Revised-BTech-CSE-2023.txt

IK Gujral Punjab Technical University,
Kapurthala
B. Tech, Computer Science &
Engineering

Study Scheme & Syllabus of
Bachelor of Technology

Computer Science & Engineering

Batch 2023 onwards
(3rd -8th Semester)
For

University Main Campus,
Constituent Campuses and
Affiliated colleges

Department of Academics

I.K. Gujral Punjab Technical University

IK Gujral Punjab Technical University, Kapurthala
B. Tech, Computetr Science & Engg.
Bachelor of Technology in Computer Science & Engineering
It is a Graduate (UG) Programme of 4 years duration (8 semesters)
Courses & Examination
Scheme: Third Semester
Course
Code

Type of Course

BTES
301-18

Engineering
Science Course

BTCS
301-18
BTCS
302-18
BTAM
302-23

Professional
Core Courses
Professional
Core Courses
Basic Science
Course

Course Title

Hours per
Week

Marks Distribution

Total
Marks

Credits

L

T

P Internal External

3

0

0

40

60

100

3

3

0

0

40

60

100

3

3

0

0

40

60

100

3

4

1

0

40

60

100

3

2

1

0

40

60

100

3

0

0

2

30

20

50

1

0

0

4

30

20

50

2

0

0

4

30

20

50

2

IT Workshop**

0

0

2

30

20

50

1

Summer
Institutional Training

0

0

0

0

0

0

Satisfactory/Un
satisfactory

15

2 12

320

380

700

21

Digital Electronics
Data structure &
Algorithms
Object Oriented
Programming
Mathematics-III*
(Probability and
Statistics)

HSMC
101/10218

Humanities &
Social Sciences
Including
Management
\Courses

BTES
302-18
BTCS
303-18
BTCS
304-18
BTCS
305-18

Engineering
Science Course
Professional
Core Courses
Professional
Core Courses
Professional
Core Courses

Total
*

Foundation Course in
Humanities
(Development of
Societies/Philosophy)
Digital Electronics
Lab
Data structure &
Algorithms Lab
Object Oriented
Programming lab.

These are the minimum contact hrs. allocated. The contact hrs.may be increased by an institute as
per the requirement of the subject.
** Syllabus to be decided by respective institute internally. It may include latest technologies.

IK Gujral Punjab Technical University,
Kapurthala
B. Tech, Computer Science &
Engineering

Fourth Semester
Course
Code

Type of Course

Course Title

Hours
per Week

Marks Distribution

L T

P Internal External

Total
Marks

Credits

BTCS
401-18

Professional
Core Courses

Discrete Mathematics

3

1

0

40

60

100

4

BTES
401-18

Engineering
Science
Course

Computer
Organization &
Architecture

3

0

0

40

60

100

3

BTCS
402-18

Professional
Core Courses

Operating Systems

3

0

0

40

60

100

3

BTCS
403-18

Professional
Core Courses

Design & Analysis of
Algorithms

3

0

0

40

60

100

3

HSMC
122-18

Humanities &
Social Sciences
including
Management
Courses

Universal Human
Values 2

2

1

0

40

60

100

3

Environmental
Sciences

3

-

-

100

-

100

S/US

EVS101- Mandatory
Courses
18
BTES
402-18

Engineering
Science Course

Computer
Organization &
Architecture Lab

0

0

2

30

20

50

1

BTCS
404-18

Professional
Core Courses

Operating Systems
Lab

0

0

4

30

20

50

2

BTCS
405-18

Professional
Core Courses

Design & Analysis of
Algorithms Lab

0

0

4

30

20

50

2

15 2 10

390

360

750

24

Total

Students will take up summer internship of 4-6 weeks at industry or organizations of repute after 4th sem, that
will be accredited in 5th semester.

2|P a g e

IK Gujral Punjab Technical University,
Kapurthala
B. Tech, Computer Science &
Engineering
Fifth Semester
Course
Code

Type of Course

BTES
501-18

Engineering
Science

BTCS
501-18

Professional
Core Courses

BTCS
502-18
BTCS
503-18

Professional
Core Courses
Professional
Core Courses

BTCS
504-18

Professional
Core Courses

Computer Networks

BTCS
XXX-18

Professional
Elective

Elective-I

MC

Mandatory
Courses

Constitution of India/
Essence of Indian
Traditional
Knowledge

BTCS
505-18

Professional
Core Courses

BTCS
506-18
BTCS
507-18
BTCS
XXX-18

Professional
Core Courses
Professional
Core Courses
Professional
Elective-I Lab
Elective
Professional
Industrial
Training
*Training
Total

Course Title

Enterprise Resource
Planning
Database
Management Systems

Hours per
Week

Marks Distribution

L T P
3 0 0

Internal
40

Total
Marks

Credits

External
60

100

3

3

0

0

40

60

100

3

Formal Language &
Automata Theory

3

0

0

40

60

100

3

Software Engineering

3

0

0

40

60

100

3

3

0

0

40

60

100

3

3

0

0

40

60

100

3

2

-

-

100

-

100

S/US

0

0

4

30

20

50

2

0

0

2

30

20

50

1

0

0

2

30

20

50

1

0

0

2

30

20

50

1

-

-

-

60

40

100

S/US

20 0

10

520

480

1000

23

Database
Management Systems
Lab
Software Engineering
Lab
Computer Networks
Lab

* 4-6 weeks industrial training undertaken after 4th semester in summer vacations.

3|P a g e

IK Gujral Punjab Technical University,
Kapurthala
B. Tech, Computer Science &
Engineering

Sixth Semester

Hours per
Marks Distribution
Week
L T P Internal External

Total Credits
Marks

3

0

0

40

60

100

3

3

0

0

40

60

100

3

3

0

0

40

60

100

3

Elective-III

3

0

0

40

60

100

3

Open Elective
Courses

Open Elective-I

3

0

0

40

60

100

3

BTCS
603-18

Project

Project-1

0

0

6

60

40

100

3

BTCS
604-18

Professional
Core Courses

Compiler Design
Lab

0

0

2

30

20

50

1

BTCS
605-18

Professional
Core Courses

Artificial
Intelligence Lab

0

0

2

30

20

50

1

BTCS

0

0

2

30

20

50

1

UUU-18

Professional
Elective Courses Elective-II lab

BTCS
YYY-18

Professional
Elective Courses

0

0

2

30

20

50

1

15 0 14

380

420

800

22

Course
Code

Type of
Course

Course Title

BTCS
601-18

Professional
Core Courses

Compiler Design

BTCS
602-18

Professional
Core Courses

Artificial
Intelligence

BTCS

Elective-II

UUU-18

Professional
Elective Courses

BTCS
YYY-18

Professional
Elective Courses

BTOE
***

Total

4|P a g e

Elective-III lab

IK Gujral Punjab Technical University,
Kapurthala
B. Tech, Computer Science &
Engineering
Seventh Semester / Eighth Semester
Hours per
Marks
Week
Distribution
L T P Internal External

Total
Marks

Credits

Professional Core Network Security
Courses
and Cryptography

3

0

0

40

60

100

3

Data Mining and
Data
Warehousing

3

0

0

40

60

100

3

Open Elective-II

3

0

0

40

60

100

3

Elective- IV

3

0

0

40

60

100

3

Course
Code

Type of Course

BTCS
701-18
BTCS
702-18

Professional Core
Courses

Course Title

BTOE
***
BTCS
ZZZ-18

Open Elective
Courses
Professional
Elective

BTCS
TTT-18
BTCS
703-18

Professional
Elective Courses

Elective-V

3

0

0

40

60

100

3

Project

Project-II

0

0

12

120

80

200

6

0

0

2

30

20

50

1

0

0

2

30

20

50

1

15

0

14

380

420

800

23

BTCS
ZZZ18
BTCS
TTT-18

Elective- IV lab
Professional
Elective
Professional
Elective
Total

Elective- V lab

Seventh Semester / Eighth Semester

Course Code
BTCS 801-18

5|P a g e

Course Title
Semester Training

Internal

External

Total
Marks

300

200

500

Marks Distribution

Credits
16

IK Gujral Punjab Technical University,
Kapurthala
B. Tech, Computer Science &
Engineering
LIST OF ELECTIVES
BTCS XXX-18: Elective-I
BTCS 510-18
BTCS 513-18
BTCS 515-18
BTCS 518-18
BTCS 520-18
BTCS 522-18
BTCS 521-18
BTCS 523-18

Programming in Python
Programming in Python Lab
Computer Graphics
Computer Graphics lab
Web Technologies
Web Technologies lab
Computational Biology
Computational Biology lab

BTCS UUU-18: Elective-II
BTCS 606-18
BTCS 607-18
BTCS 608-18
BTCS 609-18
BTCS 610-18
BTCS 611-18
BTCS 612-18
BTCS 613-18

Simulation and Modelling
Simulation and Modelling Lab
Internet of Things
Internet of Things lab
Digital Image processing
Digital Image processing lab
Cloud computing
Cloud computing lab

BTCS YYY-18: Elective-III
BTCS 614-18
BTCS 615-18
BTCS 616-18
BTCS 617-18
BTCS 618-18
BTCS 619-18
BTCS 620-18
BTCS 621-18

Software Project Management
Software Project Management Lab
Data Science
Data Science lab
Machine Learning
Machine Learning lab
Mobile Application Development
Mobile Application Development lab

BTCS ZZZ-18: Elective-IV
BTCS 704-18
BTCS 705-18
BTCS 706-18
BTCS 707-18
BTCS 708-18
BTCS 709-18
BTCS 710-18
BTCS 711-18

6|P a g e

Deep Learning
Deep Learning Lab
Distributed databases
Distributed databases lab
Computer Vision
Computer Vision lab
Agile Software Development
Agile Software Development lab

BTCS TTT-18: Elective-V
BTCS 712-18
BTCS 713-18
BTCS 714-18
BTCS 715-18
BTCS 716-18
BTCS 717-18
BTCS 718-18
BTCS 719-18

IK Gujral Punjab Technical University,
Kapurthala
B. Tech, Computer Science &
Engineering

Blockchain Technologies
Blockchain Technologies Lab
Parallel Computing
Parallel Computing lab
Adhoc and Wireless sensor networks
Adhoc and Wireless sensor networks lab
Quantum Computing
Quantum Computing lab

Open electives offered by the department:
BTCS301-18 Data Structures & Algorithms
BTCS302-18 Object Oritented Programming
BTES401-18 Computer organisation & Arcitecture
BTCS402-18 Operating system
BTCS501-18 Database Management System
BTCS504-18 Computer Networks

7|P a g e

IK Gujral Punjab Technical University,
Kapurthala
B. Tech, Computer Science &
Engineering
MINOR DEGREE IN COMPUTER SCIENCE ENGG.(Credits required 20 from Core+Electives/MOOCS*)

List of Core Courses:Minimum of 2 courses must be opted, other than studied in regular course
Course
Code

BTCS30
1-18&
BTCS30
3-18
BTCS30
2-18 &
BTCS30
4-18
BTCS50
4-18 &
BTCS50
7-18
BTCS40
2-18 &
BTCS40
4-18

Type of Course

Course Title

Hours per
Week

Marks Distribution

L

T

P Internal External

Total
Marks

Credits

PCC

Data structure Theory
& Lab

3

0

4

40T+30 60T+20
P
P

150

5

PCC

Object Oriented
Programming Theory
& Lab

3

0

4

40T+30 60T+20
P
P

150

5

PCC

Computer networks
Theory & Lab

3

0

2

40T+30 60T+20
P
P

150

4

PCC

Operating system
Theory & Lab

3

0

4

40T+30 60T+20
P
P

150

5

BTES40
1-18 &
BTCS40
2-18

ESC

Computer
Organisation and
architecture Theory &
Lab

3

0

2

40T+30 60T+20
P
P

150

4

BTCS50
1-18 &
BTCS50
4-18

PCC

Database Management
system Theory & Lab

3

0

4

40T+30 60T+20
P
P

150

5

*List of Courses through MOOCS will be provided every six months through BOS/ MOOCS Coordinator; each
course must be of minimum 12 weeks and of 4 credits after submission of successful exam in that course.

8|P a g e

IK Gujral Punjab Technical University,
Kapurthala
B. Tech, Computer Science &
Engineering

List of Electives: 3 courses can be opted, other than studied in regular course
Course
Code**

Type of Course

ELECTIVE
ELECTIVE
ELECTIVE
ELECTIVE

Course Title

Web Technologies
Theory & Lab
Machine Learning
Theory & Lab
Cloud computing
Theory & Lab
Adhoc and Sensor
network Theory &
Lab

Hours per
Week

Marks Distribution

Total
Marks

Credits

150

4

150

4

150

4

L

T

P Internal External

3

0

2

3

0

3

0

3

0

2 40T+30 60T+20
P
P

150

4

40T+30 60T+20
P
P

2 40T+30 60T+20
P
P
2 40T+30 60T+20
P
P

ELECTIVE

Data Science Theory
& Lab

3

0

2 40T+30 60T+20
P
P

150

4

ELECTIVE

Computer Graphics
Theory & Lab

3

0

2 40T+30 60T+20
P
P

150

4

ELECTIVE

Mobile Application
Development Theory
& Lab

3

0

2 40T+30 60T+20
P
P

150

4

ELECTIVE

Data Mining
&Warehousing
Theory & Lab

3

0

2 40T+30 60T+20
P
P

150

4

ELECTIVE

Information Theory &
Coding Theory & Lab

3

0

2 40T+30 60T+20
P
P

150

4

ELECTIVE

Soft Computing
Theory & Lab

3

0

2 40T+30 60T+20
P
P

150

4

** Refer to the scheme above for the course codes of respective courses.

9|P a g e

IK Gujral Punjab Technical University, Kapurthala
B. Tech, Computetr Science & Engg.

Third
Semester

IK Gujral Punjab Technical University, Kapurthala
B. Tech, Computetr Science & Engg.
Course Code: BTCS301-18 Course Title: Data Structure & Algorithms

3L:0T:P

3Credits

Detailed Contents:
Module 1:
Introduction
Basic Terminologies: Elementary Data Organizations, Data Structure Operations: insertion,
deletion, traversal etc.; Analysis of an Algorithm, Asymptotic Notations, Time-Space trade
off.
Searching: Linear Search and Binary Search Techniques and their complexity analysis.
[6 hrs] (CO1)
Module 2:
Stacks and Queues
ADT Stack and its operations: Algorithms and their complexity analysis, Applications of
Stacks: Expression Conversion and evaluation – corresponding algorithms and complexity
analysis. ADT queue, Types of Queue: Simple Queue, Circular Queue, Priority Queue;
Operations on each types of Queues: Algorithms and their analysis.
[10 hrs] (CO2, CO4, CO5)
Module 3:
Linked Lists
Singly linked lists: Representation in memory, Algorithms of several operations: Traversing,
Searching, Insertion into, Deletion from linked list; Linked representation of Stack and
Queue, Header nodes, Doubly linked list: operations on it and algorithmic analysis; Circular
Linked Lists: All operations their algorithms and the complexity analysis.
Trees: Basic Tree Terminologies, Different types of Trees: Binary Tree, Threaded Binary Tree,
Binary Search Tree, AVL Tree; Tree operations on each of the trees and their algorithms
with complexity analysis. Applications of Binary Trees. B Tree, B+ Tree: definitions,
algorithms and analysis.
[10 hrs] (CO2, CO4, CO5)
Module 4:
Sorting and Hashing
Objective and properties of different sorting algorithms: Selection Sort, Bubble Sort,
Insertion Sort, Quick Sort, Merge Sort, Heap Sort; Performance and Comparison among all
the methods, Hashing.
[10 hrs] (CO3)
Module 4:
Graph
Basic Terminologies and Representations, Graph search and traversal algorithms and
complexity analysis.
[6 hrs] (CO2, CO4)
Course Outcomes:
The student will be able to:
1. For a given algorithm student will able to analyze the algorithms to determine the time
and computation complexity and justify the correctness;
2. Student will be able to handle operation like searching, insertion, deletion, traversing on
various Data Structures and determine time and computational complexity;
3. Student will able to write an algorithm Selection Sort, Bubble Sort, Insertion Sort,
Quick Sort, Merge Sort, Heap Sort and compare their performance in term of Space and
Time complexity;
4. Students will be able to choose appropriate Data Structure as applied to specific
problem definition; &

IK Gujral Punjab Technical University, Kapurthala
B. Tech, Computetr Science & Engg.
5. Demonstrate the reusability of Data Structures for implementing complex iterative
problems.
Suggested Books:
1. “Classic Data Structures”, Samanta and Debasis, 2nd edition, PHI publishers.
2. “Fundamentals of Data Structures”, Illustrated Edition by Ellis Horowitz,
SartajSahni, Computer Science Press.
3. “Data Structures with C (Schaum's Outline Series)”, Seymour Lipschutz, 1st
edition,McGraw Hill Education.
Reference Books:
1. Algorithms, Data Structures, and Problem Solving with C++”, Illustrated Edition
by Mark Allen Weiss, Addison-Wesley Publishing Company.
2. “How to Solve it by Computer”, 2nd Impression by R. G. Dromey, Pearson
Education.

------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Course Code: BTCS302-18 Course Title: Object Oriented Programming

3L:0T:0P

3Credits

Pre-requisites: Programming in C
Detailed Contents:
Module 1:
Introduction
Overview of C++, Sample C++ program, Different data types, operators, expressions, and
statements, arrays and strings, pointers & function components, recursive functions, user defined types, function overloading, inline functions, Classes & Objects – I: classes, Scope
resolution operator, passing objects as arguments, returning objects, and object assignment.
[8 hrs] (CO1)
Module 2:
Classes & Objects –II
Constructors, Destructors, friend functions, Parameterized constructors, Static data members,
Functions, Arrays of objects, Pointers to objects, this pointer, and reference parameter, Dynamic
allocation of objects, Copyconstructors, Operator overloading using friend functions,
overloading.
[8 hrs] (CO1, CO2)
Module 3:
Inheritance
Base Class, Inheritance and protected members, Protected base class inheritance, Inheriting
multiple base classes, Constructors, Destructors and Inheritance, Passing parameters to base
class constructors, Granting access, Virtual base classes.
[8 hrs] (CO3, CO4)
Module 4:
Virtual functions, Polymorphism
Virtual function, calling a Virtual function through a base class reference, Virtual attribute is
inherited, Virtual functions are hierarchical, pure virtual functions, Abstract classes, Using
virtual functions, Early and late binding.
[8 hrs] (CO3, CO4)
Module 5:

Exception Handling

IK Gujral Punjab Technical University, Kapurthala
B. Tech, Computetr Science & Engg.
Basics of exception handling, exception handling mechanism, throwing mechanism, catching
mechanism, I/O System Basics, File I/0: Exception handling fundamentals, Exception handling
options. C++ stream classes, Formatted I/O, fstream and the File classes, Opening and closing
a file, Reading and writing text files.
[10 hrs] (CO5)
Course Outcomes:
The student will be able to:
1. Identify classes, objects, members of a class and the relationships among them needed
to solve a specific problem;
2. Demonstrate the concept of constructors and destructors. And create new definitions
for some of the operators;
3. Create function templates, overload function templates;
4. Understand and demonstrate the concept of data encapsulation, inheritance,
polymorphism with virtual functions; &
5. Demonstrate the concept of file operations, streams in C++ and various I/O
manipulators.
Suggested Books:
1. E. Balagurusamy, Object Oriented Programming with C++, Tata McGraw Hill.
Reference Books:
1. Stanley B.Lippmann, JoseeLajoie: C++ Primer, 4th Edition, Addison Wesley, 2012.
2. Herbert Schildt: The Complete Reference C++, 4th Edition, Tata McGraw Hill, 2011.
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Course Code: BTCS303-18 Course Title: Data Structure & AlgorithmsLab

0L:0T:4P

2Credits

List of Experiment:
Task 1: Write a program to insert a new element at end as well as at a given position in an
array.
Task 2: Write a program to delete an element from a given whose value is given or
whose position is given.

Task 3: Write a program to find the location of a given element using Linear Search.
Task 4: Write a program to find the location of a given element using Binary Search.
Task 5: Write a program to implement push and pop operations on a stack using linear

array.
Task 6: Write a program to convert an infix expression to a postfix expression using stacks.
Task 7: Write a program to evaluate a postfix expression using stacks.
Task 8: Write a recursive function for Tower of Hanoi problem.

Task 9: Write a program to implement insertion and deletion operations in a
queue using linear array.
Task 10:Write a menu driven program to perform following insertion

IK Gujral Punjab Technical University, Kapurthala
B. Tech, Computetr Science & Engg.
operations in a single linked list:
i.

Insertion at beginning

ii.

Insertion at end

iii.

Insertion after a given node

iv.

Traversing a linked list

Task 11:Write a menu driven program to perform following deletion operations
in a single linked list:
i.

Deletion at beginning

ii.

Deletion at end

iii.

Deletion after a given node

Task 12: Write a program to implement push and pop operations on a stack using linked list.
Task 13: Write a program to implement push and pop operations on a queue using linked

list.
Task 14:Program to sort an array of integers in ascending order using bubble sort.
Task 15:Program to sort an array of integers in ascending order using selection sort.
Task 16: Program to sort an array of integers in ascending order using insertion sort.

Task 17:Program to sort an array of integers in ascending order using quick sort.
Task 18: Program to traverse a Binary search tree in Pre-order, In-order and Post-order.
Task 19: Program to traverse graphs using BFS.
Task 20: Program to traverse graphs using DFS.

Lab Outcomes:
The student will be able to:
1. Improve practical skills in designing and implementing basic linear data structure
algorithms;
2. Improve practical skills in designing and implementing Non-linear data structure
algorithms;
3. Use Linear and Non-Linear data structures to solve relevant problems;
4. Choose appropriate Data Structure as applied to specific problem definition; &
5. Implement Various searching algorithms and become familiar with their design
methods.
Reference Books:
1. “Data Structures with C (Schaum's Outline Series)”, Seymour Lipschutz, 1st
edition,McGraw Hill Education.

------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Course Code: BTCS304-18 Course Title: Object Oriented Programming Lab
List of Experiment:

0L:0T:4P

2Credits

IK Gujral Punjab Technical University, Kapurthala
B. Tech, Computetr Science & Engg.
Task 1:

Write a program that uses a class where the member functions are defined
inside a class.

Task 2:

Write a program that uses a class where the member functions are defined
outside a class.

Task 3:

Write a program to demonstrate the use of static data members.

Task 4:

Write a program to demonstrate the use of const data members.

Task 5:

Write a program to demonstrate the use of zero argument and parameterized
constructors.

Task 6:

Write a program to demonstrate the use of dynamic constructor.

Task 7:

Write a program to demonstrate the use of explicit constructor.

Task 8:

Write a program to demonstrate the use of initializer list.

Task 9:

Write a program to demonstrate the overloading of increment and decrement
operators.

Task 10:

Write a program to demonstrate the overloading of memory management
operators.

Task 11:

Write a program to demonstrate the typecasting of basic type to class type.

Task 12:

Write a program to demonstrate the typecasting of class type to basic type.

Task 13:

Write a program to demonstrate the typecasting of class type to class type.

Task 14:

Write a program to demonstrate the multiple inheritances.

Task 15:

Write a program to demonstrate the runtime polymorphism.

Task 16:

Write a program to demonstrate the exception handling.

Task 17:

Write a program to demonstrate the use of class template.

Task 18:

Write a program to demonstrate the reading and writing of mixed type of data.

Lab Outcomes:
The student will be able to:
1. Develop classes incorporating object-oriented techniques;
2. Design and implement object-oriented concepts of inheritance and polymorphism;
3. Illustrate and implement STL class of containers and need for exceptions to handle errors
for object oriented programs; &
4. Design and implement any real world based problem involving GUI interface using
object-oriented concepts.
Reference Books:
1. Stanley B.Lippmann, JoseeLajoie: C++ Primer, 4th Edition, Addison Wesley, 2012.
2. E. Balagurusamy, Object Oriented Programming with C++, Tata McGraw Hill.

------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

IK Gujral Punjab Technical University, Kapurthala
B. Tech, Computer Science & Engg.
BTAM 302-23

Mathematics-III
(Probability and Statistics)

L-4, T-1, P-0

4 Credits

Pre-requisite: Intermediate Calculus and Basic algebra
Course Objectives: The objective of this course is to familiarize the students with fundamental
concepts of theory of probability and statistics. The major focus of the course will be on a systematic
mathematical treatment of these concepts and their applications.

Course Outcomes: At the end of the course, the student will be able to
CO1
CO2
CO3

Analyze given data using measures of central tendency, skewness and kurtosis.
Understand and deal with randomness occurring in real world phenomena.

CO4
CO5

Analyze given data using the concepts of correlation and regression and fitting of curves.
Analyze hypothesis based on small and large samples using different tests of significance.

Apply theoretical discrete and continuous probability distributions to deal with real
world problems.

Detailed Content:
Unit I
Measures of Central tendency: Moments, skewness and Kurtosis, Random experiment, Probability axioms,
Definition of Probability, conditional probability, Discrete and Continuous random variables, Expectation of
Discrete and Continuous random variables.
Unit II
Probability distributions: Binomial, Poisson and Normal, Poisson approximation to the binomial distribution,
Evaluation of statistical parameters for these three distributions, Bivariate distributions and their properties.
Unit III
Correlation and regression for bivariate data, Rank correlation. Curve fitting by the method of least squares,
fitting of straight lines, second degree parabolas and more general curves.
Unit IV
Test of significances: Sampling and standard error, Tests of significance for large samples and small samples
(t-distribution, F-distribution), Chi-square test for goodness of fit and independence of attributes.
Recommended Books:
1. S.P. Gupta, Statistical Methods, Sultan Chand & Sons, 33rd Edition, 2005.
2. S.C. Gupta and V. K. Kapoor, Fundamentals of Mathematical Statistics, Sultan Chand &
Sons, 2014.
3. S. Ross, A First Course in Probability, 6th Edition, Pearson Education India, 2002.
4. N.P. Bali and Manish Goyal, A text book of Engineering Mathematics, Laxmi Publications,
Reprint, 2010.
5. Robert V. Hogg, Joseph W. Mckean and Allen T. Craig, Introduction to Mathematical
Statistics, 7th Edition, Pearson, 2012.

Development of Societies
Course code: HSMC101-18
Credits: 3

COURSE TOPICS:
2.1 Unit I: Social Development
(5 hours)
1. Concepts behind the origin of Family, Clan and Society
2. Different Social Systems
3. Relation between Human being and Society
4. Comparative studies on different models of Social Structures and their
evolution
2.2 Unit II: Political Development
(3 hours)
1. Ideas of Political Systems as learnt from History
2. Different models of Governing system and their comparative study
2.3 Unit III: Economic Development
(18 hours)
1. Birth of Capitalism, Socialism, Marxism
2. Concept of development in pre-British, British and post British periodBarter, Jajmani
3. Idea of development in current context.
4. E. F. Schumacher's idea of development, Buddhist economics.
Gandhian idea of development. Swaraj and Decentralization.
3. READINGS
3.1 TEXTBOOK:
3.2 *REFERENCE BOOKS:
4. OTHER SESSIONS
4.1 *TUTORIALS:
4.2 *LABORATORY:
4.3 *PROJECT: Possible projects in this course could be
a) Interact with local communities and understand their issues.
b) Study local cottage industry and agricultural practices. Role of engineering
and specialized knowledge.
c) Evaluation of technology in the context of its application. Social impact of
technology. Environmental impact of technology. Evaluation from a holistic
perspective.

PHILOSOPHY Course
code: HSMC102-18
Credits: 3
COURSE TOPICS:
2.1 Unit 1:
The difference between knowledge (Vidya) and Ignorance (Avidya):
a. Upanishads;
b. Six systems orthodox and Heterodox Schools of Indian Philosophy.
c. Greek Philosophy:
2.2 Unit 2:
Origin of the Universe:
 NasidiyaSukta: "Who really knows?”
 Brhadaranyaka Upanishad; Chandogya Upanishad: Non-self, Self, real and
unreal.
 Taittiriya Upanishad: SikshaValli.
 Plato’s Symposium: Lack as the source of desire and knowledge.
 Socratic’s method of knowledge as discovery.
 Language: Word as root of knowledge (Bhartrahari’sVakyapadiyam)
 Fourteen Knowledge basis as a sources of Vidya: Four Vedas; Six auxiliary
sciences (Vedangas); Purana, Nyaya, Mimamsa and Dharma Sastras.
2.3 Unit 3:
Knowledge as Power: Francis Bacon. Knowledge as both power and self-realization in
Bagavad Gita.
2.4 Unit 4:
Knowledge as oppression: M. Foucault. Discrimination between Rtam and Satyam in
Indian Philosophy.
2.5 Unit 5:
Knowledge as invention: Modern definition of creativity; scientific activity in the
claim that science invents new things at least through technology.
2.6 Unit 6:
Knowledge about the self, transcendental self; knowledge about society, polity and
nature.
2.7 Unit 7:
Knowledge about moral and ethics codes.
2.8 Unit 8:
Tools of acquiring knowledge: Tantrayuktis, a system of inquiry (Caraka, Sushruta,
Kautilya, Vyasa)
3. READINGS

1. Copleston, Frederick, History of Philosophy, Vol. 1.Great Britain: Continuum.
2 Hiriyanna, M. Outlines of Indian Philosophy, MotilalBanarsidass Publishers; Fifth
Reprint edition (2009)
3 Sathaye, Avinash, Translation of NasadiyaSukta
4. Ralph T. H. Griffith. The Hymns of the Ŗgveda. MotilalBanarsidass: Delhi: 1973.
5. Raju, P. T. Structural Depths of Indian Thought, Albany: State University of New
York Press.
6. Plato, Symposium, Hamilton Press.
7. KautilyaArtha Sastra. Penguin Books, New Delhi.
8. Bacon, Nova Orgum
9. Arnold, Edwin. The Song Celestial.
10. Foucault, Knowledge/Power.
11. Wildon, Anthony, System of Structure.
12. Lele, W.K. The Doctrine of Tantrayukti. Varanasi: Chowkamba Series.
13. Dasgupta, S. N. History of Indian Philosophy, MotilalBanasidas, Delhi.
14. Passmore, John, Hundred Years of Philosophy, Penguin.
4. OTHER SESSIONS:
4.1 Mode of Conduct
5. ASSESSMENT (indicative only):
Ask students to do term papers, for example, writing biographical details of
founders, sustainers, transmitters, modifiers, rewriters; translating monographs of
less known philosophers such as K. C. Bhattacharys, Daya Krishna, Gopinath
Bhattacharya; comparative study of
philosophical system such as
MadhyasthaDarshan.
6. OUTCOME OF THE COURSE:
Students will develop strong natural familiarity with humanities along with
right understanding enabling them to eliminate conflict and strife in the individual
and society. Students shall be able to relate philosophy to literature, culture, society
and lived experience can be considered.

Course Code:BTES301-18

Course Title: Digital Electronics

3L:0T:0P 3Credits

Detailed Contents:
Module 1:
NUMBER SYSTEMS: Binary, Octal, Decimal, Hexadecimal. Number base
conversions, 1’s, 2’s complements, signed Binary numbers. Binary Arithmetic, Binary
codes: Weighted BCD, Gray code, Excess 3 code, ASCII.
LOGIC GATES: AND, OR, NOT, NAND, NOR, Exclusive-OR and Exclusive-NOR.
Implementations of Logic Functions using gates, NAND-NOR implementations.
Module 2 :
BOOLEAN ALGEBRA: Boolean postulates and laws – De-Morgan’s Theorem,
Principle of Duality, Boolean expression – Boolean function, Minimization of Boolean
expressions – Sum of Products (SOP), Product of Sums (POS), Minterm, Maxterm,
Canonical forms, Conversion between canonical forms, Karnaugh map Minimization,
Don’t care conditions, Quine-McCluskey method.
Module 3:
COMBINATIONAL CIRCUITS: Design procedure – Adders, Subtractors, BCD
adder, Magnitude Comparator, Multiplexer/Demultiplexer, encoder/decoder, parity
checker, code converters. Implementation of combinational logic using MUX, BCD to
7 segment decoder.
SEQUENTIAL CIRCUITS: Flip flops SR, JK, T, D and Master slave, Excitation table,
Edge triggering, Level Triggering, Realization of one flip flop using other flip flops.
Asynchronous/Ripple counters, Synchronous counters, Modulo-n counter, Ring
Counters. Design of Synchronous counters: state diagram, Circuit implementation.
Shift registers.
Module 4:
MEMORY DEVICES: Classification of memories, RAM organization, Write operation,
Read operation, Memory cycle. ROM organization, PROM, EPROM, EEPROM,
Programmable logic array, Programmable array logic, complex Programmable logic
devices (CPLDS), Field Programmable Gate Array (FPGA).
A/D & D/A CONVERTORS : Analog & Digital signals. sample and hold circuit, A/D
and D/A conversion techniques (Weighted type, R-2R Ladder type, Counter Type,
Dual Slope type, Successive Approximation type).

COURSE OUTCOME:At the end of course the student will be able to:
1. Demonstrate the operation of simple digital gates, identify the symbols,
develop the truth table for those gates; combine simple gates into more complex
circuits; change binary, hexadecimal, octal numbers to their decimal equivalent
an vice versa.
2. Demonstrate the operation of a flip-flop. Design counters and clear the
concept of shift registers.
3. Study different types of memories and their applications.Convert digital signal
into analog and vice versa.

Suggested Readings/ Books:



Morris Mano, Digital Design, Prentice Hall of India Pvt. Ltd
Donald P.Leach and Albert Paul Malvino, Digital Principles and
Applications, 5 ed., Tata McGraw HillPublishing CompanyLimited, New
Delhi, 2003.
R.P.Jain, Modern Digital Electronics, 3 ed., Tata McGraw–Hill publishing
company limited, New Delhi, 2003.
Thomas L. Floyd, Digital Fundamentals, Pearson Education, Inc, New Delhi,
2003
Ronald J. Tocci, Neal S. Widmer, Gregory L. Moss, Digital System Principles and Applications, PearsonEducation.
Ghosal ,Digital Electronics, Cengage Learning.






Course Code:BTES302-18

Course Title: Digital Electronics Lab

List of Experiments:
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.

To verify the Truth-tables of all logic gates.
To realize and verify the Half & full adder circuits using logic gates.
To realize Half & full subtractor circuits using logic gates.
To realize Encoder and Decoder circuits
To realize Multiplexer circuits
To realize 4-bit binary-gray & gray-binary converters.
To realize comparator circuit for two binary numbers of 2-bit each.
To realize Full adder & full subtractor circuits using encoder.
To design Full adder & full subtractor circuits using multiplexer.
To design and verify the Truth tables of all flip-flops.
To design Mod-6/Mod-9 synchronous up-down counter.

Course Outcomes
At the end of this course student will demonstrate the ability to:
1.

Realize combinational circuits using logic gates.

2.

Realize sequential circuits using logic gates.

3.

Realize various types of Flip-flops and counters

0L:0T:2P

1Credits

Fourth
Semester

Course Code: BTES401-18 Course Title: Computer Organization & Architecture 3L:0T:0P
Pre-requisites: Digital Electronics
Detailed Contents:
Module 1:
Functional blocks of a computer
CPU, memory, input-output subsystems, control unit. Instruction set architecture of a CPU –
registers, instruction execution cycle, RTL interpretation of instructions, addressing modes,
instruction set. Case study – instruction set of 8085 processor.
Data representation: signed number representation, fixed and floating point representations,
character representation. Computer arithmetic – integer addition and subtraction, ripple carry
adder, carry look-ahead adder, etc. multiplication – shift-andadd, Booth multiplier, carry
save multiplier, etc. Division restoring and non-restoring techniques, floating point
arithmetic.
[10 hrs] (CO1, CO2)
Module 2:
Introduction to x86 architecture.
CPU control unit design: Hardwired and micro-programmed design approaches,
Case study – design of a simple hypothetical CPU.
Memory system design: semiconductor memory technologies, memory organization.
Peripheral devices and their characteristics: Input-output subsystems, I/O device interface,
I/O transfers – program controlled, interrupt driven and DMA, privileged and non-privileged
instructions, software interrupts and exceptions. Programs and processes –role of interrupts
in process state transitions, I/O device interfaces – SCII, USB.
[12 hrs] (CO2, CO4)
Module 3:
Pipelining
Basic concepts of pipelining, throughput and speedup, pipeline hazards.
Parallel Processors: Introduction to parallelprocessors, Concurrent access to memory and
cache coherency.
[10 hrs] (CO5)
Module 4:
Memory Organization
Memory interleaving, concept of hierarchical memory organization, cache memory, cache
size vs. block size, mapping functions, replacement algorithms, write policies.
[10 hrs] (CO3)
Course Outcomes:
The student will be able to:
1. Understand functional block diagram of microprocessor;
2. Apply instruction set for Writingassembly language programs;
3. Design a memory module and analyze its operation by interfacing with the CPU;
4. Classify hardwired and microprogrammed control units; &
5. Understand the concept of pipelining and its performance metrics.
Suggested Books:
1. “ComputerOrganization and Architecture”, Moris Mano,
2. “ComputerOrganization and Design: The Hardware/Software Interface”, 5th Edition
by David A. Patterson and John L. Hennessy, Elsevier.
3. “Computer Organization and Embedded Systems”, 6th Edition by CarlHamacher,
McGraw Hill Higher Education.

3Credits

Reference Books:
1. “Computer Architecture and Organization”, 3rd Edition by John P. Hayes,
WCB/McGraw-Hill
2. “Computer Organization and Architecture: Designing for Performance”, 10th
Edition by William Stallings, Pearson Education.
3. “Computer System Design and Architecture”, 2nd Edition by Vincent P. Heuring
and Harry F. Jordan, Pearson Education.

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Course Code: BTCS402-18 Course Title: Operating Systems

3L:0T:0P

3Credits

Detailed Contents:
Module 1:
Introduction
Concept of Operating Systems, Generations of Operating systems, Types of Operating
Systems, OS Services, System Calls, Structure of an OS - Layered, Monolithic, Microkernel
Operating Systems, Concept of Virtual Machine. Case study on UNIX and WINDOWS
Operating System.
[6 hrs] (CO1)
Module 2:
Processes
Definition, Process Relationship, Different states of a Process, Process State transitions,
Process Control Block (PCB), Context switching
Thread: Definition, Various states, Benefits of threads, Types of threads, Concept of
multithreads,
Process Scheduling: Foundation and Scheduling objectives, Types of Schedulers,
Scheduling criteria: CPU utilization, Throughput, Turnaround Time, Waiting Time, Response
Time; Scheduling algorithms: Pre-emptive and Non-pre-emptive, FCFS, SJF, RR;
Multiprocessor scheduling: Real Time scheduling: RM and EDF.
[10 hrs] (CO2, CO3)
Module 3:
Inter-process Communication
Critical Section, Race Conditions, Mutual Exclusion, Hardware Solution, StrictAlternation,
Peterson’s Solution, TheProducer\ConsumerProblem, Semaphores,EventCounters,Monitors,
Message
Passing, Classical IPC Problems: Reader’s & Writer Problem, Dinning
Philosopher Problem etc.
[8 hrs] (CO2)
Module 4:
Deadlocks
Definition, Necessary and sufficient conditions for Deadlock, Deadlock Prevention, Deadlock
Avoidance: Banker’s algorithm, Deadlock detection and Recovery.
[8 hrs] (CO3)
Module 5:
MemoryManagement
Basicconcept,LogicalandPhysical address map, Memory allocation: Contiguous Memory
allocation –Fixedandvariable partition–Internaland External fragmentation and Compaction;
Paging: Principle of operation – Page allocation–Hardware support for paging, Protection and
sharing, Disadvantages of paging.
Virtual Memory: Basics of Virtual Memory – Hardware and control structures – Locality of

reference, Page fault, Working Set, Dirty page/Dirty bit – Demand paging, Page Replacement
algorithms: Optimal, First in First Out (FIFO), Second Chance (SC), Not recently used
(NRU) and Least Recently used (LRU).
[10 hrs] (CO4)
Module 6:
I/O Hardware
I/O devices, Device controllers, Direct memory access Principles of I/O Software: Goals of
Interrupt handlers, Device drivers, Device independent I/O software, Secondary-Storage
Structure: Disk structure, Disk scheduling algorithms
File Management: Concept of File, Access methods, File types, File operation, Directory
structure, File
System
structure, Allocationmethods (contiguous, linked, indexed), Free
Space Management (bit vector, linked list, grouping), directory implementation (linear list,
hash table), efficiency and performance.
Disk Management: Disk structure, Disk scheduling - FCFS, SSTF, SCAN, C-SCAN, Disk
reliability, Disk formatting, Boot-block, Bad blocks.
[8 hrs] (CO5, CO6)
Course Outcomes:
The student will be able to:
1. Explain basic operating system concepts such as overall architecture, system calls,
user mode and kernel mode;
2. Distinguish concepts related to processes, threads, process scheduling, race conditions
and critical sections;
3. Analyze and apply CPU scheduling algorithms, deadlock detection and prevention
algorithms;
4. Examine and categorize various memory management techniques like caching,
paging, segmentation, virtual memory, and thrashing;
5. Design and implement file management system; &
6. Appraise high-level operating systems concepts such as file systems, disk-scheduling
algorithms and various file systems.
Suggested Books:
1. Operating System Concepts Essentials, 9th Edition by AviSilberschatz, Peter Galvin, Greg
Gagne, Wiley Asia Student Edition.
2. Operating Systems: Internals and Design Principles, 5th Edition, William Stallings,
Prentice Hall of India.
Reference Books:
1. Operating System: A Design-oriented Approach, 1st Edition by Charles Crowley, Irwin
Publishing
2. Operating Systems: A Modern Perspective, 2nd Edition by Gary J. Nutt, Addison-Wesley
3. Design of the Unix Operating Systems, 8th Edition by Maurice Bach, Prentice-Hall of
India
4. Understanding the Linux Kernel, 3rd Edition, Daniel P. Bovet, Marco Cesati, O'Reilly and
Associates
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Course Code: BTCS403-18 Course Title: Design and Analysis of Algorithms

3L:0T:0P

3Credits

Pre-requisites: Data Structures
Detailed Contents:
Module 1:
Introduction
Characteristics of algorithm. Analysis of algorithm: Asymptotic analysis of complexity
bounds – best, average and worst-case behavior; Performance measurements of Algorithm,
Time and space trade-offs, Analysis of recursive algorithms through recurrence relations:
Substitution method, Recursion tree method and Masters’ theorem.
[8 hrs] (CO1)
Module 2:
Fundamental Algorithmic Strategies
Brute-Force, Greedy,Dynamic Programming, Branch- and-Bound and Backtracking
methodologies for the design of algorithms; Illustrations of these techniques for
Problem-Solving: Bin Packing, Knap Sack, TSP.
[10 hrs] (CO1, CO2)
Module 3:
Graph and Tree Algorithms
Traversal algorithms: Depth First Search (DFS) and Breadth First Search (BFS); Shortest
path algorithms, Transitive closure, Minimum Spanning Tree, Topological sorting, Network
Flow Algorithm.
[10 hrs] (CO3)
Module 4:
Tractable and Intractable Problems
Computability of Algorithms, Computability classes – P, NP, NP-complete and NP-hard.
Cook’s theorem, Standard NP-complete problems and Reduction techniques.
[8 hrs] (CO5)
Module 5:
Advanced Topics
Approximation algorithms, Randomized algorithms, Heuristics and their characteristics.
[6 hrs] (CO1, CO4, CO5)
Course Outcomes:
The student will be able to:
1. For a given algorithms analyze worst-case running times of algorithms based on
asymptotic analysis and justify the correctness of algorithms;
2. Explain when an algorithmic design situation calls for which design paradigm
(greedy/ divide and conquer/backtrack etc.);
3. Explain model for a given engineering problem, using tree or graph, and writethe
corresponding algorithm to solve the problems;
4. Demonstrate the ways to analyze approximation/randomized algorithms (expected
running time, probability of error); &
5. Examine the necessity for NP class based problems and explain the use of heuristic
techniques.
Suggested Books:
1. Introduction to Algorithms, 4TH Edition, Thomas H Cormen, Charles E Lieserson, Ronald
L Rivest and Clifford Stein, MIT Press/McGraw-Hill.
2. Data Structures and Algorithms in C++, Weiss, 4th edition, Pearson.
3. Fundamentals of Computer Algorithms – E. Horowitz, Sartaj Saini, Galgota Publications.

Reference Books
1. Algorithm Design, 1stEdition, Jon Kleinberg and ÉvaTardos, Pearson.
2. Algorithm Design: Foundations, Analysis, and Internet Examples, Second Edition,
Michael T Goodrich and Roberto Tamassia, Wiley.
3. Algorithms -- A Creative Approach, 3RD Edition, UdiManber, Addison-Wesley, Reading,
MA.

------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Course Code: BTES402-18 Course Title: Computer Organization & ArchitectureLab

0L:0T:2P

1Credits

List of Experiment:
Task 1: Computer Anatomy- Memory, Ports, Motherboard and add-on cards.
Task 2: Dismantling and assembling PC.
Task 3: Introduction to 8085 kit.
Task 4: 2. Addition of two 8 bit numbers, sum 8 bit.
Task 5: Subtraction of two 8 bit numbers.
Task 6: Find 1’s complement of 8-bit number.
Task 7: Find 2’s complement of 8-bit number.
Task 8: Shift an 8-bit no. by one bit.
Task 9: Find Largest of two 8 bit numbers.
Task 10: Find Largest among an array of ten numbers (8 bit).
Task 11: Sum of series of 8 bit numbers.
Task 12: Introduction to 8086 kit.
Task 13: Addition and subtraction of two 16 bit numbers, sum 16 bit.
Task 14: Implement of Booth’s algorithm for arithmetic operations.
Task 15: Find 1’s and 2’s complement of 16-bit number.
Task 16: Implement simple programs using I/O based interface.
Lab Outcomes:
The student will be able to:
1. Assemble personal computer;
2. Implement the various assembly language programs for basic arithmetic and
logical operations; &
3. Demonstrate the functioning of microprocessor/microcontroller based
systems with I/O interface.
Reference Books:
1. Fundamentals of Microprocessors and Microcontrollersby B. Ram, Dhanpat Rai
Publications.
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Course Code: BTCS404-18 Course Title: Operating Systems Lab

0L:0T:4P

2Credits

List of Experiment:
Task 1: Installation Process of various operating systems.
Task 2: Implementation of CPU scheduling algorithms to find turnaround time and
waiting time. a) FCFS b) SJF c) Round Robin (pre-emptive) d) Priority.
Task 3: Virtualization, Installation of Virtual Machine Software and installation of
Operating System on Virtual Machine.
Task 4: Commands for files & directories: cd, ls, cp, md, rm, mkdir, rmdir. Creating
and viewing files using cat. File comparisons. Disk related commands:
checking disk free spaces. Processes in linux, connecting processes with pipes,
background processing, managing multiple processes. Background process:
changing process priority, scheduling of processes at command, batch
commands, kill, ps, who, sleep. Printing commands, grep, fgrep, find, sort, cal,
banner, touch, file. File related commands ws, sat, cut, grep.
Task 5:

Shell Programming: Basic of shell programming, various types of shell, Shell
Programming in bash, conditional & looping statement, case statements,
parameter passing and arguments, shell variables, shell keywords, creating
shell programs for automate system tasks, report printing.

Task 6: Implementation of Bankers algorithm for the purpose of deadlock avoidance.

Lab Outcomes:
The student will be able to:
1. Understand and implement basic services and functionalities of the operating system;
2. Analyze and simulate CPU Scheduling Algorithms like FCFS, Round Robin, SJF, and
Priority;
3. Implement commands for files and directories;
4. Understand and implement the concepts of shell programming;
5. Simulate file allocation and organization techniques; &
6. Understand the concepts of deadlock in operating systems and implement them in
multiprogramming system.
Reference Books:
1. Operating Systems: Design and Implementation, Albert S. Woodhull and Andrew S.
Tanenbaum, Pearson Education.

------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Course Code: BTCS405-18 Course Title: Design and Analysis of Algorithms Lab

0L:0T:4P

List of Experiment:
Task 1:
Code and analyze solutions to following problem with given strategies:
i. Knap Sack using greedy approach
ii. Knap Sack using dynamic approach
Task 2:

Code and analyze to find an optimal solution to matrix chain multiplication
using dynamic programming.

Task 3:

Code and analyze to find an optimal solution to TSP using dynamic
programming.

Task 4:

Implementing an application of DFS such as:
i. to find the topological sort of a directed acyclic graph
ii. to find a path from source to goal in a maze.

Task 5:

Implement an application of BFS such as:
i. to find connected components of an undirected graph
ii. to check whether a given graph is bipartite.

Task 6:

Code and analyze to find shortest paths in a graph with positive edge weights
using Dijkstra’s algorithm.

Task 7:

Code and analyze to find shortest paths in a graph with arbitrary edge
weights using Bellman-Ford algorithm.

Task 8:

Code and analyze to find shortest paths in a graph with arbitrary edge
weights using Flyods’ algorithm.

Task 9:

Code and analyze to find the minimum spanning tree in a weighted,
undirected graph using Prims’ algorithm

Task 10:

Code and analyze to find the minimum spanning tree in a weighted,
undirected graph using Kruskals’ algorithm.

Task 11:

Coding any real world problem or TSP algorithm using any heuristic
technique.

Lab Outcomes:
The student will be able to:
1. Improve practical skills in designing and implementing complex problems with
different techniques;
2. Understand comparative performance of strategies and hence choose appropriate, to
apply to specific problem definition;
3. Implement Various tree and graph based algorithms and become familiar with their
design methods; &
4. Design and Implement heuristics for real world problems.
Reference Books
1. Data Structures and Algorithms in C++, Weiss, 4th edition, Pearson
2. Data Structures and Algorithms using Python and C++, David M. Reed and John
Zelle, 2009 edition (available as e book), Franklin Beedle& Associates.

UNIVERSAL HUMAN VALUES 2: UNDERSTANDING HARMONY

2Credit

Course code: HSMC122-18
Credits: 3
COURSE TOPICS:
The course has 28 lectures and 14 practice sessions in 5 modules:
Module 1: Course Introduction - Need, Basic Guidelines, Content and Process
for Value Education
1. Purpose and motivation for the course, recapitulation from Universal Human
Values-I
2. Self-Exploration–what is it? - Its content and process; ‘Natural Acceptance’ and
ExperientialValidation- as the process for self-exploration.
3. Continuous Happiness and Prosperity- A look at basic Human Aspirations
4. Right understanding, Relationship and Physical Facility- the basic requirements for
fulfilment of aspirations of every human being with their correct priority
5. Understanding Happiness and Prosperity correctly- A critical appraisal of the
current scenario.
6. Method to fulfil the above human aspirations: understanding and living in harmony
at various levels.
Include practice sessions to discuss natural acceptance in human being as the innate
acceptance for living with responsibility (living in relationship, harmony and coexistence) rather than as arbitrariness in choice based on liking-disliking.
Module 2: Understanding Harmony in the Human Being - Harmony in Myself!
7. Understanding human being as a co-existence of the sentient ‘I’ and the material
‘Body’
8. Understanding the needs of Self (‘I’) and ‘Body’ - happiness and physical facility
9. Understanding the Body as an instrument of ‘I’ (I being the doer, seer and enjoyer)
10. Understanding the characteristics and activities of ‘I’ and harmony in ‘I’
11. Understanding the harmony of I with the Body: Sanyam and Health; correct
appraisal of Physical needs, meaning of Prosperity in detail
12. Programs to ensureSanyam and Health.
Include practice sessions to discuss the role others have played in making material
goods available to me. Identifying from one’s own life. Differentiate between
prosperity and accumulation. Discuss program for ensuring health vs dealing with
disease.
Module 3: Understanding Harmony in the Family and Society- Harmony in
Human-Human Relationship
13. Understanding values in human-human relationship; meaning of Justice (nine
universal values in relationships) and program for its fulfilment to ensure mutual
happiness; Trust and Respect as the foundational values of relationship.
14. Understanding the meaning of Trust; Difference between intention and
competence
15. Understanding the meaning of Respect, Difference between respect and
differentiation; the other salient values in relationship.
16. Understanding the harmony in the society (society being an extension of family):
Resolution, Prosperity, fearlessness (trust) and co-existence as comprehensive Human
Goals.
17. Visualizing a universal harmonious order in society- Undivided Society,

Universal Order- from family to world family.
Include practice sessions to reflect on relationships in family, hostel and institute as
extended family, real life examples, teacher-student relationship, goal of education
etc. Gratitude as a universal value in relationships. Discuss with scenarios. Elicit
examples from students’ lives.
Module 4: Understanding Harmony in the Nature and Existence - Whole
existence as Coexistence
18. Understanding the harmony in the Nature
19. Interconnectedness and mutual fulfilment among the four orders of nature recyclability and self-regulation in nature
20. Understanding Existence as Co-existence of mutually interacting units in allpervasive space
21. Holistic perception of harmony at all levels of existence.
Include practice sessions to discuss human being as cause of imbalance in nature (film
“Home” can be used), pollution, depletion of resources and role of technology etc.
Module 5: Implications of the above Holistic Understanding of Harmony on
Professional Ethics
22. Natural acceptance of human values
23. Definitiveness of Ethical Human Conduct
24. Basis for Humanistic Education, Humanistic Constitution and Humanistic
Universal Order
25. Competence in professional ethics: a. Ability to utilize the professional
competence for augmenting universal human order b. Ability to identify the scope
and characteristics of peoplefriendly and eco -friendly production systems, c.
Ability to identify and develop appropriatetechnologies and management patterns
for above production systems.
26. Case studies of typical holistic technologies, management models and production
systems.
27. Strategy for transition from the present state to Universal Human Order: a.
At the level of individual: as socially and ecologically responsible engineers,
technologists and managers b. At the level of society: as mutually enriching
institutions and organizations.
28. Sum up.
Include practice Exercises and Case Studies will be taken up in Practice (tutorial)
Sessions eg. to discuss the conduct as an engineer or scientist etc.
3. READINGS:
3.1 Text Book
1. Human Values and Professional Ethics by R R Gaur, R Sangal, G P Bagaria, Excel
Books, New Delhi, 2010.
3.2 Reference Books
1. Jeevan Vidya: EkParichaya, A. Nagaraj, Jeevan VidyaPrakashan, Amarkantak,
1999.
2. Human Values, A.N. Tripathi, New Age Intl. Publishers, New Delhi, 2004.
3. The Story of Stuff (Book).
4. The Story of My Experiments with Truth - by Mohandas Karamchand Gandhi
5. Small is Beautiful - E. F Schumacher.

6. Slow is Beautiful - Cecile Andrews
7. Economy of Permanence - J CKumarappa
8. Bharat Mein Angreji Raj -PanditSunderlal
9. Rediscovering India - by Dharampal
10. Hind Swaraj or Indian Home Rule - by Mohandas K. Gandhi
11. India Wins Freedom - Maulana Abdul Kalam Azad
12. Vivekananda - Romain Rolland (English)
13. Gandhi - Romain Rolland (English)
OUTCOME OF THE COURSE:
By the end of the course, students are expected to become more aware of
themselves, and their surroundings (family, society, nature); they would become
more responsible in life, and in handling problems with sustainable solutions, while
keeping human relationships and human nature in mind. They would have better critical
ability. They would also become sensitive to their commitment towards what they have
understood (human values, human relationship and human society). It is hoped that they
would be able to apply what they have learnt to their own self in different day-to-day
settings in real life, at least a beginning would be made in this direction.
This is only an introductory foundational input. It would be desirable to follow it up
by
a) Faculty -student or mentor-mentee programs throughout their time with the
institution.
b) Higher level courses on human values in every aspect of living. E.g. as a
professional.
Course Code: EVS101-18

Course Title: Environmental Studies-

L:2; T:0;
P:0

0Credits

.Detailed Contents
Module 1 : Natural Resources :Renewable and non-renewable resources
Natural resources and associated problems.
a)
Forest resources : Use and over-exploitation, deforestation, case studies. Timber
extraction, mining, dams and their effects on forest and tribal people.
b)
Water resources : Use and over-utilization of surface and ground water, floods,
drought, conflicts over water, dams-benefits and problems.
c)
Mineral resources : Use and exploitation, environmental effects of extracting and
using mineral resources, case studies.
d)
Food resources : World food problems, changes caused by agriculture and
overgrazing, effects of modern agriculture, fertilizer-pesticide problems, water logging,
salinity, case studies.
e)
Energy resources : Growing energy needs, renewable and non renewable energy
sources, use of alternate energy sources. Case studies.
f)
Land resources : Land as a resource, land degradation, man induced landslides, soil
erosion and desertification.
•
Role of an individual in conservation of natural resources.
•
Equitable use of resoureces for sustainable lifestyles.
•
Module 2 : Ecosystems
Concept of an ecosystem.
Structure and function of an ecosystem.
Food chains, food webs and ecological pyramids. Introduction, types, characteristic features,

structure and function of following ecosystems:
a.
Forest ecosystem
b.
Aquatic ecosystems (ponds, streams, lakes, rivers, oceans, estuaries)
Module 3 : Biodiversity and its conservation
•
Introduction – Definition : genetic, species and ecosystem diversity.
•
Biodiversity at global, National and local levels.
•
Inida as a mega-diversity nation
•
Hot-sports of biodiversity.
•
Threats to biodiversity : habitat loss, poaching of wildlife, man-wildlife conflicts.
•
Endangered and endemic species of India
•
Module 4 : Social Issues and the Environment
•
From Unsustainable to Sustainable development
•
Resettlement and rahabilitation of people; its problems and concerns.
•
Environmental ethics : Issues and possible solutions.
•
Climate change, global warming, acid rain, ozone layer depletion, Nuclear accidents
and holocaust. Case Studies.
•
Public awareness.
*ACTIVITIES
Nature club (bird watching, recognizing plants at institute/at home, recognizing local
animals, appreciating biodiversity
Impart knowledge and inculcate the habit of taking interest and understanding biodiversity in
and around the college campus. The students should be encouraged to take interest in bird
watching, recognizing local plants, herbs and local animals. The students should be
encouraged to appreciate the difference in the local biodiversity in their hometown, in the
place of their study and other places they visit for vacation/breaks etc.
Following activities must be included.
Identify a tree fruit flower peculiar to a place or having origin from the place.
Making high resolution big photographs of small creatures (bees, spiders, ants. mosquitos
etc.) especially part of body so that people can recognize (games on recognizing
animals/plants).
Videography/ photography/ information collections on specialties/unique features of different
types of common creatures.
Search and explore patents and rights related to animals, trees etc. Studying miracles of
mechanisms of different body systems.
1(A) Awareness Activities:
a) Small group meetings about water management, promotion of recycle use, generation
of less waste, avoiding electricity waste
b) Slogan making event
c) Poster making event
d) Cycle rally
e) Lectures from experts
f) Plantation
g) Gifting a tree to see its full growth
h) Cleanliness drive
i) Drive for segregation of waste
i) To live with some eminent environmentalist for a week or so to understand his work
vi) To work in kitchen garden for mess
j) To know about the different varieties of plants
k) Shutting down the fans and ACs of the campus for an hour or so
l) Visit to a local area to document environmental assets
river/forest/grassland/hill/mountain/lake/Estuary/Wetlands

m) Visit to a local polluted site-Urban/Rural/Industrial/Agricultural
n) Visit to a Wildlife sanctuary, National Park or Biosphere Reserve
Suggested Readings
1. Agarwal, K.C. 2001 Environmental Biology, Nidi Publ. Ltd. Bikaner.
2. BharuchaErach, The Biodiversity of India, Mapin Publishing Pvt. Ltd., Ahmedabad –
380 013, India, Email:mapin@icenet.net (R)
3. Brunner R.C., 1989, Hazardous Waste Incineration, McGraw Hill Inc. 480p
4. Clark R.S., Marine Pollution, Clanderson Press Oxford (TB)
5. Cunningham, W.P. Cooper, T.H. Gorhani, E & Hepworth, M.T. 2001, Environmental
Encyclopedia, Jaico Publ. House, Mumabai, 1196p
6. Hawkins R.E., Encyclopedia of Indian Natural History, Bombay Natural History
Society, Bombay (R)
7. Heywood, V.H &Waston, R.T. 1995. Global Biodiversity Assessment. Cambridge
Univ. Press 1140p.
8. Mhaskar A.K., Matter Hazardous, Techno-Science Publication (TB)
9. Miller T.G. Jr. Environmental Science, Wadsworth Publishing Co. (TB)
10. Odum, E.P. 1971. Fundamentals of Ecology. W.B. Saunders Co. USA, 574p
11. Townsend C., Harper J, and Michael Begon, Essentials of Ecology, Blackwell
Science (TB)
12. Trivedi R.K., Handbook of Environmental Laws, Rules Guidelines, Compliances and
Stadards, Vol I and II, Enviro Media (R)
13. Trivedi R. K. and P.K. Goel, Introduction to air pollution, Techno-Science
Publication (TB)
14. Wanger K.D., 1998 Environmental Management. W.B. Saunders Co. Philadelphia,
USA 499p

Course Code: HSMC101- Course Title: Development of Societies
18

3L:0T:0P

3Credits

Detailed Contents:
Unit I: Social Development
(5 hours)
1. Concepts behind the origin of Family, Clan and Society
2. Different Social Systems
3. Relation between Human being and Society
4. Comparative studies on different models of Social Structures and their evolution
Unit II: Political Development
(3 hours)
1. Ideas of Political Systems as learnt from History
2. Different models of Governing system and their comparative study
Unit III: Economic Development
(18 hours)
1. Birth of Capitalism, Socialism, Marxism
2. Concept of development in pre-British, British and post British period- Barter, Jajmani
3. Idea of development in current context.
4. E. F. Schumacher's idea of development, Buddhist economics.
Gandhian idea of development. Swaraj and Decentralization.
PROJECT: Possible projects in this course could be
a) Interact with local communities and understand their issues.
b) Study local cottage industry and agricultural practices. Role of engineering and
specialized knowledge.
c) Evaluation of technology in the context of its application. Social impact of technology.
Environmental impact of technology. Evaluation from a holistic perspective.

Course Code: HSMC102- Course Title: PHILOSOPHY
18

3L:0T:0P

3Credits

Detailed Contents:
Unit 1:
The difference between knowledge (Vidya) and Ignorance (Avidya):
a. Upanishads;
b. Six systems orthodox and Heterodox Schools of Indian Philosophy.
c. Greek Philosophy:
Unit 2:
Origin of the Universe:
 NasidiyaSukta: "Who really knows?”
 Brhadaranyaka Upanishad; Chandogya Upanishad: Non-self, Self, real and unreal.
 Taittiriya Upanishad: SikshaValli.
 Plato’s Symposium: Lack as the source of desire and knowledge.
 Socratic’s method of knowledge as discovery.
 Language: Word as root of knowledge (Bhartrahari’sVakyapadiyam)
 Fourteen Knowledge basis as a sources of Vidya: Four Vedas; Six auxiliary sciences
(Vedangas); Purana, Nyaya, Mimamsa and Dharma Sastras.
Unit 3:
Knowledge as Power: Francis Bacon. Knowledge as both power and self-realization in
Bagavad Gita.
Unit 4:
Knowledge as oppression: M. Foucault. Discrimination between Rtam and Satyam in Indian
Philosophy.
Unit 5:
Knowledge as invention: Modern definition of creativity; scientific activity in the claim that
science invents new things at least through technology.
Unit 6:
Knowledge about the self, transcendental self; knowledge about society, polity and nature.
Unit 7:
Knowledge about moral and ethics codes.
Unit 8:
Tools of acquiring knowledge: Tantrayuktis, a system of inquiry (Caraka, Sushruta, Kautilya,
Vyasa)
READINGS
1. Copleston, Frederick, History of Philosophy, Vol. 1.Great Britain: Continuum.
2 Hiriyanna, M. Outlines of Indian Philosophy, MotilalBanarsidass Publishers; Fifth Reprint
edition (2009)
3 Sathaye, Avinash, Translation of NasadiyaSukta
4. Ralph T. H. Griffith. The Hymns of the Ŗgveda. MotilalBanarsidass: Delhi: 1973.
5. Raju, P. T. Structural Depths of Indian Thought, Albany: State University of New York
Press.
6. Plato, Symposium, Hamilton Press.
7. KautilyaArtha Sastra. Penguin Books, New Delhi.
8. Bacon, Nova Orgum
9. Arnold, Edwin. The Song Celestial.
10. Foucault, Knowledge/Power.
11. Wildon, Anthony, System of Structure.
12. Lele, W.K. The Doctrine of Tantrayukti. Varanasi: Chowkamba Series.
13. Dasgupta, S. N. History of Indian Philosophy, MotilalBanasidas, Delhi.

14. Passmore, John, Hundred Years of Philosophy, Penguin.
ASSESSMENT (indicative only):
Ask students to do term papers, for example, writing biographical details of founders,
sustainers, transmitters, modifiers, rewriters; translating monographs of less known
philosophers such as K. C. Bhattacharys, Daya Krishna, Gopinath Bhattacharya; comparative
study of philosophical system such as MadhyasthaDarshan.
OUTCOME OF THE COURSE:
Students will develop strong natural familiarity with humanities along with right
understanding enabling them to eliminate conflict and strife in the individual and society.
Students shall be able to relate philosophy to literature, culture, society and lived experience
can be considered.
BTCS401-18

Discrete Mathematics

3L:1T:0P

4 Credits

Detailed contents:
Module 1:
Sets, Relation and Function: Operations and Laws of Sets, Cartesian Products, Binary Relation,
Partial Ordering Relation, Equivalence Relation, Image of a Set, Sum and Product of Functions,
Bijective functions, Inverse and Composite Function, Size of a Set, Finite and infinite Sets,
Countable and uncountable Sets, Cantor's diagonal argument and The Power Set theorem,
Schroeder-Bernstein theorem.
Principles of Mathematical Induction: The Well-Ordering Principle, Recursive definition,
The Division algorithm: Prime Numbers, The Greatest Common Divisor: Euclidean Algorithm,
The Fundamental Theorem of Arithmetic.
CO1, CO2
Module 2:
Basic counting techniques-inclusion and exclusion, pigeon-hole principle, permutation and
combination.
CO3
Module 3:
Propositional Logic: Syntax, Semantics, Validity and Satisfiability, Basic Connectives and
Truth Tables, Logical Equivalence: The Laws of Logic, Logical Implication, Rules of Inference,
The use of Quantifiers. Proof Techniques: Some Terminology, Proof Methods and Strategies,
Forward Proof, Proof by Contradiction, Proof by Contraposition, Proof of Necessity and
Sufficiency.
CO3, CO4
Module 4:
Algebraic Structures and Morphism: Algebraic Structures with one Binary Operation, Semi
Groups, Monoids, Groups, Congruence Relation and Quotient Structures, Free and Cyclic
Monoids and Groups, Permutation Groups, Substructures, Normal Subgroups, Algebraic
Structures with two Binary Operation, Rings, Integral Domain and Fields. Boolean Algebra
and Boolean Ring, Identities of Boolean Algebra, Duality, Representation of Boolean Function,
Disjunctive and Conjunctive Normal Form
CO4
Module 5:
Graphs and Trees: Graphs and their properties, Degree, Connectivity, Path, Cycle, Sub
Graph, Isomorphism, Eulerian and Hamiltonian Walks, Graph Colouring, Colouring maps and
Planar Graphs, Colouring Vertices, Colouring Edges, List Colouring, Perfect Graph, definition
properties and Example, rooted trees, trees and sorting, weighted trees and prefix codes, Biconnected component and Articulation Points, Shortest distances. CO5
Suggested books:
1. Kenneth H. Rosen, Discrete Mathematics and its Applications, Tata McGraw – Hill

2. Susanna S. Epp, Discrete Mathematics with Applications, 4th edition, Wadsworth
Publishing Co. Inc.
3. C L Liu and D P Mohapatra, Elements of Discrete Mathematics A Computer Oriented
Approach, 3rd Edition by, Tata McGraw – Hill.
Suggested reference books:
1. J.P. Tremblay and R. Manohar, Discrete Mathematical Structure and Its Application to
Computer Science”, TMG Edition, TataMcgraw-Hill
2. Norman L. Biggs, Discrete Mathematics, 2nd Edition, Oxford University Press.
Schaum’s Outlines Series, Seymour Lipschutz, Marc Lipson,
3. Discrete Mathematics, Tata McGraw - Hill
Course Outcomes
1. To be able to express logical sentence in terms of predicates, quantifiers, and logical
connectives
2. To derive the solution for a given problem using deductive logic and prove the solution
based on logical inference
3. For a given a mathematical problem, classify its algebraic structure
4. To evaluate Boolean functions and simplify expressions using the properties of Boolean
algebra
5. To develop the given problem as graph networks and solve with techniques of graph
theory.
----------------------------------------------------------------------------------------------------------------------

Fifth
Semester

BTES501-18

Enterprise Resource Planning

3L:0T:0P

3 Credits

Course Details:
UNIT 1 INTRODUCTION
ERP: An Overview, Enterprise – An Overview, Benefits of ERP, ERP and Related Technologies,
Business Process Reengineering (BPR), Data Warehousing, Data Mining, OLAP, SCM [9hrs., CO1]
UNIT II ERP IMPLEMENTATION
ERP Implementation Lifecycle, Implementation Methodology, Hidden Costs, Organizing the
Implementation, Vendors, Consultants and Users, Contracts with Vendors, Consultants and Employees,
Project Management and Monitoring
[9hrs., CO2]
UNIT III THE BUSINESS MODULES
Business modules in an ERP Package, Finance, Manufacturing, Human Resources, Plant Maintenance,
Materials Management, Quality Management, Sales and Distribution
[9hrs., CO3]
UNIT IV THE ERP MARKET
ERP Market Place, SAP AG, Peoplesoft, Baan, JD Edwards, Oracle, QAD, SSA [9hrs., CO4]
UNIT V ERP – PRESENT AND FUTURE
Turbo Charge the ERP System, EIA, ERP and e-Commerce, ERP and Internet, Future Directions
[6hrs., CO1]
TEXT BOOK
1. Alexis Leon, “ERP Demystified”, Tata McGraw Hill, New Delhi, 2000
REFERENCES
1. Joseph A Brady, Ellen F Monk, Bret Wagner, “Concepts in Enterprise Resource Planning”,
Thompson Course Technology, USA, 2001.
2. Vinod Kumar Garg and Venkitakrishnan N K, “Enterprise Resource Planning – Concepts and
Practice”, PHI, New Delhi, 2003
Course outcomes: The students at the end will be able;
CO1: To know the basics of ERP
CO2: To understand the key implementation issues of ERP
CO3: To know the business modules of ERP
CO4: To be aware of some popular products in the area of ERP

Course Code: BTCS501-18 Course Title: Database Management Systems

3L:0T:0P 3Credits

Detailed Contents:
Module 1:

Database system architecture

Data Abstraction, Data Independence, Data Definition Language (DDL), Data Manipulation
Language (DML). Data models: Entity-relationship model, network model, relational and object
oriented Data models, integrity constraints, data manipulation operations.
[7hrs] (CO1,2)
Module 2:

Relational query languages

Relational algebra, Tuple and domain relational calculus, SQL3, DDL and DML constructs,
Open source and Commercial DBMS - MYSQL, ORACLE, DB2, SQL server. Relational
database design: Domain and data dependency, Armstrong's axioms, Normal forms,
Dependency preservation, Lossless design. Query processing and optimization: Evaluation of
relational algebra expressions, Query equivalence, Join strategies, Query optimization
algorithms.
[10hrs] (CO2,4)
Module 3:
Storage strategies, Indices, B-trees, hashing.
Module 4:
Transaction processing

[3hrs] (CO3)

Concurrency control, ACID property, Serializability of scheduling, Locking and
timestamp based schedulers, Multi-version and optimistic Concurrency Control schemes,
Database recovery.
[6hrs] (CO3)
Module 5:

Database Security

Authentication, Authorization and access control, DAC, MAC and RBAC models, Intrusion
detection, SQL injection.
[8hrs] (CO 4,5)
Module 6:
Advanced Topics
Object oriented and object relational databases, Logical databases, Web databases, Distributed
databases.
[8hrs] (CO 5)
Course Outcomes:
At the end of study the student shall be able to:
CO1: write relational algebra expressions for a query and optimize the Developed expressions
CO2: design the databases using ER method and normalization.
CO3: construct the SQL queries for Open source and Commercial DBMS-MYSQL, ORACLE,
and DB2.
CO4: determine the transaction atomicity, consistency, isolation, and durability.
CO5: Implement the isolation property, including locking, time stamping based on
concurrency control and Serializability of scheduling.

Text Books:

1. “Database System Concepts”, 6th Edition by Abraham Silberschatz, Henry F. Korth, S.
Sudarshan, McGraw-Hill.
Reference Books:
1. “Principles of Database and Knowledge–Base Systems”, Vol1 by J. D. Ullman,
Computer Science Press.
2. “Fundamentals of Database Systems”, 5th Edition by R. Elmasri and S. Navathe, Pearson
Education.
3. “Foundations of Databases”, Reprint by Serge Abiteboul, Richard Hull, Victor Vianu,
Addison-Wesley.
----------------------------------------------------------------------------------------------------------------

Course Code: BTCS502-18 Course Title: Formal Language &
Automata Theory

3L:1T:0P 3Credits 42 Hours

Detailed Contents
Module 1:
Introduction
Alphabet, languages and grammars, productions and derivation, Chomsky hierarchy of
languages.
[3hrs] (CO1 )
Module 2:
Regular languages and finite automata:
Regular expressions and languages, deterministic finite automata (DFA) and equivalence with
regular expressions, nondeterministic finite automata (NFA) and equivalence with DFA, regular
grammars and equivalence with finite automata, properties of regular languages, pumping lemma
for regular languages, minimization of finite automata.
[8hrs] (CO2 )
Module 3:
Context-free languages and pushdown automata
Context-free grammars (CFG) and languages (CFL), Chomsky and Greibach normal forms,
nondeterministic pushdown automata (PDA) and equivalence with CFG, parse trees, ambiguity
in CFG, pumping lemma for context-free languages, deterministic pushdown automata, closure
properties of CFLs.
[8hrs] (CO3 )
Module 4:
Context-sensitive languages
Context-sensitive grammars (CSG) and languages, linear bounded automata and equivalence
with CSG.
[5hrs] (CO4 )
Module 5:
Turing machines
The basic model for Turing machines (TM), Turing recognizable (recursively enumerable) and
Turing-decidable (recursive) languages and their closure properties, variants of Turing machines,
nondeterministic TMs and equivalence with deterministic TMs, unrestricted grammars and
equivalence with Turing machines, TMs as enumerators. [8hrs] (CO 5 )
Module 6:
Undecidability & Intractablity:
Church-Turing thesis, universal Turing machine, the universal and diagonalization languages,
reduction between languages and Rice s theorem, undecidable problems about languages.
Intractablity: Notion of tractability/feasibility. The classes NP and co-NP, their importance.
Polynomial time many-one reduction. Completeness under this reduction. Cook-Levin theorem:
NP-completeness of propositional satisfiability, other variants of satisfiability. NP-complete
problems from other domains: graphs (clique, vertex cover, independent sets, Hamiltonian
cycle), number problem (partition), set cover
[12hrs] (CO5 )

Course Outcomes: The student will be able to:
CO1: Write a formal notation for strings, languages and machines.
CO2: Design finite automata to accept a set of strings of a language.
CO3: Design context free grammars to generate strings of context free language .
CO4: Determine equivalence of languages accepted by Push Down Automata and languages
generated by context free grammars
CO5: Distinguish between computability and non-computability and Decidability and
undecidability.
Text Books:
1. John E. Hopcroft, Rajeev Motwani and Jeffrey D. Ullman, Introduction to Automata
Theory, Languages, and Computation, Pearson Education Asia.
Reference Books:
1. Harry R. Lewis and Christos H. Papadimitriou, Elements of the Theory of Computation,
Pearson Education Asia.
2. Dexter C. Kozen, Automata and Computability, Undergraduate Texts in Computer
Science, Springer.
3. Michael Sipser, Introduction to the Theory of Computation, PWS Publishing.
4. John Martin, Introduction to Languages and The Theory of Computation, Tata McGraw
Hill.
----------------------------------------------------------------------------------------------------------------

Course Code: BTCS503-18 Course Title: Software Engineering 3L:1T:0P

3Credits

42 Hours

Detailed Contents:
Module 1:
Evolution and impact of Software engineering, software life cycle models: Waterfall,
prototyping, Evolutionary, and Spiral models. Feasibility study, Functional and Non-functional
requirements, Requirements gathering, Requirements analysis and specification.
[10hrs] (CO 1)
Module 2:
Basic issues in software design, modularity, cohesion, coupling and layering, function-oriented
software design: DFD and Structure chart, object modeling using UML, Object-oriented software
development, user interface design. Coding standards and Code review techniques.
[8hrs] (CO2 )
Module 3:
Fundamentals of testing, White-box, and black-box testing, Test coverage analysis and test case
design techniques, mutation testing, Static and dynamic analysis, Software reliability metrics,
reliability growth modeling.
[10hrs] (CO 3)
Module 4:

Software project management, Project planning and control, cost estimation, project scheduling
using PERT and GANTT charts, cost-time relations: Rayleigh-Norden results, quality
management
[8hrs] (CO4 )
Module 5:
ISO and SEI CMMI, PSP and Six Sigma. Computer aided software engineering, software
maintenance, software reuse, Component-based software development.
[6hrs] (CO5 )
Text Books:
1. Roger Pressman, “Software Engineering: A Practitioners Approach,(6th Edition),
McGraw Hill, 1997.
Reference Books:
1. Sommerville, ”Software Engineering, 7th edition”, Adison Wesley, 1996.
2. Watts Humphrey, ”Managing software process”, Pearson education, 2003.
3. James F. Peters and Witold Pedrycz, “ Software Engineering – An Engineering
Approach”, Wiley.
4. Mouratidis and Giorgini. “Integrating Security and Software Engineering–Advances and
Future”, IGP. ISBN – 1-59904-148-0.
5. Pankaj Jalote, “An integrated approach to Software Engineering”, Springer/Narosa.
6. Fundamentals of Software Engineering by Rajib Mall, – PHI-3rd Edition, 2009.
Course Outcomes:
At the end of the course the student should be able to:
CO 1: Students should be able to identify the need for engineering approach to software
development and various processes of requirements analysis for software engineering
problems.
CO 2: Analyse various software engineering models and apply methods for design and
development of software projects.
CO 3: Work with various techniques, metrics and strategies for Testing software projects.
CO 4: Identify and apply the principles, processes and main knowledge areas for Software
Project Management
CO 5: Proficiently apply standards, CASE tools and techniques for engineering software
projects
------------------------------------------------------------------------------------

Course Code: BTCS 504-18
Detailed Contents:

Course Title: Computer Networks

3L:1T:0P 3Credits

Module 1: Data Communication Components
Representation of data and its flow Networks, Various Connection Topology, Protocols and
Standards, OSI model, Transmission Media, LAN: Wired LAN, Wireless LANs, Connecting
LAN and Virtual LAN, Techniques for Bandwidth utilization: Multiplexing - Frequency
division, Time division and Wave division, Concepts on spread spectrum.
[8hrs] (CO1)
Module 2: Data Link Layer and Medium Access Sub Layer
Error Detection and Error Correction - Fundamentals, Block coding, Hamming Distance, CRC;
Flow Control and Error control protocols - Stop and Wait, Go back – N ARQ, Selective Repeat
ARQ, Sliding Window, Piggybacking, Random Access, Multiple access protocols -Pure
ALOHA, Slotted ALOHA, CSMA/CDCDMA/CA.
[10 hrs] (CO2)

Module 3: Network Layer
Switching, Logical addressing – IPV4, IPV6; Address mapping – ARP, RARP, BOOTP and
DHCP–Delivery, Forwarding and Unicast Routing protocols.
[8 hrs] (CO3)
Module 4: Transport Layer
Process to Process Communication, User Datagram Protocol (UDP), Transmission Control
Protocol (TCP), SCTP Congestion Control; Quality of Service, QoS improving techniques:
Leaky Bucket and Token Bucket algorithm.
[8 hrs] (CO3)
Module 5: Application Layer
Domain Name Space (DNS), DDNS, TELNET, EMAIL, File Transfer Protocol (FTP),
WWW, HTTP, SNMP, Bluetooth, Firewalls, Basic concepts of Cryptography.
[8 hrs] (CO4)
Course Outcomes: The student will be able to:
CO1: Explain the functions of the different layer of the OSI Protocol;
CO2:. Describe the function of each block of wide-area networks (WANs), local area networks
(LANs) and Wireless LANs (WLANs);
CO3: Develop the network programming for a given problem related TCP/IP protocol; &
CO4: Configure DNS DDNS, TELNET, EMAIL, File Transfer Protocol (FTP), WWW, HTTP,
SNMP, Bluetooth, Firewalls using open source available software and tools.
Text Books:
1. Data Communication and Networking, 4th Edition, Behrouz A. Forouzan, McGrawHill.
2. Data and Computer Communication, 8th Edition, William Stallings, Pearson Prentice
Hall India.
Reference Books:
1. Computer Networks,
International Edition.

8th

Edition,

Andrew

S.

Tanenbaum,

Pearson

New

42 Hours

2. Internetworking with TCP/IP, Volume 1, 6th Edition Douglas Comer, Prentice Hall of
India.
3. TCP/IP Illustrated, Volume 1, W. Richard Stevens, Addison-Wesley, United States of
America.
----------------------------------------------------------------------------------------------------------------

Course Code: BTCS505-18 CourseTitle: Database management System lab 0L:0T:4P 2Credits

List of Experiments:
Task 1: Introduction to SQL and installation of SQL Server / Oracle.
Task 2: Data Types, Creating Tables, Retrieval of Rows using Select Statement,
Conditional Retrieval of Rows, Alter and Drop Statements.
Task 3: Working with Null Values, Matching a Pattern from a Table, Ordering the Result
of a Query, Aggregate Functions, Grouping the Result of a Query, Update and
Delete Statements.
Task 4: Set Operators, Nested Queries, Joins, Sequences.
Task 5: Views, Indexes, Database Security and Privileges: Grant and Revoke Commands,
Commit and Rollback Commands.
Task 6: PL/SQL Architecture, Assignments and Expressions, Writing PL/SQL Code,
Referencing Non-SQL parameters.
Task 7: Stored Procedures and Exception Handling.
Task 8: Triggers and Cursor Management in PL/SQL.
Suggested Tools – MySQL, DB2, Oracle, SQL Server 2012, Postgre SQL, SQL lite
Course Outcomes:
CO1: This practical will enable students to retrieve data from relational databases using SQL.
CO2: students will be able to implement generation of tables using datatypes
CO3: Students will be able to design and execute the various data manipulation queries.
CO4: Students will also learn to execute triggers, cursors, stored procedures etc.
------------------------------------------------------------------------------------------------------------------Course Code: BTCS506-18

Course Title: Software Engineering Lab 0L:0T:2P 1 Credits

List of Experiments:
Task 1: Study and usage of OpenProj or similar software to draft a project plan
Task 2: Study and usage of OpenProj or similar software to track the progress of a project
Task 3: Preparation of Software Requirement Specification Document, Design Documents
and Testing Phase

Task 4: related documents for some problems
Task 5: Preparation of Software Configuration Management and Risk Management related
documents
Task 6: Study and usage of any Design phase CASE tool
Task 7: To perform unit testing and integration testing
Task 8: To perform various white box and black box testing techniques
Task 9: Testing of a web site
Suggested Tools - Visual Paradigm, Rational Software Architect. Visio, Argo UML, Rational
Application Developer etc. platforms.
---------------------------------------------------------------------------------------------------------------Course Code: BTCS507-18 Course Title: Computer Networks Lab 0L:0T:2P 1 Credits

List of Experiments:
Task 1: To study the different types of Network cables and network topologies.
Task 2: Practically implement and test the cross-wired cable and straight through cable using
clamping tool and network lab cable tester.
Task 3: Study and familiarization with various network devices.
Task 4: Familiarization with Packet Tracer Simulation tool/any other related tool.
Task 5: Study and Implementation of IP Addressing Schemes
Task 6: Creation of Simple Networking topologies using hubs and switches
Task 7: Simulation of web traffic in Packet Tracer
Task 8: Study and implementation of various router configuration commands
Task 9: Creation of Networks using routers.
Task 10: Configuring networks using the concept of subnetting
Task 11: Practical implementation of basic network command and Network configuration
commands like ping, ipconfig, netstat, tracert etc. for troubleshooting network related
problems.
Task 12: Configuration of networks using static and default routes.

Course Outcomes:
The students will be able to:
CO1: Know about the various networking devices, tools and also understand the
implementation of network topologies;
CO2: Create various networking cables and know how to test these cables;
CO3: Create and configure networks in packet trace rtool using various network devices and
topologies;
CO4: Understand IP addressing and configure networks using the subnet in;
CO5: Configure routers using various router configuration commands.
Suggested Tools - NS2/3, Cisco packet tracer, Netsim etc..

ELECTIVES- I

Course Code: BTCS 510-18 Course Title: Programming in Python

3L:0T:0P 3 Credits 42 Hours

Detailed Contents:
Module 1:
Python Basics, Objects- Python Objects, Standard Types, Other Built-in Types, Internal Types,
Standard Type Operators, Standard Type Built-in Functions, Categorizing the Standard Types,
Unsupported Types Numbers - Introduction to Numbers, Integers, Floating Point Real Numbers,
Complex Numbers, Operators, Built-in Functions, Related Modules Sequences - Strings, Lists,
and Tuples, Mapping and Set Types.
[8hrs] (CO1)
Module 2:
FILES: File Objects, File Built-in Function [ open() ], File Built-in Methods, File Built-in
Attributes, Standard Files, Command-line Arguments, File System, File Execution, Persistent
Storage Modules, Related Modules
Exceptions: Exceptions in Python, Detecting and Handling Exceptions, Context Management,
*Exceptions as Strings, Raising Exceptions, Assertions, Standard Exceptions, *Creating
Exceptions, Why Exceptions (Now)?, Why Exceptions at All?, Exceptions and the sys Module,
Related Modules
Modules: Modules and Files, Namespaces, Importing Modules, Importing Module Attributes,
Module Built-in Functions, Packages, Other Features of Modules.
[10hrs] (CO1,2)
Module 3:
Regular Expressions: Introduction, Special Symbols and Characters, Res and Python
Multithreaded Programming: Introduction, Threads and Processes, Python, Threads, and the
Global Interpreter Lock, Thread Module, Threading Module, Related Modules.
[8hrs] (CO 2,3)
Module 4:
GUI Programming: Introduction, Tkinter and Python Programming, Brief Tour of Other GUIs,
Related Modules and Other GUIs
WEB Programming: Introduction, Wed Surfing with Python, Creating Simple Web Clients,
Advanced Web Clients, CGI-Helping Servers Process Client Data, Building CGI Application
Advanced CGI, Web (HTTP) Servers.
[10hrs] (CO 4,6)
Module 5:
Database Programming: Introduction, Python Database Application Programmer’s Interface
(DB-API), Object Relational Managers (ORMs), Related Modules.
[6 hrs] (CO5)
Text Books:
1. Core Python Programming, Wesley J. Chun, Second Edition, Pearson.

Course Outcomes:
The students should be able to:

CO1: Examine Python syntax and semantics and be fluent in the use of Python flow control and
functions.
CO2: Demonstrate proficiency in handling Strings and File Systems.
CO3: Create, run and manipulate Python Programs using core data structures like Lists,
Dictionaries and use Regular Expressions.
CO4: Interpret the concepts of Object-Oriented Programming as used in Python.
CO5: Implement exemplary applications related to Network Programming, Web Services and
Databases in Python.
---------------------------------------------------------------------------------------------------------------Course Code: BTCS 513-18 Course Title: Programming in Python Lab 0L:0T:2P 1 Credits 2 Hours/
week

Prerequisites: Students should install Python.
List of Experiments:
Task 1:
Task 2:
Task 3:
Task 4:
Task 5:
Task 6:
Task 7:
Task 8:
Task 9:
Task 10:

Task 11:
Task 12:
Task 13:

Task 14:
Task 15:
Task 16:

Write a program to demonstrate different number data types in Python.
Write a program to perform different Arithmetic Operations on numbers in
Python.
Write a program to create, concatenate and print a string and accessing sub-string
from a given string.
Write a python script to print the current date in the following format “Sun May
29 02:26:23 IST 2017”
Write a program to create, append, and remove lists in python.
Write a program to demonstrate working with tuples in python.
Write a program to demonstrate working with dictionaries in python.
Write a python program to find largest of three numbers.
Write a Python program to convert temperatures to and from Celsius, Fahrenheit.
[ Formula: c/5 = f-32/9]
Write a Python program to construct the following pattern, using a nested for
loop
*
*
**
***
****
***
**
*
*
Write a Python script that prints prime numbers less than 20.
Write a python program to find factorial of a number using Recursion.
Write a program that accepts the lengths of three sides of a triangle as inputs.
The program output should indicate whether or not the triangle is a right triangle
(Recall from the Pythagorean Theorem that in a right triangle, the square of one
side equals the sum of the squares of the other two sides).
Write a python program to define a module to find Fibonacci Numbers and
import the module to another program.
Write a python program to define a module and import a specific function in that
module to another program.
Write a script named copyfile.py. This script should prompt the user for the
names of two text files. The contents of the first file should be input and written
to the second file.

Task 17: Write a program that inputs a text file. The program should print all of the unique
words in the file in alphabetical order.
Task 18: Write a Python class to convert an integer to a roman numeral.
Task 19: Write a Python class to implement pow(x, n)
Task 20: Write a Python class to reverse a string word by word.
----------------------------------------------------------------------------------------------------------------

cCourse Code: BTCS521- Course Title: Computational Biology 3L:0T:0P 3 Credits 42 Hours
18
Detailed Contents:
Module 1: Introduction
Nature and scope of life science: Branches of life sciences, Characteristics of life, Levels of
Organization, Origin of life, Biochemical evolution- evolution of Proteins and Nucleotide. Cell
Biology: The cell as basic unit of life- Prokaryotic cell and Eukaryotic cell, Cell Structure and
Function- cell membrane, cell organelles, Cell Division; Mitosis & Meiosis. Cell Energetics:
Laws of Thermodynamics, Photosynthesis, Anaerobic & aerobic respiration, Structure and
function of mitochondria, respiratory pathways: Glycolysis, Kreb’s Cycle, Electron transport
chain.
[10hrs] (CO)
Module 2: More about RNA and DNA
Chromosome-Genome-Genes-Databases: Bio-molecules- DNA, RNA, Protein and amino
acids, Chargaff’s Rules, GC content.
Central Dogma: Replication, Transcription, Translation, Post transcriptional & post translational
modifications, RNA processing, RNA splicing and RNA editing. Sense/coding and antisense/template strands, Genetic code. Introduction to DNA and Protein sequencing.
[10hrs] (CO)

Module 3: Proteins
Proteins and Databases: Protein structure and function, Protein Primary structure, Amino acid
residues, Secondary, Tertiary, Quaternary Structure of Protein, Protein sequence databasesSwissProt/ TrEMBL, PIR, Sequence motif databases -Pfam, PROSITE, Protein structure
databases.
[8hrs] (CO)
Module 4: Computation and Biology
Molecular computational biology: Gene prediction, sequencing genomes, similarity search,
restriction mapping, Sequence Analysis: Principles and its uses, Hidden Markov models for
sequence analysis. Introduction of Markov Chain and Hidden Markov models. Forward
backward algorithm, Viterbi and Baum-Welch algorithms,
[14hrs] (CO)
Course Outcomes:
The student will be able to:
CO1: Understand the basic of cell structure, divisions involved in reproduction of a cell, and
its generic functionality;
CO2: Recognize the base line elements of a RNA and DNA; including fundamental behind
their complex structure;
CO3: Comprehend primary structure of the protein and various related data-sets.
CO4: Demonstrate the concept of gene sequence alignment and simulate various related
algorithms for the same.

Text books
1. Pevzner, P. A., Computational Molecular Biology, PHI Learning Pvt. Ltd, ISBN-97881-203-2550-0.
2. Ghosh, Z. and Mallick, B., Bioinformatics Principles and Applications (2008) Oxford
University Press ISBN 9780195692303
3. Mount, D. W., Bioinformatics – sequence and genome analysis.
Reference Books
1. Devasena, T. (2012). Cell Biology. Published by Oxford University Press.
2. Fall, C.P., Marland, E.S., Wagner, J.M., Tyson, J.J.(2002). Computational Cell Biology.
Springer
3. Becker, W. M., Kleinsmith, L. J., Hardin, J., & Raasch, J. (2003). The world of the cell
(Vol. 6). San Francisco: Benjamin Cummings.
4. Rastogi, S. C. (2005). Cell biology. New Age International.
5. Reece, J. B., Taylor, M. R., Simon, E. J., & Dickey, J. (2009). Biology: concepts &
connections (Vol. 3, p. 2). Pearson/Benjamin Cummings.
----------------------------------------------------------------------------------------------------------------

Course Code: BTCS523-18 Course Title: Computational Biology Lab 0L:0T:2P 1 Credits 2 Hours/
week
List of Experiments:

Task 1: Introduction of Bio Python, Various Packages and its Installation.
Task 2,3: Parsing sequence file formats
Sequences and Alphabets
Sequences act like strings
Slicing a sequence
Turning Seq objects into strings
Concatenating or adding sequences
Changing case
Nucleotide sequences and (reverse) complements
Transcription
Translation
Task 4,5: Sequence annotation objects
The SeqRecord object
Creating a SeqRecord
SeqRecord objects from scratch
SeqRecord objects from FASTA files
SeqRecord objects from GenBank files
Feature, location and position objects
SeqFeature objects
Positions and locations
Sequence described by a feature or location
Task 6,7,8: BLAST
Running BLAST over the Internet
Running BLAST locally
Introduction
Standalone NCBI BLAST+
Other versions of BLAST

Parsing BLAST output
The BLAST record class
Dealing with PSI-BLAST
Dealing with RPS-BLAST
BLAST and other sequence search tools
The SearchIO object model
QueryResult
Hit
HSP
HSPFragment
A note about standards and conventions
Reading search output files
Dealing with large search output files with indexing
Writing and converting search output files
Task 9,10: Multiple Sequence Alignment objects
Parsing or Reading Sequence Alignments
Single Alignments
Multiple Alignments
Ambiguous Alignments
Writing Alignments
Converting between sequence alignment file formats
Getting your alignment objects as formatted strings
Manipulating Alignments
Slicing alignments
Alignments as arrays
Task 11,12,13: Sequence motif analysis using Bio.motifs
Motif objects
Creating a motif from instances
Creating a sequence logo
Reading motifs
JASPAR
MEME
TRANSFAC
Writing motifs
Position-Weight Matrices

Quick Reference:
http://biopython.org/DIST/docs/tutorial/Tutorial.html#htoc106
https://biopython.readthedocs.io/en/latest/Tutorial/chapter_seq_objects.html

Course Code: BTCS 515-18

Course Title: Computer Graphics 3L:0T:0P 3 Credits

45 Hours

Detailed Contents:
Module 1:
Overview of Computer Graphics: Basics of Computer Graphics, Applications, Video Display
devices, Raster–Scan displays, Random–Scan displays, Color CRT Monitors, Flat–Panel
Displays; Video Controller, Display Processor, Common Graphic Input and Output devices,
Graphic File Formats, Graphics Software’s.
[6hrs] (CO1)
Module 2:
Output Primitives: Line Drawing, DDA, Bresenham Line Algorithm; Mid-Point Line Algorithm,
Bresenham Circle Algorithm, Midpoint Circle drawing algorithms; Midpoint Ellipse Algorithm;
Flood and Boundary Filling.
[6hrs] (CO1)
Module 3:
Two-Dimensional Geometric Transformation: Translation, Rotation, Scaling, Reflection,
Shearing, Matrix representations; Composite transformations.
[6hrs] (CO1,2)
Module 4:
Two-Dimensional Viewing: Viewing coordinate reference frame; Window to Viewport
coordinate transformation. Point Clipping, Line Clipping, text Clipping; Cohen–Sutherland and
Liang–Barskey Algorithms for line clipping; Sutherland–Hodgeman algorithm for polygon
clipping.
[6hrs](CO2)
Module 5:
Three Dimensional Transformations & Viewing: Translation, Rotation, Scaling, Reflection and
composite transformations. Parallel and Perspective Projections, Viewing Transformation: View
Plan, View Volumes and Clipping.
[6hrs] (CO2)
Module 6:
3 D Graphics and Visibility: Plane projections and its types, Vanishing points, Specification of
a 3D view. Image and object precision, Hidden edge/surface removal or visible edge/surface
determination techniques; z buffer algorithms, Depth sort algorithm, Scan line algorithm and
Floating horizon technique.
[6hrs] (CO2,3)
Module 7:
Color Models: Properties of Light, Intuitive Color Concepts, concepts of chromaticity, RGB
Color Model, CMY Color Model, HLS and HSV Color Models, Conversion between RGB and
CMY color Models, Conversion between HSV and RGB color models, Color Selection and
Applications.
[6hrs] (CO2,3)
Module 8:
Animation: Graphics Design of Animation sequences, General Computer Animation Functions
Introduction to Rendering, Raytracing, Antialiasing, Fractals, Gourard and Phong shading.
[3hrs] (CO3)
Reference Books:
1. D. Hearn and M.P. Baker, Computer Graphics: C version, 2nd Edition, PHI, 2004.
2. D.F. Rogers, Mathematical Elements for Graphics, 2nd Edition., McGraw Hill, 2004.

3. J.D. Foley et al, Computer Graphics, Principles and Practices, 2nd Edition, Addison
Wasley, 2004.
4. Roy A. Plastock, Gordon Kalley, Computer Graphics, Schaum’s Outline Series, 1986.

Course Outcomes: The students shall be able to:
CO1: Understand about fundamentals of Graphics to enable them to design animated scenes for
virtual object creations.
CO2: Make the student present the content graphically.
CO3: Work in computer aided design for content presentation for better analogy data with
pictorial representation

---------------------------------------------------------------------------------------------------------------Course Code: BTCS 518-18 Course Title: Computer Graphics Lab 0L:0T:4P 2 Credits 2 Hours/
week
List of Experiments:
Task 1: WAP to draw different geometric structures using different functions.
Task 2: Implement DDA line generating algorithm.
Task 3: Implement Bresenham’s line generating algorithm.
Task 4: Implement Mid-point circle line generating algorithm.
Task 5: Implementation of Bresenham’s circle drawing algorithm.
Task 6: Implementation of mid-point circle generating Algorithm.
Task 7: Implementation of ellipse generating Algorithm.
Task 8: WAP of color filling the polygon using Boundary fill and Flood fill algorithm.
Task 9: To translate an object with translation parameters in X and Y directions.
Task 10: To scale an object with scaling factors along X and Y directions.
Task 11: Program of line clipping using Cohen-Sutherland algorithm.
Task 12: To perform composite transformations of an object.
Task 13: To perform the reflection of an object about major.
----------------------------------------------------------------------------------------------------------------

Course Code: BTCS 520-18 Course Title: Web Technologies

3L:0T:0P 3 Credits 42 Hours

Detailed Contents:
Module 1:
Introduction: History and evolution of Internet protocols, Internet addressing, Internet Service
Provider (ISP), Introduction to WWW, DNS, URL, HTTP, HTTPS, SSL, Web browsers,
Cookies, Web servers, Proxy servers, Web applications. Website design principles, planning the
site and navigation.
[6 hrs][CO1]
Module 2:

HTML and DHTML: Introduction to HTML and DHTML, History of HTML, Structure of
HTML Document: Text Basics, Structure of HTML Document: Images, Multimedia, Links,

Audio, Video, Table and Forms, Document Layout, HTML vs. DHTML, Meta tags, Browser
architecture and Website structure. Overview and features of HTML5.
[7 hrs][CO2]
Module 3:
Style Sheets: Need for CSS, Introduction to CSS, Basic syntax and structure, Types of CSS –
Inline, Internal and External CSS style sheets.CSS Properties - Background images, Colors and
properties,Text Formatting, Margin, Padding, Positioning etc., Overview and features of CSS3.
[7 hrs][CO3]
Module 4:
Java Script: Introduction, JavaScript’s history and versions, Basic syntax, Variables, Data types,
Statements, Operators, Functions, Arrays, Objects, dialog boxes, JavaScript DOM.
[7 hrs][CO4]
Module 5:
PHP and MySQL: Introduction and basic syntax of PHP, Data types, Variables,Decision and
looping with examples, String, Functions, Array, Form processing, Cookies and Sessions, Email, PHP-MySQL: Connection to server.
[7 hrs][CO5]
Module 6:
Ajax and JSON: AJAX Introduction, AJAX Components, Handling Dynamic HTML with
Ajax, Advantages & disadvantages, HTTP request, XMLHttpRequest Server Response.
JSON– Syntax, Schema, Data types, Objects, Reading and writing JSON on client and server.
Using JSON in AJAX applications.
[8 hrs][CO6]

Students shall be able to:
CO1. Understand and apply the knowledge of web technology stack to deploy various web
services.
CO2. Analyze and evaluate web technology components for formulating web related problems.
CO3. Design and develop interactive client server internet application that accommodates user
specific requirements and constraint analysis.
CO4. Program latest web technologies and tools by creating dynamic pages with an
understanding of functions and objects.
CO5. Apply advance concepts of web interface and database to build web projects in
multidisciplinary environments.
CO6. Demonstrate the use of advance technologies in dynamic websites to provide
performance efficiency and reliability for customer satisfaction.
Text Books:
1. Jeffrey C. Jackson, “Web Technologies: A Computer Science Perspective”,
PearsonEducation

2. Rajkamal, “Internet and Web Technology”, Tata McGraw Hill
3. Ray Rischpater, “JavaScript JSON Cookbook”, Packt Publishing.
4. Ivan Bayross, “Web Enabled Commercial Application Development using HTML,
DHTML JavaScript, Perl, CGI”, BPB Publications.
5. Peter Moulding, “PHP Black Book”, Coriolis.

Course Code: BTCS 522-18 Course Title: Web Technologies Lab 0L:0T:2P 1 credits 2 Hours/
week
List of Experiments:
1. Configuration and administration Apache Web Server.
2. Develop an HTML page to demonstrate the use of basic HTML tags, Link to different HTML
page and also link within a page, insertion of images and creation of tables.
3. Develop a registration form by using various form elements like input box, text area, radio
buttons, check boxes etc.
4. Design an HTML page by using the concept of internal, inline, external style sheets.
5. Create an HTML file to implement the styles related to text, fonts, links using cascading style
sheets
6. Create an HTML file to implement the concept of document object model using JavaScript
7. Create an HTML page including JavaScript that takes a given set of integer numbers and
shows them after sorting in descending order.
8. Write an HTML page including any required JavaScript that takes a number from one text
field in the range of 0 to 999 and shows it in another text field in words. If the number is out
of range, it should show “out of range” and if it is not a number, it should show “not a
number” message in the result box.
9. Create a PHP file to print any text using variable.
10. Demonstrate the use of Loops and arrays in PHP
11. Create a PHP file using GET and POST methods.
12. A simple calculator web application that takes two numbers and an operator (+, -, /, * and %)
from an HTML page and returns the result page with the operation performed on the
operands.
13. Implement login page contains the user name and the password of the user to authenticate
with Session using PHP and MySQL, also implement this with the help of PHP-Ajax.
14. A web application for implementation:
a. The user is first served a login page which takes user’s name and password. After
submitting the details the server checks these values against the data from a database
and takes the following decisions.
b. If name and password matches, serves a welcome page with user’s full name.
c. If name matches and password doesn’t match, then serves “password mismatch” page
d. If name is not found in the database, serves a registration page, where user’s full name
is asked and on submitting the full name, it stores, the login name, password and full
name in the database (hint: use session for storing the submitted login name and
password)
15. Demonstrate the use of Ajax and JSON Technologies in programming examples.

16. Demonstrate the use of web site designing tools such as Joomla, WordPress.
17. Implement at least one minor project using different technologies mentioned in theory of the
subject.

Sixth Semester

Course Code: BTCS601-18 Course Title : Compiler Design

3L:0T:0P

3Credits

Detailed Contents:
UNIT 1: Unit I Introduction to Compilers:
Structure of a compiler – Lexical Analysis – Role of Lexical Analyzer – Input Buffering –
Specification of Tokens – Recognition of Tokens – Lex – Finite Automata – Regular
Expressions to Automata – Minimizing DFA.
[8 hrs., CO 1]
Unit II :Syntax Analysis:
Role of Parser – Grammars – Error Handling – Context-free grammars – Writing a grammar,
Top-Down Parsing – General Strategies Recursive Descent Parser – Predictive Parser-LL(1)
Parser-Shift Reduce Parser-LR Parser-LR (0) Item Construction of SLR Parsing Table Introduction to LALR Parser – Error Handling and Recovery in Syntax Analyzer-YACC.
[8 hrs., CO 2]
Unit III : Intermediate Code Generation:
Syntax Directed Definitions, Evaluation Orders for Syntax Directed Definitions, Intermediate
Languages: Syntax Tree, Three Address Code, Types and Declarations, Translation of
Expressions, Type Checking.
[8 hrs., CO 3]
Unit IV: Run-Time Environment and Code Generation:
Storage Organization, Stack Allocation Space, Access to Non-local Data on the Stack, Heap
Management – Issues in Code Generation – Design of a simple Code Generator.
[6 hrs., CO 4]
Unit V: Code Optimization:
Principal Sources of Optimization – Peep-hole optimization – DAG- Optimization of Basic
Blocks-Global Data Flow Analysis – Efficient Data Flow Algorithm. [6 hrs., CO 5]
Course Outcomes:
After undergoing this course, the students will be able to:
CO1: Build concepts on lexical analysis.
CO2: Understand strategies of syntax analysis.
CO3: Learn techniques of Intermediate code generation.
CO4: Undestand code design issues and design code generator.
CO5: Design and develop optimized codes.
Suggested Readings/ Books:
1. A.V. Aho, Monica, R.Sethi, J.D.Ullman, “Compilers, Principles, Techniques and
Tools”, Second Edition, Pearson Education/Addison Wesley, 2009.

2. Andrew W. Appel, “Modern Compiler Implementation in Java”, Second Edition, 2009.
3. J.P. Tremblay and P.G. Sorrenson, “The Theory and Practice of Compiler Writing”,
McGraw Hill, 1985.

Course Code:
BTCS604-18
Sr. No.
1

2
3
4
5
6
7
8
9
10
11
12

Course Title: Compiler Design Lab

L:0;T:0; 2P 1Credits

No. List of Experiments
Design a lexical analyser for given language and the lexical analyser should ignore redundant
spaces, tabs and new lines. It should also ignore comments. Although the syntax specification
states that identifiers can be arbitrarily long, you may restrict the length to some reasonable
value. Simulate the same in C language.
Write a C program to identify whether a given line is a comment or not.
Write a C program to recognize strings under 'a', 'a*b+', 'abb'.
Write a C program to test whether a given identifier is valid or not.
Write a C program to simulate lexical analyzer for validating operators.
Implement the lexical analyzer using JLex, flex or other lexical analyzer generating tools.
Write a C program for implementing the functionalities of predictive parser for the mini language
specified in Note 1.
a) Write a C program for constructing of LL (1) parsing.
b) Write a C program for constructing recursive descent parsing.
Write a C program to implement LALR parsing.
a) Write a C program to implement operator precedence parsing.
b) Write a C program to implement Program semantic rules to calculate the expression that takes an
expression with digits, + and * and computes the value.
Convert the BNF rules into YACC form and write code to generate abstract syntax tree for the mini
language specified in Note 1.
Write a C program to generate machine code from abstract syntax tree generated by the parser. The
instruction set specified in Note 2 may be considered as the target code.

--------------------------------------------------------------------------------------------------------------

Course Code:BTCS602-18 Course Title : Artificial Intelligence

3L:0T:0P

3Credits

Detailed Contents:
UNIT 1: Introduction (3 Hours)
Concept of AI, history, current status, scope, agents, environments, Problem Formulations,
Review
of tree and graph structures, State space representation, Search graph and Search tree.
[8hrs] (CO 1)
UNIT 2:
Search Algorithms
Random search, Search with closed and open list, Depth first and Breadth first search,
Heuristic search, Best first search, A* algorithm, Game Search.
[9hrs] (CO 2)
UNIT 3: Probabilistic Reasoning

Probability, conditional probability, Bayes Rule, Bayesian Networks- representation,
construction and inference, temporal model, hidden Markov model.
[6hrs] (CO 3)
UNIT 4 Markov Decision process
MDP formulation, utility theory, utility functions, value iteration, policy iteration and partially
observable MDPs.
[6hrs] (CO 4)
UNIT 5 Reinforcement Learning
Passive reinforcement learning, direct utility estimation, adaptive dynamic programming,
temporal difference learning, active reinforcement learning- Q learning.
[6hrs] (CO 5)
Course Outcomes:
After undergoing this course, the students will be able to:
CO1: Build intelligent agents for search and games
CO2: Solve AI problems by learning various algorithms and strategies
CO3: Understand probability as a tool to handle uncertainity
CO4: Learning optimization and inference algorithms for model learning
CO5: Design and develop programs for an reinforcement agent to learn and act in a
structured environment
Suggested Readings/ Books:
1.Stuart Russell and Peter Norvig, “Artificial Intelligence: A Modern Approach” , 3rd
Edition, Prentice Hall
2. Elaine Rich and Kevin Knight, “Artificial Intelligence”, Tata McGraw Hill
3. Trivedi, M.C., “A Classical Approach to Artifical Intelligence”, Khanna Publishing
House, Delhi.
4. Saroj Kaushik, “Artificial Intelligence”, Cengage Learning India,
5. David Poole and Alan Mackworth, “Artificial Intelligence: Foundations for
Computational
Agents”, Cambridge University Press 2010

Course Code:
BTCS 605-18

Course Title Artificial Intelligence Lab

Detailed List of Tasks:
1.Write a programme to conduct uninformed and informed search.
2. Write a programme to conduct game search.
3. Write a programme to construct a Bayesian network from given data.
4. Write a programme to infer from the Bayesian network.
5. Write a programme to run value and policy iteration in a grid world.
6. Write a programme to do reinforcement learning in a grid world

L:0;T:0;2
P:

1 Credits

ELECTIVE II

Course Code: BTCS 606-18 Course Title: Simulation and Modeling

3L:0T:0P

3Credits

Detailed Contents:
UNIT1: Introduction
Introduction to simulation and modeling, Application areas, System and system environment,
Components of a system, Discrete and continuous systems, Basic model forms and its types,
Discrete-event simulation, Steps in a simulation study, Simulation examples.[4 hrs] (CO 1)
UNIT2: General Principles
Concepts in discrete event simulation, Handling Stepped and Event-based Time in
Simulations, Event scheduling/time advance algorithms, World views, List processing using
dynamic allocation and linked list.
[4 hrs] (CO 1)
UNIT 3: Statistical and Queuing Models in Simulation
Terms and concepts, Statistical models, Discrete and continuous distributions, Poisson
distributions, Empirical distributions, Little’s equation. Characteristics of queuing systems,
Queuing notation, Long- Run measures of performance of queuing systems, Steady state
behavior of infinite and finite calling population models, Use of network of queues.
[9 hrs] (CO 2)
UNIT 4 Random Number Generation
Pseudo random numbers, Techniques for generation of pseudo random numbers, Tests for
random numbers, Random variate generation, Inverse Transform Technique- Exponential,
Uniform, Weibull, Triangular distributions, Direct transformation for Normal and lognormal
distributions.
[6hrs] (CO 2)
UNIT 5 Input Modeling and Output Analysis of a Single Model
Data collection, Identifying the distribution of data - histograms and quantile plots, Parameter
estimation, Goodness of fit tests applied to simulation inputs, Verification and validation of
simulation models, Output analysis and measures of performance and estimation.
[6hrs] (CO 3)
UNIT 6 Comparison and Evaluation of Alternative System Designs
Comparison of two system designs, Sampling with equal and unequal variances, Common
random numbers, Comparison of several system designs, Linear regression, Random number
assignment for regression.
[5 hrs] (CO 4)
Course Outcomes:
After undergoing this course, the students will be able to
CO1: Discuss the fundamental elements of discrete-event simulation including statistical
models, random processes, random variates, and inputs to simulation
CO2: Analyze a real world problem and apply modelling methodologies to develop a
discrete-event simulation model

CO3 Interpret discrete-event techniques for solving a simulation problem
CO4: Compare and evaluate alternative system designs using sampling and regression
Suggested Readings/ Books:
1. Jerry Banks, John S. Carson II, Barry L.Nelson and David M.Nicol, ”Discrete- event
system and simulation”, Prentice Hall of India.
2. Averill M.Law, “Simulation modeling and analysis (SIE)”, Tata McGraw Hill India.
3. David Cloud, Larry Rainey, “Applied Modeling and Simulation”, Tata McGraw Hill.
4. Gabriel A. Wainer, “Discrete-event modeling and simulation: a practitioner’s approach”,
CRC Press.
5. Bernard P. Zeiger, Herbert Praehofer, Tag Gon Kim, “Theory of modeling and
simulation: integrating discrete event and continuous complex dynamic systems”,
Academic Press.
6. Walter J. Karplus, George A. Bekey, Boris YakobKogan, “Modeling and simulation:
theory and practice”, Springer.
.--------------------------------------------------------------------------------------------------------------------

CourseCode: BTCS 607-18 CourseTitle: Simulation and Modeling Lab

L:0;T:0; P: Credits

Detailed List of Tasks:
1. Implementation of Basic Operations on Matrices.
2. Implementation of Chi-square goodness-of-fit test.
3. Practical implementation of Queuing Models.
4. Design Inventory System.
5. Implementation of Monte-Carlo Simulation method.
6. Analysis of Discrete and Continuous Distributions.
7. Generation of Random Numbers using Linear Congruential Method.
8. Generation of Random Numbers using Combined Linear Congruential Method.
9. Evaluation of system design using Regression Analysis.
10. Simulate a network using any network simulator.
SuggestedTools - Scilab, Tortuga and Extend. Introduction to network simulators - NS2,
CloudSim, Wireshark.

Course Code: BTCS608-18 Course Title:Internet of Things

L:3; T:0;
P:0

OBJECTIVES
The objective of this course is to impart
DETAIL CONTENTS
necessary and practical knowledge of
components of Internet of Things and develop
1. Introduction to IoT
skillsprinciples
required and
to build
real-life
IoT based
Architectural Overview, Design
needed
capabilities,
IoT Applications,
Sensing, Actuation, Basics projects.
of Networking, M2M and IoT Technology FundamentalsDevices and gateways, Data management, Business processes in IoT, Everything as a
LEARNING
OUTCOMES
Service(XaaS), Role of Cloud
in IoT, Security
aspects in IoT.
(8 Hours) , CO1
After the completion of this course, the
2. Elements of IoT
students will be able to:

3Credits

Hardware Components- Computing (Arduino, Raspberry Pi), Communication, Sensing,
Actuation, I/O interfaces. Software Components- Programming API’s (using Python
/Node.js /Arduino) for Communication, Protocols-MQTT, ZigBee, Bluetooth, CoAP, UDP,
TCP
(9 Hours), CO2
3. IoT Application Development
Solution framework for IoT applications- Implementation of Device integration, Data
acquisition and integration, Device data storage- Unstructured data storage on cloud/local
server, Authentication, authorization of devices.
(18 Hours) CO3
4. IoT Case Studies
IoT case studies and mini projects based on Industrial automation, Transportation,
Agriculture, Healthcare, Home Automation
(10 Hours),CO4

Course Outcomes: After the completion of this course, the students will be able to:
CO1: Understand internet of Things and its hardware and software components
CO2:Interface I/O devices, sensors & communication modules
CO3:Remotely monitor data and control devices
CO4:Develop real life IoT based projects
List of suggested books :
1. Vijay Madisetti, Arshdeep Bahga, Ïnternet of Things, “A Hands on Approach”,
University Press
2. Dr. SRN Reddy, Rachit Thukral and Manasi Mishra, “Introduction to Internet of Things:
A practical Approach”, ETI Labs
3. Pethuru Raj and Anupama C. Raman, “The Internet of Things: Enabling Technologies,
Platforms, and Use Cases”, CRC Press
4. Jeeva Jose, “Internet of Things”, Khanna Publishing House, Delhi
5. Adrian McEwen, “Designing the Internet of Things”, Wiley
6. Raj Kamal, “Internet of Things: Architecture and Design”, McGraw Hill
7. Cuno Pfister, “Getting Started with the Internet of Things”, O Reilly Media

Course Code: BTCS609-18 Course Title: Internet of Things Lab L:0; T:0; 1Credits
P:2
OBJECTIVES
The objective of this course is to
LIST OF PRACTICALS
impart necessary and practical
knowledge of components of Internet
of Things and develop
skills required
1. Familiarization with Arduino/Raspberry
Pi and perform
necessary software installation.
to build real-life IoT based projects.
LEARNING OUTCOMES

2. To interface LED/Buzzer with Arduino/Raspberry Pi and write a program to turn ON
LED for 1 sec after every 2 seconds.
3. To interface Push button/Digital sensor (IR/LDR) with Arduino/Raspberry Pi and write a
program to turn ON LED when push button is pressed or at sensor detection.
4. To interface DHT11 sensor with Arduino/Raspberry Pi and write a program to print
temperature and humidity readings.
5. To interface motor using relay with Arduino/Raspberry Pi and write a program to turn
ON motor when push button is pressed.
6. To interface OLED with Arduino/Raspberry Pi and write a program to print temperature
and humidity readings on it.
7. To interface Bluetooth with Arduino/Raspberry Pi and write a program to send sensor
data to smartphone using Bluetooth.
8. To interface Bluetooth with Arduino/Raspberry Pi and write a program to turn LED
ON/OFF when ‘1’/’0’ is received from smartphone using Bluetooth.
9. Write a program on Arduino/Raspberry Pi to upload temperature and humidity data to
thingspeak cloud.
10. Write a program on Arduino/Raspberry Pi to retrieve temperature and humidity data
from thingspeak cloud.
11. To install MySQL database on Raspberry Pi and perform basic SQL queries.
12. Write a program on Arduino/Raspberry Pi to publish temperature data to MQTT
broker.

Course Code:BTCS 610-18 Course Title : Digital Image Processing 3L:0T:0P

3Credits

Detailed Contents:
UNIT 1: Introduction of Digital Image Processing (DIP)
Introduction to the DIP areas and applications; Components of Digital Image Processing;
Elements of Visual Perception; Image Sensing and Acquisition; Image Sampling and
Quantization; Relationships between pixels; color models.
[7hrs] (CO 1)
UNIT 2: Image Enhancement
Spatial Domain: Gray level transformations; Histogram processing; Basics of Spatial
Filtering; Smoothing and Sharpening Spatial Filtering
Frequency Domain: Introduction to Fourier Transform; Smoothing and Sharpening
frequency domain filters; Ideal, Butterworth and Gaussian filters
[10hrs] (CO 2)
UNIT 3: Image Restoration

Noise models; Mean Filters; Order Statistics; Adaptive filters; Band reject Filters; Band pass
Filters; Notch Filters; Optimum Notch Filtering; Inverse Filtering; Wiener filtering
[8hrs] (CO 3)
UNIT4: Feature Extraction and Image Segmentation
Feature Extraction: Contour and shape dependent feature extraction, Extraction of textural
features
Segmentation: Detection of Discontinuities; Edge Linking and Boundary detection; Region
based
segmentation;
Morphological
processingerosion
and
dilation.
[10hrs] (CO 4)

UNIT 5: Image Compression and Encoding
Entropy-based schemes, Transform-based encoding, Predictive encoding and DPCM, Vector
quantization, Huffman coding.
[10hrs](CO 5)
Course Outcomes:
After undergoing this course, the students will be able to:
CO1: Understand the basic concepts of DIP.
CO2: Improve the quality of digital images.
CO3: Understand and De-noise Digital Images
CO4: Segment digital images and extract various features from digital images
CO5: Understand various image compression techniques and apply such techniques to
compress digital images for reducing the sizes of digital images.
Suggested Readings/ Books:
1. Rafael C. Gonzales, Richard E. Woods, “Digital Image Processing”, Third Edition,
Pearson Education, 2010.
2. Anil Jain K. “Fundamentals of Digital Image Processing”, PHI Learning Pvt. Ltd., 2011.
3. Willliam K Pratt, “Digital Image Processing”, John Willey, 2002.
4. Nick Efford, “Digital Image Processing a practical introduction using Java”, Third
Edition, Pearson Education, 2004.
5. R.C. Gonzalez, R.E. Woods, and S. L. Eddins “Digital Image Processing using
MATLAB”, Pearson Prentice-Hall, 2004.
6. Sandipan Dey, “Hands-On Image Processing with Python”, Packt, 2018
----------------------------------------------------------------------------------------------------------

Course Code:BTCS 611-18 Course Title: Digital Image Processing Lab L:0;T:0; P: Credits
Detailed List of Tasks:
1. WAP to draw Histogram of digital Image
2. WAP to enhance the quality of digital image using various gray level transformations.
3. WAP to enhance the quality of digital image using Average and median filters in
spatial domain.
4. WAP to convert digital image from spatial domain to frequency domain.
5. Implement low pass filters in frequency domain for image enhancement.
6. Implement high pass filters in frequency domain for image enhancement.
7. Implement Optimum Notch Filtering for de-noising of digital image.
8. WAP to segment digital image using thresholding approach.
9. WAP to extract shape and texture based features from image.
10. WAP to compress digital image using entropy based approach.
Suggested Tools – MATLAB/Python/JAVA

Course Code: BTCS 612-18

Course Title: Cloud Computing

3L:0T:0P

3Credits

Detailed Contents:
UNIT1: Introduction : Definition of cloud, characteristics of cloud, historical developments
& challenges ahead, the vision of cloud computing, Driving factors towards cloud, Comparing
grid with utility computing, cloud computing and other computing systems, types of workload
patterns for the cloud, IT as a service, Applications of cloud computing.
[8hrs] (CO1)
UNIT2: Cloud computing concepts: Introduction to virtualization techniques,
Characteristics of virtualization, Pros and Cons of virtualization Technology, Hypervisors,
Types of hypervisors, Multitenancy, Application programming interfaces (API), Elasticity
and scalability.
[9hrs] (CO2)
UNIT 3: Cloud service models: Cloud service models, Infrastructure as a service (IaaS)
architecture- details and example, Platform as a service (PaaS) architecture- details and
example, Software as a service (SaaS) architecture-- details and example, Comparison of
cloud service delivery models.
[6hrs] (CO3)
UNIT 4: Cloud deployment models: Introduction to cloud deployment models, Public
clouds, Private clouds, Hybrid clouds, Community clouds, Migration paths for cloud,
Selection criteria for cloud deployment.
[6hrs] (CO4)
UNIT 5: Security in cloud computing: Understanding security risks, Principal security
dangers to cloud computing, Internal security breaches, User account and service hijacking,
measures to reduce cloud security breaches
Case Studies: Comparison of existing Cloud platforms /Web Services.
[6hrs] (CO5)
Course Outcomes:
After undergoing this course, the students will be able to:
CO1: Understand the core concepts of the cloud computing paradigm
CO2: Understanding importance of virtualization along with their technologies
CO3: Analyze various cloud computing service and deployment models and apply them to
solve problems on the cloud.
CO4: Implementation of various security strategies for different cloud platform
Suggested Readings/ Books:
1. Raj Kumar Buyya, James Broberg, Andrezei M.Goscinski, “Cloud Computing:
Principles and Paradigms”,Wiley 2011
2. Anthony T. Velte, Toby J. Velte and Robert Elsenpeter, “Cloud Computing: A practical
Approach”, McGraw Hill, 2010.
3. Barrie Sosinsky, “Cloud Computing Bible”, Wiley, 2011.
4. Judith Hurwitz, Robin Bllor, Marcia Kaufman, Fern Halper, “Cloud Computing for
dummies”, 2009.

1.

Reference Books
Rajkumar Buyya, Christian Vecchiola, S.Thamarai Selvi, “Mastering Cloud Computing”
TMH 2013.
2. George Reese “Cloud Application Architectures”, First Edition, O‟Reilly Media 2009.
3. Dr. Kumar Saurabh “Cloud Computing” 2nd Edition, Wiley India 2012.

Course Code: BTCS 612-18 Course Title: Cloud Computing Lab

L:0;T:0; P:2

Detailed List of Tasks:
11. Install VirtualBox/VMware Workstation on different OS.
12. Install different operating systems in VMware.
13. Simulate a cloud scenario using simulator.
14. Implement scheduling algorithms.
15. To study cloud security management.
16. To study and implementation of identity management
17. Case Study - Amazon Web Services/Microsoft Azure/Google cloud services.
Suggested Tools –Matlab, Cloudsim

1 Credits

ELECTIVE III

Course Code: BTCS 614-18 Course Title: Software Project Management

3L:0T:0P 3 Credits

Detailed Contents:
MODULE 1: Introduction
Project Evaluation and Planning - Activities in Software Project Management, Overview of
Project Planning, Stepwise planning, contract management, Software processes and process
models.
[5hrs] (CO1)
MODULE 2: Cost Benefit Analysis
Cost Benefit Analysis, Cash Flow Forecasting, Cost-Benefit Evaluation Techniques, Risk
Evaluation. Project costing, COCOMO 2, Staffing pattern, Effect of schedule compression,
Putnam’s equation, Capers Jones estimating rules of thumb.
[6hrs] (CO2)
MODULE 3: Project Scheduling
Project Sequencing and Scheduling Activities, Scheduling resources, Critical path analysis,
Network Planning, Risk Management, Nature and Types of Risks, Managing Risks, Hazard
Identification, Hazard Analysis, Risk Planning and Control, PERT and Monte Carlo
Simulation techniques.

[8hrs] (CO3)
MODULE 4: Monitoring & Control
Monitoring and Control- Collecting Data, Visualizing Progress, Cost Monitoring, review
techniques, project termination review, Earned Value analysis, Change Control, Software
Configuration Management (SCM), Managing Contracts, Types of Contracts, Stages in
Contract Placement, Typical Terms of a Contract, Contract Management and Acceptance.
[8hrs] (CO4)
MODULE 5: Quality Management
Quality Management and People Management- Introduction, Understanding Behavior,
Organizational Behavior, Selecting the Right Person for The Job, Motivation, The Oldman –
Hackman Job Characteristics Model, Working in Groups, Organization and team structures,
Decision Making, Leadership, Organizational Structures, Stress, Health and Safety. ISO and
CMMI models, Testing, and Software reliability, test automation, Overview of project
management tools.
[9hrs] (CO5)

Course Outcomes:
After undergoing this course, the students will be able to:


CO1: Explain project management in terms of the software development process



CO2: Estimate project cost and perform cost-benefit evaluation among projects



CO3: Apply the concepts of project scheduling and risk management.



CO4: Explain Software configuration management and the concepts of contract
management.



CO5: Apply quality models in software projects for maintaining software quality and
reliability

Suggested Readings/Books:
1. Bob Hughes, Mike Cotterell, “Software Project Management”, Tata McGraw Hill. (2009)
2. Royce, “Software Project Management”, Pearson Education. (2005).
3. Robert K. Wysocki, “Effective Software Project Management”, Wiley.(2006)
4. Ian Sommerville, Software Engineering, Seventh Edition, Pearson Education.
5. R.S. Pressman, Software Engineering: A Practitioner's Approach, Sixth Edition, Tata
McGraw-Hill.
6. Kassem, Software Engineering, Cengage Learning

Course Code: BTCS 615-18 Course Title: Software Project Management L:0;T:0; P:2 1 Credits
Lab
Detailed List of Tasks:
Task 1: Introduction to MS Project
Task 2: Create a Project Plan


Specify project name and start (or finish) date.



Identify and define project tasks.



Define duration for each project task.



Define milestones in the plan



Define dependency between tasks

Task 3: Create Project Plan contd.


Define project calendar.



Define project resources.



Specify resource type and resource rates



Assign resources against each task



Baseline the project plan

Task 4: Execute and Monitor the Project Plan


Update % Complete with current task status.



Review the status of each task.



Compare Planned vs Actual Status



Review the status of Critical Path



Review resources assignation status

Task 5: Generate Dashboard and Reports


Dashboard



Resource Reports



Cost Reports



Progress Reports

Suggested Tools – MS Project, Rational Team Concert
Course Outcomes:
After undergoing this course, the students will be able to:
CO1: Plan and manage projects.
CO2: Consolidate and communicate information about their project.
CO3: Create Gantt charts and PERT (Project Evaluation Review Technique) chart of their
project
CO4: Manage resources, assignments, work allocation and generate reports to assess project
status, project cost status and resource utilization.
CO5: Identify factors affecting the critical path of their project.

Course Code: BTCS 618-18 Course Title : Machine Learning

3L:0T:0P

3Credits

Detailed Contents:
UNIT 1: Introduction: Well-Posed learning problems, Basic concepts, Designing a
learning system, Issues in machine learning. Types of machine learning: Learning
associations, Supervised learning, Unsupervised learning and Reinforcement learning.
[4hrs] (CO 1)
UNIT 2: Data Pre-processing: Need of Data Pre-processing, Data Pre-processing Methods:
Data Cleaning, Data Integration, Data Transformation, Data Reduction; Feature Scaling
(Normalization and Standardization), Splitting dataset into Training and Testing set.
[4hrs] (CO 2)
UNIT 3: Regression: Need and Applications of Regression, Simple Linear Regression,
Multiple Linear Regression and Polynomial Regression, Evaluating Regression Models
Performance (RMSE, Mean Absolute Error, Correlation, RSquare, Accuracy with acceptable
error, scatter plot, etc.)
[6hrs] (CO 3)

UNIT 4 Classification: Need and Applications of Classification, Logistic Regression,
Decision tree, Tree induction algorithm – split algorithm based on information theory, split
algorithm based on Gini index; Random forest classification, Naïve Bayes algorithm; KNearest Neighbours (K-NN), Support Vector Machine (SVM), Evaluating Classification
Models Performance (Sensitivity, Specificity, Precision, Recall, etc.). Clustering: Need and
Applications of Clustering, Partitioned methods, Hierarchical methods, Density-based
methods.
[12hrs] (CO 4)
UNIT 5 Association Rules Learning: Need and Application of Association Rules Learning,
Basic concepts of Association Rule Mining, Naïve algorithm, Apriori algorithm. Artificial
Neural Network: Need and Application of Artificial Neural Network, Neural network
representation and working, Activation Functions. Genetic Algorithms: Basic concepts,
Gene Representation and Fitness Function, Selection, Recombination, Mutation and Elitism.
[14hrs] (CO 5)

Course Outcomes:
After undergoing this course, the students will be able to:
CO1: Analyse methods and theories in the field of machine learning
CO2: Analyse and extract features of complex datasets
CO3: Deploy techniques to comment for the Regression
CO4: Comprehend and apply different classification and clustering techniques
CO5: Understand the concept of Neural Networks and Genetic Algorithm
Suggested Readings/ Books:
Text Books:
1. Mitchell M., T., Machine Learning, McGraw Hill (1997) 1stEdition.
2. Alpaydin E., Introduction to Machine Learning, MIT Press (2014) 3rdEdition.
3. Vijayvargia Abhishek, Machine Learning with Python, BPB Publication (2018)
Reference Books:
1. Bishop M., C., Pattern Recognition and Machine Learning, Springer-Verlag (2011)
2ndEdition.
2. Michie D., Spiegelhalter J. D., Taylor C. C., Campbell, J., Machine Learning, Neural and
Statistical Classification. Overseas Press (1994).

Course Code: BTCS619-18 Course Title: machine Learning Lab L:0;T:0;2
P:
Detailed List of Tasks:
1.
2.
3.
4.

Implement data pre-processing
Deploy Simple Linear Regression
Simulate Multiple Linear Regression
Implement Decision Tree

1Credits

5. Deploy Random forest classification
6. Simulate Naïve Bayes algorithm
7. Implement K-Nearest Neighbors (K-NN), k-Means
8. Deploy Support Vector Machine, Apriori algorithm
9. Simulate Artificial Neural Network
10. Implement the Genetic Algorithm code
Suggested Tools Python/R/MATLAB

Course Code: BTCS620-18 Course Title:Mobile Application L:3; T:0;
Development
P:0

3Credits

OBJECTIVES
The objective of this course is to
impart necessary and practical
Unit-1
knowledge of components of
Internet of Things and develop
Introduction to Android: The
Android
Developing
Android SDK, Introduction
skills
required
to buildenvironment,
real-life
to Open Handset Alliance,IoT
Development
Framework, Application Fundamentals; Device
based projects.
Compatibility, System permissions, Understanding Anatomy of Android Application,
LEARNING OUTCOMES
Android Development Tools
6 hrs. ,CO 1
Details of course:

After the completion of this
course, the students will be able
to:
Getting started with Mobility:
Mobility Landscape, Mobile Platforms, Mobile apps
development, Android terminologies, Application Context, Activities, Services, Intents,
Understand
internet
Things
Receiving and Broadcasting Intents,
Setting
up the of
mobile
apps development environment
and its hardware and software
with emulator
6 hrs., CO1
components Interface I/O
devices, sensors &
Unit-III
communication modules
monitor
data andDesigning, Layout, User Interface
Building block of MobileRemotely
apps: App
user Interface
control
devices
Develop
real
elements, VUIs and Mobile Apps, Text to Speech Techniques,
Designing the Right UI,
life IoT based projects
Activity states and lifecycle, Interaction among activities
6 hrs., CO 2
Unit-II

Unit-IV

DETAIL CONTENTS 1.
to IoTbeyond
(8 Hours)
Sprucing up Mobile apps:Introduction
App functionality
user interface- Threads, sync task,
Architectural Overview, Design
Services-states and life cycle, Notifications, Broadcast receivers, Telephony and SMS APIs
principles and needed
Native data handling: on device
file I/O,
preferences, mobile databases such as
capabilities,
IoTshared
Applications,
SQLite, Working with a content
provider
8 hrs., CO 3,4
Sensing,
Actuation, Basics of
Networking, M2M and IoT
Unit-V
Technology FundamentalsDevices and gateways, Data
Factors in Developing Mobile
Applications:
Mobile processes
Software Engineering, Frameworks and
management,
Business
Tools, Generic UI Development,
User as a
in IoT,Android
Everything
Service(XaaS), Role of Cloud in
Graphics and Multimedia: Performance
Multithreading,
Graphics and UI Performance,
IoT, Securityand
aspects
in IoT.
Android Graphics, Mobile Agents
and Peer-to-Peer
Architecture,
Android Multimedia
2. Elements
of IoT
(9
Hours)
Hardware Components8 hrs., CO 4,5
Computing (Arduino, Raspberry
Pi), Communication, Sensing,
Unit-VI
Actuation, I/O interfaces.

Platforms and Additional Issues: Development Process, Architecture, Design, Technology
Selection, Testing, Security and Hacking, Active Transactions, More on Security
8hrs., CO 5
Course Outcomes:
CO 1: Describe those aspects of mobile programming that make it unique from
programming for other platforms,
CO 2: Critique mobile applications on their design pros and cons,
CO 3: Utilize rapid prototyping techniques to design and develop sophisticated mobile
interfaces,
CO 4: Program mobile applications for the Android operating system that use basic and
advanced phone features, and
CO 5: Deploy applications to the Android marketplace for distribution

References:
1. Rick Rogers, John Lombardo, Meike Blake, “Android application development”, Ist
Edition, O’Reilly, 2010
2. Lauren Darcey and Shane Conder,“Android Wireless Application Development”, 2nd
ed. Pearson Education, 2011
3. Wei-Meng Lee , Beginning Android 4 development ,2012 by John Wiley & Sons
4. Jeff Mewherter, Scott Gowell,Wrox Publisher,”Professional Mobile Application
Development”, Ist Edition, 2012
5. Reto Meier, “Professional Android 4 Application Development”, Wrox, 2012

Course Code: BTCS621-18 Course Title:Mobile Application
Development Lab

L:0; T:0;
P:2

1Credits

OBJECTIVES
LIST OF PRACTICALS
The objective of this course is to impart
necessary and practical knowledge of
components of Internet of Things and
develop skills
required
1. Introduction to Android platform.
Introduction
to to
thebuild
tools real-life
used in the lab. Create a simple
IoT
based
projects.
application
OUTCOMES
2. Understand the app idea LEARNING
and design user
interface/wireframes of mobile app
After the
completion of this course, the
3. Set up mobile app development
environment
students will be able to:
4. Write a program using activity class to show different events.
Understand internet of Things and its
5. Write a program to convert
text to speech.
hardware
and software components
Interface I/O devices, sensors &
6. Develop and debug mobile
app components
– UserRemotely
interface, services, notifications,
communication
modules
broadcast receivers, data components
monitor data and control devices
Develop real life IoT based projects
7. Using emulator to deploy and run mobile apps
DETAIL CONTENTS 1. Introduction

8. Testing mobile app- unit testing, black box testing and test automation

SEVENTH /
EIGHTTH
SEMESTER

Course Code: BTCS 701-18 Course Title : Network Security and

3L:0T:0P

3Credits

Cryptography

Detailed Contents:
UNIT 1: Introduction (3 Hours)
Introduction to Cryptography, Security Threats, Vulnerability, Active and Passive attacks,
Security services and mechanism, Conventional Encryption Model, CIA model
[5hrs] (CO 1)
UNIT 2: Math Background
Modular Arithmetic, Euclidean and Extended Euclidean algorithm, Prime numbers, Fermat
and Euler’s Theorem
[5hrs]
(CO 1)
UNIT 3: Cryptography
Dimensions of Cryptography, Classical Cryptographic Techniques Block Ciphers (DES,
AES) : Feistal Cipher Structure, Simplifies DES, DES, Double and Triple DES, Block Cipher
design Principles, AES, Modes of Operations Public-Key Cryptography : Principles Of
Public-Key Cryptography, RSA Algorithm, Key Management, Diffie-Hellman Key Exchange,
Elgamal Algorithm, Elliptic Curve Cryptography

[12hrs] (CO 2)
UNIT 4 Hash and MAC Algorithms
Authentication Requirement, Functions, Message Authentication Code, Hash Functions,
Security Of Hash Functions And Macs, MD5 Message Digest Algorithm, Secure Hash
Algorithm, Digital Signatures, Key Management : Key Distribution Techniques, Kerberos

[6hrs] (CO 3)
UNIT 5 Security in Networks
Threats in networks, Network Security Controls – Architecture, Encryption, Content Integrity,
Strong Authentication, Access Controls, Wireless Security, Honeypots, Traffic flow security,
Firewalls – Design and Types of Firewalls, Personal Firewalls, IDS, Email Security – PGP,
S/MIME
[7hrs] (CO 4)
Course Outcomes:
After undergoing this course, the students will be able to:
CO1: Understand the fundamental principles of access control models and techniques,
authentication and secure system design

CO2: Have a strong understanding of different cryptographic protocols and techniques and
be able to use them.
CO3: Apply methods for authentication, access control, intrusion detection and prevention.
CO4: Identify and mitigate software security vulnerabilities in existing systems.
Suggested Readings/ Books:
1. Cryptography And Network Security Principles And Practice Fourth Edition, William
Stallings, Pearson Education
2. Modern Cryptography: Theory and Practice, by Wenbo Mao, Prentice Hall PTR
3. Network Security Essentials: Applications and Standards, by William Stallings. Prentice
Hall
4. Cryptography: Theory and Practice by Douglas R. Stinson, CRC press.
-----------------------------------------------------------------------------------------------------------------

Course Code: BTCS
-702-18

Course Title: Data Warehousing and 3L: 0T: 0P
Data Mining

Credits: 3

Detailed Contents:
UNIT 1:
Data Warehousing Introduction:
design guidelines for data warehouse implementation,
Multidimensional Models; OLAP- introduction, Characteristics, Architecture, Multidimensional view
Efficient processing of OLAP Queries, OLAP server Architecture ROLAP versus MOLAP Versus
HOLAP and data cube, Data cube operations, data cube computation.
Data mining: What is data mining, Challenges, Data Mining Tasks, Data: Types of Data, Data
Quality, Data Pre-processing, Measures of Similarity and Dissimilarity

[10hrs]

UNIT 2:
Data mining: Introduction, association rules mining, Naive algorithm, Apriori algorithm, direct hashing
and pruning (DHP), Dynamic Item set counting (DIC), Mining frequent pattern without candidate
generation (FP, growth), performance evaluation of algorithms
Classification: Introduction, decision tree, tree induction algorithms – split algorithm based on
information theory, split algorithm based on Gini index; naïve Bayes method; estimating predictive
accuracy of classification method

[10 hrs]

UNIT 3:
Cluster analysis: Introduction, partition methods, hierarchical methods, density based methods, dealing
with large databases, cluster software

Search engines: Characteristics of Search engines, Search Engine Functionality, Search Engine
Architecture, Ranking of web pages, The search engine history, Enterprise Search, Enterprise Search
Engine Software.

[10 hrs]

UNIT 4:
Web data mining: Web Terminology and Characteristics, Locality and Hierarchy in the web, Web
Content Mining, Web Usage Mining, Web Structure Mining, Web mining Software.[8 hrs]
Suggested Readings / Books:
1. Carlo Vercellis, Business Intelligence: Data mining and Optimization for Decision Making, WILEY.
2. Han J., Kamber M. and Pei J. , b Data mining concepts and techniques, Morgan Kaufmann Publishers
(2011) 3rd ed.
3. Pudi V., Krishana P.R., Data Mining, Oxford University press, (2009) 1st ed.
4. Adriaans P., Zantinge D., Data mining, Pearsoneducation press (1996), 1st ed.
5. Pooniah P. , Data Warehousing Fundamentals, Willey interscience Publication, (2001), 1st ed.

ELECTIVE IV

Course Code: BTCS 704-18 Course Title : Deep Learning

3L:0T:0P

3Credits

Detailed Contents:

UNIT 1: Machine Learning Basics: Learning, Under-fitting, Overfitting, Estimators,
Bias, Variance, Maximum Likelihood Estimation, Bayesian Statistics, Supervised
Learning, Unsupervised Learning and Stochastic Gradient Decent.
[4hrs] (CO 1)
UNIT 2: Deep Feedforward Network: Feed-forward Networks, Gradient-based Learning,
Hidden Units, Architecture Design, Computational Graphs, Back-Propagation,
Regularization, Parameter Penalties, Data Augmentation, Multi-task Learning, Bagging,
Dropout and Adversarial Training and Optimization.
[4hrs] (CO 2)
UNIT 3: Convolution Networks: Convolution Operation, Pooling, Basic Convolution
Function, Convolution Algorithm, Unsupervised Features and Neuroscientific for
convolution Network. [6hrs] (CO 3)
UNIT 4: Sequence Modelling: Recurrent Neural Networks (RNNs), Bidirectional RNNs,
Encoder- Decoder Sequence-to-Sequence Architectures, Deep Recurrent Network, Recursive
Neural Networks and Echo State networks.
[12hrs] (CO 4)
UNIT 5: Deep Generative Models: Boltzmann Machines, Restricted Boltzmann Machines,
Deep Belief Networks, Deep Boltzmann Machines, Sigmoid Belief Networks, Directed
Generative Net, Drawing Samples from Auto –encoders.
[14hrs] (CO 5)

Course Outcomes:
After undergoing this course, the students will be able to:
CO1: Comprehend the advancements in learning techniques
CO2: Compare and explain various deep learning architectures and algorithms.
CO3: Demonstrate the applications of Convolution Networks
CO4: Apply Recurrent Network for Sequence Modelling
CO5: Deploy the Deep Generative Models

Suggested Readings/ Books:

Text Books:
1. Goodfellow L., Bengio Y. and Courville A., Deep Learning, MIT Press (2016).
2. Patterson J. and Gibson A., Deep Learning: A Practitioner's Approach, O’Reilly
(2017), 1st
ed.
Reference Books:
1. Haykin S., Neural Network and Machine Learning, Prentice Hall Pearson (2009), 3rd
ed.
2. Geron A., Hands-on Machine Learning with Sci-kit and TensorFlow, O’Reilly Media
(2017)

Course Code: BTCS 705-18 Course Title: Deep Learning Lab

L:0;T:0;
2P:

Credits;1

Detailed List of Tasks:












Creating a basic network and analyze its performance
Deploy the Confusion matrix and simulate for Overfitting
Visualizing a neural network
Demo: Object Detection with pre-trained RetinaNet with Keras
Neural Recommender Systems with Explicit Feedback
Backpropagation in Neural Networks using Numpy
Neural Recommender Systems with Implicit Feedback and the Triplet Loss
Fully Convolutional Neural Networks
ConvNets for Classification and Localization
Text Classification and Word Vectors
Character Level Language Model (GPU required)

Suggested Tools Python/R/MATLAB

Course Code:
BTCS706-18

Course Title: Distributed Databases

3L: 0T: 0P

Credits: 3

Detailed Contents:
Unit 1:
INTRODUCTION: Distributed data processing; What is a DDBS; Advantages and disadvantages of
DDBS; Problem areas; Overview of database and computer network concepts
DISTRIBUTED DATABASE MANAGEMENT SYSTEM ARCHITECTURE: Transparencies in a
distributed DBMS; Distributed DBMS architecture; Global directory issues.
6 hrs., CO1

Unit 2:
DISTRIBUTED DATABASE DESIGN: Alternative design strategies; Distributed design issues;
Fragmentation; Data allocation.
SEMANTICS DATA CONTROL: View management; Data security; Semantic Integrity Control.
QUERY PROCESSING ISSUES: Objectives of query processing; Characterization of query
processors; Layers of query processing; Query decomposition; Localization of distributed data.
10 hrs., CO1
Unit 3:
DISTRIBUTED QUERY OPTIMIZATION: Factors governing query optimization; Centralized
query optimization; Ordering of fragment queries; Distributed query optimization algorithms.
TRANSACTION MANAGEMENT: The transaction concept; Goals of transaction management;
Characteristics of transactions; Taxonomy of transaction models.
CONCURRENCY CONTROL: Concurrency control in centralized database systems; Concurrency
control in DDBSs; Distributed concurrency control algorithms; Deadlock management.
10
hrs., CO2
Unit 4:
RELIABILITY:Reliability issues in DDBSs; Types of failures; Reliability techniques; Commit
protocols; Recovery protocols.
PARALLEL DATABASE SYSTEMS : Parallel architectures; parallel query processing and
optimization; load balancing.
ADVANCED TOPICS: Databases, Distributed Object Management, Multi-databases. 10 hrs.,
CO2,3

COURSE OUTCOMES
After completion of course, students would be able to:
CO1: Design trends in distributed systems.
CO2: Apply network virtualization in distributed environment.
CO3: Apply remote method invocation and objects.

References:
1. Principles of Distributed Database Systems, M.T. Ozsu and P. Valduriez, Prentice-Hall, 1991.
2. Distributed Database Systems, D. Bell and J. Grimson, Addison-Wesley, 1992.

Course Code:
BTCS707-18

Course Title: Distributed Databases
lab

L: T: 2P

Credits: 1

Detailed list of Tasks:
Programs may be implemented using any open source tool
Expt. 1: Installation and configuration of database packages.
Expt. 2: Creating and managing database objects (Tables, views, indexes etc.)
Expt. 3: Creating and managing database security through user management.
Expt. 4: Creating and maintaining database links.
Expt. 5: Implement Partitioning on the database tables.
Expt. 6: Implement various Transaction concurrency control methods [i.e. lock’s] by executing
multiple update and queries.
Expt. 7: Performance tuning of SQL queries.
Mini Project: Student has to do a project assigned from course contents in a group of two or three
students. The team will have to demonstrate as well as have to give a presentation of the same.
Course Code:
BTCS708-18

Course Title: Computer Vision

3L: 0T: 0P

Credits: 3

Detailed Contents:
Unit 1: Overview, computer imaging systems, lenses, Image formation and sensing, Image analysis, preprocessing and Binary image analysis.
6 hrs., CO1
Unit 2:
Edge detection, Edge detection performance, Hough transform, corner detection. 4 hrs., CO1
Unit 3:
Segmentation, Morphological filtering, Fourier transform.

4 hrs., CO1

Unit 4:
Feature extraction, shape, histogram, color, spectral, texture, using CVIP tools, Feature analysis, feature
vectors, distance /similarity measures, data pre- processing.
8
hrs., CO2
Unit 5:
Pattern Analysis: Clustering: K-Means, K-Medoids, Mixture of Gaussians Classification: Discriminant
Function, Supervised, Un-supervised, Semi- supervised. Classifiers: Bayes, KNN, ANN models;
Dimensionality Reduction: PCA, LDA, ICA, and Non- parametric methods.
Recent trends in Activity Recognition, computational photography, Biometrics
COURSE OUTCOMES
After completion of course, students would be able to:

8 hrs., CO1

CO1: Understand image detection and analysis
CO2: Identify features to recognize object , scene and categorization from images.
CO3: Develop the skills necessary to build computer vision applications.

References:
1. Computer Vision: Algorithms and Applications by Richard Szeliski.
2. Deep Learning, by Goodfellow, Bengio, and Courville.
3. Dictionary of Computer Vision and ImageProcessing, by Fisheretal.

Course Code:
BTCS708-18

Course Title: Computer Vision lab

L: T: 2P

Credits: 1

Detailed list of Tasks:
Programs may be implemented using MATLAB/C/C++/Java/Python on binary/grayscale/color
images.
Expt. 1: Implementation of basic image transformations:
a. Log
b. Power law
c. Negation
Expt. 2: Implementation the following:
a. Histogram processing
b. Histogram equalization/matching
Expt. 3: Implementation of piecewise linear transformations
a. Contrast stretching
b. Grey level slicing
c. Bit plane slicing
Expt. 4: Implementation of image enhancement/smoothing using
a. Linear (weighted and non-weighted filters)
b. Order statistics filters (Nonlinear filters)
i. Mean
ii. Median
iii. Min
iv. Max
v. Average
Expt. 5: Implementation of image enhancement/sharpening using
a.
b.
c.

Laplacian operators
Sobel’s operators
Robert’s cross operators

Expt. 6: Implement the 2D-DFT to obtain Fourier coefficients and reconstruct the image, i.e., IDFT.
Expt. 7: Implement image enhancement using Fourier low pass filters

a. Ideal
b. Butterworth
c. Gaussian
Expt. 8: Implement image enhancement using Fourier high pass filters
a. Ideal
b. Butterworth
c. Gaussian
Expt. 9: Implement algorithms to detect the following in an image
a. Point
b. Line
c. Boundary
Expt. 10: Implement Hough transform to detect a line.
Mini Project: Student has to do a project assigned from course contents in a group of two or three
students. The team will have to demonstrate as well as have to give a presentation of the same.

Course Code: BTCS 710-18 Course Title : Agile Software Development

3L:0T:0P

3Credits

Detailed Contents:
UNIT 1: Introduction
Need of Agile software development, History of Agile, Agile context– manifesto, principles,
methods, values. The benefits of agile in software development.
[6hrs] (CO 1)
UNIT 2: Agile Design Methodologies
Fundamentals, Design principles–Single responsibility, Open-closed, Liskov-substitution,
Dependency-inversion, Interface-segregation.
[6hrs] (CO 2)
UNIT 3: Scrum
Introduction to scrum framework, Roles: Product owner, team members and scrum master,
Events: Sprint, sprint planning, daily scrum, sprint review, and sprint retrospective, Artifacts:
Product backlog, sprint backlog and increments. User stories- characteristics and contents.
[8hrs] (CO 3)
UNIT 4: Kanban
Introduction to Kanban framework, Workflow, Limit the amount of work in progress, pulling
work from column to column, Kanban board, Adding policies to the board, Cards and their
optimization. [6hrs] (CO 4)

UNIT 5: Extreme Programming
Basic values and principles, Roles, Twelve practices of XP, Pair programming, XP team, Life
cycle and tools for XP.
[6hrs] (CO 5)
UNIT 6: Agile Testing
The Agile lifecycle and its impact on testing, Test driven development– Acceptance tests and
verifying stories, writing a user acceptance test, Developing effective test suites, Continuous
integration, Code refactoring. Risk based testing, Regression tests, Test automation. [6hrs]
(CO 6)
Course Outcomes:
After undergoing this course, the students will be able to:
CO1: Understand concept of agile software engineering and its advantages in software
development.
CO2 Explain the role of design principles in agile software design.
CO3 Define the core practices behind Scrum framework.
CO4 Understand key principles of agile software development methodology-Kanban.

CO5 Describe implications of functional testing, unit testing, and continuous integration.
CO6 Understand the various tools available to agile teams to test the project.
Suggested Readings/ Books:
1. Ken Schawber, Mike Beedle, “Agile Software Development with Scrum”, Pearson.
2. Robert C. Martin, “Agile Software Development, Principles, Patterns and Practices”,
Prentice Hall.
3. Mike Cohn, “User Stories Applied: For Agile Software Development”, Addison Wesley
Signature Series.
4. Lisa Crispin, Janet Gregory, “Agile Testing: A Practical Guide for Testers and Agile
Teams”, Addison Wesley.
5. Paul VII, “Agile: The Complete Overview of Agile Principles and Practices (Agile
Product Management)”.
6. Robert Martin, “Agile Software Development, Principles, Patterns, and Practices”,
Pearson New International Edition.
7. Greene Jennifer,” Learning Agile”, O’Reilly Series.

Course Code:
BTCS 711-18

Course Title : Agile Software Development Lab

Detailed List of Tasks:

L:0T:2P

Credits:1

1. Understand the background and driving forces for taking an Agile Approach to
Software Development.
2. Build out a backlog and user stories.
3. To study and use automated build tool.
4. To study-- version control tool.
5. To study Continuous Integration tool.
6. Apply Design principle and Refactoring to achieve agility.
7. Perform Testing activities within an agile project.
8. Mini Project: based on tools

ELECTIVE V

Course Code:
BTCS721-18

Course Title: Block Chain
Technology

3L:0 T: 0P

Credits: 3

Detailed Contents:
INTRODUCTION TO BLOCKCHAIN
Blockchain- Public Ledgers, Blockchain as Public Ledgers -Bitcoin, Blockchain 2.0, Smart Contracts,
Block in a Blockchain, Transactions-Distributed Consensus, The Chain and the Longest Chain Cryptocurrency to Blockchain 2.0 - Permissioned Model of Blockchain, Cryptographic -Hash Function,
Properties of a hash function-Hash pointer and Merkle tree
BITCOIN AND CRYPTOCURRENCY
A basic crypto currency, Creation of coins, Payments and double spending, FORTH – the precursor for
Bitcoin scripting, Bitcoin Scripts , Bitcoin P2P Network, Transaction in Bitcoin Network, Block Mining,
Block propagation and block relay, Consensus introduction, Distributed consensus in open environmentsConsensus in a Bitcoin network
BITCOIN CONSENSUS
Bitcoin Consensus, Proof of Work (PoW)- Hashcash PoW , Bitcoin PoW, Attacks on PoW ,monopoly
problem- Proof of Stake- Proof of Burn - Proof of Elapsed Time - Bitcoin Miner, Mining Difficulty,
Mining Pool-Permissioned model and use cases, Design issues for Permissioned Blockchains, Execute
contracts- Consensus models for permissioned blockchain-Distributed consensus in closed environment
Paxos
DISTRIBUTED CONSENSUS
RAFT Consensus-Byzantine general problem, Byzantine fault tolerant system-Agreement Protocol,
Lamport-Shostak-Pease BFT Algorithm-BFT over Asynchronous systems, Practical Byzantine Fault
Tolerance
HYPER LEDGER FABRIC & ETHERUM
Architecture of Hyperledger fabric v1.1-Introduction to hyperledger fabric v1.1, chain code- Ethereum:
Ethereum network, EVM, Transaction fee, Mist Browser, Ether, Gas, Solidity, Smart contracts, Truffle
Design and issue Crypto currency, Mining, DApps, DAO
BLOCKCHAIN APPLICATIONS
Internet of Things-Medical Record Management System-Block chain in Government and Block chain
Security-Block chain Use Cases –Finance
COURSE OUTCOMES
CO1: Understand emerging abstract models for Block chain Technology.

CO2: Identify major research challenges and technical gaps existing between theory and practice in
crypto currency domain.
CO3: It provides conceptual understanding of the function of Blockchain as a method of securing
distributed ledgers, how consensus on their contents is achieved, and the new applications that they
enable.
CO4: Apply hyperledger Fabric and Etherum platform to implement the Block chain Application.
REFERENCES
1. Mastering Blockchain: Deeper insights into decentralization, cryptography, Bitcoin, and popular
Blockchain frameworks by Bashir, Imran,2017.
2. Arvind Narayanan, Joseph Bonneau, Edward Felten, Andrew Miller, and Steven Goldfeder. Bitcoin
and cryptocurrency technologies: a comprehensive introduction. Princeton University Press, 2016.
3. Joseph Bonneau et al, SoK: Research perspectives and challenges for Bitcoin and cryptocurrency,
IEEE Symposium on security and Privacy, 2015.

Course Code: 71318

Course Title: Block chain
Technology lab

L: T: 2P

Credits:1

1. To Develop Naive Block chain construction.
2. Design Memory Hard algorithm and its Implementation
3. Design Toy application using Blockchain
5. Program to Solve a Mining puzzles using Block chain
6. The ability to formulate mathematical models and problem-solving skills through programming
techniques for addressing real-time problems using appropriate data structures and algorithms.
7. The ability to provide design, build, and deploy a distributed application and provide solutions using
block chain applications to enhance business measures by sharing information safely and effectively.
8. The ability to create crypto currencies and give a strong technical understanding of Block chain
technologies with an in-depth understanding of applications, open research challenges, and future
directions.

Course Code:
BTCS714-18

Course Title: Parallel Computing

3L: 0T: 0P

Credits: 3

Detailed Contents:
Introduction: Paradigms of parallel computing: Synchronous - vector/array, SIMD, Systolic;
Asynchronous - MIMD, reduction paradigm.
Hardware taxonomy: Flynn's classifications, Handler's classifications. Software taxonomy: Kung's
taxonomy, SPMD.

Abstract parallel computational models: Combinational circuits, Sorting network, PRAM models,
Interconnection RAMs. Parallelism approaches - data parallelism, control parallelism
Performance Metrices: Laws governing performance measurements. Metrices - speedups, efficiency,
utilization, communication overheads, single/multiple program performances, bench marks.
Parallel Processors: Taxonomy and topology - shared memory mutliprocessors, distributed memory
networks. Processor organization - Static and dynamic interconnections. Embeddings and simulations.
Parallel Programming: Shared memory programming, distributed memory programming, object
oriented programming, data parallel programming, functional and dataflow programming.
Scheduling and Parallelization: Scheduling parallel programs. Loop scheduling. Parallelization of
sequential programs. Parallel programming support environments.
Books and References:
1. M. J. Quinn. Parallel Computing: Theory and Practice , McGraw Hill, New York, 1994.
2. T. G. Lewis and H. El-Rewini. Introduction to Parallel Computing , Prentice Hall, New Jersey,
1992.
3. T. G. Lewis. Parallel Programming: A Machine-Independent Approach , IEEE Computer Society
Press, Los Alamitos, 1994.
Research articles.
Course Code:
BTCS715-18

Course Title: Parallel Computing lab

L: T: 2P

Credits: 1

The details may be designed by course instructor as per the theory.

BTCS 716-18

Adhoc and Wireless Sensor
Networks

L:03, T:0, P: 0

Credits: 3

Detailed Contents:
UNIT 1:
{07hrs}(CO1)
ADHOC AND SENSORS NETWORKS – INTRODUCTION AND ROUTING PROTOCOLS:
Wireless Sensor Networks (WSNs): concepts and architectures - Applications of Ad Hoc and Sensor
Networks - Design Challenges in Ad hoc and Sensor Networks. Wireless Networks, Issues in Ad hoc
wireless networks, Routing Protocol for Ad Hoc Wireless Networks, Classifications of Routing Protocols,
Table Driven Routing Protocols – Destination Sequenced Distance Vector (DSDV), On–Demand Routing
protocols –Ad hoc On–Demand Distance Vector Routing (AODV).
UNIT2:
{09hrs}(CO2)
WSN NETWORKING CONCEPT AND MAC PROTOCOLS :

Issues in Designing a MAC Protocol for Ad Hoc Wireless Networks - Design Goals of a MAC Protocol
for Ad Hoc Wireless Networks, MAC Protocols for wireless sensors Networks, Low duty cycle Protocols
and Wakeup concepts, Classification of MAC Protocols , S-MAC, Contention based protocols -PAMAS
schedule based protocols –LEACH, IEEE 802.15.4. MAC protocols , Energy efficient routing challenges
and issues in transport layer
UNIT 3:
{06hrs}(CO3)
ROUTING PROTOCOLS AND TRANSPORT LAYER IN AD HOC WIRELESS NETWORKS:
Routing Protocol: Issues in designing a routing protocol for Ad hoc networks - Classification- proactive
routing - reactive routing (on-demand) - hybrid routing - Transport Layer protocol for Ad hoc networks Design Goals of a Transport Layer Protocol for Ad Hoc Wireless Networks -Classification of Transport
Layer solutions-TCP over Ad hoc wireless ,
UNIT4:
{06hrs}(CO4)
SENSOR NETWORKS INTRODUCTION AND ARCHITECTURES:
Challenges for Wireless Sensor Networks, Enabling Technologies for Wireless Sensor Networks, WSN
application examples, Single-Node Architecture – Hardware Components, Energy Consumption of
Sensor Nodes, Network Architecture – Sensor Network Scenarios, Transceiver Design Considerations.
UNIT 5:
{07hrs}(CO5)
SENSOR NETWORK SECURITY- NETWORK SECURITY :
Security in Ad Hoc Wireless Networks - Network Security Requirements. Network Security requirements
issues and Challenges in security provisioning Network, Security Attacks. Layer wise attack in wireless
sensor networks, possible solutions for Jamming, tampering black hole attack, Flooding attack, Key
distribution and Management, Secure Routing -SPINS reliability requirements in sensors Networks.
Sensor Network Platforms and Tools

Course Outcomes:
After undergoing this course, the students will be able to:
CO Nos.

Course Outcomes:

CO1

Explain the Fundamental Concepts and applications of ad hoc and wireless sensor
networks
and apply this knowledge to identify the suitable routing algorithm based
on the network.

CO2

Apply the knowledge to identify appropriate physical and MAC layer protocols

CO3:

Understand the transport layer and Describe routing protocols for ad hoc wireless
networks with respect to TCP design issues

CO 4

Be familiar with the OS used in Wireless Sensor Networks and build basic modules
CO 5:

Understand the Challenges in security provisioning ,Security Attacks and security issues
possible in Adhoc and Sensors Networks

CO 5

Suggested Readings/ Books:
Text Books:
1. C. Siva Ram Murthy, and B. S. Manoj, "Ad Hoc Wireless Networks: Architectures and Protocols ",
Pearson Education, 2008.
2. Labiod. H, “Wireless Adhoc and Sensor Networks”, Wiley, 2008.
3. 3. Li, X, “Wireless ad -hoc and sensor Networks: theory and applications”, Cambridge University
Press, 2008.
Reference Books
1. Carlos De Morais Cordeiro, Dharma Prakash Agrawal “Ad Hoc & Sensor Networks: Theory and
Applications”, world Scientific Publishing Company, 2nd edition, 2011.
2. Feng Zhao and Leonides Guibas, "Wireless Sensor Networks", Elsevier Publication
3. Holger Karl and Andreas Willig “Protocols and Architectures for Wireless Sensor Networks”, Wiley,
2005
(soft copy available) .
4. Kazem Sohraby, Daniel Minoli, & Taieb Znati, “Wireless Sensor Networks Technology, Protocols,
and Applications”, John Wiley, 2007. (soft copyavailable).
5. Anna Hac, “Wireless Sensor Network Designs”, John Wiley, 2003.(soft copy available)
Online Resources:
1. www.wirelessnetworksonline.com
2. www.securityinwireless.com
3. www.ida.liu.se/~petel71/SN/lecture-notes/sn.pdf Practice Aspects 1. NS2 Simulator tool

BTCS 717-18

Adhoc and Wireless Sensor Networks Lab

L:0, T:0, P: 2

Credits: 1

List of Experiments :

Sr.
No

Name and list of Practical

1

Introduction of Wireless sensor network applications and its simulation

2

Network Simulator installation of wireless sensor network.

3

Implementation of routing protocol in NS2 for DSR protocol

4

Study other wireless sensor network simulators (Mannasim. Contiki

5

Implementation of routing protocol in NS2 for AODV protocol for TORA protocol

Course Code:
BTCS718-18

Course Title: Quantum Computing

3L: 0T: 0P

Credits: 3

Detailed Syllabus
UNIT-1 Fundamental Concepts: Global Perspectives, Quantum Bits, Quantum
Computation, Quantum Algorithms, Quantum Information, Postulates of Quantum
Mechanisms.
8
Hrs.
UNIT-II Quantum Computation: Quantum Circuits – Quantum algorithms, Single Orbit
operations, Control Operations, Measurement, Universal Quantum Gates, Simulation of
Quantum Systems, Quantum Fourier transform, Phase estimation, Applications,
Quantum search algorithms – Quantum counting – Speeding up the solution of NP –
complete problems – Quantum Search for an unstructured database.
8 Hrs.
UNIT-III Quantum Computers: Guiding Principles, Conditions for Quantum
Computation, Harmonic Oscillator Quantum Computer, Optical Photon Quantum
Computer – Optical cavity Quantum electrodynamics, Ion traps, Nuclear Magnetic
resonance.
8
Hrs.
UNIT-IV Quantum Information: Quantum noise and Quantum Operations – Classical
Noise and Markov Processes, Quantum Operations, Examples of Quantum noise and
Quantum Operations – Applications of Quantum operations, Limitations of the Quantum
operations formalism, Distance Measures for Quantum information.
8
Hrs.
UNIT-V Quantum Error Correction: Introduction, Shor code, Theory of Quantum Error
–Correction, Constructing Quantum Codes, Stabilizer codes, Fault – Tolerant Quantum
Computation, Entropy and information – Shannon Entropy, Basic properties of Entropy,
Von Neumann, Strong Sub Additivity, Data Compression, Entanglement as a physical
resource .
8
Hrs.
Course Outcomes;
At the end of the course students should:

CO1: understand the quantum model of computation and the basic principles of
quantum mechanics;
CO2: be familiar with basic quantum algorithms and their analysis;
CO3: be familiar with basic quantum protocols such as teleportation and super dense
coding;
CO4: see how the quantum model relates to classical models of deterministic and
probabilistic computation.
Text books:
1. Micheal A. Nielsen. &Issac L. Chiang, “Quantum Computation and Quantum
Information”, Cambridge University Press, Fint South Asian edition, 2002.
2. Eleanor G. Rieffel , Wolfgang H. Polak , “Quantum Computing - A Gentle
Introduction” (Scientific and Engineering Computation) Paperback – Import, 3 Oct 2014
3. Computing since Democritus by Scott Aaronson

4. Computer Science: An Introduction by N. DavidMermin 5. Yanofsky's and Mannucci,
Quantum Computing for Computer Scientists.

Course Code:
BTCS719-18

Course Title: Quantum
Computing lab

L: T: 2P

Credits: 1

1. List modern relevant quantum algorithms and their purposes.
2. Explain the key principles of the various models of quantum computation (circuit,
measurement-based, adiabatic model).
3. Explain the basic structure of the quantum algorithms addressed in the course that
are based on the circuit model, and to compute the outcome of basic quantum
circuits.
4. Compare, in terms of time complexity, what quantum advantage is expected from the
quantum algorithms addressed in the course with respect to their classical
counterparts.
5. Program simple quantum algorithms on a cloud quantum computer or a cloud
simulator.
6. Understand the basic principles of the continuous variable encoding for quantum
information processing.
7. Give examples of the motivation for applying quantum computing to machine
learning and of what the obstacles are to achieving an advantage from doing so.