, 4, 5]) should return 3,
as there are three elements in the list larger than 2. Solve this task without using any existing
higher- order function.
14. Brute force solution to the Knapsack problem. Write a function that allows you to generate
random problem instances for the knapsack program. This function should generate a list of items
containing N items that each have a unique name, a random size in the range 1 5 and a random
value in the range 1.10.
Next, you should perform performance measurements to see how long the given knapsack solver
take to solve different problem sizes. You should perform at least 10 runs with different randomly
generated problem instances for the problem sizes 10,12,14,16,18,20 and 22. Use a backpack size
of 2:5 x N for each value problem size N. Please note that the method used to generate random
numbers can also affect performance, since different distributions of values can make the initial
conditions of the problem slightly more or less demanding.
How much longer time does it take to run this program when we increase the number of items?
Does the backpack size affect the answer?
Try running the above tests again with a backpack size of 1 x N and with 4:0 x N.
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15. Assume that you are organising a party for N people and have been given a list L of people who,
for social reasons, should not sit at the same table. Furthermore, assume that you have C tables (that
are infinitely large).
Write a function layout (N,C,L) that can give a table placement (i.e. a number from 0 : : :C -1)
for each guest such that there will be no social mishaps.
For simplicity we assume that you have a unique number 0....... N-1 for each guest and that the list of
restrictions is of the form [(X, Y) .... ] denoting guests X, Y that are not allowed to sit together. Answer
with a dictionary mapping each guest into a table assignment, if there are no possible layouts of the
guests you should answer False.
References:
1. David Poole, Alan Mackworth, Randy Goebel,”Computational Intelligence: a logical
approach”, Oxford University Press, 2004.
2. G. Luger, “Artificial Intelligence: Structures and Strategies for complex problem solving”,
Fourth Edition, Pearson Education, 2002.
3. J. Nilsson, “Artificial Intelligence: A new Synthesis”, Elsevier Publishers, 1998.
4. Artificial Neural Networks, B. Yagna Narayana, PHI
5. Artificial Intelligence, 2nd Edition, E.Rich and K.Knight, TMH.
6. Artificial Intelligence and Expert Systems, Patterson, PHI.

https://www.tensorflow.org/
https://pytorch.org/
https://github.com/pytorch
https://keras.io/
https://github.com/keras-team
http://deeplearning.net/software/theano/
https://github.com/Theano/Theano
https://caffe2.ai/
https://github.com/caffe2
https://deeplearning4j.org/Scikit-learn:https://scikit-learn.org/stable/
https://github.com/scikit-learn/scikit-learn
https://www.deeplearning.ai/
https://opencv.org/
https://github.com/qqwweee/keras-yolo3
https://www.pyimagesearch.com/2018/11/12/yolo-object-detection-with-opencv/
https://developer.nvidia.com/cuda-math-library
http://vlabs.iitb.ac.in/vlabs-dev/labs/machine_learning/labs/index.php
Page 14 of 65
1 0 2 3
MOBILE APPLICATION DEVELOPMENT
(Skill Oriented Course - III)
Course Objectives:
 Learn the configuration of Android Studio, SDK Manager, and AVD Emulators
 Understand Android UI Components and make use of Material Design for Android
 Learn the usage of Libraries, APIs and handle messages
 Explore various Hybrid App Development Platforms
 Acquire the knowledge of app releases and publishing and app to the play store
Course Outcomes:
• Demonstrate the configuration of Android Software Development tools
• Design and develop Mobile Applications using Android and Kotlin
• Develop a complex android application by using apis, Libraries, and message handling
techniques
• Construct the mobile application using a hybrid framework or SDK
• Release and publish an application on Google Play Store
Activities:
Module 1:
Android OS Architecture: Application Layer, Framework Layer, Libraries and Runtime, Hardware
Abstraction Layer, and Kernel
Task: Select any two Mobile Apps used in your mobile phone and note the various functionalities
and their corresponding layers.
Module 2:
Android Studio: Install Android Studio, SDK Manager, Configure Plugins, Android Virtual
Device(AVD) Emulators
Task: Install Android Studio and Configure Latest Android SDKs and Android Virtual Devices
Module 3:
Building your First Application: Understanding Activities and Intents, Activity Lifecycle and
Managing State, Activities and Implicit Intents
Task: Build and Run Hello World Application on the virtual Device and also test the app on your
mobile phone
Module 4:
Android UI components: Text Controls, Buttons, Widgets, Layouts, Containers
Task: Explore all the UI Controls and design a Student Registration Activity
Module 5:
Material Design for Android: Material theme and widgets, Elevation shadows, Cards, Animations,
Drawables
Task: Design the Student Registration Activity using Material Design for Android Components
Module 6:
Navigation: Back-button navigation, Hierarchical navigation patterns, Ancestral navigation (Up
button), Descendant navigation, Lateral navigation with tabs and swipes
Task: Design a complete Student Management Application using Android and provide effective
navigation between various Activities.
Page 15 of 65
Module 7:
Connect to the Internet: Security best practices for network operations, Including permissions in
the manifest, Performing network operations on a worker thread, Making an HTTP connection,
Parsing the results, Managing the network state
Task: Develop an Android Application that stores Student Details into the hosting server and retrieve
student details from the server
Module 8:
Messages and Storage: Creating a Snackbar object, Showing the message to the user, instantiate a
Toast object, Show the toast, Add Notification to your App, Customize Notifications, App-specific
storage, Preferences, Room persistence library
Task: Secure the Student Management Application with proper hints, messages, notifications, and
logging
Module 9:
Geo Location: Set up the project and get an API Key, Add Markers on the map, map Styles, Enable
location tracking
Task: Add your college location on maps and also provide a location tracking feature in your app
Module 10:
Authentication: Add Firebase to the project, Email Authentication, Phone Authentication, Gmail
Authentication
Task: Design and implement an effective student Login System with OTP feature and email
authentication using firebase
Module 11:
Hybrid App Development: Hybrid App vs Native App, React-Native, Flutter, Ionic, Xamarin
Task: Design Student Management App using any one of the Hybrid Frameworks or SDKs.
Module 12:
Publish App to Play Store: Add a launcher icon and Application ID, Specify API Level targets and
version number, Disable logging and debugging, Generate signed APK for release, Create a Google
Developer Account, Run pre-launch reports, Review criteria for publishing, Submit your app for
publishing.
Task: Prepare and Publish Your Android Apps in Google Play Store
References:
1. Smyth, Neil. Android Studio 4.2 Development Essentials - Kotlin Edition: Developing Android
Apps Using Android Studio 4.2, Kotlin, and Android Jetpack, Payload Media,
Incorporated, 2021.
2. Cheng, Fu. Build Mobile Apps with Ionic 4 and Firebase: Hybrid Mobile App
Development. Germany, Apress, 2018.
3. Derks, Roy, and Boduch, Adam. React and React Native: A Complete Hands-on Guide to Modern
Web and Mobile Development with React.js, 3rd Edition. United Kingdom, Packt
Publishing, 2020.

https://developer.android.com/
https://material.io/
https://kotlinlang.org/
https://google-developer-training.github.io/android-developer-fundamentals-course-concepts/
https://developers.google.com/
Page 16 of 65
OPEN
ELECTIVES
(III-V SEMESTER)
Page 17 of 65
3 0 0 3
OPTICAL COMMUNICATIONS
(Open Elective –I)
Course Objectives:
 To understand the construction and characteristics of optical fibre cable.
 To develop the knowledge of optical signal sources and power launching.
 To identify and understand the operation of various optical detectors.
 To understand the design of optical systems and WDM.
Course Outcomes:
At the end of the course, the student will be able to:
 Understand and analyze the constructional parameters of optical fibres.
 Estimate the losses due to attenuation, absorption, scattering and bending.
 Compare various optical detectors and choose suitable one for different applications
UNIT I
Overview of Optical Fiber Communication:
- Historical development, The general system, Advantages of Optical Fiber Communications,
Optical Fiber Wave Guides- Introduction, Ray Theory Transmission, Total Internal
Reflection, Acceptance Angle, Numerical Aperture, Skew Rays, Cylindrical Fibers- Modes, V
number, Mode Coupling, Step Index Fibers, Graded Index Fibers. Single Mode Fibers- Cut
Off Wavelength, Mode Field Diameter, Effective Refractive Index, Fiber Materials Glass,
Halide, Active Glass, Chalgenide Glass, Plastic Optical Fibers.
UNIT II
Signal Distortion in Optical Fibers:
Attenuation, Absorption, Scattering and Bending Losses, Core and Cladding Losses,
Information Capacity Determination, Group Delay, Types of Dispersion - Material Dispersion,
Wave-Guide Dispersion, Polarization Mode Dispersion, Intermodal Dispersion, Pulse
Broadening, Optical Fiber Connectors- Connector Types, Single Mode Fiber Connectors,
Connector Return Loss.
UNIT III
Fiber Splicing:
Splicing Techniques, Splicing Single Mode Fibers, Fiber Alignment and Joint Loss-
Multimode Fiber Joints, Single Mode Fiber Joints. Optical Sources- LEDs, Structures,
Materials, Quantum Efficiency, Power, Modulation, Power Bandwidth Product, Injection
Laser Diodes- Modes, Threshold Conditions, External Quantum Efficiency, Laser Diode Rate
Equations, Resonant Frequencies, Reliability of LED & ILD.
UNIT IV
Optical Detectors:
Physical Principles of PIN and APD, Detector Response Time, Temperature Effect on
Avalanche Gain, Comparison of Photo Detectors, Optical Receiver Operation- Fundamental
Receiver Operation, Digital Signal Transmission, Error Sources, Receiver Configuration,
Digital Receiver Performance, Probability of Error, Quantum Limit, Analog Receivers.
UNIT V
Optical System Design:
Considerations, Component Choice, Multiplexing, Point-to- Point Links, System
Considerations, Link Power Budget with Examples, Overall Fiber Dispersion in Multi-Mode
and Single Mode Fibers, Rise Time Budget with Examples. Transmission Distance, Line
Coding in Optical Links, WDM, Necessity, Principles, Types of WDM, Measurement of
Attenuation and Dispersion, Eye Pattern.
Page 18 of 65
Textbooks:
1. Optical Fiber Communications – Gerd Keiser, MC GRAW HILL EDUCATION, 4th Edition, 2008.
2. Optical Fiber Communications – John M. Senior, Pearson Education, 3rd Edition, 2009.
References:
1. Fiber Optic Communications – D.K. Mynbaev , S.C. Gupta and Lowell L. Scheiner,
Pearson Education, 2005.
2. Text Book on Optical Fibre Communication and its Applications – S.C.Gupta, PHI, 2005.
3. Fiber Optic Communication Systems – Govind P. Agarwal , John Wiley, 3rd Ediition, 2004.
4. Introduction to Fiber Optics by Donald J.Sterling Jr. – Cengage learning, 2004.
Page 19 of 65

3 0 0 3
WIRELESS COMMUNICATION SYSTEM
(Open Elective –I)
UNIT I
Unit 1 Introduction: Wireless Networks, Structured and Unstructured Networks, Mobile Systems, 3G
Networks, Next Generation Networks (NGN), Mobile Computing in Next Generation Networks (NGN)
Unit 2
Mobile Computing Architectures: Global Systems for Mobile Communications (GSM),
General Packet Radio Service (GPRS), International Telecommunications Union (ITU) – T
standards, NGN Architecture, Core Network, Access Network, Wi-Fi, Wi MAX, Cellular
Networks, Bluetooth.
Unit 3
Mobility Management: Entities and Terminology, Mobility Management in GSM and GPRS,
Home Location Register (HLR), Visitor Location Register (VLR), Features of IPv4 and IPv6,
Mobile IP, IP Packet Delivery.
Unit 4
Mobile Transport Layer: Traditional TCP, Implications of Traditional TCP for Mobility
Management
Handover Management:
Entities and Terminology, Types of Handovers, Handover Detection, Strategies for Handover
Detection- Mobile Controlled Handover, Network Controlled Handover.
Unit 5
Operating Systems for Mobile: Distributed Operating Systems, Issues related to Mobile
Computing Systems, Features of Mobile Operating Systems - Apple i OS, Blackberry OS,
Android, Windows Phone, Symbian OS.
Text book:
1. Aditya K.Jagannatham “Principles of Modern Wireless Communications Systems – Theory and
Practice”, McGraw-Hill International,2015.
2. Theodore S.Rappaport, “Wireless Communications – Principles and Practice”, 2nd Edition,
PHI,2004.
3. Asoke K Talukder and Roopa R Yavaga Mobile Computing TMH (2008)
4. Jochen Schiller, Mobile Communications 2nd Edition Pearson Education
Reference books:
1. William C.Y.Lee, “Mobile Cellular Telecommunications”, 2nd Edition,McGraw-Hill

International, 1995.
2. Mukesh Singhal and Niranjan G Shivaratri , Advanced Concepts in Operating Systems, TMH
Page 20 of 65

3 0 0 3
OPTIMIZATION TECHNIQUES
(Open Elective- I)
Course Objectives:
This course enables the students to classify and formulate real-life problem for modeling as
optimization problem, solving and applying for decision making.
Course Outcomes: Student will be able to

 formulate a linear programming problem and solve it by various methods.
 give an optimal solution in assignment jobs, give transportation of items from sources to
destinations.
 identify strategies in a game for optimal profit.
 implement project planning.
UNIT I
Introduction to operational research-Linear programming problems (LPP)-Graphical method-
Simplex method-Big M Method-Dual simplex method.
UNIT II
Transportation problems- assignment problems-Game theory.
UNIT III
CPM and PERT –Network diagram-Events and activities-Project Planning-Reducing critical events
and activities-Critical path calculations.
UNIT IV
Sequencing Problems-Replacement problems-Capital equipment- Discounting costs- Group
replacement.
UNIT V
Inventory models-various costs- Deterministic inventory models-Economic lot size-Stochastic
inventory models- Single period inventory models with shortage cost.
Textbooks:
1. Operations Research , S.D. Sharma.
2. Operations Research, An Introduction, Hamdy A. Taha, Pearson publishers.
3. Operations Research, Nita H Shah, Ravi M Gor, Hardik Soni, PHI publishers
Reference Books:
1. Problems on Operations Research, Er. Prem kumargupta, Dr.D.S. Hira, Chand publishers
2. Operations Research, CB Gupta, PK Dwivedi, Sunil kumaryadav

https://nptel.ac.in/content/storage2/courses/105108127/pdf/Module_1/M1L2slides.pdf
https://slideplayer.com/slide/7790901/
https://www.ime.unicamp.br/~andreani/MS515/capitulo12.pdf
Page 21 of 65

3 0 0 3
MATERIALS CHARACTERIZATION TECHNIQUES
(Open Elective- I)
Course Objectives:
 To provide an exposure to different characterization techniques.
 To enlighten the basic principles and analysis of different spectroscopic techniques.
 To explain the basic principle of Scanning electron microscope along with its limitations and
applications.
 To identify the Resolving power and Magnification of Transmission electron microscope and
its applications.
 To educate the uses of advanced electric and magnetic instruments for characterization.
Course Outcomes: At the end of the course the student will be able
 To explain the structural analysis by X-ray diffraction.
 To understand the morphology of different materials using SEM and TEM.
 To recognize basic principles of various spectroscopic techniques.
 To study the electric and magnetic properties of the materials.
 To make out which technique can be used to analyse a material
UNIT I
Structure analysis by Powder X-Ray Diffraction: Introduction, Bragg’s law of diffraction, Intensity of
Diffracted beams, Factors affecting Diffraction, Intensities, Structure of polycrystalline Aggregates,
Determination of crystal structure, Crystallite size by Scherrer and Williamson-Hall (W-H) Methods,
Small angle X-ray scattering (SAXS) (in brief).
UNIT II
Microscopy technique -1 –Scanning Electron Microscopy (SEM)
Introduction, Principle, Construction and working principle of Scanning Electron Microscopy,
Specimen preparation, Different types of modes used (Secondary Electron and Backscatter Electron),
Advantages, limitations and applications of SEM.
UNIT III
Microscopy Technique -2 - Transmission Electron Microscopy (TEM): Construction and Working
principle, Resolving power and Magnification, Bright and dark fields, Diffraction and image
formation, Specimen preparation, Selected Area Diffraction, Applications of Transmission Electron
Microscopy, Difference between SEM and TEM, Advantage and Limitations of Transmission
Electron Microscopy.
UNIT IV
Spectroscopy techniques – Principle, Experimental arrangement, Analysis and advantages of the
spectroscopic techniques – (i) UV-Visible spectroscopy (ii) Raman Spectroscopy, (iii) Fourier
Transform infrared (FTIR) spectroscopy, (iv) X-ray photoelectron spectroscopy (XPS).
UNIT V
Electrical & Magnetic Characterization techniques: Electrical Properties analysis techniques (DC
conductivity, AC conductivity) Activation Energy, Effect of Magnetic field on the electrical
properties (Hall Effect). Magnetization measurement by induction method, Vibrating sample
Magnetometer (VSM) and SQUID.
Textbooks:
1. Material Characterization: Introduction to Microscopic and Spectroscopic Methods –Yang
Leng – John Wiley & Sons (Asia) Pvt. Ltd. 2008
2. Handbook of Materials Characterization -by Sharma S. K. - Springer
References:
1. Fundamentals of Molecular Spectroscopy – IV Ed. – Colin Neville Banwell and Elaine M.
McCash, Tata McGraw-Hill, 2008.
2. Elements of X-ray diffraction – Bernard Dennis Cullity& Stuart R Stocks, Prentice Hall, 2001
3. Materials Characterization: Introduction to Microscopic and Spectroscopic Methods-Yang Leng- John Wiley
& Sons4. Characterization of Materials 2nd Edition, 3 Volumes-Kaufmann E N -John Wiley (Bp)
Page 22 of 65

3 0 0 3
CHEMISTRY OF ENERGY MATERIALS
(Open Elective- I)
Course Objectives:
 To make the student understand basic electrochemical principles such as standard
electrode potentials, emf and applications of electrochemical principles in the design
of batteries.
 To understand the basic concepts of processing and limitations of fossil fuels and
Fuel cells & their applications.
 To impart knowledge to the students about fundamental concepts of hydrogen
storage in different materials and liquification method
 Necessasity of harnessing alternate energy resources such as solar energy and its
basic concepts.
 To understand and apply the basics of calculations related to material and energy
flow in the processes.
Course Outcomes:
 Ability to perform simultaneous material and energy balances.
 Student learn about various electrochemical and energy systems
 Knowledge of solid, liquid and gaseous fuels
 To know the energy demand of world, nation and available resources to fulfill the demand
 To know about the conventional energy resources and their effective utilization
 To acquire the knowledge of modern energy conversion technologies
 To be able to understand and perform the various characterization techniques of fuels
 To be able to identify available nonconventional (renewable) energy resources and
techniques to utilize them effectively
UNIT I: Electrochemical Systems: Galvanic cell, standard electrode potential, application of EMF,
electrical double layer, dipole moments, polarization, Batteries-Lead-acid and Lithium ion batteries.
UNIT II: Fuel Cells: Fuel cell working principle, Classification of fuel cells, Polymer
electrolyte membrane (PEM) fuel cells, Solid-oxide fuel cells (SOFC), Fuel cell efficiency,
Basic design of fuel cell,.
UNIT III: Hydrogen Storage: Hydrogen Storage, Chemical and Physical methods of
hydrogen storage, Hydrogen Storage in metal hydrides, metal organic frame works (MOF),
Carbon structures, metal oxide porous structures, hydrogel storage by high pressure methods.
Liquifaction method.
UNIT IV:Solar Energy: Solar energy introduction and prospects, photo voltaic (PV)
technology, concentrated solar power (CSP), Solar Fuels, Solar cells.
UNIT V: Photo and Photo electrochemical Conversions: Photochemical cells and

applications of photochemical reactions, specificity of photo electrochemical cell, advantage
of photoelectron catalytic conversions.
References:
1. Physical chemistry by Ira N. Levine
2. Essentials of Physical Chemistry, Bahl and Bahl and Tuli.
3. Inorganic Chemistry, Silver and Atkins
4. Fuel Cell Hand Book 7th Edition, by US Department of Energy (EG&G technical
services and corporation)
5. Hand book of solar energy and applications by Arvind Tiwari and Shyam.
6. Solar energy fundamental, technology and systems by Klaus Jagar et.al.
7. Hydrogen storage by Levine Klebonoff
Page 23 of 65
OPEN ELECTIVES
OFFERED TO OTHER
BRANCHES
(III-V SEMESTER)
Page 24 of 65
OBJECT ORIENTED PROGRAMMING THROUGH JAVA

(Open Elective -I)
Course Objectives:
 To understand object oriented concepts and problem solving techniques

 To obtain knowledge about the principles of inheritance and polymorphism
 To implement the concept of packages, interfaces, exception handling and concurrency
mechanism.
 To design the GUIs using applets and swing controls.
 To understand the Java Database Connectivity Architecture

 Solve real-world problems using OOP techniques.
 Apply code reusability through inheritance, packages and interfaces
 Solve problems using java collection framework and I/O classes.
 Develop applications by using parallel streams for better performance.
 Develop applets for web applications.
 Build GUIs and handle events generated by user interactions.
 Use the JDBC API to access the database
UNIT - I Introduction
Introduction: Introduction to Object Oriented Programming, The History and Evolution of Java,
Introduction to Classes, Objects, Methods, Constructors, this keyword, Garbage Collection, Data Types,
Variables, Type Conversion and Casting, Arrays, Operators, Control Statements, Method Overloading,
Constructor Overloading, Parameter Passing, Recursion, String Class and String handling methods.
UNIT - II Inheritance, Packages, Interfaces
Inheritance: Basics, Using Super, Creating Multilevel hierarchy, Method overriding, Dynamic Method
Dispatch, Using Abstract classes, Using final with inheritance, Object class,
Packages: Basics, Finding packages and CLASSPATH, Access Protection, Importing packages.
UNIT - III Exception handling, Stream based I/O (java.io)
Exception handling - Fundamentals, Exception types, Uncaught exceptions, using try and catch, multiple
catch clauses, nested try statements, throw, throws and finally, built-in exceptions, creating own exception
subclasses.
UNIT - IV Multithreading, The Collections Framework (java.util)
Multithreading: The Java thread model, Creating threads, Thread priorities, Synchronizing threads,
Interthread communication.
The Collections Framework (java.util): Collections overview, Collection Interfaces, The Collectionclasses-
Array List, Linked List, Hash Set, Tree Set, Priority Queue, Array Deque. Hashtable, Properties, Stack,
Vector
UNIT - V Applet, GUI Programming with Swings, Accessing Databases with JDBC
Applet: Basics, Architecture, Applet Skeleton, requesting repainting, using the status window, passing
parameters to applets,GUI Programming with Swings – The origin and design philosophy of swing,
components and containers, layout managers, event handling, using a push button, jtextfield, jlabel and
image icon, the swing buttons, jtext field, jscrollpane, jlist, jcombobox, trees, jtable
Page 25 of 65
Textbooks:
1.Java The complete reference, 9th edition, Herbert Schildt, McGraw Hill Education (India) Pvt. Ltd.
2.Java How to Program, 10th Edition, Paul Dietel, Harvey Dietel, Pearson Education.
Reference Books:
1.Understanding Object-Oriented Programming with Java, updated edition, T. Budd, Pearson Education.
2.Core Java Volume – 1 Fundamentals, Cay S. Horstmann, Pearson Education.
3.Java Programming for core and advanced learners, Sagayaraj, Dennis, Karthik andGajalakshmi,
University Press
4. Introduction to Java programming, Y. Daniel Liang, Pearson Education.
https://www.w3schools.com/java/java_oop.asp
http://peterindia.net/JavaFiles.html
Page 26 of 65
DATABASE MANAGEMENT SYSTEMS

(OPEN ELECTIVE -I)
Course Objectives:

 Train in the fundamental concepts of database management systems, database modeling and design, SQL, PL/SQL
and system implementation techniques.
 Enable students to model ER diagrams for any customized application
 Inducting appropriate strategies for optimization of queries.
 Provide knowledge on concurrency techniques
 Demonstrate the organization of Databases
 After completion of the course, students will be able to

 Design a database for a real-world information system
 Define transactions that preserve the integrity of the database
 Generate tables for a database
 Organize the data to prevent redundancy
 Pose queries to retrieve the information from the database.
UNIT - I Introduction, Introduction to Relational Model
Introduction: Database systems applications, Purpose of Database Systems, view of Data, Database Languages,
Relational Databases, Database Design, Data Storage and Querying, Transaction Management, Database
Architecture, Data Mining and Information Retrieval, Specialty Databases, Database users and Administrators,
Introduction to Relational Model: Structure of Relational Databases, Database Schema, Keys, Schema Diagrams,
Relational Query Languages, Relational Operations
UNIT - II Introduction to SQL, Advanced SQL
Introduction to SQL: Overview of the SQL Query Language, SQL Data Definition, Basic Structure of SQL
Queries, Additional Basic Operations, Set Operations, Null Values, Aggregate Functions, Nested Sub-queries,
Modification of the Database. Intermediate SQL: Joint Expressions, Views, Transactions, Integrity Constraints, SQL
Data types and schemas, Authorization.
Advanced SQL: Accessing SQL from a Programming Language, Functions and Procedures, Triggers, Recursive
Queries, OLAP, Formal relational query languages.
UNIT - III Database Design and the E-R Model, Relational Database Design
Database Design and the E-R Model: Overview of the Design Process, The Entity-Relationship Model,
Constraints, Removing Redundant Attributes in Entity Sets, Entity-Relationship Diagrams, Reduction to Relational
Schemas, Entity-Relationship Design Issues.
Relational Database Design:
Features of Good Relational Designs, Atomic Domains and First Normal Form, Decomposition Using Functional
Dependencies, Functional-Dependency Theory, Algorithms for Decomposition, Decomposition Using Multivalued
Dependencies, More Normal Forms.
UNIT - IV Query Processing, Query optimization
Query Processing: Overview, Measures of Query cost, Selection operation, sorting, Join Operation, other
operations, Evaluation of Expressions.
Query optimization: Overview, Transformation of Relational Expressions, Estimating statistics of Expression
results, Choice of Evaluation Plans, Materialized views, Advanced Topics in Query Optimization.
Page 27 of 65
UNIT - V Transaction Management, Concurrency Control, Recovery System
Transaction Management:
Transactions: Concept, A Simple Transactional Model, Storage Structures, Transaction Atomicity and Durability,
Transaction Isolation, Serializability, Isolation and Atomicity, Transaction Isolation Levels, Implementation of
Isolation Levels, Transactions as SQL Statements.
Concurrency Control: Lock-based Protocols, Deadlock Handling, Multiple granularity, Timestamp-based

Protocols, and Validation-based Protocols.
Recovery System: Failure Classification, Storage, Recovery and Atomicity, Recovery Algorithm, Buffer
Management, Failure with Loss of Nonvolatile Storage, Early Lock Release and Logical Undo Operations.
Textbooks:
1. A.Silberschatz, H.F.Korth, S.Sudarshan, “Database System Concepts”,6/e, TMH 2019
Reference Books:
1. Database Management System, 6/e RamezElmasri, Shamkant B. Navathe, PEA

2. Database Principles Fundamentals of Design Implementation and Management, Carlos Coronel, Steven Morris,
Peter Robb, Cengage Learning.
3.Database Management Systems, 3/e, Raghurama Krishnan, Johannes Gehrke,TMH
https://onlinecourses.nptel.ac.in/noc21_cs04/preview
Page 28 of 65
COMPUTER GRAPHICS
(Open Elective – I)
Course Objectives:
 This course is designed to enable the students to familiarize themselves with basic concepts of Computer
Graphics and different image transforms and learn various processing techniques.
Course Outcomes:
 Perform image manipulations and different image processing techniques
 Illustrate basic operations like – Enhancement, segmentation, compression, Image transforms and
restoration techniques on image.
 Apply various morphological operators on images
UNIT I
Overview of Computer Graphics: Video Display devices, raster scan displays, random scan displays, color CRT
Monitors, Direct view storage tubes, Flat panel displays, raster scan systems, random scan systems, input devices.
Graphical User Interfaces and Interactive Input Methods: The User Dialogue, Windows and icons, input of
graphical data, input functions.
UNIT II
Output Primitives: Points and Lines, Line-Drawing Algorithms: DDA Algorithm, Bresenham’s Line Algorithm,
Line Algorithm, Line Function, Circle Generation Algorithms, Ellipse Generation Algorithms.
UNIT III
Attributes of output Primitives: Line Attributes, Color and Gray scale levels, area fill attributes, character
attributes, bundled attributes, antialiasing.
UNIT IV
Two dimensional geometric transformations: Basic transformations, matrix representation and homogenous
coordinates transformations, other transformations.
Two Dimensional Viewing: the viewing pipeline, viewing coordinates reference frame, window to viewport
coordinate transformations, two dimensional viewing functions, clipping operations, point clipping, line clipping:
Cohen-Sutherland Line Clipping, Polygon Clipping: Sutherland-Hodgeman Polygon Clipping, Curve Clipping,
Text Clipping, Exterior Clipping.
UNIT V
Three Dimensional Concepts: Three Dimensional display methods.
Three Dimensional Geometric and Modeling Transformations: Translation, rotation, scaling, other
transformations, Composite transformations, three dimensional transformation functions.
Three Dimensional Viewing: viewing pipeline, viewing coordinates, projections, clipping
Page 29 of 65
Textbooks:
1. Donlad Hearn and M.Pauling Baker , Computer Graphics, PHI (Second Edition)
Reference Books:
1.Shalini Govil-Pai, Principles of Computer Graphic Theory and practice using open GL and Maya
Springer(2007)
2. ISRD group,Computer Graphicsace series, TMG(2006)
Page 30 of 65
( III B.TECH - II SEMESTER)
Page 31 of 65

B. Tech (CSE)– III- VI Semester L T P C
3 0 0 3
COMPILER DESIGN
Course Objectives:
 Teach the concepts related to assemblers, loaders, linkers and editors
 Introduce the basic principles of the compiler construction
 Explain the Concept of Context Free Grammars, Parsing and various Parsing Techniques.
 Expose the process of intermediate code generation.
 Instruct the process of Code Generation and various Code optimization techniques
Course Outcomes:
• Differentiate the various phases of a compiler
• Design code generator
• Apply code optimization techniques
• Identify the tokens and verify the code
UNIT I Introduction
Introduction: The structure of a compiler, the science of building a compiler, programming
language basics
Lexical Analysis: The Role of the Lexical Analyzer, Input Buffering, Recognition of Tokens, The
Lexical-Analyzer Generator Lex, Finite Automata, From Regular Expressions to Automata, Design
of a Lexical-Analyzer Generator, Optimization of DFA-Based Pattern Matchers.
UNIT II Syntax Analysis

Introduction, Context-Free Grammars, Writing a Grammar, Top-Down Parsing, Bottom-Up Parsing,
Introduction to LR Parsing: Simple LR, More Powerful LR Parsers, Using Ambiguous Grammars
and Parser Generators.
UNIT III Syntax-Directed Translation

Syntax-Directed Translation: Syntax-Directed Definitions, Evaluation Orders for SDD's,
Applications of Syntax-Directed Translation, Syntax-Directed Translation Schemes, Implementing
L-Attributed SDD's.
Intermediate-Code Generation: Variants of Syntax Trees, Three-Address Code, Types and
Declarations, Type Checking, Control Flow, Switch-Statements, Intermediate Code for Procedures.
UNIT IV Code Generation

Run-Time Environments: Stack Allocation of Space, Access to Nonlocal Data on the Stack, Heap
Management, Introduction to Garbage Collection, Introduction to Trace-Based Collection.
Code Generation: Issues in the Design of a Code Generator, The Target Language, Addresses in the
Target Code, Basic Blocks and Flow Graphs, Optimization of Basic Blocks, A Simple Code
Generator, Peephole Optimization, Register Allocation and Assignment, Dynamic Programming
Code-Generation.
UNIT V Machine-Independent Optimization

Machine-Independent Optimization: The Principal Sources of Optimization, Introduction to Data-
Flow Analysis, Foundations of Data-Flow Analysis, Constant Propagation, Partial-Redundancy
Elimination, Loops in Flow Graphs
Textbooks:
1. Alfred V. Aho, Monica S. Lam, Ravi Sethi, Jeffrey D. Ullman, “Compilers Principles,
Techniques and Tools”, 2nd Edition, Pearson.
Page 32 of 65
Reference Books:
1. Yunlin Su, Song Y. Yan, “Principles of Compilers”, Springer, 2012.
2. Andrew W. Appel, “Modern Compiler Implementation in JAVA”, 2nd edition, Cambridge
University Press, 2004.
3. Lex &Yacc – John R. Levine, Tony Mason, Doug Brown, O’reilly
4. Compiler Construction, Louden, Thomson.

1. https://nptel.ac.in/courses/106108052/
2. http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=Compilers
Page 33 of 65

3 0 0 3
MACHINE LEARNING
Course Objectives:
The course is introduced for students to
 Understand basic concepts of Machine Learning
 Study different learning algorithms
 Illustrate evaluation of learning algorithms
 Identify machine learning techniques suitable for a given problem
 Solve the problems using various machine learning techniques
 Design application using machine learning techniques
UNIT I Introduction to Machine Learning &Preparing to Model

Introduction: What is Human Learning? Types of Human Learning, what is Machine
Learning?Types of Machine Learning, Problems Not to Be Solved Using Machine Learning,
Applications of Machine Learning, State-of-The-Art Languages/Tools in Machine Learning, Issues
in Machine Learning
Preparing to Model: Introduction, Machine Learning Activities, Basic Types of Data in Machine
Learning, Exploring Structure of Data, Data Quality and Remediation, Data Pre-Processing
UNIT II Modelling and Evaluation &Basics of Feature Engineering

Introduction, selecting a Model, training a Model (for Supervised Learning), Model Representation
and Interpretability, Evaluating Performance of a Model, Improving Performance of a Model
Basics of Feature Engineering: Introduction, Feature Transformation, Feature Subset Selection
UNIT III Bayesian Concept Learning & Supervised Learning: Classification

Introduction, Why Bayesian Methods are Important? Bayes’ Theorem, Bayes’ Theorem and Concept
Learning, Bayesian Belief Network
Supervised Learning: Classification: Introduction, Example of Supervised Learning, Classification
Model, Classification Learning Steps, Common Classification Algorithms-k-Nearest
Neighbour(kNN), Decision tree, Random forest model, Support vector machines
UNIT IV Supervised Learning: Regression

Introduction, Example of Regression, Common Regression Algorithms-Simple linear regression,
Multiple linear regression, Assumptions in Regression Analysis, Main Problems in Regression
Analysis, Improving Accuracy of the Linear Regression Model, Polynomial Regression Model,
Logistic Regression, Maximum Likelihood Estimation.
UNIT V Unsupervised Learning

Introduction, Unsupervised vs Supervised Learning, Application of Unsupervised Learning,
Clustering – Clustering as a machine learning task, Different types of clustering techniques,
Partitioning methods, K-Medoids: a representative object-based technique, Hierarchical clustering,
Density-based methods- DBSCAN,Finding Pattern using Association Rule- Definition of common
terms, Association rule, Theapriori algorithm for association rule learning, Build the apriori principle
rules
Page 34 of 65
Textbooks:
1. Machine Learning, SaikatDutt, Subramanian Chandramouli, Amit Kumar Das, Pearson,
2019.
Reference Books:
1. EthernAlpaydin, “Introduction to Machine Learning”, MIT Press, 2004.
2. Stephen Marsland, “Machine Learning -An Algorithmic Perspective”, Second Edition,
Chapman and Hall/CRC Machine Learning and Pattern Recognition Series,2014.
2. Andreas C. Müller and Sarah Guido “Introduction to Machine Learning with Python: A
Guide for Data Scientists”, Oreilly.
 Andrew Ng, “Machine Learning Yearning”
 https://www.deeplearning.ai/machine-learning- yearning/
 Shai Shalev-Shwartz , Shai Ben-David, “Understanding Machine Learning: From
Theory to Algorithms” , Cambridge University Press
https://www.cse.huji.ac.il/~shais/UnderstandingMachineLearning/index.html
Page 35 of 65

3 0 0 3
DATA WAREHOUSING AND DATA MINING

Course Objectives:
The main objective of the course is to
 Introduce basic concepts and techniques of data warehousing and data mining
 Examine the types of the data to be mined and apply pre-processing methods on raw data
 Discover interesting patterns, analyze supervised and unsupervised models and estimate the accuracy of the
algorithms.
Course Outcomes:
By the end of the course student will be able to
 Illustrate the importance of Data Warehousing, Data Mining and its functionalities and Design schema for
real time data warehousing applications.
 Demonstrate on various Data Preprocessing Techniques viz. data cleaning, data integration, data
transformation and data reduction and Process raw data to make it suitable for various data mining
algorithms.
 Choose appropriate classification technique to perform classification, model building and evaluation.
 Make use of association rule mining techniques viz. Apriori and FP Growth algorithms and analyze on
frequent itemsets generation.
 Identify and apply various clustering algorithm (with open source tools), interpret, evaluate and report the
result.
UNIT I:
Data Warehousing and Online Analytical Processing: Data Warehouse: Basic concepts, Data Warehouse
Modeling: Data Cube and OLAP, Data Warehouse Design and Usage, Data Warehouse Implementation,
Introduction: Why and What is data mining, What kinds of data need to be mined and patterns can be mined, Which
technologies are used, Which kinds of applications are targeted.
UNIT II:
Data Pre-processing: An Overview, Data Cleaning, Data Integration, Data Reduction, Data Transformation and
Data Discretization.
UNIT III:
Classification: Basic Concepts, General Approach to solving a classification problem, Decision Tree Induction:
Attribute Selection Measures, Tree Pruning, Scalability and Decision Tree Induction, Visual Mining for Decision
Tree Induction.
UNIT IV:
Association Analysis: Problem Definition, Frequent Item set Generation, Rule Generation: Confident Based
Pruning, Rule Generation in Apriori Algorithm, Compact Representation of frequent item sets, FP- Growth
Algorithm.
UNIT V:
Cluster Analysis: Overview, Basics and Importance of Cluster Analysis, Clustering techniques, Different Types of
Clusters; K-means: The Basic K-means Algorithm, K-means Additional Issues, Bi-secting K Means.
Page 36 of 65
Text Books:
1. Data Mining concepts and Techniques, 3/e, Jiawei Han, Michel Kamber, Elsevier,2011.
2. Introduction to Data Mining: Pang-Ning Tan & Michael Steinbach, Vipin Kumar, Pearson,2012.
Reference Books:
1. Data Mining Techniques and Applications: An Introduction, Hongbo Du, Cengage Learning.
2. Data Mining: VikramPudi and P. Radha Krishna, Oxford Publisher.
3. Data Mining and Analysis - Fundamental Concepts and Algorithms; Mohammed J. Zaki, Wagner
Meira, Jr, Oxford
4. Data Warehousing Data Mining & OLAP, Alex Berson, Stephen Smith, TMH.
http://onlinecourses.nptel.ac.in/noc18_cs14/preview
5. (NPTEL course by Prof.Pabitra Mitra)
http://onlinecourses.nptel.ac.in/noc17_mg24/previe
w
6. (NPTEL course by Dr. Nandan Sudarshanam& Dr. Balaraman Ravindran)
http://www.saedsayad.com/data_mining_map.htm
Page 37 of 65

3 0 0 3
ADVANCED COMPUTER ARCHITECTURE
(Professional Elective Course-II)
Course Objectives:
 Understand the Concept of Parallel Processing and its applications
 Implement the Hardware for Arithmetic Operations
 Analyse the performance of different scalar Computers
 Develop the Pipelining Concept for a given set of Instructions
 Distinguish the performance of pipelining and non-pipelining environment in a processor
Course Outcomes:
 Illustrate the types of computers, and new trends and developments in computer
architecture
 Outline pipelining, instruction set architectures, memory addressing
 Apply ILP using dynamic scheduling, multiple issue, and speculation
 Illustrate the various techniques to enhance a processors ability to exploit Instruction-
level parallelism (ILP), and its challenges
 Apply multithreading by using ILP and supporting thread-level parallelism (TLP)
UNIT I
Computer Abstractions and Technology:
Introduction, Eight Great Ideas in Computer Architecture, Below Your Program, Under the Covers,
Technologies for Building Processors and Memory, Performance, The Power Wall, The Sea
Change: The Switch from Uni-processors to Multiprocessors, Benchmarking the Intel Core i7,
Fallacies and Pitfalls.
UNIT II
Instructions: Language of the Computer:
Operations of the Computer Hardware, Operands of the Computer Hardware, Signed and Unsigned
Numbers, Representing Instructions in the Computer, Logical Operations, Instructions for Making
Decisions, Supporting Procedures in Computer Hardware, Communicating with People, MIPS
Addressing for 32-Bit Immediates and Addresses, Parallelism and Instructions: Synchronization,
Translating and Starting a Program, A C Sort Example to Put It All Together, Arrays versus Pointers,
ARMv7 (32-bit) Instructions, x86 Instructions, ARMv8 (64-bit) Instructions.
UNIT III
Arithmetic for Computers:
Introduction, Addition and Subtraction, Multiplication, Division, Floating Point, Parallelism and
Computer Arithmetic: Subword Parallelism, Streaming SIMD Extensions and Advanced Vector
Extensions in x86, Subword Parallelism and Matrix Multiply.
UNIT IV
The Processor:
Introduction, Logic Design Conventions, Building a Datapath, A Simple Implementation Scheme,
An Overview of Pipelining, Pipelined Datapath and Control, Data Hazards: Forwarding versus
Stalling, Control Hazards, Exceptions, Parallelism via Instructions, The ARM Cortex-A8 and Intel
Core i7 Pipelines.
UNIT V
Large and Fast: Exploiting Memory Hierarchy:
Introduction, Memory Technologies, The Basics of Caches, Measuring and Improving Cache
Performance, Dependable Memory Hierarchy, Virtual Machines, Virtual Memory, A Common
Framework for Memory Hierarchy, Using a Finite-State Machine to Control a Simple Cache,
Page 38 of 65
Parallelism and Memory Hierarchies: Cache Coherence,Parallelism and Memory Hierarchy:

Redundant Arrays of Inexpensive Disks, Advanced Material: Implementing Cache Controllers, The
ARM Cortex-A8 and Intel Core i7 Memory Hierarchies.
Textbooks:
2) Computer Organization and Design: The hardware and Software Interface, David A Patterson,
John L Hennessy, 5th edition, MK.
3) Computer Architecture and Parallel Processing – Kai Hwang, Faye A.Brigs, Mc Graw Hill.
Reference Books:
1) Modern Processor Design: Fundamentals of Super Scalar Processors, John P. Shen and
Miikko H. Lipasti, Mc Graw Hill.
2) Advanced Computer Architecture – A Design Space Approach – DezsoSima, Terence
Fountain, Peter Kacsuk , Pearson.

Page 39 of 65

3 0 0 3
COMPUTER GRAPHICS
(Professional Elective Course – II)
Course Objectives:
This course is designed to enable the students to familiarize themselves with basic concepts of
Computer Graphics and different image transforms and learn various processing techniques.
Course Outcomes:
 Perform image manipulations and different image processing techniques
 Illustrate basic operations like – Enhancement, segmentation, compression, Image
transforms and restoration techniques on image.
 Apply various morphological operators on images
UNIT I
Overview of Computer Graphics: Video Display devices, raster scan displays, random scan displays, color
CRT Monitors, Direct view storage tubes, Flat panel displays, raster scan systems, random scan systems, input
devices.
Graphical User Interfaces and Interactive Input Methods: The User Dialogue, Windows and icons, input
of graphical data, input functions.
UNIT II
Output Primitives: Points and Lines, Line-Drawing Algorithms: DDA Algorithm, Bresenham’s Line
Algorithm, Line Algorithm, Line Function, Circle Generation Algorithms, Ellipse Generation Algorithms.
UNIT III
Attributes of output Primitives: Line Attributes, Color and Gray scale levels, area fill attributes, character attributes,
bundled attributes, antialiasing.
UNIT IV
Two dimensional geometric transformations: Basic transformations, matrix representation and homogenous
coordinates transformations, other transformations.
Two Dimensional Viewing: the viewing pipeline, viewing coordinates reference frame, window to viewport
coordinate transformations, two dimensional viewing functions, clipping operations, point clipping, line
clipping: Cohen-Sutherland Line Clipping, Polygon Clipping: Sutherland-Hodgeman Polygon Clipping, Curve
Clipping, Text Clipping, Exterior Clipping.
UNIT V
Three Dimensional Concepts: Three Dimensional display methods.
Three Dimensional Geometric and Modeling Transformations: Translation, rotation, scaling,

other transformations, Composite transformations, three dimensional transformation functions.
Three Dimensional Viewing: viewing pipeline, viewing coordinates, projections, clipping
Page 40 of 65
Textbooks:
2. Donlad Hearn and M.Pauling Baker , Computer Graphics, PHI (Second Edition)
Reference Books:
Author Title Publisher
1. Shalini Govil-Pai Principles of Computer Graphics

Theory and practice using open GL and Maya Springer(2007)
2. ISRD group Computer Graphics ace series, TMG(2006)
3. Amearendra N.Sinha, Computer Graphics TMH(2008)

Arun D Udai
Page 41 of 65

3 0 0 3
NATURAL LANGUAGE PROCESSING

(Professional Elective – II)
Course Objectives
 Explain and apply fundamental algorithms and techniques in the area of natural language
processing (NLP)
 Discuss approaches to syntax and semantics in NLP.
 Examine current methods for statistical approaches to machine translation.
 Teach machine learning techniques used in NLP.
Course Outcomes:
 Understand the various NLP Applications and Organization of Natural language, able to learn
and implement realistic applications using Python.
 Apply the various Parsing techniques, Bayes Rule, Shannon game, Entropy and Cross
Entropy.
 Understand the fundamentals of CFG and parsers and mechanisms in ATN’s.
 Apply Semantic Interpretation and Language Modelling.
 Apply the concept of Machine Translation and multilingual Information Retrieval systems
and Automatic Summarization.
UNIT II
Introduction to Natural language
The Study of Language, Applications of NLP, Evaluating Language Understanding Systems,Different
Levels of Language Analysis, Representations and Understanding, Organization ofNatural language
Understanding Systems, Linguistic Background: An outline of English Syntax.
UNIT II
Grammars and Parsing
Grammars and Parsing- Top-Down and Bottom-Up Parsers, Transition Network Grammars, Feature
Systems and Augmented Grammars, Morphological Analysis and the Lexicon, Parsing with Features,
Augmented Transition Networks, Bayees Rule, Shannon game, Entropy and Cross Entropy.
UNIT III
Grammars for Natural Language
Grammars for Natural Language, Movement Phenomenon in Language, Handling questions in
Context Free Grammars, Hold Mechanisms in ATNs, Gap Threading, Human Preferences in Parsing,
Shift Reduce Parsers, Deterministic Parsers.
UNIT IV
Semantic Interpretation
Semantic & Logical form, Word senses & ambiguity, The basic logical form language, Encoding
ambiguity in the logical Form, Verbs & States in logical form, Thematic roles, Speech acts
&embedded sentences, Defining semantics structure model theory.
Language Modeling
Introduction, n-Gram Models, Language model Evaluation, Parameter Estimation, Language Model
Adaption, Types of Language Models, Language-Specific Modeling Problems, Multilingual and
Cross lingual Language Modeling.
Page 42 of 65
UNIT V
Machine Translation
Survey: Introduction, Problems of Machine Translation, Is Machine Translation Possible, Brief
History, Possible Approaches, Current Status. Anusaraka or Language Accessor: Background, Cutting
the Gordian Knot, The Problem, Structure of Anusaraka System, User Interface, Linguistic Area,
Giving up Agreement in Anusarsaka Output, Language Bridges.
Multilingual Information Retrieval

Introduction, Document Pre-processing, Monolingual Information Retrieval, CLIR, MLIR, Evaluation
in Information Retrieval, Tools, Software and Resources.
Multilingual Automatic Summarization

Introduction, Approaches to Summarization, Evaluation, How to Build a Summarizer, Competitions
and Datasets.
Textbooks:
1. James Allen, Natural Language Understanding, 2nd Edition, 2003, Pearson Education.
2. Multilingual Natural Language Processing Applications: From Theory To Practice-Daniel
M.Bikel and ImedZitouni, Pearson Publications.
3. Natural Language Processing, A paninian perspective, Akshar Bharathi, Vineetchaitanya,
Prentice–Hall of India.
Reference Books:
1. Charniack, Eugene, Statistical Language Learning, MIT Press, 1993.
2. Jurafsky, Dan and Martin, James, Speech and Language Processing, 2nd Edition, Prentice
Hall, 2008.
3. Manning, Christopher and Henrich, Schutze, Foundations of Statistical Natural Language
Processing, MIT Press, 1999.
http://www.nptelvideos.in/2012/11/natural-language-processing.html
Page 43 of 65

0 0 3 1.5
COMPILER DESIGN LAB
Course Objectives:
 To introduce LEX and YACC tools
 To learn to develop algorithms to generate code for a target machine
 To implement LL and LR parsers
Course Outcomes:
 Design, develop, and implement a compiler for any language
 Use LEX and YACC tools for developing a scanner and a parser
 Design and implement LL and LR parsers
 Design algorithms to perform code optimization in order to improve the performance of a
program in terms of space and time complexity
1. Design and implement a lexical analyzer for given language using C and the lexical analyzer
should ignore redundant spaces, tabs and new lines.
2. Implementation of Lexical Analyzer using Lex Tool
3. Generate YACC specification for a few syntactic categories.
a. Program to recognize a valid arithmetic expression that uses operator +, – , * and /.
b. Program to recognize a valid variable which starts with a letter followed by any number
of letters or digits.
c. Implementation of Calculator using LEX and YACC
d. Convert the BNF rules into YACC form and write code to generate abstract syntax tree
4. Write program to find ε – closure of all states of any given NFA with ε transition.
5. Write program to convert NFA with ε transition to NFA without ε transition.
6. Write program to convert NFA to DFA
7. Write program to minimize any given DFA.
8. Develop an operator precedence parser for a given language.
9. Write program to find Simulate First and Follow of any given grammar.
10. Construct a recursive descent parser for an expression.
11. Construct a Shift Reduce Parser for a given language.
12. Write a program to perform loop unrolling.
13. Write a program to perform constant propagation.
14. Implement Intermediate code generation for simple expressions.
References:
1. Compilers: Principles, Techniques and Tools, Second Edition, Alfred V. Aho, Monica S. Lam,
Ravi Sethi, Jeffry D. Ullman, Pearson.
2. Compiler Construction-Principles and Practice, Kenneth C Louden, Cengage Learning.
3. Modern compiler implementation in C, Andrew W Appel, Revised edition, Cambridge
University Press.
4. The Theory and Practice of Compiler writing, J. P. Tremblay and P. G. Sorenson, TMH
5. Writing compilers and interpreters, R. Mak, 3rd edition, Wiley student edition.

http://cse.iitkgp.ac.in/~bivasm/notes/LexAndYaccTutorial.pdf
Page 44 of 65

0 0 3 1.5
MACHINE LEARNING LAB
Course Objectives:
 Make use of Data sets in implementing the machine learning algorithms
 Implement the machine learning concepts and algorithms in any suitable language of choice.
 Understand the Mathematical and statistical prospectives of machine learning algorithms
through python programming
 Appreciate the importance of visualization in the data analytics solution.
 Derive insights using Machine learning algorithms
Note:
a. The programs can be implemented in either JAVA or Python.
b. For Problems 1 to 6 and 10, programs are to be developed without using the built-in classes
or APIs of Java/Python.
c. Datasets can be taken from standard repositories
(https://archive.ics.uci.edu/ml/datasets.html) or constructed by the students.
1. Implement and demonstrate the FIND-S algorithm for finding the most specific hypothesis
based on a given set of training data samples. Read the training data from a .CSV file.
2. For a given set of training data examples stored in a .CSV file, implement and demonstrate
the Candidate-Elimination algorithm to output a description of the set of all hypotheses
consistent with the training examples.
3. Write a program to demonstrate the working of the decision tree based ID3 algorithm.
Use an appropriate data set for building the decision tree and apply this knowledge to
classify a new sample.
4. Build an Artificial Neural Network by implementing the Back-propagation algorithm and
test the same using appropriate data sets.
5. Write a program to implement the naïve Bayesian classifier for a sample training data set
stored as a .CSV file. Compute the accuracy of the classifier, considering few test data sets.
6. Assuming a set of documents that need to be classified, use the naïve Bayesian Classifier
model to perform this task. Built-in Java classes/API can be used to write the program.
Calculate the accuracy, precision, and recall for your data set.
7. Write a program to construct a Bayesian network considering medical data. Use this model
to demonstrate the diagnosis of heart patients using standard Heart Disease Data Set. You
can use Java/Python ML library classes/API.
8. Apply EM algorithm to cluster a set of data stored in a .CSV file. Use the same data set for
clustering using k-Means algorithm. Compare the results of these two algorithms and
comment on the quality of clustering. You can add Java/Python ML library classes/API in
the program.
9. Write a program to implement k-Nearest Neighbour algorithm to classify the iris data set.
Print both correct and wrong predictions. Java/Python ML library classes can be used for
this problem.
10. Implement the non-parametric Locally Weighted Regression algorithm in order to fit data
points. Select appropriate data set for your experiment and draw graph
Projects
1. Predicting the Sale price of a house using Linear regression
2. Spam classification using Naïve Bayes algorithm
3. Predict car sale prices using Artificial Neural Networks
4. Predict Stock market trends using LSTM
5. Detecting faces from images
Page 45 of 65
References:
1. Python Machine Learning Workbook for beginners, AI Publishing, 2020.

1) Machine Learning A-Z (Python & R in Data Science Course) | Udemy
2) Machine Learning | Coursera
Page 46 of 65

0 0 3 1.5
DATA WAREHOUSING AND DATA MINING LAB
Course Objectives: The main objective of the course is to
 Inculcate Conceptual, Logical, and Physical design of Data Warehouses OLAP applications and OLAP
deployment
 Design a data warehouse or data mart to present information needed by management in a form that is usable
 Emphasize hands-on experience working with all real data sets.
 Test real data sets using popular data mining tools such as WEKA, Python Libraries
 Develop ability to design various algorithms based on data mining tools.
Course Outcomes: By the end of the course student will be able to

 Design a data mart or data warehouse for any organization
 Extract knowledge using data mining techniques and enlist various algorithms used in information analysis
of Data Mining Techniques
 Demonstrate the working of algorithms for data mining tasks such as association rule mining, classification
for realistic data
 Implement and Analyze on knowledge flow application on data sets and Apply the suitable visualization
techniques to output analytical results
Software Requirements: WEKA Tool/Python/R-Tool/Rapid Tool/Oracle Data mining
1. Creation of a Data Warehouse.
 Build Data Warehouse/Data Mart (using open source tools like Pentaho Data Integration Tool, Pentaho
Business Analytics; or other data warehouse tools like Microsoft-SSIS, Informatica, Business Objects,etc.,)
 Design multi-dimensional data models namely Star, Snowflake and Fact Constellation schemas for any one
enterprise (ex. Banking, Insurance, Finance, Healthcare, manufacturing, Automobiles, sales etc).
 Write ETL scripts and implement using data warehouse tools.
 Perform Various OLAP operations such slice, dice, roll up, drill up and pivot
2. Explore machine learning tool “WEKA”

 Explore WEKA Data Mining/Machine Learning Toolkit.
 Downloading and/or installation of WEKA data mining toolkit.
 Understand the features of WEKA toolkit such as Explorer, Knowledge Flow interface, Experimenter,
command-line interface.
 Navigate the options available in the WEKA (ex. Select attributes panel, Preprocess panel, Classify panel,
Cluster panel, Associate panel and Visualize panel)
 Study the arff file format Explore the available data sets in WEKA. Load a data set (ex. Weather dataset,
Iris dataset, etc.)
 Load each dataset and observe the following:
1. List the attribute names and they types
2. Number of records in each dataset
3. Identify the class attribute (if any)
4. Plot Histogram
5. Determine the number of records for each class.
6. Visualize the data in various dimensions
Page 47 of 65
3. Perform data preprocessing tasks and Demonstrate performing association rule mining on data sets
 Explore various options available in Weka for preprocessing data and apply Unsupervised filters like
Discretization, Resample filter, etc. on each dataset
 Load weather. nominal, Iris, Glass datasets into Weka and run Apriori
Algorithm with different support and confidence values.
 Study the rules generated. Apply different discretization filters on numerical attributes and run the Apriori
association rule algorithm. Study the rules generated.
 Derive interesting insights and observe the effect of discretization in the rule generation process.
4. Demonstrate performing classification on data sets

 Load each dataset into Weka and run 1d3, J48 classification algorithm. Study the classifier output. Compute
entropy values, Kappa statistic.
 Extract if-then rules from the decision tree generated by the classifier, Observe the confusion matrix.
 Load each dataset into Weka and perform Naïve-bayes classification and k-Nearest Neighbour
classification. Interpret the results obtained.
 Plot RoC Curves
 Compare classification results of ID3, J48, Naïve-Bayes and k-NN classifiers for each dataset, and deduce
which classifier is performing best and poor for each dataset and justify.
5. Demonstrate performing clustering of data sets

 Load each dataset into Weka and run simple k-means clustering algorithm with different values of k
(number of desired clusters).
 Study the clusters formed. Observe the sum of squared errors and centroids, and derive insights.
 Explore other clustering techniques available in Weka.
 Explore visualization features of Weka to visualize the clusters. Derive interesting insights and explain.
6. Demonstrate knowledge flow application on data sets

 Develop a knowledge flow layout for finding strong association rules by using Apriori, FP Growth
algorithms
 Set up the knowledge flow to load an ARFF (batch mode) and perform a cross validation using J48
algorithm
 Demonstrate plotting multiple ROC curves in the same plot window by using j48 and Random forest tree
7. Demonstrate ZeroR technique on Iris dataset (by using necessary preprocessing technique(s)) and share your
observations
8. Write a java program to prepare a simulated data set with unique instances.
9. Write a Python program to generate frequent item sets / association rules using Apriori algorithm
10. Write a program to calculate chi-square value using Python. Report your observation.
11. Write a program of Naive Bayesian classification using Python programming language.
12. Implement a Java program to perform Apriori algorithm
13. Write a program to cluster your choice of data using simple k-means algorithm using JDK
14. Write a program of cluster analysis using simple k-means algorithm Python programming language.
15. Write a program to compute/display dissimilarity matrix (for your own dataset containing at least four instances
with two attributes) using Python
16. Visualize the datasets using matplotlib in python.(Histogram, Box plot, Bar chart, Pie chart etc.,)
Page 48 of 65

1 0 2 2
SOFT SKILLS
(Skill Oriented Course-IV)
Course Objectives:
 To encourage all round development of the students by focusing on soft skills
 To make the students aware of critical thinking and problem-solving skills
 To develop leadership skills and organizational skills through group activities
 To function effectively with heterogeneous teams
By the end of the program students should be able to
 Memorize various elements of effective communicative skills
 Interpret people at the emotional level through emotional intelligence
 apply critical thinking skills in problem solving
 analyse the needs of an organization for team building
 Judge the situation and take necessary decisions as a leader
 Develop social and work-life skills as well as personal and emotional well-being
UNIT – I Soft Skills & Communication Skills
Introduction, meaning, significance of soft skills – definition, significance, types of communication skills -
Intrapersonal & Inter-personal skills - Verbal and Non-verbal Communication
Activities:
Intrapersonal Skills- Narration about self- strengths and weaknesses- clarity of thought – self- expression
– articulating with felicity
(The facilitator can guide the participants before the activity citing examples from the lives of the great,
anecdotes and literary sources)
Interpersonal Skills- Group Discussion – Debate – Team Tasks - Book and film Reviews by groups -
Group leader presenting views (non- controversial and secular) on contemporary issues or on a given topic.
Verbal Communication- Oral Presentations- Extempore- brief addresses and speeches- convincing-
negotiating- agreeing and disagreeing with professional grace.
Non-verbal communication – Public speaking – Mock interviews – presentations with an objective to
identify non- verbal clues and remedy the lapses on observation
UNIT – II Critical Thinking

Active Listening – Observation – Curiosity – Introspection – Analytical Thinking – Open-mindedness –
Creative Thinking
Activities:
Gathering information and statistics on a topic - sequencing – assorting – reasoning – critiquing issues –
placing the problem – finding the root cause - seeking viable solution – judging with rationale – evaluating
the views of others - Case Study, Story Analysis
UNIT – III Problem Solving & Decision Making

Meaning & features of Problem Solving – Managing Conflict – Conflict resolution – Methods of decision
making – Effective decision making in teams – Methods & Styles
Activities:
Placing a problem which involves conflict of interests, choice and views – formulating the problem –
exploring solutions by proper reasoning – Discussion on important professional, career and organizational
decisions and initiate debate on the appropriateness of the decision.
Case Study & Group Discussion
Page 49 of 65
UNIT – IV Emotional Intelligence & Stress Management

Managing Emotions – Thinking before Reacting – Empathy for Others – Self-awareness – Self-Regulation
– Stress factors – Controlling Stress – Tips
Activities:
Providing situations for the participants to express emotions such as happiness, enthusiasm, gratitude,
sympathy, and confidence, compassion in the form of written or oral presentations. Providing opportunities
for the participants to narrate certain crisis and stress –ridden situations caused by failure, anger, jealousy,
resentment and frustration in the form of written and oral presentation, Organizing Debates
UNIT – V Leadership Skills

Team-Building – Decision-Making – Accountability – Planning – Public Speaking – Motivation – Risk-
Taking - Team Building - Time Management
Activities:
Forming group with a consensus among the participants- choosing a leader- encouraging the group
members to express views on leadership- democratic attitude- sense of sacrifice – sense of adjustment –
vision – accommodating nature- eliciting views on successes and failures of leadership using the past
knowledge and experience of the participants, Public Speaking, Activities on Time Management,
Motivation, Decision Making, Group discussion etc.
NOTE-:
1. The facilitator can guide the participants before the activity citing examples from the lives of the great,
anecdotes, epics, scriptures, autobiographies and literary sources which bear true relevance to the prescribed
skill.
2. Case studies may be given wherever feasible for example for Decision Making- The decision of King
Lear or for good Leadership – Mahendar Singh Dhoni etc.
Textbooks:
1. Personality Development and Soft Skills (English, Paperback, Mitra BarunK.)Publisher: Oxford
University Press; Pap/Cdr edition (July 22, 2012)
2. Personality Development and Soft Skills: Preparing for Tomorrow, Dr Shikha KapoorPublisher : I
K International Publishing House; 0 edition (February 28, 2018)
Reference Books:
1. Soft skills: personality development for life success by Prashant Sharma, BPB publications
2018.
2. Soft Skills By Alex K. Published by S.Chand
3. Soft Skills: An Integrated Approach to Maximise Personality Gajendra Singh Chauhan,
Sangeetha Sharma Published by Wiley.
4. Communication Skills and Soft Skills (Hardcover, A. Sharma) Publisher: Yking books
5. SOFT SKILLS for a BIG IMPACT (English, Paperback, RenuShorey) Publisher: Notion Press
6. Life Skills Paperback English Dr. Rajiv Kumar Jain, Dr. Usha Jain Publisher: Vayu Education
of India
1. https://youtu.be/DUlsNJtg2L8?list=PLLy_2iUCG87CQhELCytvXh0E_y-bOO1_q
2. https://youtu.be/xBaLgJZ0t6A?list=PLzf4HHlsQFwJZel_j2PUy0pwjVUgj7KlJ
3. https://youtu.be/-Y-R9hDl7lU
4. https://youtu.be/gkLsn4ddmTs
5. https://youtu.be/2bf9K2rRWwo
6. https://youtu.be/FchfE3c2jzc
Page 50 of 65

2 0 0 0
INTELLECTUAL PROPERTY RIGHTS AND PATENTS
(Mandatory Non-Credit Course)
Course Objectives:
• This course introduces the student to the basics of Intellectual Property Rights, Copy Right Laws,
Cyber Laws, Trade Marks and Issues related to Patents. The overall idea of the course is to help and
encourage the student for startups and innovations
Course Outcomes:
 Understand IPR law & Cyber law
 Discuss registration process, maintenance and litigations associated with trademarks
 Illustrate the copy right law
 Enumerate the trade secret law.
UNIT I
Introduction to Intellectual Property Law – Evolutionary past – Intellectual Property Law Basics – Types of
Intellectual Property – Innovations and Inventions of Trade related Intellectual Property Rights – Agencies
Responsible for Intellectual Property Registration – Infringement – Regulatory – Overuse or Misuse of
Intellectual Property Rights – Compliance and Liability Issues.
UNIT II
Introduction to Copyrights – Principles of Copyright – Subject Matters of Copyright – Rights Afforded by
Copyright Law –Copyright Ownership – Transfer and Duration – Right to Prepare Derivative Works –Rights
of Distribution – Rights of performers – Copyright Formalities and Registration – Limitations – Infringement
of Copyright – International Copyright Law-Semiconductor Chip Protection Act.
UNIT III
Introduction to Patent Law – Rights and Limitations – Rights under Patent Law – Patent Requirements –
Ownership and Transfer – Patent Application Process and Granting of Patent – Patent Infringement and
Litigation – International Patent Law – Double Patenting – Patent Searching – Patent Cooperation Treaty –
New developments in Patent Law- Invention Developers and Promoters.
UNIT IV
Introduction to Trade Mark – Trade Mark Registration Process – Post registration procedures – Trade Mark
maintenance – Transfer of rights – Inter parties Proceedings – Infringement – Dilution of Ownership of Trade
Mark – Likelihood of confusion – Trade Mark claims – Trade Marks Litigation – International Trade Mark
Law.
UNIT V
Introduction to Trade Secrets – Maintaining Trade Secret – Physical Security – Employee Access Limitation
– Employee Confidentiality Agreement – Trade Secret Law – Unfair Competition – Trade Secret Litigation –
Breach of Contract – Applying State Law. Introduction to Cyber Law – Information Technology Act – Cyber
Crime and E-commerce – Data Security – Confidentiality – Privacy – International aspects of Computer and
Online Crime.
Textbooks:
1. Deborah E.Bouchoux: “Intellectual Property”. Cengage learning, New Delhi
2. Kompal Bansal &Parishit Bansal “Fundamentals of IPR for Engineers”, BS Publications (Press)
3. Cyber Law. Texts & Cases, South-Western’s Special Topics Collections
References:
1. Prabhuddha Ganguli: ‘ Intellectual Property Rights” Tata Mc-Graw – Hill, New Delhi
2. Richard Stim: “Intellectual Property”, Cengage Learning, New Delhi.
3. R. Radha Krishnan, S. Balasubramanian: “Intellectual Property Rights”, Excel Books. New Delhi.
4. M. Ashok Kumar and Mohd. Iqbal Ali: “Intellectual Property Right” Serials Pub.
Page 51 of 65
OPEN ELECTIVES
(III-VI SEMESTER)
Page 52 of 65

3 0 0 3
SATELLITE COMMUNICATIONS
(Open Elective-II)
UNIT I
Origin of Satellite Communications, Historical Back-ground, Basic Concepts of Satellite
Communications, Frequency allocations for Satellite Services, Applications, Future Trends of Satellite
Communications.
UNIT II
ORBITAL MECHANICS AND LAUNCHERS : Orbital Mechanics, Orbital perturbations, Orbit
determination, launches and launch vehicles, Orbital effects in communication systems performance.
UNIT III
SATELLITE SUBSYSTEMS : Attitude and orbit control system, telemetry, tracking, Command and
monitoring, power systems, communication subsystems, Satellite antenna Equipment reliability and
Space qualification.
UNIT IV
MULTIPLE ACCESS: Frequency division multiple access (FDMA) Intermodulation. Time division
Multiple Access (TDMA) Frame structure, Examples. Satellite Switched TDMA Onboard processing,
DAMA, Code Division Multiple access (CDMA),Spread spectrum transmission and reception.
UNIT V
SATELLITE NAVIGATION & THE GLOBAL POSITIONING SYSTEM: Radio and Satellite
Navigation, GPS Position Location principles, GPS Receivers and codes, Satellite signal acquisition,
GPS Navigation Message, GPS signal levels, GPS receiver operation, GPS C/A code accuracy,
Differential GPS.
TEXT BOOKS:
1. Satellite Communications – Timothy Pratt, Charles Bostian and Jeremy Allnutt, WSE, Wiley
Publications, 2nd Edition, 2003.
2. Satellite Communications Engineering – Wilbur L. Pritchard, Robert A Nelson and Henri
G.Suyderhoud, 2nd Edition, Pearson Publications, 2003.
REFERENCE BOOKS:
1. Satellite Communications : Design Principles – M. Richharia, BS Publications, 2nd Edition, 2003.

2. Satellite Communication – D.C Agarwal, Khanna Publications, 5th Ed.
3. Fundamentals of Satellite Communications – K.N. Raja Rao, PHI, 2004
4. Satellite Communications – Dennis Roddy, McGraw Hill, 2nd Edition, 1996.
Page 53 of 65

3 0 0 3
EMBEDDED SYTEMS
(Open Elective –II)
EMBEDDED SYSTEMS OBJECTIVES:

The main objectives of this course are given below:
The basic concepts of an embedded system are introduced.
 The various elements of embedded hardware and their design principles are explained.
 Different steps involved in the design and development of firmware for embedded systems is elaborated.
 Internals of Real-Time operating system and the fundamentals of RTOS based embedded firmware design is
discussed.
Fundamental issues in hardware software co-design were presented and explained.
 Familiarize with the different IDEs for firmware development for different family of processors/controllers and
embedded operating systems.
Embedded system implementation and testing tools are introduced and discussed.
Outcomes:
At the end of this course the student can able to: Understand the basic concepts of an embedded system and able
to know an embedded system design approach to perform a specific function.
The hardware components required for an embedded system and the design approach of an embedded hardware.
The various embedded firmware design approaches on embedded environment.
 Understand how to integrate hardware and firmware of an embedded system using real time operating system.
Syllabus
UNIT-I INTRODUCTION: Embedded system-Definition, history of embedded systems, classification of
embedded systems, major application areas of embedded systems, purpose of embedded systems, the typical
embedded system-core of the embedded system, Memory, Sensors and Actuators, Communication Interface,
Embedded firmware.
UNIT-II EMBEDDED HARDWARE DESIGN: Analog and digital electronic components, I/O types and
examples, Serial communication devices, Parallel device ports, Wireless devices, Timer and counting devices,
Watchdog timer, Real time clock.
UNIT-III EMBEDDED FIRMWARE DESIGN: Embedded Firmware design approaches, Embedded Firmware
development languages, ISR concept, Interrupt sources, Interrupt servicing mechanism, Multiple interrupts, DMA,
Device driver programming.
UNIT-IV REAL TIME OPERATING SYSTEM: Operating system basics, Types of operating systems, Tasks,
Process and Threads, Multiprocessing and Multitasking, Task Scheduling, Threads, Processes and Scheduling,
Task communication, Task synchronization, Device Drivers.
UNIT-V EMBEDDED SYSTEM DEVELOPMENT: The integrated development environment, Types of files
generated on cross-compilation, Deassembler/Decompiler, Simulators, Emulators and Debugging, Target hardware
debugging, Boundary Scan, Embedded Software development process and tools.
Text Books:
1. Embedded Systems Architecture- By Tammy Noergaard, Elsevier Publications, 2013.
2. Embedded Systems-By Shibu.K.V-Tata McGraw Hill Education Private Limited, 2013.
References:
1. Embedded System Design, Frank Vahid, Tony Givargis, John Wiley Publications, 2013.
2. Embedded Systems-Lyla B.Das-Pearson Publications, 2013. IV Year - II Semester L T
Page 54 of 65
KRISHNA UNIVERISTY COLLEGE OF ENGINEERING & TECHNOLOGY B. Tech (CSE)–

III- VI Semester L T P C
3 0 0 3
WAVELET TRANSFORMS AND ITS APPLICATIONS
(Open Elective-II)
Course Objectives:
This course provides the students to understand Wavelet transforms and its applications.
Course Outcomes:
 Understand wavelets and wavelet expansion systems.
 Illustrate the multi resolution analysis ad scaling functions.
 Form fine scale to coarse scale analysis.
 Find the lattices and lifting.
 Perform numerical complexity of discrete wavelet transforms.
 Find the frames and tight frames using fourier series.
UNIT I Wavelets
Wavelets and Wavelet Expansion Systems - Wavelet Expansion- Wavelet Transform- Wavelet
System- More Specific Characteristics of Wavelet Systems -Haar Scaling Functions and Wavelets -
effectiveness of Wavelet Analysis -The Discrete Wavelet Transform the Discrete-Time and
Continuous Wavelet Transforms.
UNIT II A Multiresolution Formulation of Wavelet Systems

Signal Spaces -The Scaling Function -Multiresolution Analysis - The Wavelet Functions - The
Discrete Wavelet Transform- A Parseval's Theorem - Display of the Discrete Wavelet Transform and
the Wavelet Expansion.
UNIT III Filter Banks and the Discrete Wavelet Transform

Analysis - From Fine Scale to Coarse Scale- Filtering and Down-Sampling or Decimating -Synthesis
- From Coarse Scale to Fine Scale -Filtering and Up-Sampling or Stretching - Input Coefficients -
Lattices and Lifting - -Different Points of View.
UNIT IV Time-Frequency and Complexity

Multiresolution versus Time-Frequency Analysis- Periodic versus Nonperiodic Discrete Wavelet
Transforms -The Discrete Wavelet Transform versus the Discrete-Time Wavelet Transform-
Numerical Complexity of the Discrete Wavelet Transform.
UNIT V Bases and Matrix Examples

Bases, Orthogonal Bases, and Biorthogonal Bases -Matrix Examples - Fourier Series Example - Sine
Expansion Example - Frames and Tight Frames - Matrix Examples -Sine Expansion as a Tight
Frame Example.
Textbooks:
1. C. Sidney Burrus, Ramesh A. Gopinath, “Introduction to Wavelets and Wavelets
Transforms”,Prentice Hall, (1997).
2. James S. Walker, “A Primer on Wavelets and their Scientific Applications”, CRC Press,
(1999).
Reference Books:
1. Raghuveer Rao, “Wavelet Transforms”, Pearson Education, Asia.

https://www.slideshare.net/RajEndiran1/introduction-to-wavelet-transform-51504915
Page 55 of 65

3 0 0 3
PHYSICS OF ELECTRONIC MATERIALS AND DEVICES
(Open Elective-II)
Course Objectives:
 To impart the fundamental knowledge on various materials, their properties and applications.
 To provide insight into various semiconducting materials, and their properties.
 To enlighten the characteristic behavior of various semiconductor devices.
 To provide the basics of dielectric and piezoelectric materials and their properties.
 To explain different categories of magnetic materials, mechanism and their advanced
applications.
Course Outcome: At the end of the course the student will be able
 To understand the fundamentals of various materials.
 To exploit the physics of semiconducting materials
 To familiarize with the working principles of semiconductor-based devices.
 To understand the behaviour of dielectric and piezoelectric materials.
 To identify the magnetic materials and their advanced applications.
UNIT I Fundamentals of Materials Science
Introduction, Phase rule, Phase Diagram, Elementary idea of Nucleation and Growth, Methods of
crystal growth. Basic idea of point, line and planar defects. Concept of thin films, preparation of thin
films, Deposition of thin film using sputtering methods (RT and glow discharge).
UNIT II Semiconductors
Introduction, charge carriers in semiconductors, effective mass, Diffusion and drift, Diffusion and
recombination, Diffusion length. The Fermi level & Fermi-Dirac distribution, Electron and Hole in
quantum well, Change of electron-hole concentration- Qualitative analysis, Temperature dependency
of carrier concentration, Conductivity and mobility, Effects of temperature and doping on mobility,
High field effects.
UNIT III Physics of Semiconductor devices
Introduction, Band structure, PN junctions and their typical characteristics under equilibrium and
under bias, Construction and working principles of: Light emitting diodes, Heterojunctions,
Transistors, FET and MOSFETs.
UNIT IV Dielectric Materials and their applications:
Introduction, Dielectric properties, Electronic polarizability and susceptibility, Dielectric constant and
frequency dependence of polarization, Dielectric strength and dielectric loss, Piezoelectric properties.
UNIT V Magnetic Materials and their applications
Introduction, Magnetism & various contributions to para and dia magnetism, Ferro and Ferri
magnetism and ferrites, Concepts of Spin waves and Magnons, Anti-ferromagnetism, Domains and
domain walls, Coercive force, Hysteresis, Nano-magnetism, Super-paramagnetism – Properties and
applications.
Textbooks
1. Principles of Electronic Materials and Devices- S.O. Kasap, McGraw-Hill Education (India) Pvt. Ltd.,
3rd edition, 2007.
2. Electronic Components and Materials- Grover and Jamwal, Dhanpat Rai and Co.
Reference Books:
1. Solid State Electronic Devices -B.G. Streetman and S. Banerjee, PHI Learning, 6th edition
2. Electronic Materials Science- Eugene A. Irene, , Wiley, 2005
3. An Introduction to Electronic Materials for Engineers-Wei Gao, Zhengwei Li, Nigel Sammes, World
Scientific Publishing Co. Pvt. Ltd., , 2nd Edition,2011
4. A First Course In Material Science- by Raghvan, McGraw Hill Pub.
5. The Science and Engineering of materials- Donald R.Askeland, Chapman& Hall Pub.
NPTEL courses links
https://onlinecourses.nptel.ac.in/noc20_mm02/preview,
https://nptel.ac.in/noc/courses/noc17/SEM1/noc17-mm07
Page 56 of 65
KRISHNA UNIVERISTY COLLEGE OF ENGINEERING &

TECHNOLOGY B. Tech (CSE)– III- VI Semester L T
P C
3 0 0 3
CHEMISTRY OF POLYMERS AND ITS APPLICATIONS

(Open Elective-II)
Course Objectives:
• To understand the basic principles of polymers
• To synthesize the different polymeric materials and their characterization by
various instrumental methods.
• To impart knowledge to the students about fundamental concepts of Hydro gels of
polymer networks, surface phenomenon by micelles
• To enumerate the applications of polymers in engineering
Course Outcome
• At the end of the course, the student will be able to:
• Understand the state of art synthesis of Polymeric materials
• Understand the hydro gels preparation, properties and applications in drug delivery system.
• Characterize polymers materials using IR, NMR, XRD.
• Analyze surface phenomenon of micelles and characterise using photoelectron
spectroscopy, ESCA and Auger spectroscopy
UNIT I : Polymers-Basics and Characterization Basic concepts: monomers, repeat units, degree of
polymerization, linear, branched and network polymers, classification of polymers, Polymerization:
condensation, addition, radical chain, ionic and coordination and copolymerization. Average
molecular weight concepts: number, weight and viscosity average molecular weights, polydispersity
and molecular weight distribution Measurement of molecular weight: end group, viscosity, light
scattering, osmotic and ultracentrifugation methods, analysis and testing of polymers.
Unit II : Synthetic Polymers Addition and condensation polymerization processes – Bulk, Solution,
Suspension and Emulsion polymerization. Preparation and significance, classification of polymers
based on physical properties, Thermoplastics, Thermosetting plastics, Fibers and elastomers, General
Applications.
Preparation of Polymers based on different types of monomers, Olefin polymers,
UNIT III : Natural Polymers & Modified cellulosics Natural Polymers: Chemical & Physical
structure, properties, source, important chemical modifications, applications of polymers such as
cellulose, lignin, starch, rosin, shellac, latexes, vegetable oils and gums, proteins. Modified cellulosics:
Cellulose esters and ethers such as Ethyl cellulose, CMC, HPMC, cellulose acetals, Liquid crystalline
polymers; specialty plastics- PES, PAES, PEEK, PEAK. Learning Outcomes:
UNIT IV: Hydrogels of Polymer networks and Drug delivery Definitions of Hydrogel, polymer
networks, Types of polymer networks, Methods involved in hydrogel preparation, Classification,
Properties of hydrogels, Applications of hydrogels in drug delivery. Introduction to drug systems
including, drug development, regulation, absorption and disposition, routes of administration and
dosage forms. Advanced drug delivery systems and controlled release.
UNIT V : Surface phenomena Surface tension, adsorption on solids, electrical phenomena at
interfaces including electrokinetics, micelles, reverse micelles, solubilization. Application of
photoelectron spectroscopy, ESCA and Auger spectroscopy to the study of surfaces.
References :
Page 57 of 65
1. A Text book of Polymer science, Billmayer

2. Organic polymer Chemistry, K.J.Saunders, Chapman and Hall
3. Advanced Organic Chemistry, B.Miller, Prentice Hall
4. Polymer Chemistry – G.S.Mishra
5. Polymer Chemistry – Gowarikar
6. Physical Chemistry –Galston
7. Drug Delivery- Ashim K. Misra
Page 58 of 65
OPEN ELECTIVES
OFFERED TO OTHER
BRANCHES
(III-VI SEMESTER)
Page 59 of 65
ARTIFICIAL INTELLIGENCE
(Open Elective -II)
Course Objectives:
 This course is designed to:
 Introduce Artificial Intelligence
 Teach about the machine learning environment
 Present the searching Technique for Problem Solving
 Introduce Natural Language Processing and Robotics
Course Outcomes:
 Apply searching techniques for solving a problem
 Design Intelligent Agents
 Develop Natural Language Interface for Machines
 Design mini robots
 Summarize past, present and future of Artificial Intelligence
UNIT - I Introduction
Introduction: What is AI, Foundations of AI, History of AI, The State of Art.
Intelligent Agents: Agents and Environments, Good Behaviour: The Concept of Rationality, The Nature of
Environments, The Structure of Agents.
UNIT - II Solving Problems by searching
Problem Solving Agents, Example problems, Searching for Solutions, Uninformed Search Strategies,
Informed search strategies, Heuristic Functions, Beyond Classical Search: Local Search Algorithms and
Optimization Problems, Local Search in Continues Spaces, Searching with Nondeterministic Actions,
Searching with partial observations, online search agents and unknown environments.
UNIT - III Reinforcement Learning & Natural Language Processing
Reinforcement Learning: Introduction, Passive Reinforcement Learning, Active Reinforcement Learning,

Generalization in Reinforcement Learning, Policy Search, applications of RL.
Natural Language Processing: Language Models, Text Classification, Information Retrieval, Information
Extraction.
UNIT - IV Natural Language for Communication
Natural Language for Communication: Phrase structure grammars, Syntactic Analysis, Augmented
Grammars and semantic Interpretation, Machine Translation, Speech Recognition.
Perception: Image Formation, Early Image Processing Operations, Object Recognition by appearance,
Reconstructing the 3D World, Object Recognition from Structural information, Using Vision.
Page 60 of 65
UNIT - V Robotics
Robotics: Introduction, Robot Hardware, Robotic Perception, planning to move, planning uncertain
movements, Moving, Robotic software architectures, application domains Philosophical foundations. Weak
AI, Strong AI, Ethics and Risks of AI, Agent Components, Agent Architectures, Are we going in the right
direction, What if AI does succeed.
Textbooks:
1. Stuart J.Russell, Peter Norvig, “Artificial Intelligence A Modern Approach”, 3rd Edition, Pearson
Education, 2019.
Reference Books:
1. Nilsson, Nils J., and Nils Johan Nilsson. Artificial intelligence: a new synthesis. Morgan Kaufmann,
1998.
2. Johnson, Benny G., Fred Phillips, and Linda G. Chase. "An intelligent tutoring system for the
accounting cycle: Enhancing textbook homework with artificial intelligence." Journal of
Accounting Education 27.1 (2009): 30-39.
http://peterindia.net/AILinks.html
http://nptel.ac.in/courses/106106139/
Page 61 of 65
Mobile Computing
(Open elective –II)
Course Objectives:
 Facilitate students to understand android SDK.

 Help students to gain a basic understanding of Android application development.
 Inculcate working knowledge of Android Studio development tool.
Course Outcomes:
 Identify various concepts of mobile programming that make it unique from programming for
other platforms.
 Evaluate mobile applications on their design pros and cons.
 Utilize rapid prototyping techniques to design and develop sophisticated mobile interfaces.
 Develop mobile applications for the Android operating system that use basic and advanced
phone features.
 Demonstrate the deployment of applications to the Android marketplace for distribution.
Unit 1 Introduction: Wireless Networks, Structured and Unstructured Networks, Mobile Systems, 3G
Networks, Next Generation Networks (NGN), Mobile Computing in Next Generation Networks (NGN),
Applications of Mobile Computing in NGN, Location Based Services.
Unit 2 Mobile Computing Architectures: Global Systems for Mobile Communications

(GSM), General Packet Radio Service (GPRS), International Telecommunications Union (ITU) – T
standards, NGN Architecture, Core Network, Access Network, Wi-Fi, WiMAX, Cellular Networks,
Bluetooth.
Unit 3 Mobility Management: Entities and Terminology, Mobility Management in GSM and GPRS,
Home Location Register (HLR), Visitor Location Register (VLR), Features of IPv4 and IPv6, Mobile IP, IP
Packet Delivery, changes in IPv6 for Mobile IPv6.
Unit 4 Mobile Transport Layer: Traditional TCP, Implications of Traditional TCP for Mobility
Management, Classical Improvements of TCP – Indirect TCP, Snooping TCP, Mobile TCP, Fast
Retransmit/Fast Recovery, Transmission/ Time-out Freezing, Selective Retransmission, Transaction-
oriented TCP.
Handover Management: Entities and Terminology, Types of Handovers, Handover Detection, Strategies
for Handover Detection- Mobile Controlled Handover, Network Controlled Handover, Mobile assisted
Handover, Handover Failures.
Unit 5 Operating Systems for Mobile Computing: Distributed Operating Systems, Issues related to
Mobile Computing Systems, Features of Mobile Operating Systems - Apple iOS, Blackberry OS, Android,
Windows Phone, Symbian OS.
Text book:
5. Asoke K Talukder and Roopa R Yavaga Mobile Computing TMH (2008)
6. Jochen Schiller, Mobile Communications 2nd Edition Pearson Education
Page 62 of 65
Reference books:
3. Rajkamal Mobile Computing Oxford (2008)

4. Mukesh Singhal and Niranjan G Shivaratri Advanced Concepts in Operating
System
Page 63 of 65

PRINCI PLES OF OPERATING SYSTEMS
(Open Elective -II)
Course Objectives:
● Understand basic concepts and functions of operating systems

● Understand the processes, threads and scheduling algorithms.
● Expose the students with different techniques of handling deadlocks
● Provide good insight on various memory management techniques
● Explore the concept of file-system and its implementation issues
Course Outcomes:
• Demonstrate and understand of computer systems and operating systems functions

• Distinguish between process and thread and classify scheduling algorithms
• Solve synchronization and deadlock problems
• Compare various memory management schemes
• Explain file systems concepts and i/o management
UNIT I Introduction to Computer and Operating system
Computer Types, Functional Units, Basic Operational Concepts, Number Representation and Arithmetic
Operations, Character Representation, Performance, Historical Perspective, Memory Locations and
Addresses, Memory operations, Instructions and Instruction Sequencing, Addressing modes
Architecture Operating System Structure, Operations Process, Memory, Storage Management, Protection
and Security Computing Environments Operating System Services User Operating System Interface
System Calls Types System Programs OS Structure OS Generation System Boot.
UNIT II Process, Threads and Scheduling
Process Concept Scheduling Operations on Processes Cooperating Processes Inter-Process Communication

Threads - Multithreading Models -Thread Libraries- Threading Issues – Scheduling Criteria Scheduling
Algorithms Algorithm Evaluation.
UNIT III Process Synchronization and Deadlocks
The Critical-Section Problem Synchronization Hardware Mutex Locks -Semaphores Classic Problems of
Synchronization Critical Regions Monitors Deadlocks System Model Deadlock Characterization Methods
for Handling Deadlocks Deadlock Prevention Deadlock Avoidance Deadlock Detection Recovery from
Deadlock.
UNIT IV Memory Management
Introduction - Swapping Contiguous Memory Allocation Paging Segmentation- Structure of the Page Table
- Virtual Memory- Background Demand Paging Copy on Write Page Replacement Allocation of Frames
Thrashing.
UNIT V Input/ Output and Files

Overview of Mass Storage Structure - Disk Structure - Disk Scheduling and Management-File System
Interface File Concept - Access Methods -Directory and Disk Structure- Directory Implementation -
Page 64 of 65
Allocation Methods- I/O Systems I/O Hardware- Application I/O Interface - Kernel I/O Subsystem.
Textbooks:
1. Carl Hamacher, ZvonkoVranesic, SafwatZaky and NaraigManjikian, Computer Organization and

Embedded Systems, Sixth Edition, Tata McGraw Hill, 2012.
2. Abraham Silberschatz, Peter B. Galvin and Greg Gagne, Operating Systems Concepts, Ninth
Edition, Wiley,2012.
Reference Books:
1. William Stallings, Operating Systems: Internals and Design Principles, Ninth Edition, Prentice-
Hall, 2018.
2. Andrew Tanenbaum, Modern Operating Systems, Third Edition, Prentice Hall, 2009.
http://peterindia.net/OperatingSystems.html
Page 65 of 65

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KRUCET B.Tech.

B.TECH. - COMPUTER SCIENCE & ENGINEERING

S.No. Course Code Course Name Offered by the Dept.

S.No Course Code Course Name Offered by the Dept.

1 Satellite Communications ECE

OPEN ELECTIVES TO BE OFFERED BY CSE FOR OTHER BRANCHES

Open Elective-I Open Elective-II

KRISHNA UNIVERISTY COLLEGE OF ENGINEERING & TECHNOLOGY

UNIT I Computer Networks and the Internet

UNIT II The Data Link Layer, Access Networks, and LANs

UNIT III The Network Layer

Routing Algorithms, Internetworking, The Network Layer in The Internet (Textbook 1)

UNIT V Principles of Network Applications

Online Learning Resources:

KRISHNA UNIVERISTY COLLEGE OF ENGINEERING & TECHNOLOGY

KRISHNA UNIVERISTY COLLEGE OF ENGINEERING & TECHNOLOGY

UNIT V: Formal Languages: Chomsky classification of Languages, Operations on Languages, Operations

Online Learning Resources:

KRISHNA UNIVERISTY COLLEGE OF ENGINEERING & TECHNOLOGY

Online Learning Resources:

UNIT I Introduction to IoT

UNIT II Prototyping IoT Objects using Microprocessor/Microcontroller

UNIT III IoT Architecture and Protocols

UNIT IV Device Discovery and Cloud Services for IoT

UNIT V UAV IoT

Online Learning Resources:

KRISHNA UNIVERISTY COLLEGE OF ENGINEERING & TECHNOLOGY

COMPUTER NETWORKS LAB

Online Learning Resources/Virtual Labs:

ARTIFICIAL INTELLIGENCE LAB

Online Learning Resources/Virtual Labs:

Online Learning Resources/Virtual Labs:

KRISHNA UNIVERISTY COLLEGE OF ENGINEERING & TECHNOLOGY

1. William C.Y.Lee, “Mobile Cellular Telecommunications”, 2nd Edition,McGraw-Hill

KRISHNA UNIVERISTY COLLEGE OF ENGINEERING & TECHNOLOGY

Course Outcomes: Student will be able to

Online Learning Resources:

KRISHNA UNIVERISTY COLLEGE OF ENGINEERING & TECHNOLOGY

KRISHNA UNIVERISTY COLLEGE OF ENGINEERING & TECHNOLOGY

UNIT V: Photo and Photo electrochemical Conversions: Photochemical cells and

OBJECT ORIENTED PROGRAMMING THROUGH JAVA

 To understand object oriented concepts and problem solving techniques

Course Outcomes (CO):

After completion of the course, students will be able to

UNIT - II Inheritance, Packages, Interfaces

UNIT - III Exception handling, Stream based I/O (java.io)

UNIT - IV Multithreading, The Collections Framework (java.util)

KRISHNA UNIVERISTY COLLEGE OF ENGINEERING & TECHNOLOGY

DATABASE MANAGEMENT SYSTEMS

This course is designed to:

Course Outcomes (CO):

 After completion of the course, students will be able to

UNIT - I Introduction, Introduction to Relational Model

UNIT - II Introduction to SQL, Advanced SQL

UNIT - IV Query Processing, Query optimization

UNIT - V Transaction Management, Concurrency Control, Recovery System

Concurrency Control: Lock-based Protocols, Deadlock Handling, Multiple granularity, Timestamp-based

1. A.Silberschatz, H.F.Korth, S.Sudarshan, “Database System Concepts”,6/e, TMH 2019

1. Database Management System, 6/e RamezElmasri, Shamkant B. Navathe, PEA