M. S. RAMAIAH INSTITUTE OF TECHNOLOGY
BANGALORE-54
(Autonomous Institute, Affiliated to VTU)
Computer Science and Engineering
Outcomes Based Education Curricula
(Academic Year 2015 – 2016)
V & VI Semester
History of the Institute
M. S. Ramaiah Institute of Technology was started in 1962 by the late Dr. M.S. Ramaiah, our
Founder Chairman who was a renowned visionary, philanthropist, and a pioneer in creating
several landmark infrastructure projects in India. Noticing the shortage of talented engineering
professionals required to build a modern India, Dr. M.S. Ramaiah envisioned MSRIT as an
institute of excellence imparting quality and affordable education. Part of Gokula Education
Foundation, MSRIT has grown over the years with significant contributions from various
professionals in different capacities, ably led by Dr. M.S. Ramaiah himself, whose personal
commitment has seen the institution through its formative years. Today, MSRIT stands tall as
one of India’s finest names in Engineering Education and has produced around 35,000
engineering professionals who occupy responsible positions across the globe.
History of Department of Computer Science and Engineering
Year of Establishment
Names of the Programmes
offered
1984
1. UG: B.E. in Computer science and Engineering
2. PG: M.Tech. in Computer Science and Engineering
3. PG: M.Tech. in Computer Networks and Engineering
4. Ph.D
5. M.Sc(Engg.) by research
Faculty
Name
Sl. No.
Qualification
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
Dr. K G Srinivasa
M.E, Ph.D
Dr. R. Srinivasan
D.Sc.
Dr .S Ramani
Ph.D
Nagabhushan A.M
M.Tech
Dr. Anita Kanavalli
M.E., Ph.D
Dr. Seema S
M.S., Ph.D
Dr. Annapurna P. Patil
M. Tech, Ph.D
Jagadish S Kallimani
M.Tech, (Ph.D)
Jayalakshmi D S
M.Sc(Engg), (Ph.D)
Dr. Monica R Mundada
M.Tech, Ph.D
Sanjeetha R
M.Tech
A Parkavi
M.E. (Ph.D)
Veena G S
M.Tech (Ph.D)
J Geetha
M.Tech, (Ph.D)
Dr. T N R Kumar
M. Tech Ph.D
Mamatha Jadav V
M.Tech
Chethan C T
B.E.
Sini Anna Alex
M.E, (Ph.D)
Vandana Sardar
M.E.
Meera Devi
M.Tech
Mallegowda M
M.Tech
Divakar Harekal
M.E.
Chandrika Prasad
M.Tech
S Rajarajeswari
M.E, (Ph.D)
Sowmyarani C N
M.E. (Ph.D)
Pramod S Sunagar
M.Tech
Sowmya B J
M.Tech
Pradeep Kumar D
M.Tech
Ganeshayya I Shidaganti
M.Tech
Chetan
M.Tech
Darshana A Naik
M.Tech
Srinidhi H
M.Tech
Aparna R
B.E, M.Tech
Hanumantha Raju R
B.E, M.Tech
Visiting Faculty Members from Industry
35.
Dr. Ramamurthy Badrinath
Ph.D
36.
N. Pramod
B.E.
37.
Jayasimha Rao
38.
Sriram Kashyap
M.S. in Machine Learning and
Data Mining from Aalto
University School of Science
MTech from IIT Madras
Designation
HOD, Professor
Emeritus Professor
Emeritus Professor
Emeritus Professor
Professor
Associate Professor
Associate Professor
Associate Professor
Associate Professor
Associate Professor
Assistant Professor
Assistant Professor
Assistant Professor
Assistant Professor
Assistant Professor
Assistant Professor
Assistant Professor
Assistant Professor
Assistant Professor
Assistant Professor
Assistant Professor
Assistant Professor
Assistant Professor
Assistant Professor
Assistant Professor
Assistant Professor
Assistant Professor
Assistant Professor
Assistant Professor
Assistant Professor
Assistant Professor
Assistant Professor
Assistant Professor
Assistant Professor
AICTE-INAE distinguished
Visiting Professor
Application Engineering at
Thoughtworks Pvt. Ltd.
Entrepreneur
Intel, Bangalore
Vision and Mission of the Institute
Vision
To evolve into an autonomous institution of International standards for imparting quality
Technical Education
Mission
MSRIT shall deliver global quality technical education by nurturing a conducive learning
environment for a better tomorrow through continuous improvement and customization.
Quality Policy
“We at M. S. Ramaiah Institute of Technology, Bangalore strive to deliver comprehensive,
continually enhanced, global quality technical and management education through an established
Quality Management system complemented by the synergistic interaction of the stakeholders
concerned”.
Vision and Mission of the Department
Vision
To build a strong learning and research environment in the field of Computer Science and
Engineering that responds to the challenges of 21st century.
Mission
To produce computer science graduates who, trained in design and implementation of
computational systems through competitive curriculum and research in collaboration with
industry and other organizations.
To educate students in technology competencies by providing professionally committed faculty
and staff.
To inculcate strong ethical values, leadership abilities and research capabilities in the minds of
students so as to work towards the progress of the society.
Process for Defining the Vision and the Mission of the Department
Programme Educational Objectives (PEOs)
A B.E. (Computer Science & Engineering) graduate of M. S. Ramaiah Institute of Technology
should, within three to five years of graduation
1. Pursue a successful career in the field of Computer Science & Engineering or a related
field utilizing his/her education and contribute to the profession as an excellent employee,
or as an entrepreneur
2. Be aware of the developments in the field of Computer Science & Engineering,
continuously enhance their knowledge informally or by pursuing graduate studies
3. Be able to work effectively in multidisciplinary environments and be responsible
members/leaders of their communities
PEO Derivation Process
Programme Outcomes (POs)
The outcomes of the Bachelor of Engineering in Computer Science & Engineering Programme
are as follows:
A B.E. (Computer Science & Engineering) graduate must demonstrate
1. An ability to apply knowledge of mathematics, science, and engineering as it applies to
Computer Science & Engineering
2. An ability to identify, formulate, study, and analyze and solve complex computing
problems
3. An ability to design a computer-based system, component, software or process to meet the
desired needs
4. An ability to design and conduct experiments, evaluate results and provide valid
conclusions
5. An ability to use modern computing techniques, technologies and tools necessary for
computing engineering practice.
6. An ability to understand and assess the societal, legal and security issues related to the
practice of computer science and engineering.
7. An ability to understand the impact of computing solutions in an economic, environmental
and societal context.
8. An understanding of professional and ethical responsibilities in professional engineering
practice.
9. An ability to function effectively individually and in team, and in multi-disciplinary
environment.
10. An ability to communicate effectively.
11. An understanding of the engineering and management principles required for project and
finance management.
12. Recognition of the need for, and an ability to engage in life-long learning.
PO Derivation Process
Mapping of PEOs and POs
Sl.
No.
1
2
3
Programme
Educational
Objectives
Excel in
career
Life-long
learning
Work in
diverse
teams and
show
Leadership
Programme Outcomes
PO1
PO2
PO3
PO4
PO5
PO6
PO7
PO8
PO9
PO10
PO11
PO12
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
Curriculum Breakdown Distribution
Sl. No.
Courses
Weightage
1
Basic Science Core Courses
13%
2
Basic Engineering Science Core Courses
13%
3
Humanities and Social Science Core Courses
3%
4
Professional Courses and Electives
62%
5
Major Project
9%
6
Mandatory Learning Courses
0%
x
x
x
x
Board of Studies for the Term 2015-2016
1. Head of the Department
concerned:
2. At least five faculty members at
different
levels
covering
different
specializations
constituting nominated by the
Academic Council
Dr. K G Srinivasa
Chairperson
Dr. Anita Kanavalli
Prof. Jagadish S Kallimani
Prof. Jayalakshmi D S
Prof. H V Divakar
Prof. Sanjeetha R
Prof. A Parkavi
Prof. Chandrika Prasaad
Member
Member
Member
Member
Member
Member
Member
Dr. R. Srinivasan
Dr. S. Ramani
Prof. Nagabhushan A M
Member
Member
Member
Dr. Kavi Mahesh, Professor, PESIT
Dr. G Varaprasad Associate
Professor, BMSCE
Member
Member
Dr. N.K. Srinath, Professor, RVCE
Member
6. One
representative
from
industry/corporate sector allied
area relating to placement
nominated by the Academic
Council
Mr. Rajesh Vijayarajan, HewlettPackard
Member
7. One postgraduate meritorious
alumnus to be nominated by the
Principal
Sriram Kashyap, Intel Corporation
Member
3. Special invitees
4. Two experts in the subject from
outside the college
5. One expert from outside the
college, nominated by the Vice
Chancellor
9
Department Advisory Board for the term 2015-2016
1. Head of the Department concerned
Dr. K G Srinivasa
Member
2. Experts from other organizations for
Department Advisory Board
Dr. Satish Vadhiyar, SERC, IISC Bangalore
Dr. Srinivasaraghavan, IIIT Bangalore
Dr. K Sangeeta Iyer
Member
Member
Member
Industry Advisory Board for the Term 2015-2016
1. Head of the Department concerned
Dr. K G Srinivasa
Member
2. Experts from industry constituting
the Industry Advisory Board
Dr. Badrinath Ramamurthy, HP Labs, India
Dr. N.C. Narendra, CTS
Dr. Yogesh Simhan, SERC, IISC
Mr. Sreekanth Iyer, IBM
Mr. Nishant Kulkarni, IBM
Mr. Muthuraman Ranganath, SAP Technologies
Mr. K Murali, Amazon
Member
Member
Member
Member
Member
Member
Member
10
Scheme of Studies for Third Year B.E. (CSE) for the batch 2013-2017
V Semester
Total Credits: 25
Code
Subject
L T P Credit
CS512
Operating Systems
4 0 0 4
CS513
Database Systems
4 0 0 4
CS514
Computer Networks
4 0 0 4
CS515
Software Engineering
3 0 1 4
Elective Group I
* * * 4
CSL511 Java Laboratory
0 1 1 2
CSL512 DBMS and OS Laboratory
0 0 2 2
CSL514 Computer Networks Laboratory
0 0 1 1
VI Semester
Total Credits: 25
Code
Subject
L T P Credit
CS612
Compiler Design
3 1 0 4
CS613
Embedded Systems
4 0 0 4
CS621
Computer Security
3 1 0 4
CS622
Computer Graphics
3 0 1 4
Elective Group II
* * * 4
CSL611 Unix Systems Programming Laboratory
0 1 1 2
CSL613 Embedded Systems Laboratory
0 0 1 1
CSL612 Internet Technologies & Software
0 1 1 2
Development for Portable Devices
Laboratory
List of Electives:
Elective Group I
Elective Group II
1 CSPE01 Data Mining (3:1:0)
13 CSPE13 Analysis of Computer Data
Networks (4:0:0)
2 CSPE02 Machine Learning (3:1:0)
14 CSPE14 Operations Research (4:0:0)
3 CSPE03 Cloud Computing (4:0:0)
15 CSPE15 Advanced Algorithms (3:0:1)
4 CSPE04 Distributed Systems (4:0:0)
16 CSPE16 Artificial Intelligence (4:0:0)
5 CSPE05 Big Data and Data Science
17 CSPE17 System Simulation (4:0:0)
(3:0:1)
6 CSPE06 Storage Area Networks (4:0:0) 18 CSPE18 Pattern Recognition (4:0:0)
7 CSPE07 Information Retrieval (3:0:1)
19 CSPE19 Advanced Computer Architecture
(4:0:0)
8 CSPE08 Social Network Analysis
20 CSPE20 Computer Systems Performance
(3:1:0)
Analysis (4:0:0)
9 CSPE09 Wireless Sensor Networks
21 CSPE21 Software Testing (3:0:1)
(4:0:0)
10 CSPE10 Mobile Computing (4:0:0)
22 CSPE22 Software Architecture and Design
Patterns (4:0:0)
11 CSPE11 Multimedia Computing (3:0:1) 23 CSPE23 Service Oriented Architectures
and Web Services (4:0:0)
12 CSPE12 Software Defined Networks
24 CSPE24 Object Oriented Modelling and
(4:0:0)
Design (4:0:0)
11
Course Title: Operating Systems
Course Code: CS512
Credits (L:T:P) : 4:0:0
Core/ Elective: Core
Type of Course: Lecture
Total Contact Hours: 56 Hrs
Prerequisites:
Nil
Course Contents:
Unit 1
Operating system Overview: Operating system objectives and Functions, Major Achievements, Developments
leading to Modern operating systems, Linux, Real-Time System Concepts
Unit 2
Process Description and Control: What is Process? Process states, Process Description, Process Control, Processes
and Threads. Process Scheduling: Basic Concepts, Scheduling criteria, Scheduling Algorithms
Unit 3
Concurrency: Mutual Exclusion and Synchronization: Principles of Concurrency, Mutual Exclusion: Hardware
Support, Semaphores, Monitors, Message Passing, Readers/Writers Problem. Concurrency: Deadlock and
Starvation: Principles of deadlock, Deadlock Prevention, Deadlock avoidance, Deadlock Detection, Dining
Philosopher’s Problem
Unit 4
Memory Management - Memory Management Strategies: Background, Swapping, Contiguous memory allocation,
Paging, Structure of page table, Segmentation. Virtual Memory Management: Background, Demand paging,
Copy-on-write, Page replacement, Allocation of frames, Thrashing.
File System: Implementation of File System - File System, File concept, Access methods, Directory structure, File
system mounting, File sharing, Protection.
Unit 5
Implementing File System: File system structure, File system implementation, Directory implementation,
Allocation methods, Free space management. Secondary Storage Structures: Mass storage structures: Disk
structure, Disk scheduling, Disk management, Swap space management
Text Book:
1. Abraham Silberschatz, Peter Baer Galvin, Greg Gagne: Operating System Principles, 8th edition, WileyIndia, 2011.
2. William Stallings: Operating Systems internals and Design Principles, 8th Edition, Pearson Education,
2014
3. Jean J Labrosse: MicroC/OS-II The Real-Time Kernel, Second Edition, CMP Books, 2006
Reference Books:
1. D.M Dhamdhere: Operating systems - A concept based Approach, 3rd Edition, Tata McGraw- Hill, 2012.
2. P.C.P. Bhatt: Introduction to Operating Systems Concepts and Practice, 3rd Edition, PHI, 2010.
3. Harvey M Deital: Operating systems, 3rd Edition, Pearson Education, 2011.
Course Delivery:
The course will be delivered through lectures, class room interaction, Online courses
12
Direct & Indirect Assessment Methods
Course Assessment and Evaluation:
What
To whom
C
I
E
S
E
E
When/ Where
(Frequency in the
course)
Max
marks
Evidence
collected
Contributing to
Course
Outcomes
Internal
assessment
tests
Thrice(Average of
the best two will
be computed)
30
Blue books
1,2, 3,4,5,6 & 7
Online
Courses/Quiz
Once
20
Quiz answers/
Course
certificates
1,2, 3,4,5,6 & 7
End of course
(Answering 5 of
10 questions)
100
Answer scripts
1,2, 3,4,5,6 & 7
Standard
examination
Students
End of course
survey
End of course
Questionnaire &
Responses
-
1,2, 3,4,5,6 & 7
Effectiveness of
Delivery of
instructions &
Assessment
Methods
Course Outcomes:
At the end of the course the student will be able to:
1. Describe the functions, major achievement and development of Operating Systems.
2. Implement concepts of Process scheduling using preemptive and non preemptive scheduling algorithms.
3. Illustrate the concepts of classical synchronization problems.
4. Demonstrate different methods for handling deadlocks using detection, prevention and avoidance
techniques.
5. Explain the concepts of memory management techniques including virtual memory.
6. Describe file systems and its implementation.
7. Illustrate secondary storage structure including disk scheduling algorithm.
Mapping course outcomes with programme outcomes:
Course Outcomes
Programme Outcomes
1
x
2
4
5
Implement concepts of Process scheduling using
preemptive and non preemptive scheduling
algorithms.
Illustrate
the
concepts
of
classical
synchronization problems.
x
x
x
x
x
x
x
x
Demonstrate different methods for handling
deadlocks using detection, prevention and
avoidance techniques.
Explain the concepts of memory management
techniques including virtual memory.
Describe file systems and its implementation.
x
x
x
x
x
x
x
x
x
x
x
x
Illustrate secondary storage structure including
disk scheduling algorithm.
x
x
Describe the functions, major achievement and
development of Operating Systems.
13
3
x
6
7
8
9
10
11
12
x
Course Title: Database Systems
Course Code: CS513
Credits (L:T:P) : 4:0:0
Core/ Elective: Core
Type of course: Lecture
Total Contact Hours: 56 Hours
Prerequisites: Nil
Course Contents:
Unit 1
Introduction: Characteristics of Database approach, Actors on the Scene, Workers behind the scene, Advantages of
using DBMS approach, Data models, schemas and instances, Three-schema architecture and data independence,
Database languages and interfaces, the database system environment, Centralized and client-server architectures,
Classification of Database Management systems, Entity-Relationship Model: Conceptual Database using high level
conceptual data models for Database Design, A Sample Database Application, Entity types, Entity sets Attributes
and Keys Relationship types, Relationship Sets, Roles and Structural Constraints Weak Entity Types..
Unit 2
Relational Model and Relational Algebra: Relational Model Concepts, Relational Model Concepts, Relational
Model Constraints and Relational Database Schema Update Operations, Transactions and Dealing with Constraint
violations, Unary Relational operations, Relational Algebra Operations from Set Theory, Binary Relational
Operations, JOIN and DIVISION, Additional Relational Operations, Examples of Queries in Relational Algebra
Relational Database Design Using ER- to-Relational Mapping.
Unit 3
Introduction to SQL: Overview of the SQL Query Language, SQL Data Definition, Basic structure of SQL
Queries, Additional Basic Operations, Null values, Aggregate Functions, nested Sub queries, Modification of the
Database, Join Expressions, Views, Transactions, Integrity Constraints, SQL Data Types and Schemas,
Authorization. Database programming issues and techniques, Embedded SQL.
Unit 4
Database Design: Informal Design Guidelines for Relation Schemas, Functional Dependencies, Normal Forms
based on Primary Keys, General Definitions of 2nd and 3rd Normal Forms, Boyce Codd Normal Forms, Multivalued
Dependencies and IV Normal Forms, Join Dependencies and V Normal Forms, Inference Rules, Equivalence and
Minimal Cover, Properties of Relational Decomposition, Algorithms for relational database schema design.
Unit 5
Transaction Management: Transaction Concept, A Simple Transaction Model, Transaction Atomicity and
Durability, Serializability, Transaction Isolation and Atomicity, Transaction Isolation Levels, Implementation of
Isolation Levels. Concurrency Control: Lock-Based Protocols, Deadlock Handling. Recovery System: Failure
Classification, Storage, Recovery and Atomicity, Recovery Algorithm
Text Book:
1. Elmasri and Navathe: Fundamentals of Database Systems, 5th Edition, Addison-Wesley, 2011.
2. Silberschatz, Korth and Sudharshan: Data base System Concepts, 6th Edition, Tata McGraw Hill, 2011
Reference Books:
1. .C.J. Date, A. Kannan, S. Swamynatham: An Introduction to Database Systems, 8th Edition, Pearson education,
2009.
Course Delivery:
The course will be delivered through lectures, class room interaction, group discussion, lab exercises and projects.
14
Course Assessment and Evaluation:
CIE
To Whom
Internal
Assessment
Tests
When/ Where
(Frequency in
the course)
Thrice(Average
of the best two
will be
computed)
Max
Marks
Evidence
Collected
Contribution to
Course Outcomes
30
Blue Books
1,2,3,4,5 & 6
Once
20
Project
Reports/Online
Course
certificates
2,3,4,5
End of Course
100
Answer scripts
1,2,3,4,5 & 6
Questionnaire
1,2,3,4,5 & 6
ffectiveness of
Delivery of
instructions &
Assessment Methods
Mini
Projects/Online
Course
Students
SEE
Direct & Indirect Assessment Methods
What
Standard
Examination
End of Course
Survey
End of the
course
-
Course Outcomes:
At the end of the course the student will be able to:
1. Differentiate database systems from traditional file systems by enumerating the features provided by database
systems..
2. Design entity-relationship diagrams to represent simple database applications
3. Construct relational algebraic expressions for queries using the concepts of relational database theory
4. Formulate using SQL, solutions to a broad range of query and data update problems
5. Apply Normalization to improve database design
6. Identify the basic issues of transaction processing and concurrency control.
Mapping Course Outcomes with Programme Outcomes:
Course Outcomes
Programme Outcomes
1
Differentiate database systems from traditional
file systems by enumerating the features
provided by database systems
Design entity-relationship diagrams to represent
simple database applications
Construct relational algebraic expressions for
queries using the concepts of relational database
theory
Formulate using SQL, solutions to a broad range
of query and data update problems
Apply Normalization to improve database
design
Identify
the basic issues of transaction
processing and concurrency control
x
2
3
4
5
x
x
x
x
x
x
x
6
x
x
x
x
x
x
15
x
x
7
8
9
10
11
12
Course Title: Computer Networks
Course Code: CS514
Credits (L:T:P:) : 4:0:0
Core/ Elective: Core
Type of course: Lecture
Total Contact Hours: 56 Hrs
Prerequisites: Data Communication
Course Contents:
Unit 1
Application Layer: Principles of Network Applications: Network Application Architectures,Application-Layer
Protocols,The Web and HTTP: Overview of HTTP, Non-Persistent and Persistent Connections, HTTP Message
Format, User-Server Interaction-Cookies,Web Caching,The Conditional GET.File Transfer-FTP:FTP Commands
and Replies, Electronic Mail in the Internet: SMTP, Comparison with HTTP, Mail Access Protocols. DNS—The
Internet’s Directory Service: Services Provided by DNS,Overview of How DNS Works,DNS Records and
Messages, Peer-to Peer Applications: P2P File Distribution, Distributed Hash Tables (DHTs), Case Study: P2P
Internet Telephony with Skype
Unit 2
Transport layer: Multiplexing and Demultiplexing,Connectionless Transport-UDP:UDP Segment Structure, UDP
Checksum,Connection-Oriented Transport-TCP: The TCP Connection, TCP Segment Structure, Round-Trip Time
Estimation and Timeout, Reliable Data Transfer, Flow Control,TCP Connection Management,Principles of
Congestion Control, Approaches to Congestion Control,TCP Congestion Control
Unit 3
Network Layer: Forwarding and Routing, The Internet Protocol (IP): Forwarding and addressing in the Internet,
Datagram Format, IPv4 Addressing, classful Addressing, Classless addressing,IPv6 Addressing, The IPv6 protocol,
ICMPv4, The ICMPv6 protocol,Transition from IPv4 to IPv6,Routing Algorithms- The Distance-Vector
Routing,Link-State Routing, path vector routing
Unit 4
Unicast routing protocols-RIP, OSPF, BGP4, Multicast Routing: Introduction, Multicasting basics, Intradomain and
interdomain multicast protocols, IGMP
Wireless and Mobile Networks: Introduction, WiFi 802.11 Wireless LANs- The 802.11 Architecture,Mobility in
the Same IP Subnet, Advanced Features in 802.11, Beyond 802.11, WiMAX, Cellular Internet Access-An Overview
of Cellular Architecture, Mobility Management Principles, Addressing, Routing to a Mobile Node, Mobile IP,
Managing Mobility in Cellular Networks, Routing Calls to a Mobile User, Handoffs in GSM, Wireless and
Mobility: Impact on Higher-layer Protocols
Unit 5
Multimedia Networking: Multimedia Networking Applications- Examples of Multimedia Applications, Streaming
Stored Audio and Video - RTSP, Removing Jitter at the Receiver for Audio, Recovering from Packet Loss,
Distributing Multimedia in Today’s Internet - Content Distribution Networks, Protocols for Real-Time Interactive
Applications, RTP, RTCP, SIP, H.323, Flow control to improve QoS, Integrated Services, Differentiated services.
Text Book:
1. James F. Kurose and Keith W. Ross: Computer Networking: A Top-Down Approach, 5th edition, AddisonWesley, 2009.
2. Forouzan: Data Communications and Networking, 5th edition, McGraw Hill Education 2013.
Reference Books:
1. Larry L. Peterson and Bruce S Davie: Computer Networks: A Systems Approach, Fifth Edition, Elsevier, 2011.
2. Tanenbaum: Computer Networks, 4th Ed, Pearson Education/PHI, 2003.
3. William Stallings: Data and Computer Communications, 8th Edition, Pearson Education, 2012.
4. Behrouz A. Forouzan: Data communication and Networking, 4th edition, Tata McGraw-Hill, 2012.
Course Delivery:
The course will be delivered through lectures, class room interaction, group discussion and lab exercises.
16
Course Assessment and Evaluation:
To
Whom
Direct & Indirect Assessment Methods
What
Internal
Assessment Tests
When/ Where
(Frequency in
the course)
Thrice(Average of
the best two will
be computed)
Max
Marks
Evidence
Collected
Contribution to
Course Outcomes
30
Blue Books
1,2 3,4,5,6 & 7
Soft copy of
the ppt and
report
1,2 3,4,5,6 & 7
Answer
scripts
1,2 3,4,5,6 & 7
Questionnaire
1,2 3,4,5,6 & 7
Effectiveness of
Delivery of
instructions &
Assessment
Methods
CIE
Theory
Assignments
SEE
Standard
Examination
Students
End of Course
Survey
Once
20
End of Course
(Answering
5 of 10 questions)
100
End of the course
-
Course Outcomes:
At the end of the course students should be able to:
1. Describe the various application layer protocols used by TCP/IP reference model
2. Differentiate between connection oriented and connection less services of transport layer.
3. Solve problems of routing using various routing protocols and algorithms.
4. Identify the drawbacks of classful addressing and the need for classless & IPv6 addressing.
5. Manage mobility in Internet and cellular networks.
6. Compare features of different wireless network standards
7. Identify the challenges in multimedia networking with respect to content delivery and Quality of Service.
Mapping Course Outcomes with Programme Outcomes:
Course Outcomes
Programme Outcomes
1
2
3
4
5
6
x
x
x
x
x
x
x
x
x
x
x
x
Solve problems of routing using various routing
protocols and algorithms.
x
x
x
x
x
x
Identify the drawbacks of classful addressing and the
need for classless & IPv6 addressing.
x
x
x
x
x
Manage mobility in Internet and cellular networks.
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
Describe the various application layer protocols used
by TCP/IP reference model
Differentiate between connection oriented and
connection less services of transport layer.
Compare features of different wireless network
standards
Identify the challenges in multimedia networking
with respect to content delivery and Quality of
Service.
17
x
7
8
9
10
11
12
Course Title: Software Engineering
Course Code: CS515
Credits (L:T:P) : 3:0:1
Core/ Elective: Core
Type of course: Lecture, Practical
Total Contact Hours: 70
Prerequisites:
Nil
Course Contents:
Unit 1
The Software Problem & Processes: Cost, Schedule & Quality, Scale & Change, Software Processes: Process &
Project, Component Software Processes, Software Development Process Models, Project Management Process
Unit 2
Requirements Analysis & Project Planning: Requirements Analysis & Specification: Value of a Good SRS,
Requirements Process, Requirements Specification, Functional Specification with Use Cases, Other Approaches for
Analysis,
Planning a Software Project: Effort Estimation, Project Schedule & Staffing, Quality Planning, Risk Management
Planning, Project Monitoring Plan
Unit 3
Design, Coding: Design: Design Concepts, Function-oriented Design, Object-oriented Design, Detailed Design,
Metrics,
Coding: Programming Principles & Guidelines, Incrementally Developing Code, Managing Evolving Code.
Unit 4
Unit Testing and Testing: Unit Testing, Code Inspection, Metrics Testing Concepts, Testing Process, Black-box
Testing, White-box Testing, Metrics.
Unit 5
Software Engineering for new paradigms- Web and Cloud Web Engineering: Web Applications vs
Conventional Software
Impact of Cloud computing on Software Development life cycle: Limitations and Challenges in Cloud-Based
Applications Development- Introduction and Challenges. Impact of Cloud computing on Software Development life
cycle.
Textbooks
1. Pankaj Jalote: A Concise Introduction to Software Engineering , Springer, 2008 (Chapters: 1-4, 6-8)
2. Emilia Mendes, Nile Mosley: Web Engineering, Springer, 2006 (Chapter: 1)
3. Zaigham Mahmood, Saqib Saeed: Software Engineering framework for the cloud computing Paradigms,
Springer, 2013(Chapters 3,4)
Reference Books
1. Roger S. Pressman: Software Engineering A Practitioner's Approach, 7th Edition, McGraw Hill, 2010
2. David Gustafson: Software Engineering, Schaum's Outline Series, McGraw Hill, 2002 (Chapters: 6)
Course Delivery:
The course will be delivered through lectures in the classroom
18
Course Assessment and Evaluation:
To
Whom
Direct & Indirect Assessment Methods
What
Internal
Assessment
Test
Software
Engineering
Lab
using
open source
tools
CIE
CIE
Standard
Examination
SEE
End of Course
Survey
Students
When/
Where
(Frequency in the
course)
Thrice (Average of
the best two will be
computed)
Max
Marks
Evidence
Collected
Contribution
to
Course
Outcomes
30
Blue Books
1, 2, 3, 4 & 5
Daily
Evaluation
based
on
the
Performance(20M)
20
Report
1,2,3,4 & 5
End of Course
(Answering
5 of 10 questions)
100
Answer
scripts
1,2,3,4 & 5
Questionnaire
1, 2, 3, 4 & 5
Effectiveness of
Delivery
of
instructions &
Assessment
Methods
End of the course
-
Course Outcomes:
At the end of the course the students should be able to:
1. Recall the principles and techniques of Software Engineering.
2. Identify the activities in project management, requirement engineering process and the different types of
system models.
3. Illustrate the knowledge of design engineering in software development.
4. Formulate different testing methods and tools.
5. Familiarize and differentiate the need for software engineering practices for Web Engineering and Cloud
Computing.
Mapping Course Outcomes with Programme Outcomes:
Course Outcomes
Recall the principles and techniques of Software
Engineering
Identify the activities in project management,
requirement engineering process and the different
types of system models
Illustrate the knowledge of design engineering in
software development
Formulate different testing methods and tools
Familiarize and differentiate the need for software
engineering practices for Web Engineering and Cloud
Computing.
Programme Outcomes
1
2
x
3
4
5
6
7
8
9
10
11
12
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
19
Course Title: Java Laboratory
Course Code: CSL511
Credits (L:T:P) : 0:1:1
Core/ Elective: Core
Type of course: Practical
Total Contact Hours: 28
Prerequisites: OOPS with C++
Course Contents:
There shall be a minimum of 2 exercises conducted on each of the following topics.
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
Classes, objects, overloading
Inheritance, interface and packages
Exception Handling
Multi-Threading
Event Handling
Collection Frame Works
Swings and Strings
JDBC
Servlets
JSP
JSP and Servlets using JDBC
Java Bean
Network Programming using sockets.
Mini-Project
Reference Books:
1. Herbert Schildt: Java The Complete Reference, 8th Edition, Tata McGraw Hill, 2013.
2. Y. Daniel Liang: Introduction to JAVA Programming, 7th Edition, Pearson Education, 2012.
3. Jim Keogh: J2EE The Complete Reference, first edition, Tata McGraw Hill, 2011.
4. Ivan Bayross, Sharanam Shah, Cyntiha Bayross and Vishali Shah:Java EE 5 for
Beginners, 2nd edition SPD (Sharoff Publishers & Distributors Pvt. Ltd.), August 2008.
Course Delivery:
The course will be delivered through lectures in the laboratory with exercises.
20
Course Assessment and Evaluation:
To
Whom
What
Direct & Indirect Assessment Methods
Lab Test
CIE
Demo at the end of
semester
Mini project
Viva
SEE
Lab
Examination
End of Course
Survey
When/
Where
(Frequency in the
course)
1Lab Test
Students
Every
Week(Average of
the total score will
be computed)
End
of
Course
(Executing
2
programs)
End of the course
Max
Marks
Evidence
Collected
Contribution
to
Course Outcomes
20
Data sheets
1,2,3,4,5,6 & 7
20
code
1,2,3,4,5,6 & 7
10
Viva Result
Sheets
Recollection Skills
50
-
Answer
scripts
1,2,3,4,5,6 & 7
Questionnaire
1,2,3,4,5,6 & 7
Effectiveness of
Delivery of
instructions &
Assessment Methods
Course Outcomes:
At the end of the course the students should be able to:
1. Design Java applications with inheritance and interface concepts.
2. Design Java applications with multithreading concepts and demonstrate the error handling concepts.
3. Design GUI applications with the help of swings and handle events.
4. Implement Data structures using Java Collection Frame Works.
5. Design client server applications using socket Programming
6. Design J2EE applications with database access.
7. Develop web applications using JSP and Servlets.
Mapping Course Outcomes with Programme Outcomes:
Course Outcomes
Design Java applications with inheritance and
interface concepts.
Design Java applications with multithreading
concepts and demonstrate the error handling
concepts.
Design GUI applications with the help of swings
and handle events
Implement Data structures using Java Collection
Frame Works
Design client server applications using socket
Programming
Design J2EE applications with database access
Develop web applications using JSP and Servlets
Programme Outcomes
1
2
3
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
21
4
5
6
7
8
9
10
11
12
Course Title: DBMS and OS Laboratory
Course Code: CSL512
Credits (L:T:P) : 0:0:2
Core/ Elective: Core
Type of course: Practical
Total Contact Hours: 56
Prerequisites: Nil
Course Contents:
1. Introduction to MongoDB and CRUD Operations
2. MongoDBUsage in Enterprise Applications
3. Develop an Entity-Relationship(ER) Model and Mapping to Relational Model
4. Impalement SQL Queries using DDL,DML Statements
5. Build an Application model in Oracle DB using Nested queries, Triggers and Views
6. Design a Database application for a particular case study using Visual Basic/Java Script in visual studio
/Eclipse Tool.
7. UNIX operating system calls
8. Implement various Scheduling Algorithms
9. Develop an Application using Threading , Synchronization and Inter Process communication Applications
10. Implement Various File Organization Techniques
11. Implement Bankers Algorithm for Dead Lock Avoidance
12. Demonstration of working of Operating System using VM Emulators
Reference Books:
1. Database Management Systems” by Raghu Ramakrishnan, JohannersGehrke, Second Edition. McGrawHill Education
2. Fundamentals of Database Systems” by RamezElmasri, Shamkant B. Navathe ,Fifth Edition, Pearson
Publications
3. Database System Concepts” by Abraham Silberschatz , Henry F. Korth, sixth Edition ,McGraw Hill
Education
4. Operating System Concepts” by Abraham Silberschatz ,Peter B. Galvin, Seventh edition Addison-Wesley
Publisher
Course Delivery:
The course will be delivered through lab exercises using Software Tools
Course Assessment and Evaluation:
Direct & Indirect
Assessment Methods
What
To
Whom
Internal
Assessment
Tests
CIE
When/ Where
(Frequency in the
course)
Twice(Average of
the best two will
be computed)
Max
Marks
Evidence
Collected
40
Record Book /
Data sheets
Contribution
to Course
Outcomes
1,2,3,4,5,6,7 &
8
Students
Mini Project
End of the Course
22
10
Project
Documentation
1,2,3,4,5,6,7 &
8
SEE
Practical
Examination
End of Course
Survey
End of Course
50
End of the course
Answer scripts
1,2,3,4,5,6,7 &
8
Questionnaire
1,2,3,4,5,6,7 &
8 Effectiveness
of Delivery of
instructions &
Assessment
Methods
-
Course Outcomes:
At the end of the course the student will be able to
1. Recognize the Core MongoDB Operations
2. Create an Enterprise Application using MongoDB
3. Design an OracleDB ApplicationUsing SQL Queries
4. Develop a Real-time DB Application Using IDE
5. Construct a program on, Dead lock Avoidance to ensure deadlock is avoided.
6. Illustrate the concepts of Threading, Synchronization and IPC.
7. Create Programs on Various File Allocation Technique.
8. Compare the efficiency of various Scheduling Algorithms.
Mapping Course Outcomes with Programme Outcomes:
Course Outcomes
Programme Outcomes
1
Recognize the Core MongoDB Operations
Create an Enterprise Application using MongoDB
Design an Oracle DB Application Using SQL
Queries
Develop a Real-time DB Application Using IDE
Construct a program on, Dead lock Avoidance to
ensure deadlock is avoided.
2
x
3
4
x
x
x
x
x
x
x
x
x
x
x
Illustrate the concepts of Threading,
Synchronization and IPC.
x
x
Create Programs on Various File Allocation
Technique.
x
x
Compare the efficiency of various Scheduling
Algorithms.
x
23
5
x
x
x
6
7
8
9
10
11
12
Course Title: Computer Networks Laboratory
Course Code: CSL514
Credits (L:T:P) : 0:0:1
Core/ Elective: Core
Type of course: Practical
Total Contact Hours: 28
Prerequisites: Data Communication
Course Contents:
Note: Student is required to solve one problem from PART-A and one problem from PART-B. The questions
are allotted based on lots. Both questions carry equal marks.
PART - A
Implement the following in C/C++ or Wireshark as suitable.
1. Write a program for error detection using CRC-CCITT (16-bits).
2. Write a program to generate Hamming Code for error detection and correction.
3. Trace Hypertext Transfer Protocol.
4. Trace Domain Name Server.
5. Write a client-server program using TCP/IP sockets in which client requests for a file by sending the file
name to the server, and the server sends back the contents of the requested file if present.
6. Trace Internet Protocol and Internet Control Message Protocol
7.
Trace Dynamic Host Configuration Protocol.
8. Write a program to implement traffic policing using Leaky bucket algorithm.
9. Write a program to implement traffic policing using Token bucket algorithm.
PART-B
The following experiments shall be conducted using either NS-2/NS3/OMNET++ or any other suitable
simulator.
10. Simulate a three nodes point-to-point network with duplex links between them. Set the queue size vary the
bandwidth and find the number of packets dropped.
11. Simulate a four node point-to-point network, and connect the links as follows: n0-n2, n1-n2 and n2-n3.
Apply TCP agent between n0-n3 and UDP agent between n1-n3. Apply relevant applications over TCP and
UDP agents by changing the parameters and determine the number of packets sent by TCP/UDP.
12. Simulate simple Extended Service Set with transmitting nodes in wireless LAN and determine the
performance with respect to transmission of packets.
13. Simulate a wireless network, generate traffic and analyze its performance.
14. Simulate a transmission of ping message over a network topology consisting of 6 nodes and find the
number of packets dropped due to congestion.
Reference Books:
1. James F. Kurose and Keith W. Ross: Computer Networking: A Top-Down Approach, 5th edition, AddisonWesley, 2009.
2. Larry L. Peterson and Bruce S Davie: Computer Networks: A Systems Approach, Fifth Edition, Elsevier, 2011.
3. Behrouz A. Forouzan: Data communication and Networking, 4th edition, Tata McGraw-Hill, 2012.
4. W. Richard Stevens, Bill Fenner, Andrew M. Rudoff: Unix Network programming, The sockets networking
API, Addison-Wesley Professional, 2004
Course Delivery: The course will be delivered through lab exercises & practical assignments.
24
Course Assessment and Evaluation:
When/ Where
(Frequency in
the course)
Max
Marks
Evidence
Collected
Contribution to
Course Outcomes
Internal
Assessment Tests
Once
30
Blue Books
1,2 3,4 & 5
Practical
Assignment
Once
20
Soft copy of
ppt & report
1,2,3,4 & 5
Answer
scripts
1,2,3,4 &5
Questionnaire
1, 2, 3,4,5
Effectiveness of
Delivery of
instructions &
Assessment
Methods
Direct Assessment Methods
What
CIE
SEE
Practical
Examination
End of Course
Survey
To
Whom
End of Course
50
Students
End of the course
-
Course Outcomes:
At the end of the course the student will be able to:
1. Illustrate networking concepts using programming languages like C/C++.
2. Use packet sniffing tools like Wireshark to intercept & analyze the packets at different network layers.
3. Use simulators like NS2/NS3/OMNET++.
4. Design client-server applications using socket programming.
5. Implement networking modules and protocols.
Mapping Course Outcomes with Programme Outcomes:
Course Outcomes
Illustrate networking concepts using programming
languages like C/C++.
Use packet sniffing tools like Wireshark to
intercept & analyze the packets at different
network layers.
Use simulators like NS2/NS3/OMNET++
Design client-server applications using socket
programming.
Implement networking modules and protocols.
Programme Outcomes
1
2
3
4
5
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
25
6
7
8
9
10
11
12
Course Title: Compiler Design
Course Code: CS612
Credits (L:T:P) : 3:1:0
Core/ Elective: Core
Type of Course: Lecture, Tutorial
Total Contact Hours: 70
Prerequisites: Nil
Course Contents:
Unit 1
Introduction, Lexical Analysis: Language processors, The structure of Compilers, Lexical analysis: The role of
Lexical Analyzer, Input Buffering, Specifications of Tokens, Recognition of Tokens. Syntax Analysis: Introduction,
Writing a Grammar.
Unit 2
Parsing: Top-down Parsing, Bottom-up Parsing, Introduction to LR Parsing: Simple LR parser. More powerful LR
Parsers: Canonical parser, LALR parser.
Unit 3
Syntax-Directed Definitions: Evaluation order for SDDs, Applications of Syntax-directed translation, Syntaxdirected translation schemes. Run-Time Environments: Storage organization, Stack allocation of space.
Unit 4
Intermediate Code Generation: Variants of syntax trees, Three-address code, Types and declarations, Translation
of expressions, Type checking, Control flow, Back patching, Switch statements, Intermediate code for procedures.
Unit 5
Code Generation: Issues in the design of Code Generator, The Target language, Addresses in the target code, Basic
blocks and Flow graphs, Optimization of basic blocks, A Simple Code Generator.
Text Books:
1. Alfred V Aho, Monica S. Lam, Ravi Sethi, Jeffrey D Ullman: Compilers- Principles, Techniques and Tools, 2nd
Edition, Pearson education, 2012.
Reference Books:
1. Kenneth C Louden: Compiler Construction - Principles & Practice, First Edition, Brooks/Cole, CENGAGE
learning, 1997.
2. Andrew W Appel: Modern Compiler Implementation in C, First Edition, Cambridge University Press, 2010.
Course Delivery:
The course will be delivered through lectures, class room interaction, group discussion and exercises and self-study
cases.
26
Course Assessment and Evaluation:
Direct & Indirect Assessment
Methods
When/ Where
(Frequency in the
course)
Thrice(Average of
the best two will
be computed)
To
Whom
What
Internal
Assessment
Tests
Practical
Assignment
CIE
SEE
Standard
Examination
Students
End of Course
Survey
Max
Marks
Evidence
Collected
Contribution to
Course Outcomes
30
Blue Books
1,2,3,4,5 & 6
Twice
20
Online
Submission
Working with
Compiler Tools
End of Course
(Answering
5 of 10 questions)
100
Answer
scripts
1,2,3,4,5 & 6
1,2,3,4,5 & 6
Effectiveness of
Delivery of
instructions &
Assessment Methods
End of the course
Course Outcomes:
At the end of the course students should be able to:
1. Construct lexical analyser to recognize inputs using patterns
2. Devise different types of syntax analyzers using grammars
3. Illustrate syntax-directed translation schemes for grammars
4. Formulate intermediate code generators for programming statements
5. Develope assemlby language code for the given itnermediate codes
6. Compose optimized code for intermediate codes and assembly langugage code
Mapping Course Outcomes with Programme Outcomes:
Course Outcomes
Construct
lexical
analyser to recognize
inputs using patterns
Devise different types
of syntax analyzers
using grammars
Illustrate
syntaxdirected
translation
schemes for grammars
Formulate
intermediate
code
generators
for
programming
statements
Develope
assemlby
language code for the
given
itnermediate
codes
Compose optimized
code for intermediate
codes and assembly
langugage code
Programme Outcomes
PO1
PO2
PO3
PO4
PO5
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
27
PO6
PO7
PO8
PO9
PO10
PO11
PO12
Course Title: Embedded systems
Course Code: CS613
Credits (L:T:P) : 4:0:0
Core/ Elective: Core
Type of course: Lecture
Total Contact Hours: 56
Prerequisites: Microprocessors and Computer Organization.
Course Contents:
Unit 1
Embedded Computing: Introduction, Complex Systems and Microprocessors Embedded Systems Design Process,
design metrics, flow and optimization,. Specifications and modeling, Cortex-M0 Technical Overview,
Implementation Features System Features , Debug Features, Advantages
Unit 2
Programming model , Operation Modes and States , Architecture, Registers and Special Registers Behaviors of the
Application Program Status Register (APSR), Memory System Overview, Stack Memory , Operations Introduction
to Cortex-M0 Programming, Instruction Set
Unit 3
Instruction Usage Examples, implementation of various structures like loop, switch, function, subroutine, Memory
System, Exceptions and Interrupts, Interrupt Control and System Control
Unit 4
Simple Application Programming, Simple Input /Output, Simple Interrupt Programming, Sensors, Thermistors,
LDRs, LEDs 7 segment, LCD, Stepper motor , relays, Actuator, and ADCs
Unit 5
Embedded/ Real time tasks, real time systems, types of real time systems Quality of good real time systems. Real
Time Operating System Concepts: Architecture of the kernel. Task and task scheduler. ISR. Semaphores. Mutex.
Mailboxes. Message queues. Message queues Pipes. Signals. Memory management. Priority inversion problemText
Text Books:
1. Wayne Wolf “Computers as Components Principles of Embedded Computer System Design”, Second
Edition, Elsevier, 2008.
2. Joseph Yiu, “ The Definitive Guide to the ARM Cortex-M0 “, 1st edition, Newnes - an imprint of Elsevier,
2011
3. Lyla B. Das, “Embedded Systems an integrated approach “ , 1st edition, Pearson, 2013.
4. KVVK Prasad, “Embedded/Real Time Systems : Concepts, Design and Programming “ , 1st edition,
Dreamtech, 2011 .
Reference Books:
1. Frank Wahid/Tony Givargis “Embedded System Design A Unified Hardware/Software Introduction “ 1st
Edition, John Wiley & Sons, 2002.
2. Raj Kamal , “Embedded Systems: Architecture ,Programming and Design”, Tata McGrawhill, New
Delhi,2003.
3. Tammy Noergaard, “Embedded Systems Architecture- Comprehensive Guide for Engineer and
Programmers – Elsevier Publication, 2005
4. Barnett, Cox & O’cull,”Embedded C programming”,Thomson ,2005.
Course Delivery: lectures, ppt, videos
28
Course Assessment and Evaluation:
Direct & Indirect Assessment Methods
What
To
Whom
Internal
Assessment Tests
CIE
SEE
Class-room
Surprise Quiz
Standard
Examination
Students
End of Course
Survey
When/ Where
(Frequency in the
course)
Thrice(Average of
the best two will be
computed)
Twice(Summation
of the two will be
computed)
Max
Marks
Evidence
Collected
Contribution to
Course Outcomes
30
Blue Books
1,2 3,4,5,6 & 7
20
Quiz papers
100
Answer scripts
1,2 3,4,5,6 & 7
Questionnaire
1,2 3,4,5,6 & 7
Effectiveness of
Delivery of
instructions &
Assessment
Methods
End of Course
(Answering
5 of 10 questions)
End of the course
-
1,2 3,4,5,6 & 7
Course Outcomes:
At the end of the course the students should be able to
1. Identify embedded system requirements and goals
2. Summarize the Cortex M0 architecture memory mapping and its advantages for designing embedded
system
3. Compare the different Programming models for embedded systems
4. Evaluate cortex M0 in assembly instructions and write embedded C programs using CMSIS features .
5. Devise programs using interrupt capabilities
6. Compare the working of various sensors and actuators and their interface with microcontrollers
7. Estimate the kernel of RTOS and its various deployments for embedded systems.
Mapping Course Outcomes with Programme Outcomes:
Course Outcomes
Programme Outcomes
1
Identify embedded system requirements and goals
Summarize the Cortex M0 architecture memory
mapping and its advantages for designing embedded
system
Compare the different Programming models for
embedded systems
Evaluate cortex M0 in assembly instructions and write
embedded C programs using CMSIS features .
Devise programs using interrupt capabilities
2
x
3
4
5
6
x
x
Compare the working of various sensors and actuators
and their interface with microcontrollers
Estimate the kernel of RTOS and its various
deployments for embedded systems.
29
8
9
10
x
11
12
x
x
x
x
x
x
x
x
x
x
x
x
x
x
7
x
x
x
x
x
x
x
x
x
x
x
Course Title: Computer Security
Course Code: CS621
Credits (L:T:P) : 3:1:0
Core/ Elective: Core
Type of course: Lecture, Tutorial
Total Contact Hours: 70
Prerequisites:
Nil
Course Contents:
Unit 1
Introduction: Security Goals, Cryptographic Attacks, Services and Mechanism, Techniques. Mathematics of
Cryptography: Integer Arithmetic, Modular Arithmetic, Matrices, Linear Congruence.
Unit 2
Traditional Symmetric-Key Ciphers: Introduction, Substitution Ciphers, Transpositionl Ciphers, Stream and
Block Ciphers. Data Encryption Standard (DES): Introduction, DES Structure, DES Analysis, Security of DES,
Multiple DES, Examples of Block Ciphers influenced by DES.
Advanced Encryption Standard: Introduction, Transformations, Key Expansion, The AES Ciphers, Examples,
Analysis of AES.
Unit 3
Encipherment using Modern Symmetric-Key Ciphers: Use of Modern Block Ciphers, Use of Stream Ciphers,
Other Issues.
Asymmetric Key Cryptography: Introduction, RSA Cryptosystem, Rabin Cryptosystem, Elgamal Cryptosystem,
Elliptic Curve Cryptosystems
Unit 4
Message authentication: Authentication Requirements, Authentication Functions, Message Authentication Codes,
Security of MACs, MACs based on Hash Functions: MAC.
Digital signatures: Digital Signatures, Digital Signature Standard.
Key management and distribution: Symmetric Key distribution using symmetric encryption, Symmetric Key
distribution using Asymmetric encryption, Distribution of public keys, X.509 certificates, Kerberos.
Unit 5
Transport level security: Web Security considerations, Secure Sockets Layer and Transport Layer Security.
Transport Layer Security, HTTPS, Secure Shell.
Internet security: Electronic Mail Security: Pretty Good Privacy, S/MIME. IP Security: Overview, IP Security
Policy.
System security: Intruders: Intruders, Intrusion detection.
Malicious Software: Types of Malicious Software, Viruses. Firewalls: The need for Firewalls, Firewall
Characteristics, Types of Firewalls.
Text Books:
1. Behrouz A. Forouzan, Debdeep Mukhopadhyay: Cryptography and Network Security, 2nd Edition, Special
Indian Edition, Tata McGraw-Hill, 2011.
2. William Stallings, Cryptography and Network Security, Fifth Edition, Prentice Hall of India, 2005
Reference Books:
1. Josef Pieprzyk, Thomas Hardjono, Jennifer Serberry Fundamentals of Computer Security, Springer.
Course Delivery:
The course will be delivered through lectures in the classroom
30
Course Assessment and Evaluation:
To
Whom
Direct & Indirect Assessment Methods
What
Internal
Assessment
Test
Cryptography
and Network
Security
Tutorials
CIE
CIE
Standard
Examination
SEE
End of Course
Survey
Student
When/
Where
(Frequency in the
course)
Thrice (Average of
the best two will be
computed)
Max
Marks
Evidence
Collected
Contribution to
Course
Outcomes
30
Blue Books
1, 2, 3, 4 & 5
Daily
Evaluation
based
on
the
Performance(20M)
20
Report
1,2,3,4 & 5
End of Course
(Answering
5 of 10 questions)
100
Answer
scripts
1,2,3,4 & 5
Questionna
ire
1, 2, 3, 4 & 5
Effectiveness of
Delivery
of
instructions &
Assessment
Methods
End of the course
-
Course Outcomes:
At the end of the course the students should be able to:
1. Interpret the security goals and the threats to security
2. Identify and formulate the type of encryption method DES or AES depending on the need and security
threat perception
3. Compare and Contrast the need of Symmetric-key and Asymmetric-key Ciphers.
4. Summarize the fundamentals of Key Management and Identity need for Digital Signatures and its utility
5. Identify security goals achieved at application layer, and appraise the need for firewalls.
Mapping Course Outcomes with Programme Outcomes:
Course Outcomes
Interpret the security goals and the threats to security
Identify and formulate the type of encryption method
DES or AES depending on the need and security threat
perception
Compare and Contrast the need of Symmetric-key and
Asymmetric-key Ciphers
Summarize the fundamentals of Key Management and
Identity need for Digital Signatures and its utility
Identify security goals achieved at application layer,
and Appraise the need for firewalls.
Programme Outcomes
1
x
2
x
3
x
x
x
x
x
x
x
x
x
x
31
4
5
x
x
6
x
7
8
9
x
10
x
11
12
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
Course Title: Computer Graphics
Course Code: CS622
Credits (L:T:P) : 3:0:1
Core/ Elective: Core
Type of course: Lecture, Practicals
Total Contact Hours: 70
Prerequisites:
Mathematics, data structures
Course Contents:
Unit 1
Introduction: Applications of computer graphics, A graphics system, Images: Physical and synthetic, Imaging
Systems, The synthetic camera model, The programmer’s interface, Graphics architectures, Programmable Pipelines
Graphics Programming: Programming two-dimensional applications, OpenGL application programming interface,
Primitives and attributes, color, viewing, control functions, the gasket program, polygons and recursions, the threedimensional gasket, adding interactions, menus.
Unit 2
Geometric Objects and Transformations: Scalars, Points, and Vectors, Three-dimensional Primitives, Coordinate
Systems and Frames, Modeling a Colored Cube, Affine Transformations, Rotation, Translation and Scaling,
Transformation in Homogeneous Coordinates, Concatenation of Transformations, OpenGL Transformation
Matrices, Spinning of cube, Interfaces to three-dimensional applications.
Unit 3
Implementation: Basic Implementation Strategies, Four major tasks, Clipping, Line-segment clipping, Polygon
clipping, Clipping of other primitives. Clipping in three dimensions, Rasterization, Bresenham’s algorithm, Polygon
Rasterization, Hidden-surface removal,
Unit 4
Viewing: Classical and computer viewing, Viewing with a Computer, Positioning of the camera, Parallel
Projections, Perspective projections, Projections in OpenGL, Hidden-surface removal, Parallel-projection matrices,
Perspective-projection matrices, Interactive Mesh Displays, Projections and Shadows.
Unit 5
Lighting and Shading: Light and Matter, Light sources, The Phong reflection model, Polygon shading,
Approximation of sphere by recursive subdivision, Specifying lighting parameters, Implementing a lighting model
Text Books:
1. Edward Angel and Dave Shreiner: Interactive Computer Graphics - A Top-Down Approach with Shader-based
OpenGL, 6th Edition, Pearson Education, 2011.
Reference Books:
1. Donald Hearn and Pauline Baker: Computer Graphics with OpenGL, 3rd Edition, Pearson Education, 2011.
2. F.S. Hill Jr.: Computer Graphics Using OpenGL, 3rd Edition, Pearson Education, 2009.
3. James D Foley, Andries Van Dam, Steven K Feiner, John F Hughes: Computer Graphics, 2nd Edition, Pearson
Education, 2011
Course Delivery:
The course will be delivered through lectures, classroom interactions, presentations and code demo.
Course Assessment and Evaluation:
Direct &
Indirect
Assessment
Methods
CIE
What
Internal
Assessment
Tests
Assignment/Qu
iz
To Whom
Students
When/ Where
(Frequency in
the course)
Thrice(Average
of the best two
will be
computed)
Max
Mark
s
Evidence
Collected
Contribution to
Course Outcomes
30
Blue Books
1,2,3,4,5,6,7
Once
20
Quiz Papers
1,2,3,4,5,6,7
32
SEE
Standard
Examination
End of Course
Survey
End of Course
(Answering
5 of 10 questions)
Answer
scripts
1,2,3,4,5,6,7
Questionnaire
1, 2 ,3,4,5,6,7
Effectiveness of
Delivery of
instructions &
Assessment Methods
100
End of the course
-
Course Outcomes:
1.
2.
3.
4.
5.
6.
7.
Explain the image formation process and the pipeline architecture of computer graphics
Describe the software and hardware components of a computer graphics system and basics of OpenGL
API’s
Derive the geometrical transformations used in interactive computer graphics in different coordinate
systems
Discuss the different algorithms for clipping and rasterization of lines and polygons, and for hidden surface
removal.
Derive matrix formulations for different types of viewing and projections.
Explain different lighting and shading models.
Write 3D computer graphics applications in OpenGL using knowledge of display systems, image synthesis,
and interactive control.
Mapping Course Outcomes with Programme Outcomes:
Course Outcomes
Programme Outcomes
1
Explain the image formation process and the
pipeline architecture of computer graphics.
x
Describe the software and hardware components
of a computer graphics system and basics of
OpenGL API’s.
x
Derive the geometrical transformations used in
interactive computer graphics in different
coordinate systems.
x
Discuss the different algorithms for clipping and
rasterization of lines and polygons, and for
hidden surface removal.
Derive matrix formulations for different types of
viewing and projections.
Explain different lighting and shading models.
x
2
3
x
x
x
x
x
x
4
5
6
7
8
9
10
11
12
x
x
x
x
x
Write 3D computer graphics applications in
OpenGL using knowledge of display systems,
image synthesis, and interactive control.
x
33
x
Course Title: Unix Systems Programming Laboratory
Course Code: CSL611
Credits (L:T:P) : 0:1:1
Core/ Elective: Core
Type of course: Tutorials, Laboratory
Total Contact Hours: 56
Prerequisites: NIL
Course Contents:
1. Basic file I/O functions & properties of a file.
2. File Types, File access permission and File links.
3. Creating the process and process accounting.
4. Feature provided by different signal implementation.
5. Coding rules and Characteristics of Daemon Process
6. Process termination and setjmp, longjmp function.
7. Client-Server Models using pipes
8. Orphaned process group and Interprocess communication.
Text Books:
1.
W. Richard Stevens: Advanced Programming in the UNIX Environment, Second Edition, Pearson
education, 2011.
Reference Books:
1. Terrence Chan: UNIX System Programming Using C++, First edition, Prentice Hall India, 2011.
2. Kay A Robbins and Steve Robbins: Unix Systems Programming, First Edition, Pearson Education, 2009.
3. Marc J. Rochkind: Advanced UNIX Programming, 2nd Edition, Pearson
Course Delivery:
This course is delivered using lectures and Practical to enable the students to: apply the UNIX system Programming
concepts while programming, explore the knowledge by applying on suitable applications
Course Assessment and Evaluation:
Direct & Indirect Assessment
Methods
What
C
I
E
S
E
E
To
Whom
Continuous Evaluation
in Lab
Internal Assessment
Tests: Write-up,
Execution & Viva
Standard Examination
End of Course
Survey
Students
When/ Where
(Frequency in
the course)
Every Lab
Session
Twice(Summati
on of the two
will be
computed)
End of Course
End of the
course
34
Max
Mar
ks
Evidence
Collected
Contribution to
Course Outcomes
10
Data Sheets
1,2,3,4 & 5
40
Quiz sheets /
LMS
1,2,3,4 & 5
50
Answer scripts
1,2,3,4 & 5
Questionnaire
1,2,3,4 & 5
Effectiveness of
Delivery of
instructions &
Assessment
Methods
Course Outcomes:
At the end of the course, the students will be able to:
1. Identify UNIX system calls and commands.
2. Describe the functions available for file I/O and changing the properties of the file in Unix OS.
3. Identify different types of files supported by UNIX operating System.
4. Illustrate the basic IPC issues and techniques in UNIX system programming
5. Explain the creation of new process, process accounting and process termination.
Mapping Course Outcomes with ProgrammeOutcomes:
Course Outcomes
ProgrammeOutcomes
1
2
x
x
x
Describe the functions available for file I/O and
changing the properties of the file in Unix OS
x
x
x
Identify different types of files supported by UNIX
operating System
x
x
x
Illustrate the basic IPC issues and techniques in
UNIX system programming
x
x
x
Explain the creation of new process, process
accounting and process termination
x
x
x
Identify UNIX system calls and commands
35
3
4
5
6
7
8
9
10
11
12
Course Title: Embedded Systems Laboratory
Course Code: CSL613
Credits (L:T:P):
Core/ Elective: Core
0:0:1
Type of course: Practicals
Total Contact Hours: 28
Prerequisites: The student should have undergone the course on Microprocessors and Computer organization.
Course Contents:
Experiments that are to be conducted as a part of the course
Introduction to the nuvoton 140 board, introduction to all the ports peripherals Introduction to Kiel software 4.0
simulator
Part A : write Assembly programs
1. Program for 64 bit addition, to use a counter
2. Program for 64 bit subtraction, multiplication
3. Program for copy 10 locations
4. Program for copy swap 10 memory locations
5. Program for copy factorial, using branch instruction square root
6. Program for copy use of fuctions use of bit extraction
7. Program for use of bit clear
8. Program for copy sign extention
9. Program using multiple load and store instruction
Part B : write Embedded C programs using CMSIS
1. programs in c introduction to cmsis files. Using gpio use rgb leds
2. program to use the leds to toggle using gpio
3. GPIO to scan 3x3 keypad (Smpl_7seg_Keypad)
4. GPIO controlled by 3x3 keypad (Smple_GPIO_Keypad)
5. GPIO to read Body IR Sensor (Smpl_GPIO_BodyInfrared)
6. GPIO interfacing with LCM (Smpl_GPIO_LCM16x2)
7. Smpl_GPIO_Buzzer
8. Counter program using seg_display routine
9. Smpl_GPIO_Interrupt
10. using ADC to read variable resistor
11. Exercise using ADC using variable resistor and display on LCD using built in function
Text Books:
1. Wayne Wolf “Computers as Components Principles of Embedded Computer System Design”, Second
Edition, Elsevier, 2008.
2. Joseph Yiu, “ The Definitive Guide to the ARM Cortex-M0 “, 1st edition, Newnes - an imprint of Elsevier,
2011
3. Lyla B. Das, “Embedded Systems an integrated approach “ , 1st edition, Pearson, 2013.
Reference Books:
1. Rob Toulson ans Tim Wilmshurst “ Fast and Effective Embedded systems Design applying the ARM
mbed” First edition 2012, Newnes - an imprint of Elsevier
Course Delivery:
Through lab experiments in lab with nuvoton 140 board, with introduction to various all the ports peripherals AND
introduction to Kiel software 4.0 simulator
36
Course Assessment and Evaluation:
Direct & Indirect Assessment
Methods
What
CIE
SEE
To
Whom
When/ Where
(Frequency in the
course)
Max
Marks
Evidence
Collected
Contribution to
Course Outcomes
Internal Lab Test
30
Blue Books
1,2 3,4,5,6 & 7
20
Project report
and evaluation
1,2 3,4,5,6 & 7
100
Answer scripts
1,2 3,4,5,6 & 7
Questionnaire
1,2 3,4,5,6 & 7
Effectiveness of
Delivery of
instructions &
Assessment
Methods
Internal
Assessment Tests
Class-room
Surprise Quiz
Standard
Examination
End of Course
Survey
Mini Project
Students
End of Course
(Answering
5 of 10 questions)
End of the course
-
Course Outcomes:
At the end of the course, the students will be able to:
1. Explore various CortexM0 instructions.
2. Examine GPIO ports connected to various on board interfaces.
3. Devise programs using multiple interfaces, interrupts and RTC
4. Explore use of multiple interfaces, interrupts, setting priority
5. Demonstrate combination of assembly and embedded c programming
6. Evaluate the use of real-time OS for doing multiple jobs.
7. Design a new task using different concepts learnt in this course and demonstrate a team project.
Mapping Course Outcomes with Programme Outcomes:
Course Outcomes
Explore various CortexM0 instructions
Examine GPIO ports connected to various on board
interfaces
Devise programs using multiple interfaces, interrupts
and RTC
Explore use of multiple interfaces, interrupts, setting
priority
Demonstrate combination of assembly and embedded
c programming
Evaluate the use of Real-time OS for doing multiple
jobs
Design a new task using different concepts learnt in
this course and demonstrate a team project.
Programme Outcomes
1
x
2
3
4
5
6
7
x
x
8
9
x
10
11
12
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
37
x
x
x
x
x
x
x
x
x
x
Course Title: Internet Technologies & Software Development for
Portable Devices Laboratory
Course Code: CSL612
Credits (L:T:P) : 0:1:1
Core/ Elective: Core
Type of course: Practical
Total Contact Hours: 56
Prerequisites: Knowledge of OOP Language
Course Contents:
There shall be a minimum of 2 exercises conducted on each of the following topics.
1.
2.
3.
4.
5.
6.
7.
8.
9.
HTML5
Java Script
PHP and MYSQL
Ruby on Rails
Develop an android application to investigate the activity life cycle and the fragments
Develop an android application to create user interfaces with different layouts and views.
Develop an android application on using implicit & explicit Intents.
Develop an android application to work with SQLite data storage.
Mini-Project
Reference Books:
1. Robert W. Sebesta: Programming the World Wide Web, 4th edition, Pearson education, 2012.
2. M. Deitel, P.J. Deitel, A. B. Goldberg: Internet & World Wide Web How to H program, 4th Edition,
Pearson education, 2011.
3. Professional Android 4 Application Development, by Reto Meier, WROX Press, Wiley Publishing.
4. Android Application Development, Programming with the Google SDK, by, Rick Rogers, John Lombardo,
ZigurdMednieks, Blake Meike, SPD, Oreilly, ISBN10: 81-8404-733-9, ISBN13:978-81-8404-733-2
Course Delivery:
The course will be delivered through lectures in the laboratory with exercises.
Course Assessment and Evaluation:
To
Whom
What
Lab Test
CIE
Demo at the end of
semester
Miniproject
Viva
Direct Assessment
Methods
Students
SEE
Lab
Examination
End of Course
Survey
When/
Where
(Frequency in the
course)
One Lab Test
Every
Week(Average of
the total score will
End
of
Course
(Executing
2
End of the course
38
Max
Marks
Evidence
Collected
Contribution
to
Course Outcomes
20
Data sheets
1,2,3,4 & 5
20
code
1,2,3,4 & 5
10
Viva Result
Sheets
Answer
scripts
Recollection Skills
50
-
Questionnaire
1,2,3,4 & 5
1,2,3,4 & 5
Effectiveness of
Delivery of
instructions &
Assessment Methods
Course Outcomes:
At the end of the course the students should be able to:
1. Develop web pages with various media contents usingHTML5.
2. Create a robust Client side validation with java script.
3. Design dynamic data-driven Web sites using MySQL and PHP.
4. Design a database application using Ruby on Rail framework.
5. Demonstrate the activity life cycle and fragment life cycle.
6. Illustrate the usage of different Views & ViewGroups.
7. Develop android applicationsto invoke different applications using Intents.
8. Design an android database application using SQLite.
Mapping Course Outcomes with Programme Outcomes:
Course Outcomes
Develop web pages with various media contents
usingHTML5.
Create a robust Client side validation with java
script
Design dynamic data-driven Web sites using
MySQL and PHP.
Design a database application using Ruby on Rail
framework
Demonstrate the activity life cycle and fragment
life cycle.
Illustrate the usage of different Views &
ViewGroups.
Develop android applications to invoke different
applications using Intents.
Design an android database application using
SQLite
Programme Outcomes
1
2
3
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
39
4
5
6
7
8
9
10
11
12
Course Title: Data Mining
Course Code: CSPE01
Credits (L:T:P) : 3:1:0
Type of course: Lecture, Tutorials
Core/ Elective: Elective
Total Contact Hours: 70
Prerequisites: Nil
Course Contents:
Unit 1
DATA MINING Introduction – Data – Types of Data – Data Mining Functionalities –Classification of Data Mining
Systems – Issues –Data Preprocessing- C4.5 Algorithm Description- C4.5 Features - Two Illustrative Examples.
Unit 2
ASSOCIATION RULE MINING Mining Frequent Patterns – Apriori Algorithm Description,. - Two Illustrative
Examples- Mining various Kinds of Association Rules – Correlation Analysis – Constraint Based Association
Mining.
Unit 3
CLASSIFICATION AND PREDICTION Basic Concepts - Decision Tree Induction - Bayesian Classification –
Rule Based Classification – Classification by Back propagation – Support Vector Machines – Associative
Classification – Lazy Learners – Other Classification Methods – Prediction.
Unit 4
CLUSTERING AND TRENDS IN DATA MINING Cluster Analysis - Types of Data – Categorization of Major
Clustering Methods – K-means– Partitioning Methods – Hierarchical Methods - Density-Based Methods –Grid
Based Methods – Model-Based Clustering Methods – Clustering High Dimensional Data - Constraint – Based
Cluster Analysis – Outlier Analysis – Data Mining Applications.
Unit 5
MINING Mining the World Wide Web - Page Rank Algorithm, Text mining, Mining Time Series Data, The CART
Algorithm Briefly Stated, Ensemble methods-Increasing the Accuracy, Mining genomic data.
References:
Jiawei Han and Micheline Kamber: Data Mining Concepts and Techniques, Elsevier, 2nd Edition, 2009.
Xindong Wu and Vipin Kumar: The top ten Algorithms in Data Mining, Chapman and Hall/CRC press.
Pang-Ning Tan, Michael Steinbach and Vipin Kumar, “Introduction to Data Mining”, Person Education, 2007.
K.P. Soman, Shyam Diwakar and V. Aja, “Insight into Data Mining Theory and Practice”, Eastern Economy
Edition, Prentice Hall of India, 2006.
G. K. Gupta, “Introduction to Data Mining with Case Studies”, Eastern Economy Edition, Prentice Hall
of India, 2006.
7. Daniel T.Larose, “Data Mining Methods and Models”, Wiley-Interscience, 2006.
1.
2.
3.
4.
5.
6.
Course outcomes:
At the end of the course the students will be able to:
1.
2.
3.
4.
5.
6.
7.
Understand ways in which the methods of data mining differ from the more classical statistical approaches
to data analysis, the rationale for data mining methods.
Illustrate the kinds of patterns that can be discovered by association rule mining.
Implement different classification and prediction techniques in numerous applications.
Compute dissimilarities between objects represented by various attributes or variable types and examine
several clustering techniques.
Analyze the results generated from the constructed artifact to determine if patterns of clusters were detected
in the data sets.
Assess time series data from web and use it for prediction.
Perform data Preprocessing, Classification algorithms using WEKA, R.
40
Mapping Course Outcomes with Programme Outcomes:
Programme Outcomes
Course Outcomes
1
Understand ways in which the methods of data mining
differ from the more classical statistical approaches to
data analysis, the rationale for data mining methods.
Illustrate the kinds of patterns that can be discovered by
association rule mining.
Implement different classification and prediction
techniques in numerous applications.
Compute dissimilarities between objects represented by
various attributes or variable types and examine several
clustering techniques.
Analyze the results generated from the constructed
artifact to determine if patterns of clusters were detected
in the data sets.
Assess time series data from web and use it for
prediction.
Perform data Preprocessing, Classification algorithms
using WEKA, R.
41
2
x
3
4
5
6
7
x
x
x
x
x
8
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
9
10
11
12
x
x
x
Course Title: Machine Learning
Course Code: CSPE02
Credits (L:T:P) : 3:1:0
Core/ Elective: Elective
Type of Course: Lecture, Practicals
Total Contact Hours: 70
Prerequisites: NIL
Course Contents:
Unit 1
Introduction: Probability theory (Bishop ch-1 & Appendix B,C); What is machine learning, example machine
learning applications (Alpaydin ch-1) Supervised Learning:Learning a Class from Examples, VC-dimension, PAC
learning, Noise, Learning multiple classes, Regression, Model selection and generalisation. (Alpaydin ch-2)
Unit 2
Bayesian Learning: Classification, losses and risks, utility theory (Alpaydin ch3 (3.1, 3.2, 3.3, 3.5)) MLE,
Evaluating an estimator, bayes estimator, parametric classificaion (Alpaydin ch4 - 4.1-4.5) (Bishop 4.2);
Discriminant functions Introduction, Discriminant functions, Least squares classification, Fisher’s linear
discriminant, fixed basis functions, logistic regression (Bishop 4.1,4.3.1,4.3.2)
Unit 3
Multivariate methods: Multivariate Data,Parameter Estimation,Estimation of Missing Values,Multivariate Normal
Distribution,Multivariate Classification,Tuning Complexity,Discrete Features,Multivariate Regression (Alpaydin
ch-5)
Nonparametric methods: Nearest Neighbor Classifier, Nonparametric Density Estimation (Alpaydin ch-8 selected
topics)
Unit 4
Maximum margin classifiers: SVM, Introduction to kernel methods, Overlapping class distributions, Relation to
logistic
regression,Multiclass SVMs, SVMs for regression (Bishop ch 6 and 7 only covered topics). Mixture models and
EM K-means clustering, Mixture of gaussians, Hierarchical Clustering, Choosing the Number of Clusters (Bishop
9.1,9.2, Alpaydin 7.7,7.8)
Unit 5
Dimensionality reduction - (Alpaydin ch6) Combining Models (Bishop ch-14)
Text Books:
1.
Ethem Alpaydin "Introduction To Machine Learning" 2nd Edition PHI Learning Pvt. Ltd-New Delhi
2.
Christopher Bishop "Pattern Recognition and Machine Learning" CBS Publishers & Distributors-New
Delhi
Course Outcomes:
At the end of the course, a student should be able to
1. Explain the concepts and issues of learning systems.
2. Evaluate decision tree based learning algorithm.
3. Evaluate Bayesian learning algorithm.
4. Determine sample complexity for infinite hypothesis spaces
5. Evaluate rule- based learning algorithm.
42
Course Title: Cloud Computing
Credits (L:T:P): 4:0:0
Type of course: Lecture
Course Code: CSPE03
Core/ Elective: Elective
Total Contact Hours: 56
Prerequisites: NIL
Course Contents:
Unit 1
Introduction: Definition, characteristics, Benefits, challenges of cloud computing, cloud models: service-IaaSPaaS-SaaS, deployment-Public,Private,hybrid,Community, cloud services: Amazon, Google, Azure, online
services, open source private clouds, SLA. Applications: Healthcare, Energy systems, transportation,
manufacturing, Education, Government, mobile communication, application development.
Cloud Application Development: Amazon web Services: EC2 Instances, Connecting Clients, Security Rules,
Launch an EC2 Linux Instance and connect it, create EC2 placement Group, to use S3 in java, to manage SQS
services in C#, to install simple notification service on Ubuntu 10.04.
Unit 2
Cloud Architecture, programming model: NIST reference architecture, Architectural styles of cloud applications ,
single , multi ,hybrid cloud site, redundant, non redundant , 3 tier, multi tier architectures , programming model :
compute and data intensive , Compute intensive model: parallel computation – BSP , Workflows , coordination of
multiple activities- zoo keeper, data intensive model: big data- map reduce programming model ,map reduce in
cloud, map reduce applications – hadoop distributed file system, Grep the Web, graph processing- SSSP, SSSP in
mapreduce, Pregel programming model , other big data programming models.
Unit 3
Cloud Resource Virtualization: basics of virtualization, types of virtualization techniques, merits and demerits of
virtualization, full vs para-virtualization – virtual machine monitor/hypervisor - virtual machine basics, taxonomy of
virtual machines, process vs system virtual machines – emulation: interpretation and binary translation – HLL,
virtual machines , storage, desktop and application virtualization, applying virtualization.
Unit 4
Cloud Resource Management and Scheduling: Policies and mechanisms for resource management, Resource
bundling, combinatorial , fair queuing, Start time fair queuing, borrowed virtual time, Cloud scheduling subject to
deadlines, Scheduling map reduce applications subject to deadlines, Resource management and application scaling.
Unit 5
Cloud Security : Risks, privacy and privacy impacts assessments, Multi-tenancy Issues, Security in VM, OS,
Virtualization system security issues and vulnerabilities, Virtualization System-Specific Attacks: Technologies for
Virtualization-Based Security Enhancement, Legal , Compliance Issues: Responsibility, ownership of data, right to
penetration test, local law where data is held, examination of modern Security Standards (eg PCIDSS), how
standards deal with cloud services and virtualization, compliance for the cloud provider vs. compliance for the
customer.
References:
1. Cloud Computing: Theory and Practice, Dan Marinescu, 1st edition, MK Publishers, 2013.
2. Distributed and Cloud Computing, From Parallel Processing to the Internet of Things, Kai Hwang, Jack
Dongarra, Geoffrey Fox. MK Publishers.
3. Cloud Computing: A Practical Approach, Anthony T. Velte, Toby J. Velte, Robert Elsenpeter, McGraw Fill,
2010
4. Cloud computing A Hands on Approach Arshdeep Bahga,Vijay Madisetti Universities Publications
5. Online Readings : http://www.pds.ewi.tudelft.nl/ , http://csrc.nist.gov/publications/nistpubs
43
Course Outcomes:
At the end of the course the students will be able to:
1. To derive the best practice model for developing and deploying cloud based applications
2. Develop cloud applications by integrating commercial cloud services as saas provider
3. Propose an Enterprise/private cloud by designing own cloud architecture and provide Iaas
4. Compose services in a distributed computing environment to achieve tasks relevant to a knowledge-based
business or public service.
5. Deploy programme using the APIs of Cloud Computing , create Virtual Machine images and to deploy
them on a Cloud.
6. Evaluate the security issues related to multi-tenancy and Appraise compliance issues that arise from cloud
computing
7. Performance evaluations and critical evaluations of a small scale virtual environment developed in the lab.
Mapping Course Outcomes with Programme Outcomes:
Course Outcomes
To derive the best practice model for developing and
deploying cloud based applications
Develop cloud applications by integrating commercial
cloud services as saas provider
Propose an Enterprise/private cloud by designing own
cloud architecture and provide Iaas as service
Compose services in a distributed computing
environment to achieve tasks relevant to a knowledgebased business or public service.
Deploy programme using the APIs of Cloud
Computing create Virtual Machine images and to
deploy them on a Cloud.
Evaluate the security issues related to multi-tenancy
and Appraise compliance issues that arise from cloud
computing
Performance evaluations and critical evaluations of a
small scale virtual environment developed in the lab.
Programme Outcomes
1
2
3
4
5
x
x
x
x
x
6
x
x
x
x
x
x
x
x
x
x
44
x
8
9
x
x
x
x
10
11
x
x
x
x
x
7
x
x
x
x
x
x
12
Course Title: Distributed Systems
Credits (L:T:P) : 4:0:0
Type of Course: Lecture
Course Code: CSPE04
Core/ Elective: Elective
Total Contact Hours: 56
Prerequisites: Basic knowledge of internet technologies
Course Contents:
Unit 1
Introduction: Distributed Systems - Need and motivation, Challenges(Fallacies); Architecture: System Models,
common terminologies; Distributed System Kinds: Storage, Compute, Coordination
Unit 2
Distributed Storage Systems - 1: Shared Memory: Model, Distributed Shared Memory, Linearizability; Data
Aspects: Partitioning, Sharding, Fault-Tolerance, Consistency, ACID; CAP Theorem, BASE, Brewer’s Conjecture
explained;
Unit 3
Distributed Storage Systems - 2: Distributed Replication: Consistency and Paxos Algorithm; Access Patterns
and Data Models: Key-Value, Document, Columnar, Files, Graph, Inverted Index; DynamoDb: Distributed KeyValue Store, Architecture, CAP Implications HDFS: Distributed File System, Architecture, CAP Implications;
Elasticsearch: Distributed search solution, CAP Implications; Other distributed storage solution as Case Study and
assignment
Unit 4
Distributed Compute Systems: Synchronous and Asynchronous Processing, Data and Task Parallel systems, Data
locality; Fault Tolerance in Processing; Pipelines and Workflows; MapReduce: Programming paradigm, Google
MapReduce - Architecture and phases. Stream Processing: Introduction and Concepts, Comparative study of S4,
Storm and Borealis. [Additional Reading - Split Query processing, other forms of distributed query processing]
Unit 5
Distributed Coordination: Why coordination in Distributed systems?, Zookeeper: Architecture and applications;
Paxos revisited; Leader election, Byzantine Problem simple description, Quorum Systems
Reading Material:
Main References:
 Coulouris et.al. Distributed Systems Concepts and Design
 Links: http://azmuri.files.wordpress.com/2013/09/george-coulouris-distributed-systems-concepts-anddesign-5th-edition.pdf
 https://drive.google.com/file/d/0B3XfLh7sFg6xdmVRdE1ZcWcwbHM/preview (Just save locally)
 Notes on Theory of Distributed Systems,James Apnes
 http://cs-www.cs.yale.edu/homes/aspnes/classes/465/notes.pdf
 Distributed algorithms Nancy Lynch
Unit1: Recommended Reading  https://gist.github.com/jboner/2841832
 Building a distributed system requires a methodical approach to requirements https://queue.acm.org/detail.cfm?id=2482856
 Distributed Systems Fallacies - http://www.rgoarchitects.com/Files/fallacies.pdf
 Chapter 1 and 2 of Coulouris et.al Distributed Systems Concepts and Design Coulouris.pdf
 Lecture Notes: Introduction to DistSys
 http://www.cs.helsinki.fi/u/jakangas/Teaching/DistSys/DistSys-08f-1.pdf
 www.cis.upenn.edu/~lee/00cse380/lectures/ln13-ds.ppt
45
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Unit2:
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Unit3:
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Unit4:
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Unit5:
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http://www0.cs.ucl.ac.uk/staff/ucacwxe/lectures/ds98-99/dsee3.pdf
For discussion on Common Terminologies, refer Coulouris. Section 1.5 but use terminologies in the above
presentations
Notes on Theory of Distributed Systems,James Apnes: Chapter 15 and 16 http://cswww.cs.yale.edu/homes/aspnes/classes/465/notes.pdf
CAP Theorem - http://www.cs.berkeley.edu/~brewer/cs262b-2004/PODC-keynote.pdf
Proof/Explanation for Brewer's CAP Conjecture https://github.com/machinelearner/distSysTheory/blob/master/literature/brewersconjecture/BrewersConjecture-SigAct.pdf?raw=true
http://webpages.cs.luc.edu/~pld/353/gilbert_lynch_brewer_proof.pdf
Distributed Databases: http://pcbunn.cithep.caltech.edu/DistributedDatabasesPakistan.pdf(Partitioning),
Sharding ,Replication and CAP implications - http://www.dia.uniroma3.it/~torlone/bigdata/L7-NoSQL.pdf
http://www.inf.unibz.it/dis/teaching/DDB/ln/ddb03.pdf(Explained partitioning as fragmentation)
Linearizability:
https://github.com/machinelearner/distSysTheory/blob/master/literature/linearizability/p463herlihy.pdf?raw=true(Can leave out the axioms and theorems - not in scope for under-grad class)
Consistency - http://www.cs.uoi.gr/~pitoura/courses/ds04_gr/cons-repl.pdf
Failures and distributed Systems http://www.cse.psu.edu/~gcao/teach/513-00/c7.pdf
Replication: http://www.cs.cmu.edu/~dga/15-440/F12/lectures/15-replication.pdf
Coulouris Chapter 18 and http://book.mixu.net/distsys/replication.html
Paxos Lecture - https://www.youtube.com/watch?v=WX4gjowx45E
Paxos Algorithm Explained - http://research.microsoft.com/en-us/um/people/lamport/pubs/paxossimple.pdf
http://the-paper-trail.org/blog/consensus-protocols-paxos/
DynamoDb http://www.allthingsdistributed.com/files/amazon-dynamo-sosp2007.pdf
HDFS Architecture: http://hadoop.apache.org/docs/r1.2.1/hdfs_design.html
,http://aosabook.org/en/hdfs.html
https://github.com/machinelearner/distSysTheory/blob/master/literature/hdfs/hdfs.pdf?raw=true
Parallel Computing Paradigm http://www.buyya.com/cluster/v2chap1.pdf,
http://www.cs.unc.edu/~dewan/242/s07/notes/ipc/node9.html
http://www.cse.iitd.ernet.in/~dheerajb/parallel_paradigms.pdf
MapReduce Google
http://static.googleusercontent.com/media/research.google.com/en//archive/mapreduce-osdi04.pdf
S4 - Stream Processing http://www.4lunas.org/pub/2010-s4.pdf
Borealis - http://www.cs.harvard.edu/~mdw/course/cs260r/papers/borealis-cidr05.pdf
http://grids.ucs.indiana.edu/ptliupages/publications/survey_stream_processing.pdf
Distributed Algorithms by Nancy Lynch - Chapters on Asynchronous and Synchronous Networks
Refer to Notes - James Apnes 6,7,10,14
Consensus Survey - http://courses.csail.mit.edu/6.897/fall04/papers/Fischer/fischer-survey.ps
46
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


Refer: Distributed algorithms Nancy Lynch for further reading
Zookeeper Overview: http://zookeeper.apache.org/doc/trunk/zookeeperOver.pdf
Zookeeper - Yahoo Research
https://github.com/machinelearner/distSysTheory/blob/master/literature/zookeeper/Hunt.pdf?raw=true
Byzantine generals problem: http://research.microsoft.com/en-us/um/people/lamport/pubs/byz.pdf
Course Outcomes :
At the end of the course the students will be able to:
1. Describe the architecture models of the distributed systems
2. Survey the available distributed storage systems through case study and assignment
3. Defend the use of various techniques in distributed compute systems
4. Explain the modern architectures of co-ordintation in distributed systems
Mapping Course Outcomes with Programme Outcomes:
Course Outcomes
Describe the architecture models of the distributed
systems
Survey the available distributed storage systems
through case study and assignment
Defend the use of various techniques in distributed
compute systems
Explain the modern architectures of co-ordintation
in distributed systems
Programme Outcomes
1
x
47
2
3
4
5
6
7
8
9
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
10
11
x
12
Course Title: Big Data and Data Science
Course Code: CSPE05
Credits (L:T:P) : 3:0:1
Core/ Elective: Elective
Type of Course: Lecture, Practical
Total Contact Hours: 70
Prerequisites: Nil
Course Contents:
Unit 1
Introduction to Big Data and related technologies: Dawn of the Big Data Era, Definition and Features of Big
Data, Big Data Value, The Development of Big Data, Challenges of Big Data.
Cloud Computing, Relationship Between Cloud Computing and Big Data, IoT, IoT Preliminaries, Relationship
Between IoT and Big Data, Data Center, Hadoop Preliminaries, Relationship between Hadoop and Big Data
Unit 2
Big Data Acquisition and Analysis: Enterprise Data, IoT Data, Bio-medical Data, Data Generation from Other
Fields. Big Data Acquisition, Data Collection, Data Transportation, Data Pre-processing, Storage Mechanism for
Big Data Big Data Analysis: Traditional Data Analysis, Big Data Analytic Methods, Architecture for Big Data
Analysis, Real-Time vs. Offline Analysis, Analysis at Different Levels, Analysis with Different Complexity, Tools
for Big Data Mining and Analysis.
Unit 3
Big Data Analysis Using Hadoop: Hadoop: MapReduce Basics: Functional Programming Roots, Mappers and
Reducers, The Execution Framework, Partitioners and Combiners, The Distributed File System, Hadoop Cluster
Architecture. Graph Algorithms: Graph Representations, Parallel Breadth-First Search, PageRank, Issues with
Graph Processing.
Unit 4
Big data analysis using R: Introduction to R: Understanding datasets, Data structures: Vectors, Matrices, Arrays,
Data frames, Factors, Lists Modeling Methods: Mapping problems to machine learning tasks: Solving
classification problems, scoring Problems, Working without known targets, Problem-to-method mapping.
Unit 5
Big Data applications: Application Evolution, Big Data Analysis Fields, Structured Data Analysis, Text Data
Analysis, Web Data Analysis, Multimedia Data Analysis, Network Data Analysis, Mobile Traffic Analysis.
Key Applications, Application of Big Data in Enterprises, Application of IoT Based Big Data, Application of Online
Social Network-Oriented Big Data, Applications of Healthcare and Medical Big Data, Collective Intelligence, Smart
Grid.
Reference Material: (In no particular order of precedence)
1. Principles of Big Data: Preparing, Sharing and Analyzing Complex Information, Jules J Berman, First
Edition, MK Publishers, 2013.
2. The Field Guide to Data Science:http://www.boozallen.com/media/file/TheFieldGuidetoDataScience.pdf
3. Understanding Big Data: ftp://129.35.224.12/software/tw/Defining_Big_Data_through_3V_v.pdf
4. Ghemawat et.al Google, MapReduce: Simplied Data Processing on Large Clusters
http://static.googleusercontent.com/media/research.google.com/en//archive/mapreduceosdi04.pdf
5. Hadoop Tutorial: http://developer.yahoo.com/hadoop/tutorial/
Course Delivery
The course will be delivered through lectures, presentations, classroom discussions, practice exercises and practical
sessions.
Text Books:
1. Chen, M., Mao, S., Zhang, Y., & Leung, V. C.“Big Data-Related Technologies, Challenges and Future
Prospect”s , 2014, Springer (Chapters: 1-6)
2. Lin, J., & Dyer, C. “Data-intensive text processing with MapReduce”,2010, (Chapters: 2and 5)
3. Robert I. Kabacoff, “R in Action”, 2011,(Chapter 2 Pages:21-32)
4. Zumel, N., & Mount, J. “Practical data science with R”, 2014, (Chapters: 5, Pages 81-91 )
48
Reference Books
1. O. R. Team. Big Data Now: Current Perspectives from O’Reilly Radar. O’Reilly Media, 2011.
2. Adler, Joseph. R in a Nutshell, Second Edition. O’Reilly Media, 2012.
Course Assessment and evaluation:
Direct & Indirect Assessment Methods
What
CIE
To
Whom
Internal
Assessment
Tests
When/ Where
(Frequency in
the course)
Thrice (Average
of the best two
will be
computed)
Lab test
SEE
Semester
End
Examination
Students
End of Course
Survey
Once
End of Course
(Answering
5 of 10
questions)
Max
Marks
Evidence
Collected
Contribution to
Course
Outcomes
25
Blue Books
1,2,3,4,5,6 & 7
25
Data Sheets
1,2,3,4,5,6 & 7
50
Answer
scripts
1,2,3,4,5,6 & 7
Questionnaire
1,2,3,4,5,6 & 7
Effectiveness of
Delivery of
instructions &
Assessment
Methods
End of the
course
-
Course Outcomes
At the end of the course the students should be able to:
1. Review the features of Big Data and its types
2. Identify the relationship of big data to Cloud Computing and IoT.
3. Ilustrate different steps involved in acquisition of Big data, pre-processing
4. Identify the different storage systems for Big data.
5. Examine different types of analysis techniques involved in processing of Big Data.
6. Illustrate different types of analysis through evaluation models for Big data.
7. Estimate the need for Big data applications.
Mapping Course Outcomes with Programme Outcomes:
Course Outcomes
Review the features of Big Data and its types
Identify the relationship of big data to Cloud
Computing and IoT.
Ilustrate different steps involved in acquisition of Big
data, pre-processing
Identify the different storage systems for Big data.
Examine different types of analysis techniques
involved in processing of Big Data.
Illustrate different types of analysis through
evaluation models for Big data.
Estimate the need for Big data applications.
Programme Outcomes
1
x
2
x
3
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
49
4
5
6
7
8
9
10
11
12
x
x
x
x
Course Title: Storage Area Networks
Course Code: CSPE06
Credits (L:T:P) : 4:0:0
Core/ Elective: Elective
Type of course: Lecture
Total Contact Hours: 56
Prerequisites:
Computer Networks, Computer Organization, Operating system
Course Contents:
Unit 1
Introduction: Server Centric IT Architecture and its Limitations; Storage – Centric IT Architecture and its
advantages. Case study: Replacing a server with Storage Networks The Data Storage and Data Access problem; The
Battle for size and access.
Intelligent Disk Subsystems: Architecture of Intelligent Disk Subsystems; Hard disks and Internal I/O Channels;
JBOD, Storage virtualization using RAID and different RAID levels; Caching: Acceleration of Hard Disk Access;
Intelligent disk subsystems, Availability of disk subsystems.
Unit 2
I/O Techniques: The Physical I/O path from the CPU to the Storage System; SCSI; Fibre Channel Protocol Stack;
Fibre Channel SAN; IP Storage.
Unit 3
Network Attached Storage: The NAS Architecture, The NAS hardware Architecture, The NAS Sotfware
Architecture, Network connectivity, NAS as a storage system.
File System and NAS: Local File Systems; Network file Systems and file servers; Shared Disk file systems;
Comparison of fibre Channel and NAS.
Unit 4
Storage Virtualization: Definition of Storage virtualization ; Implementation Considerations; Storage virtualization
on Block or file level; Storage virtualization on various levels of the storage Network; Symmetric and Asymmetric
storage virtualization in the Network.
Unit 5
SAN Architecture and Hardware devices: Overview, Creating a Network for storage; SAN Hardware devices;
The fibre channel switch; Host Bus Adaptors; Putting the storage in SAN; Fabric operation from a Hardware
perspective.
Software Components of SAN: The switch’s Operating system; Device Drivers; Supporting the switch’s
components; Configuration options for SANs.
Management: Planning Business Continuity; Managing availability; Managing Serviceability; Capacity planning;
Security considerations.
Text Book:
1. Ulf Troppens, Rainer Erkens and Wolfgang Muller: Storage Networks Explained, 1st edition, Wiley India,
2012.
Reference Books:
1. Marc Farley: Storage Networking Fundamentals – An Introduction to Storage Devices, Subsystems,
Applications, Management, and File Systems, Cisco Press, 2005.
2. Robert Spalding: “Storage Networks The Complete Reference”, 1st edition, Tata McGraw-Hill, 2011.
3. Richard Barker and Paul Massiglia: “Storage Area Network Essentials A CompleteGuide to understanding
and Implementing SANs”, 1st edition, John Wiley India, 2011.
50
Course Delivery: Lecture,ppt and videos
Course Assessment and Evaluation:
Direct & Indirect Assessment Methods
What
To
Whom
Internal
Assessment Tests
CIE
Class-room
Surprise Quiz
SEE
Standard
Examination
When/ Where
(Frequency in the
course)
Thrice(Average of
the best two will be
computed)
Twice(Summation
of the two will be
computed)
Students
End of Course
Survey
Max
Marks
Evidence
Collected
Contribution to
Course Outcomes
30
Blue Books
1,2 3,4,5 & 6
20
Quiz papers
100
Answer scripts
1,2 3,4,5 & 6
Questionnaire
1,2 3,4,5 & 6
Effectiveness of
Delivery of
instructions &
Assessment
Methods
End of Course
(Answering
5 of 10 questions)
End of the course
-
1,2 3,4,5 & 6
Course Outcomes:
At the end of the course the students should be able to
1. Describe storage centric architecture as a necessity for an enterprise data storage
2.
Recognize Storage Area Networks and Local area Networks (Ethernet)
3.
Illustrate the working of various layers of Fiber Channel protocol stack.
4.
Identify the need for NAS and IP SAN at an Enterprise requirements
5.
Evaluate virtualizations Block /File level in SAN networks
6.
Identify SAN and NAS Hardware and Software components
Mapping Course Outcomes with Programme Outcomes:
Course Outcomes
Describe storage centric architecture as a necessity for
an enterprise data storage
Programme Outcomes
1
2
x
3
4
5
x
x
Recognize San networks / Lan networks
x
x
x
Illustrate the working of various layers of Fiber
Channel protocol stack.
x
x
x
Identify the need for NAS and IP SAN for an
Enterprise storage requirements
x
x
Evaluate virtualizations Block /File level in SAN
networks
x
Identify SAN and NAS Hardware and Software
components
x
51
6
x
x
x
7
8
9
10
x
x
x
x
x
x
x
x
x
x
11
12
x
x
x
x
x
x
x
Course Title: Information Retrieval
Course Code: CSPE07
Credits (L:T:P:SS) : 3:0:1
Core/ Elective: Elective
Type of course: Lecture, Practicals
Total Contact Hours: 70
Prerequisites: NIL
Course Content:
Unit 1
Introduction: Overview, History of IR, Text Operations: Document preprocessing, Document Clustering, Text
Compression, Indexing: Inverted files, Mathematics for IR: Set Theory, Mathematical Logic, Probability and
Linear algebra, Classic IR Models: Boolean Model, Vector space model: tf-idf weighing, Probabilistic Model.
Unit 2
Evaluation Measures: Precision, Recall, Alternative Measures, Reference Collections: TREC, Relevance
Feedback and Query Expansion, Text Classification: The text classification problem, Flat clustering:
Clustering in information retrieval, Problem Statement, Hierarchical clustering: Hierarchical agglomerative
clustering,, Single-link and Complete-link clustering.
Unit 3
String Matching algorithms: Knuth Morris Pratt and Rabin Karp, Stemming algorithm: Porter, Map reduce
algorithms: tf- idf calculation and indexing, Classification: Naive Bayes algorithm, Clustering: k-means
algorithm.
Unit 4
Web search engines: Architecture, Web Crawling and Indexing: Overview, Crawling, Distributing Indexes,
Connectivity Servers. Link analysis: Web as a graph, Page Rank, Hubs and Authorities.
Unit 5
XML Retrieval: Basic XML Concepts, Challenges in XML retrieval, a vector space model for XML retrieval,
Introduction to Semantic Web: Purpose, Semantic Web Stack, RDF, RDFS, Ontology, Web ontology language
(OWL) and ontology tools.
Text Books/ Online Books
1. Ricardo Baeza-Yates, Berthier Ribeiro-Neto: Modern Information Retrieval, Pearson Education, 1999.
2. Introduction to Information Retrieval. C.D. Manning, P. Raghavan, H. Schütze. Cambridge UP, 2008.
3.
Mathematics for Classical Information Retrieval: Roots and Applications: Dariush Alimohammadi,
http://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1002&context=zeabook
4.
http://www.dcc.fc.up.pt/~zp/aulas/1213/pde/geral/bibliografia/MIT.Press.A.Semantic.Web.Primer.eBookTLFeBOOK.pdf
Reference Books:
1. William B Frakes, Ricardo Baeza Yates: Information Retrieval Data Structures and Algorithms, PH PTR,
1992.
2. David A Grossman, Ophir Frieder: Information Retrieval Algorithms and Heuristics, 2e, Springer, 2004.
Course Delivery:
The course will be delivered through lectures and presentations.
52
Course Assessment and Evaluation:
Direct & Indirect Assessment Methods
What
CIE
To Whom
Internal
Assessment Tests
Lab Assignments
SEE
Students
Standard
Examination
When/ Where
(Frequency in
the course)
Thrice
(Average of
the best two
will be
computed)
Throughout
Semester
End of Course
(Answering
5 of 10
questions)
End of Course
Survey
End of the
course
Max
Mark
s
Evidence
Collected
Contribution to
Course Outcomes
30
Blue Books
1,2,3,4,5 & 6
20
Softcopy of lab
assignments
1,2,3,4,5 & 6
100
Answer scripts
1,2,3,4,5 & 6
Questionnaire
1,2,3,4,5 & 6
Effectiveness of
Delivery of
instructions &
Assessment Methods
-
Course Outcomes:
At the end of the course, a student should be able to
1. Describe text operations and various information retrieval models.
2. Assess an IR system using various evaluation measures.
3. Explain text classification and clustering techniques.
4. Illustrate algorithms such as string matching, map reduce, classification and clustering.
5. Discuss techniques for web information retrieval such as web search engine, web crawling and link analysis.
6. Explain XML Retrieval and various semantic web technologies.
Mapping Course Outcomes with Programme Outcomes:
Course Outcomes
Describe text operations
and various information
retrieval models.
Assess an IR system
using various evaluation
measures.
Explain
text
classification
and
clustering techniques.
Illustrate algorithms such
as string matching, map
reduce, classification and
clustering.
Discuss techniques for
web information retrieval
such as web search
engine, web crawling
and link analysis.
Explain XML Retrieval
and various semantic
web technologies
PO1
PO2
PO3
PO4
Programme Outcomes
PO5 PO6 PO7 PO8
PO9
PO10
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
53
PO11
PO12
Course Title: Social network analysis
Course Code: CSPE08
Credits (L:T:P) : (3:1:0)
Core/ Elective: Elective
Type of course: Lecture, Tutorial
Total Contact Hours: 70
Prerequisites:
Nil
Course Contents:
Unit 1
Social Structure : Sociometry and Sociogram, Exploratory Social Network Analysis, Assembling a Social Network.
Attributes and Relations: The World System, Partitions, Reduction of a Network, Vectors and Coordinates, Network
Analysis and Statistics
Unit 2
Cohesive Subgroups: Density and Degree, Components, Cores, Cores. Sentiments and Friendship: Balance Theory,
Detecting Structural Balance and Clusterability, Development in Time. Affiliations: Two-Mode and One-Mode
Networks, m-Slices, The Third Dimension
Unit 3
Center and Periphery: Distance, Betweenness. Brokers and Bridges: Bridges and Bi-Components, Ego-Networks
and Constraint, Affiliations and Brokerage Roles. Diffusion: Contagion, Exposure and Thresholds, Critical Mass.
Unit 4
Prestige: Popularity and Indegree, Correlation, Domains, Proximity Prestige. Ranking: Triadic Analysis, Acyclic
Networks, Symmetric-Acyclic Decomposition, Genealogies and Citations: Genealogy of the Ragusan Nobility,
Family Trees, Family Trees, Citations among Papers on Network Centrality, Citations
Unit 5
Blockmodels: Matrices and Permutation, Roles and Positions: Equivalence, Blockmodeling. Pajek: Creating
Network Files for Pajek, Exporting Visualizations
Text Books:
1.
2.
3.
Exploratory social Network Analysis with Pajek, WOUTER DE NOOY, ANDREJ MRVAR, VLADIMIR
BATAGELJ, Cambridge University press, 2005
Introduction to social network methods, Hanneman, Robert A. and Mark Riddle. 2005. University of
California, Riverside ( published in digital form at http://faculty.ucr.edu/~hanneman/ )
Social Network Analysis for Startups, Maksim Tsvetovat and Alexander Kouznetsov, Oreilly,2011
Course Delivery:
The course will be delivered through lectures, software tools in laboratory, class room interaction, group discussion,
exercises and self study cases.
54
Course Outcomes:
At the end of the course, a student should be able to
1.
2.
3.
4.
5.
6.
7.
Describe social structure and attributes of real world scenarios.
Find the statistical details of different social networks
Indicate the cohesive nature of different social networks
Estimate the affiliations of social network elements
Examine the brokerage properties in social networks
Assess ranking of real world social networks
Verify the roles of social network elements
Mapping Course Outcomes with Programme Outcomes:
Course Outcomes
Programme Outcomes
4
5
×
×
×
×
×
×
×
×
×
×
×
×
×
×
×
×
×
×
×
Examine the brokerage properties in social networks
×
×
×
×
×
Assess ranking of real world social networks
×
×
×
×
×
Verify the roles of social network elements
×
×
×
×
×
Describe social structure and attributes of real world
scenarios.
Find the statistical details of different social
networks
Indicate the cohesive nature of different social
networks
Estimate the affiliations of social network elements
1
2
×
55
3
6
7
8
9
10
11
12
Course Title: Wireless Sensor Networks
Course Code: CSPE09
Credits (L:T:P) : 4:0:0
Core/ Elective: Elective
Type of course: Lecture, Laboratory
Total Contact Hours: 70
Prerequisites:
Computer Networks
Course Contents:
Unit 1
Introduction: Definition of Wireless Sensor Networks (WSNs), difference between the adhoc and sensor n/ws,
challenges for WSN and applications of WSN. Single node architecture: Hardware components, energy
consumption in sensor nodes, brief study of operating systems like TinyOS and NesC. Ntework architecture:
Network scenarios, QOS parameters, design principles of WSN and the interfaces.
Unit 2
Communication Protocols: Physicallayer protocols - Introduction, Wireless channel and communication
fundamentals, Physical layer & transceiver design considerations in WSNs. MAC layer protocolsFundamentals of
(wireless) MAC protocols, Low duty cycle protocols and wakeup concepts, Contention-based protocols, Schedulebased protocols, The IEEE 802.15.4 MAC protocol, IEEE 802.11 and Bluetooth.
Unit 3
Link layer protocols: Fundamentals: Tasks and requirements, Error control, Framing, Link management. Naming
and addressing: Fundamentals, Address and name management in wireless sensor networks, Assignment of MAC
addresses, Distributed assignment of locally unique addresses, Content-based and geographic addressing.
Unit 4
Time synchronization: Introduction to the time synchronization problem, Protocols based on sender/receiver
synchronization, Protocols based on receiver/receiver synchronization. Localization and Positioning: Properties of
positioning, Possible approaches, Mathematical basics for the lateration problem, Single-hop localization,
Positioning in multi-hop environments, Impact of anchor placement.
Unit 5
Routing Protocols: The many faces of forwarding and routing, Gossiping and agent-based unicast forwarding,
Energy-efficient unicast, Broadcast and multicast, Geographic routing for Mobile nodes.
Text Books:
1.
2.
Holger Karl & Andreas Willig, " Protocols And Architectures for Wireless Sensor Networks" , John Wiley,
2005.
Feng Zhao & Leonidas J. Guibas, “Wireless Sensor Networks- An Information Processing Approach",
Elsevier, 2007.
Reference Books:
1. KazemSohraby, Daniel Minoli, &TaiebZnati, “Wireless Sensor Networks- Technology, Protocols, And
Applications”, John Wiley, 2007.
2. Anna Hac, “Wireless Sensor Network Designs”, John Wiley, 2003.
Course Delivery:
The course will be delivered through lectures, class room interaction, group discussion and lab exercises and selfstudy cases.
56
Course Outcomes:
At the end of the course the students should be able to:
1. Explain the hardware and software components of wireless sensor networks.
2. Summarize the fundamentals of MAC protocols.
3. Recognize the important tasks of Link layer addressing and naming schemes in WSNs
4. Identify the time synchronization problems and understand the principal design trade-offs for positioning
nodes in the network.
5. Compare the different mechanisms available for routing in the network.
6. Devise new applications for WSNs.
Mapping Course Outcomes with Programme Outcomes:
Course Outcomes
Programme Outcomes
1
4
5
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
Explain the hardware and software components of
wireless sensor networks.
Identify the time synchronization problems and
understand the principal design trade-offs for
positioning nodes in the network.
Compare the different mechanism available for
routing in the network
Devise new applications for WSNs
3
x
Summarize the fundamentals of MAC protocols.
Recognize the important tasks of Link layer
addressing and naming schemes in WSNs
2
57
x
6
7
8
9
10
11
12
Course Title: Mobile Computing
Course Code: CSPE10
Credits (L:T:P) : 4:0:0
Core/ Elective: Elective
Type of course: Lecture
Total Contact Hours: 56
Prerequisites:
Nil
Course Contents:
Unit 1
Introduction; Types of computing; wireless transaction - Signals, antennas, signal propagation, multiplexing
modulation, spread spectrum; medium access: SDMA, FDMA,TDMA, CDMA. OFDM, Wireless LAN: IEEE
802.11, HiperLAN, Blue tooth, Wi Max.
Unit 2
Mobile Network layer – mobile IP, Dynamic host configuration protocol, Mobile adhoc networks. Mobile transport
layer-Traditional and classical TCP, TCP over 2.5 (3.0) G wireless networks, performance enhancing proxies,
Communication Systems: GSM,DECT, TETRA, UMTS, IMIT 200, LTE.
Unit 3
Databases: Database Hoarding Techniques, Data Caching, Transactional Models, Query Processing.Data
Dissemination and Broadcasting Systems: Communication Asymmetry, Classification of Data-Delivery
Mechanisms, Data Dissemination Broadcast Models, Selective Tuning and Indexing Techniques.
Unit 4
Data Synchronization in Mobile Computing Systems: Synchronization, Synchronizationsoftware for mobile devices,
Synchronization protocols, SyncML – Synchronization language for mobile computing, Sync4J (Funambol),
Synchronized Multimedia Markup Language (SMIL). Mobile Devices: Server and Management: Mobile agent,
Application server, Gateways, Portals, Service Discovery, Device management, Mobile file systems, security.
Unit 5
Support Mobility- File Systems, Mobile Security, Mobile operating systems: PalmOs, Windows, Symbian OS,
Android, iOS, Linux for Mobile devices.
Text Books:
1. Mobile Computing, RajKamal, Oxford University Press, 2nd Edition, 2012
2. Jochen Schiller, Mobile Communications, 2nd Edition, Pearson 2003
Reference Books:
1. Reza B, Mobile Computing Principles, Cambridge University press 2005
2. Dr. Ashok Talukder, MsRoopaYavagal, Mr. Hasan Ahmed: Mobile Computing, Technology, Applications and
Service Creation, 2nd Edition, Tata McGraw Hill, 2010.
3. Martyn Mallik: Mobile and Wireless Design Essentials, First Edition, Wiley, 2011.
Course Delivery:
The course will be delivered through lectures, presentations, classroom discussions, and practical implementations.
Questions for CIE and SEE are designed in accordance with the Revised Bloom’s taxonomy.
58
Course Assessment Methods:
When/ Where
(Frequency in the
course)
Max
Marks
Evidence
Collected
Contribution to
Course
Outcomes
Internal
Tests
Thrice(Average of
the best two will be
computed)
30
Blue Books
1,2,3,4 & 5
Quiz
Twice
20
Quiz Papers
1,2,3,4 & 5
End of Course
(Answering 5 out
of 10 Questions)
50
Answer scripts
1,2,3,4 & 5
Questionnaire
1, 2, 3, 4 & 5
Effectiveness of
Delivery of
instructions &
Assessment
Methods
Direct & Indirect Assessment Methods
What
To Whom
CIE
SEE
Standard
Examination
Students
End of Course Survey
End of the course
-
Course Outcomes:
At the end of the course, a student should be able to:
1. Identify the basic networking concepts, principles and techniques in mobile networks.
2. Identify the concept of mobile network and transport layer for mobile networks and telecommunication
systems.
3. Outline the database handling, data dissemination, Synchronization with respect to different Mobile OS.
4. Estimate data synchronization in mobile computing systems.
5. Illustrate the mobility support using different file systems and platforms.
Mapping Course Outcomes with Programme Outcomes:
Course Outcomes
Identify the basic networking concepts, principles and
techniques in mobile networks.
Identify the concept of mobile network and transport
layer for mobile networks and telecommunication
systems.
Outline the database handling, data dissemination,
Synchronization with respect to different Mobile OS.
Estimate data synchronization in mobile computing
systems.
Illustrate the mobility support using different file
systems and platforms.
Programme Outcomes
1
2
3
4
x
x
5
x
x
x
x
x
x
x
59
x
x
x
x
x
6
7
8
9
10
11
12
Course Title: Multimedia Computing
Course Code: CSPE11
Credits (L:T:P) : 3:0:1
Core/ Elective: Elective
Type of Course: Lecture, Practicals
Total Contact Hours: 56
Prerequisites: NIL
Unit 1
Introduction, Media and Data Streams, Audio Technology: Multimedia Elements, Multimedia Applications,
Multimedia Systems Architecture, Evolving Technologies for Multimedia Systems, Defining Objects for
Multimedia Systems, Multimedia Data Interface Standards, The need for Data Compression, Multimedia Databases.
Media: Perception Media, Representation Media, Presentation Media, Storage Media, Characterizing Continuous
Media Data Streams. Sound: Frequency, Amplitude, Sound Perception and Psychoacoustics, Audio Representation
on Computers, Three Dimensional Sound Projection, Music and MIDI Standards, Speech Signals, Speech Output,
Speech Input, Speech Transmission. Graphics and Images, Video Technology, Computer-Based Animation:
Capturing Graphics and Images Computer Assisted Graphics and Image Processing, Reconstructing Images,
Graphics and Image Output Options. Basics, Television Systems, Digitalization of Video Signals, Digital
Television, Basic Concepts, Specification of Animations, Methods of Controlling Animation, Display of Animation,
Transmission of Animation, Virtual Reality Modeling Language.
Unit 2
Data Compression: Storage Space, Coding Requirements, Source, Entropy, and Hybrid Coding, Basic
Compression Techniques, JPEG: Image Preparation, Lossy Sequential DCT-based Mode, Expanded Lossy DCTbased Mode, Lossless Mode, Hierarchical Mode. H.261 (Px64) and H.263: Image Preparation, Coding Algorithms,
Data Stream, H.263+ and H.263L, MPEG: Video Encoding, Audio Coding, Data Stream, MPEG-2, MPEG-4,
MPEG-7, Fractal Compression.
Unit 3
Optical Storage Media: History of Optical Storage, Basic Technology, Video Discs and Other WORMs, Compact
Disc Digital Audio, Compact Disc Read Only Memory, CD-ROM Extended Architecture, Further CD-ROM-Based
Developments, Compact Disc Recordable, Compact Disc Magneto-Optical, Compact Disc Read/Write, Digital
Versatile Disc. Content Analysis : Simple Vs. Complex Features, Analysis of Individual Images, Analysis of Image
Sequences, Audio Analysis, Applications.
Unit 4
Data and File Format Standards: Rich-Text Format, TIFF File Format, Resource Interchange File Format (RIFF),
MIDI File Format, JPEG DIB File Format for Still and Motion Images, AVI Indeo File Format, MPEG Standards,
TWAIN.
Unit 5
Multimedia Application Design: Multimedia Application Classes, Types of Multimedia Systems, Virtual Reality
Design, Components of Multimedia Systems, Organizing Multimedia Databases, Application Workflow Design
Issues, Distributed Application Design Issues.
Text Books:
1. Ralf Steinmetz, Klara Narstedt: Multimedia Fundamentals: Vol 1-Media Coding and Content Processing,
First Edition, PHI, 2010.
2. Prabhat K. Andleigh, Kiran Thakrar: Multimedia Systems Design,1st Edition, PHI, 2011.
Reference Books:
1. K.R. Rao, Zoran S. Bojkovic and Dragorad A. Milovanovic: Multimedia Communication Systems:
Techniques, Standards, and Networks, 1st Edition, PHI , 2010.
2. Nalin K Sharad: Multimedia information Networking, PHI, 2002.
60
Course Outcomes:
At the end of the course, a student should be able to
1. Identify the basic concepts of media, data streams and audio technology
2. Implement different data compression techniques including video, audio and fractal compression.
3. Demonstrate different optical storage media including content Analysis
4. Identify the different data and file format standards like TIFF, RIFFF, MIDI and MPEG.
5. Analyze multimedia application design methods like Virtual Reality design and workflow design.
61
Course Title: Software Defined Networks
Course Code: CSPE12
Credits (L:T:P:SS) : 4:0:0
Core/ Elective: Elective
Type of course: Lecture
Total Contact Hours: 56
Prerequisites:
Data Communications and Computer Networks
Course Contents:
Unit 1
Introduction - Basic Packet-Switching Terminology, The Modern Data Center, Traditional Switch Architecture,
Autonomous and Dynamic Forwarding Tables, Can We Increase the Packet-Forwarding IQ? Open Source and
Technological Shifts.
Why SDN? Evolution of Switches and Control Planes, Cost, SDN Implications for Research and Innovation, Data
Center Innovation, Data Center Needs
Unit 2
The Genesis of SDN: The Evolution of Networking Technology, Forerunners of SDN, Software Defined
Networking is Born, Sustaining SDN Interoperability, Open Source Contributions, Legacy Mechanisms Evolve
Toward SDN, Network Virtualization.
How SDN Works - Fundamental Characteristics of SDN, SDN Operation, SDN Devices, SDN Controller, SDN
Applications, Alternate SDN Methods
Unit 3
The OpenFlow Specification - OpenFlow Overview, OpenFlow 1.0 and OpenFlow Basics, OpenFlow 1.1
Additions, OpenFlow 1.2 Additions, OpenFlow 1.3 Additions, OpenFlow Limitations.
Alternative Definitions of SDN - Potential Drawbacks of Open SDN, SDN via APIs, SDN via Hypervisor-Based
Overlays, SDN via Opening Up the Device, Network Functions Virtualization, Alternatives Overlap and Ranking
Unit 4
SDN in the Data Center- Data Center Definition, Data Center Demands, Tunneling Technologies for the Data
Center, Path Technologies in the Data Center, Ethernet Fabrics in the Data Center, SDN Use Cases in the Data
Center, Open SDN versus Overlays in the Data Center, Real-World Data Center Implementations.
SDN in Other Environments - Consistent Policy Configuration, Global Network View, Wide Area Networks,
Service Provider and Carrier Networks, Campus Networks, Hospitality Networks, Mobile Networks, In-Line
Network Functions, Optical Networks, SDN vs. P2P/Overlay Networks
Unit 5
SDN Applications- Reactive versus Proactive Applications, Reactive SDN Applications, Proactive SDN
Applications, Analyzing Simple SDN Applications, Creating Network Virtualization Tunnels, Offloading Flows in
the Data Center, Access Control for the Campus, Traffic Engineering for Service Providers
SDN Futures - Potential Novel Applications of Open SDN, Applying Programming Techniques to Networks,
Security Applications, Hiding IP Addresses, Segregating IPSec Traffic in Mobile Networks, Roaming in Mobile
Networks, Traffic Engineering in Mobile Networks, Energy Savings, SDN-Enabled Switching Chips
Text Book:
1. Paul Goransson, Chuck Black: Software Defined Networks A Comprehensive Approach , Elsevier, 2014.
Reference Book:
1. Thomas D.Nadeau & Ken Gray: SDN Software Defined Networks O'Reilly publishers, First edition, 2013.
Course Delivery:
The course will be delivered through lectures and assignments.
62
Course Outcomes:
At the end of the course students should be able to:
1.
2.
3.
4.
5.
6.
7.
Differentiate between traditional networks and software defined networks
Identify the characteristics of SDN
Describe the working of SDN
Compare the performance of various open flow versions
Define SDN via APIs and Hypervisor based overlays
Identify various SDN applications and environments that benefits from its use.
Discuss future applications for SDN with respect to security and mobility of networks.
Mapping Course Outcomes with Programme Outcomes:
Course Outcomes
Differentiate between traditional networks and
software defined networks
Identify the characteristics of SDN
Describe the working of SDN
Compare the performance of various open flow
versions
Programme Outcomes
1
2
3
4
5
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
Identify various SDN applications and environments
that benefits from its use.
x
x
x
x
x
Discuss future applications for SDN with respect to
security and mobility of networks.
x
x
x
x
x
Define SDN via APIs and Hypervisor based overlays
63
6
x
7
8
9
10
11
12
x
Course Title: Analysis of Computer Data Networks
Course Code: CSPE13
Credits (L:T:P) : 4:0:0
Core/ Elective: Core
Type of course: Lecturer
Total Contact Hours: 56
Prerequisites:
Computer Network and Data Communication
Course contents:
Unit 1
Review of Digital Transmission, Error Detection, Effectiveness of Error Detection codes, Two-dimensional Parity
checks, Polynomial codes. Standardized Polynomial Codes, Error Detecting Capability of a Polynomial code, Linear
Codes, Performance of Linear Codes, Error Correction(Garcia’s Book pages 166 to 190)
Unit 2
Introduction to Data Link Layer, Framing, Character – oriented Framing, Bit- oriented Framing, Length fields,
Framing with errors, Maximum Frame size-Variable Frame Length, Fixed Frame Length, Little’s theorem,
Probabilistic Form of Little’s theorem, Applications of Little’s theorem, Occupancy distribution upon arrival,
Occupancy Distribution upon arrival, Occupancy distribution up to departure, Brief review of
Queuing models (Gallager’s book, pages 86-96, 152-162, 171-173)
Unit 3:
Delay Models in Data Networks – Introduction, Multiplexing of Traffic on a Communication Link, Queuing
Models. Statistical Multiplexing, Poisson arrival process and packet loss probability, M/M/m/m: The m-server loss
system, M/G/1 system, Priority Queuing, nonpreemptive priority, Preemptive resume priority, Network of queues,
Jackson’s theorem (Garcia’s book pages 340-348, Gallager’s book pages 178, 179, 186-190,
203-209, 221-229)
Unit 4:
Multiaccess communication-Review of Aloha Networks, Idealized Slotted Multiaccess Model, Stabilized Slotted
Aloha, Splitting algorithms, Tree Algorithms, FCFS Splitting Algorithms, Improvements in the FCFS splitting
Algorithm, CSMA Slotted Aloha, FCFS Splitting Algorithms for CSMA (Gallager’s book pages 275-277, 282 –
283, 289 – 302, 305 – 307, 310, 311)
Unit 5:
Topological Design of Networks: Flow Models, An overview of Topological Design Problems, Subnet Design
Problem, Capacity Assignment Problem, Heuristic Methods for Capacity Assignment, Network Reliability Issues,
Spanning Tree Topology Design (Gallager’s book Pages 433-448)
Text Books:
1. Alberto Leon-Garcia and Indra Widjaja, Communication Networks, Fundmental Concepts and Key
Architectures, Second Reprint, Tata McGraw-Hill, 2004
2. Dimitri Bertsekas and Robert Gallager, Data Networks, Second Edition, Prentics Hall of India, 2000
Reference Books:
1. Anurag Kumar, Manjunath and Joy Kuri: Communication Networking2. An Analytical Approach, Indian Reprint, Morgan Kaufman Publishers, 2006
64
Course Outcomes:
At the end of the course student should be able to
1.
2.
3.
4.
5.
6.
Illustrate mathematical and engineering aspects of the performance in Data Networks.
Create a mathematical model of the given problem in computer Networks
Identify the role of Little’s theorem in the analysis typically in Occupancy distribution on arrival and
Occupancy Distribution on Departure
Review the study on Queuing models and apply to the cases of Priority Queuing, non-preemptive
priority and preemptive resume priority
Describe special theorems like Jackson’s theorem and its applications, splitting algorithms and application
to ALOHA Networks
Discuss Topological design of networks, flow models, spanning tree topology, subnet design problem, etc.
65
Course Title: Operations Research
Course Code: CSPE14
Credits (L:T:P) : 4:0:0
Core/ Elective: Elective
Type of course: Lecture
Total Contact Hours: 56
Prerequisites: NIL
Course Contents:
Unit 1
Introduction, Linear Programming – 1: Introduction: The origin, nature and impact of OR, Defining the problem
and gathering data, Formulating a mathematical model, Deriving solutions from the model, Testing the model,
Preparing to apply the model, Implementation. Introduction to Linear Programming: Prototype example, The linear
programming (LP) model.
Unit 2
LP – 2, Simplex Method: Assumptions of LP, Additional examples. The essence of the simplex method, Setting
up the simplex method, Algebra of the simplex method, the simplex method in tabular form, Tie breaking in the
simplex method, Adapting to other model forms, Post optimality analysis, Computer implementation, Foundation of
the simplex method.
Unit 3
Simplex Method – 2, Duality Theory: The revised simplex method, a fundamental insight. The essence of duality
theory, Economic interpretation of duality, Primal dual relationship, Adapting to other primal forms.
Unit 4
Duality Theory and Sensitivity Analysis, Other Algorithms for LP : The role of duality in sensitive analysis,
The essence of sensitivity analysis, Applying sensitivity analysis. The dual simplex method, Parametric linear
programming, The upper bound technique.Transportation and Assignment Problems: The transportation problem, A
streamlined simplex method for the transportation problem, The assignment problem, A special algorithm for the
assignment problem.
Unit 5
Game Theory, Decision Analysis: Game Theory: The formulation of two persons, zero sum games, Solving
simple games- a prototype example, Games with mixed strategies, Graphical solution procedure, Solving by linear
programming, Extensions.
Decision Analysis: A prototype example, Decision making without experimentation, Decision making with
experimentation, Decision trees. Metaheuristics: The nature of Metaheuristics, Tabu Search, Simulated Annealing,
Genetic Algorithms.
Text Book:
1. Frederick S. Hillier and Gerald J. Lieberman: Introduction to Operations Research: Concepts and Cases, 8th
Edition, Tata McGraw Hill, 2005.
Reference Books:
1. Wayne L. Winston: Operations Research Applications and Algorithms, 4th Edition, Cengage Learning,
2003.
2. Hamdy A Taha: Operations Research: An Introduction, 8th Edition, Pearson Education, 2007.
66
Course Outcomes:
At the end of the course, a student should be able to
1.
2.
3.
4.
5.
Formulate the mathematical model and Apply the Linear Programming techniques.
Describe the simplex method in tabular form, algebra of simplex method and post optimality analysis.
Apply and identify the simplex method-2, essence of duality theory and primal dual relationship.
Apply and analyze essence of sensitivity analysis, algorithms for transportation and assignment problems.
Demonstrate the algorithm for the formulation of zero sum game, decision making with experimentation
and simulated annealing.
67
Course Title: Advanced Algorithms
Course Code: CSPE15
Credits (L:T:P:S) : 3:0:1
Core/ Elective: Elective
Type of course: Lecture, Practical
Total Contact Hours: 56
Prerequisites: Algorithms
Course Contents:
Unit 1
Analysis Techniques: Growth of Functions, Asymptotic notations, Standard notations and common functions,
Recurrences and Solution of Recurrence equations – The Substitution method, The recurrence – tree method, The
master method, Amortized Analysis: Aggregate, Accounting and Potential Methods.
Unit 2
Graph Algorithms: Bellman-Ford Algorithm, Single source shortest paths in a DAG, Johnson’s Algorithm for
sparse graphs, Maximum bipartite matching. Trees: B-trees, Red-Black trees Hashing: General Idea, Hash
Function, Separate Chaining, Open addressing, Rehashing, Extendible hashing
Unit 3
Number – Theoretic Algorithms: Elementary notations, GCD, Modular Arithmetic, Solving modular linear
equations, The Chinese remainder theorem, Powers of an element, RSA cryptosystem. Heaps: Heaps, Priority
Queues, Binomial Heaps, Fibonacci Heaps.
Unit 4
String Matching Algorithms: Naïve string matching, Rabin – Karp algorithm, String matching with finite
automata, Knuth-Morris-Pratt algorithm, Boyer-Moore Algorithms.
Unit 5
Algorithmic Puzzles: Magic Square, n-queens problem, Glove Selection, Ferrying Soldiers, Jigsaw Puzzle
Assembly, A Stack of Fake Coins, Maximum Sum Descent, Hats of Two Colors, Pluses and Minuses, Searching for
a Pattern, Locker Doors, Palindrome Counting, Inverting a Coin Triangle, Sorting 5 in 7.
Text Books:
1. T H Cormen, C E Leiserson, R L Rivest and C Stein: Introduction to Algorithms, 3/e, PHI, 2011.
2. Mark Allen Weiss: Data Structures and Algorithm Analysis in C++, 3rd Edition, Pearson Education, 2011.
3. Anany Levitin and Maria Levitin: Algorithmic Puzzle, Oxford University Press, 2011
Reference Books:
1. Ellis Horowitz, Sartaj Sahni, S Rajasekharan: Fundamentals of Computer Algorithms, University Press,
2007.
2. Alfred V Aho, John E Hopcroft, J D Ullman: The Design and Analysis of Computer Algorithms, Pearson
Education, 2011.
Course Delivery:
The course will be delivered through lectures and presentations.
Course Assessment and Evaluation Scheme:
Direct &
Indirect
Assessment
Methods
What
CIE
SEE
Internal
Assessment
Tests
Assignment
Standard
Examination
To
Whom
Student
s
When/ Where
(Frequency in the
course)
Thrice(Average of the
best two will be
computed)
Once
End of Course
(Answering
5 of 10 questions)
68
Max
Marks
Evidence
Collected
Contribution to
Course
Outcomes
30
Blue Books
1,2,3,4,5,6 & 7
20
Solutions
1,2,3,4,5,6 & 7
100
Answer scripts
1,2,3,4,5,6 & 7
End of Course
Survey
End of the course
-
1,2 ,3,4,5 & 6
Effectiveness of
Delivery of
instructions &
Assessment
Methods
Questionnaire
Course Outcomes:
At the end of the course, a student should be able to
1 Devise recurrence relations and amortized cost of various operations.
2 Illustrate graph algorithms such as Bellman-Ford, Shortest path, and Bipartite matching.
3 Explain B-trees, Red-Black trees and hashing techniques.
4 Identify the methods for solving modular linear equations, Chinese remainder theorem and RSA
cryptosystem.
5 Describe types of heaps such as Binomial and Fibonacci heaps.
6 Assess the string matching algorithms such as Boyer-Moore and Knuth-Morris-Pratt algorithm.
7 Compose mathematical models, objective functions and constraints to solve algorithmic puzzles.
Mapping Course Outcomes with Programme Outcomes:
Course Outcomes
Devise recurrence relations
and
amortized cost of various
operations.
Illustrate graph algorithms
such
as
Bellman-Ford,
Shortest path, and Bipartite
matching.
Explain B-trees, Red-Black
trees and hashing techniques
Identify the methods for
solving
modular
linear
equations, Chinese remainder
theorem
and
RSA
cryptosystem.
Describe types of heaps such
as
Binomial and Fibonacci heaps.
Assess the string matching
algorithms
such as Boyer-Moore and
Knuth
Morris-Pratt algorithm.
Compose
mathematical
models, objective functions
and constraints to solve
algorithmic puzzles.
PO
1
PO
2
PO
3
x
x
x
x
x
x
x
x
x
x
x
x
PO
4
x
PO
10
PO
11
PO
12
x
x
x
Programme Outcomes
PO PO PO PO PO
5
6
7
8
9
x
69
x
x
x
x
x
x
x
x
x
Course Title: Artificial Intelligence
Course Code: CSPE16
Credits (L:T:P) : 4:0:0
Core/ Elective: Elective
Type of course: Lecture
Total Contact Hours: 56
Prerequisites:
Knowledge of any advance programming language
Course Content:
Unit 1
Introduction: What is AI? Intelligent Agents: Agents and environment, Rationality, the nature of environment, the
structure of agents. Problem-solving by search: Problem-solving agents, Example problems, searching for solution,
Uninformed search strategies.
Unit 2
Informed Search and Exploration: Informed search strategies, Heuristic functions, On-line search agents and
unknown environment. Logical Agents: Knowledge-based agents, The wumpus world, Logic, propositional logic,
Reasoning patterns in propositional logic, Effective propositional inference, Agents based on propositional logic
Unit 3
First-Order Logic: Representation revisited, Syntax and semantics of first-order logic, Using first-order logic,
Knowledge engineering in first-order logic. Interference in First-order Logic: Propositional versus first-order
inference, Unification and lifting, Forward chaining, Backward chaining, Resolution.
Unit 4
Planning: The Planning problem, planning with state-space approach, Planning graphs, Planning with propositional
logic. Uncertainty: Acting under uncertainty, Basic probability Notations, Inference using full joint distributions,
Independence, Bayes’ rule and its use. Learning from observations: Forms of Learning, Inductive learning,
learning decision trees, Ensemble learning, Computational learning theory
Unit 5
Natural Language Processing: Introduction, syntactic processing, semantic Analysis, discourse and pragmatic
processing, statistical natural language processing.
Genetic Algorithms: GA, Significance of genetic operators, Termination parameters, Niching and Speciation,
Evolving Neural Networks, Theoretical grounding, Ant Algorithms.
AI: Present and Future: Peer reviews in class room on Advance Topics in AI, AI Programming Languages,
Current state of art of AI and its future.
Text Book:
1. Stuart Russel, Peter Norvig: Artificial Intelligence - A Modern Approach, 2nd Edition, Pearson Education,
2012.
2. Elaine Rich, Kevin Knight, Shivashankar B Nair: Artificial Intelligence, 3rd Edition, Tata McGraw Hill, 2011.
Reference Books:
1. Nils J. Nilsson: Principles of Artificial Intelligence, First Edition, Elsevier, 2002.
2. Luger, G. F., & Stubblefield, W. A., Artificial Intelligence - Structures and Strategies for Complex Problem
Solving. New York, NY: Addison Wesley, 5th edition (2005).
Course Delivery:
The course will be delivered through lectures, presentations, classroom discussions, and practical implementations.
70
Course Assessment and Evaluation:
Direct & Indirect Assessment Methods
What
To
Whom
Internal
Assessment
Tests
CIE
Implementation
of AI
Techniques
SEE
When/
Where
(Frequency
in the
course)
Thrice
(Average of
the best two
will be
computed)
Max
Marks
Evidence
Collected
Contribution
to Course
Outcomes
30
Blue Books
1,2,3,4,5 & 6
Review 1
Review 2
20
Soft copies of
the mini
projects/ AI
techniques
implemented
1,2,3,4,5 & 6
End of
Course
(Answering
5 of 10
questions)
100
Answer scripts
1,2,3,4,5 & 6
End of the
course
-
Questionnaire
1,2,3,4,5 & 6,
Relevance of
the course
Students
Semester End
Examination
End of Course
Survey
Course Outcomes:
At the end of the course, a student should be able to
1. Identify the modern view of AI and its application based on agent philosophy.
2. Compare the various searching algorithms commonly used in AI by implementing them.
3. Summarize the various knowledge representation and inference techniques.
4. Examine the various learning and planning techniques available and its application.
5. Review the various methods of handling uncertainty.
6. Examine the role of Natural Language processing and Genetic Algorithms in building Intelligent Systems.
Mapping Course Outcomes with Programme Outcomes:
Course Outcomes
Programme Outcomes
1
Identify the modern view of AI and its application
based on agent philosophy
Compare the various searching algorithms commonly
used in AI by implementing them.
Summarize the various knowledge representation and
inference techniques
Examine the various learning and planning techniques
available and its application.
Review the various methods of handling uncertainty
Examine the role of Natural Language processing and
Genetic Algorithms in building Intelligent Systems.
71
2
3
4
5
6
7
8
9
10
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
11
12
Course Title: System Simulation
Course Code: CSPE17
Credits (L:T:P) 4:0:0
Core/ Elective: Elective
Type of course: Lecture
Total Contact Hours: 56
Prerequisites: NIL
Course Content:
Unit 1
Introduction: When simulation is the appropriate tool and when it is not appropriate, Advantages and
disadvantages of Simulation, Areas of application, Systems and system environment, Components of a system,
Discrete and continuous systems, Model of a system, Types of Models, Discrete-Event System Simulation, Steps in
a Simulation Study. Simulation examples: Simulation of queuing systems, Simulation of inventory systems, Other
examples of simulation.
Unit 2
General Principles, Simulation Software: Concepts in Discrete-Event Simulation: The Event-Scheduling / TimeAdvance Algorithm, World Views, Manual simulation Using Event Scheduling, List processing. Simulation in Java,
Simulation in GPSS. Queuing Models: Characteristics of Queuing Systems.
Unit 3
Random-Number Generation, Random-Variate Generation: Properties of random numbers, Generation of
pseudo-random numbers, Techniques for generating random numbers, Tests for Random Numbers. Random-Variate
Generation: Inverse transform technique, Acceptance-Rejection technique, Special properties.
Unit 4
Input Modeling: Data Collection, Identifying the distribution with data, Parameter estimation, Goodness of Fit
Tests, Fitting a non-stationary Poisson process, Selecting input models without data, Multivariate and Time-Series
input models.
Unit 5
Output Analysis for a Single Model: Types of simulations with respect to output analysis, Stochastic nature of
output data, Measures of performance and their estimation, Output analysis for terminating simulations, Output
analysis for steady-state simulations. Verification and Validation of Simulation Models: Model building, verification
and validation, Verification of simulation models, Calibration and validation of models. Optimization via
Simulation.
Text Book:
1. Jerry Banks, John S. Carson II, Barry L. Nelson, David M. Nicol: Discrete-Event System Simulation, 4th
Edition, Pearson Education, 2012.
Reference Books:
1. Lawrence M. Leemis, Stephen K. Park: Discrete – Event Simulation: A First Course, First edition, Pearson
/ Prentice-Hall, 2006.
2. Averill M. Law: Simulation Modeling and Analysis, 4th Edition, Tata McGraw-Hill, 2011.
Course Delivery:
The course will be delivered through lectures, presentations, classroom discussions, and practical implementations.
72
Mapping Course Outcomes with Programme Outcomes:
Course Outcomes
Programme Outcomes
1
2
Identify modelling a system and types of simulation
tools.
x
x
Describe the concepts of scheduleing /Queueing
system using simulation software
x
x
Test random function generation through various
transform techniques
x
x
x
x
x
x
Figure out the data collection process
Interpret the stochastic nature of output data.
3
4
5
6
7
Course Outcomes:
At the end of the course, a student should be able to
1.
2.
3.
4.
5.
Identify modelling a system and types of simulation tools.
Describe the concepts of scheduleing /Queueing system using simulation software
Test random function generation through various transform techniques
Figure out the data collection process
Interpret the stochastic nature of output data.
73
8
9
10
11
12
Course Title: Pattern Recognition
Course Code: CSPE18
Credits (L:T:P) : 4:0:0
Core/ Elective: Elective
Type of course: Lecture
Total Contact Hours: 56 Hrs
Prerequisites: NIL
Course Contents:
Unit 1
Introduction: Machine perception, an example, Pattern Recognition System, The Design Cycle, Learning and
Adaptation. Bayesian Decision Theory: Introduction, Bayesian Decision Theory, Continuous Features, Minimum
error rate, classification, classifiers, discriminant functions, and decision surfaces, the normal density, Discriminant
functions for the normal density.
Unit 2
Maximum-likelihood and Bayesian Parameter Estimation: Introduction, Maximum-likelihood estimation,
Bayesian Estimation, Bayesian parameter estimation: Gaussian Case, general theory, Hidden Markov Models. Nonparametric Techniques: Introduction, Density Estimation, Parzen windows, KN – Nearest- Neighbor Estimation,
The Nearest- Neighbor Rule, Metrics and Nearest-Neighbor Classification.
Unit 3
Linear Discriminant Functions: Introduction, Linear Discriminant Functions and Decision Surfaces, Generalized
Linear Discriminant Functions, The Two-Category Linearly Separable case, Minimizing the Perception Criterion
Functions, Relaxation Procedures, Non-separable Behavior, Minimum Squared-Error procedures, The Ho-Kashyap
procedures. Stochastic Methods: Introduction, Stochastic Search, Boltzmann Learning, Boltzmann Networks and
Graphical Models, Evolutionary Methods.
Unit 4
Non-Metric Methods: Introduction, Decision Trees, CART, Other Tree Methods, Recognition with Strings,
Grammatical Methods.
Unit 5
Unsupervised Learning and Clustering: Introduction, Mixture Densities and Identifiability, Maximum-Likelihood
Estimates, Application to Normal Mixtures, Unsupervised Bayesian Learning, Data Description and Clustering,
Criterion Functions for Clustering.
Text Book:
1. Richard O. Duda, Peter E. Hart, and David G. Stork: Pattern Classification, 2nd Edition, WileyInterscience, 2012.
Reference Book:
1. Earl Gose, Richard Johnsonbaugh, Steve Jost: Pattern Recognition and Image Analysis, HAR/DSK Edition,
Pearson Education, 2007.
Course Delivery:
PPT presentations, Black Board approach
74
Course Outcomes:
At the end of the course the student should be able to
1.
2.
3.
4.
5.
6.
7.
Analyse using Top-down approach the pattern recognition System
Interpret Bayesian decision theorem
Analyse the bayesian estimation, Density estimation
Explain linear discriminant functions
Use different stochastic methods for linear discriminant functions.
Check different non-parametric methods.
Assess the criteria for unsupervised learning and clustering
Mapping Course Outcomes with Programme Outcomes:
Course Outcomes
Analyse using Top-down approach the pattern
recognition System
Interpret Bayesian decision theorem
Check the bayesian estimation, Density estimation
Infer about linear discriminant functions
Use different stochastic methods for linear
discriminant functions.
Check different non-parametric methods.
Assess the criteria for unsupervised learning and
clustering
Programme Outcomes
1
2
x
x
x
x
x
x
x
x
x
x
x
x
x
x
75
3
4
5
6
7
8
9
10
11
12
Course Title: Advanced Computer Architecture
Course Code: CSPE19
Credits (L:T:P) : 4:0:0
Core/ Elective: Core
Type of course: Lecture
Total Contact Hours: 56
Prerequisites: Computer Organization, Introduction to Microprocessors
Course Contents:
Unit 1
Fundamentals of Quantitative Design and Analysis: Classes of Computers, Defining Computer Architecture,
Trends in Technology, Trends in Cost, Dependability, Measuring Reporting and Summarizing Performance,
Quantitative Principles of Computer Design, Introduction to Pipelining and Pipeline Hazards.
Unit 2
Instruction–Level Parallelism: Concepts and Challenges, Basic Compiler Techniques for Exposing ILP, Reducing
Branch cost with Advanced branch Prediction, Overcoming Data Hazards with Dynamic Scheduling examples and
the Algorithm, Exploiting ILP Using Multiple Issue and Static Scheduling and Dynamic Scheduling, Case studyThe Intel Core i7.
Unit 3
Thread–Level Parallelism: Introduction, Centralized Shared-Memory Architectures, Performance of symmetric
shared memory Multiprocessors, Distributed Shared Memory and Directory-Based Coherence, Synchronization:
The Basics, Models of Memory Consistency.
Unit 4
Memory Hierarchy Design: Introduction, Ten Advanced Optimizations of Cache Performance, Memory
Technology and Optimizations, Protection: Virtual Memory and Virtual Machines, Memory Hierarchies in the ARM
Cortex-A8.
Unit 5
Data Level Parallelism in Vector, SIMD Architectures and Warehouse-Scale Computers: Introduction, Vector
Architecture, SIMD Instruction set Extensions for Multimedia, Introduction to Warehouse-scale Computers,
Programming Models and Workloads for Warehouse-scale Computers, Computer Architecture for Warehouse-scale
Computers. Case Study: Google Warehouse Scale Computer.
Text Book:
1. John L. Hennessey and David A. Patterson: Computer Architecture, A Quantitative Approach, 5th Edition,
Elsevier, 2012.
Reference Books:
1. Kai Hwang, Naresh Jotwani: Advanced Computer Architecture - Parallelism, Scalability, Programmability,
2th Edition, Tata McGraw Hill, 2011.
2. David E. Culler, Jaswinder Pal Singh: Parallel Computer Architecture, A Hardware / Software Approach,
Morgan Kauffman, 1st edition, 2010.
Course Delivery: The course will be delivered through lectures, presentations, classroom discussions, and practical
implementations. Questions for CIE and SEE are designed in accordance with the Bloom’s taxonomy.
76
Course Assessment and Evaluation:
CIE
To Whom
Internal
Assessment
Tests
When/ Where
(Frequency in
the course)
Thrice(Average
of the best two
will be
computed)
Max
Marks
Evidence
Collected
Contribution to
Course Outcomes
30
Blue Books
1,2,3,4 & 5
Twice
20
Test Data
Sheets
1,2,3,4 & 5
End of Course
(Answering
5 of 10
questions)
100
Answer
scripts
1,2,3,4 & 5
Questionnaire
1, 2,3,4 & 5
Effectiveness of
Delivery of
instructions &
Assessment Methods
Quiz
SEE
Direct & Indirect Assessment Methods
What
Standard
Examination
Students
End of Course
Survey
End of the course
-
Course Outcomes:
At the end of the course students should be able to:
1. Interpret the growth in processor performance, development of IC for higher reliability and availability
2. Explain the concept of parallelism and challenges associated with instruction level parallelism.
3. Organize the complexity of different types of memory architectures.
4. Find the techniques to optimize the cache, and virtual machines.
5. Examine the different architectures under data level parallelism and warehouse scale computers.
Mapping Course Outcomes with program Outcomes:
Course Outcomes
1
Interpret
the growth in processor
performance, development of IC for higher
reliability and availability
Explain the concept of parallelism and
challenges associated with instruction level
parallelism.
Organize the complexity of different types of
memory architectures
Find the techniques to optimize the cache,
and virtual machines
Examinethe different architectures under
data level parallelism and warehouse scale
computers
2
3
Program Outcomes
5
6 7
8 9
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
77
x
4
10
x
x
11
12
x
x
x
x
x
Course Title: Computer Systems Performance Analysis
Course Code: CSPE20
Credits (L:T:P) :4:0:0
Core/ Elective: Elective
Type of course: Lecture
Total Contact Hours: 56
Prerequisites: Students should have undergone a course on probability theory, matrices, software engineering
aspects and queuing theory
Course Contents:
Unit 1
Introduction: The art of Performance Evaluation; Common Mistakes in Performance Evaluation, A Systematic
Approach to Performance Evaluation, Selecting an Evaluation Technique, Selecting Performance Metrics,
Commonly used Performance Metrics, Utility Classification of Performance Metrics, Setting Performance
Requirements.
Unit 2
Workloads, Workload Selection and Characterization: Types of Work loads, addition instructions, Instruction
mixes, Kernels; Synthetic programs, Application benchmarks, Popular benchmarks. Work load Selection: Services
exercised, level of detail; Representativeness; Timeliness, Other considerations in workload selection. Work load
characterization Techniques: Terminology; Averaging, Specifying dispersion, Single Parameter Histograms, Multi
Parameter Histograms, Principle Component Analysis, Markov Models, Clustering.
Unit 3
Monitors, Program Execution Monitors and Accounting Logs: Monitors: Terminology and classification;
Software and hardware monitors, Software versus hardware monitors, Firmware and hybrid monitors, Distributed
System Monitors, Program Execution Monitors and Accounting Logs, Program Execution Monitors, Techniques for
Improving Program Performance, Accounting Logs, Analysis and
Interpretation of Accounting log data, using accounting logs to answer commonly asked questions.
Unit 4
Capacity Planning and Benchmarking: Steps in capacity planning and management; Problems in Capacity
Planning; Common Mistakes in Benchmarking; Benchmarking Games; Load Drivers; Remote-Terminal Emulation;
Components of an RTE; Limitations of RTEs.Experimental Design and and Analysis: Introduction: Terminology,
Common mistakes in experiments, Types of experimental designs, 2k Factorial Designs, Concepts, Computation of
effects, Sign table method for computing effects; Allocation of variance; General 2k Factorial Designs, General full
factorial designs with k factors: Model, Analysis of a General Design, Informal Methods.
Unit 5
Queuing Models: Introduction: Queuing Notation; Rules for all Queues; Little’s Law, Types of Stochastic Process.
Analysis of Single Queue: Birth-Death Processes; M/M/1 Queue; M/M/m Queue; M/M/m/B Queue with finite
buffers; Results for other M/M/1 Queuing Systems. Queuing Networks: Open and Closed Queuing Networks;
Product form networks, queuing Network models of Computer Systems. Operational Laws: Utilization Law; Forced
Flow Law; Little’s Law; General Response Time Law; Interactive Response Time Law; Bottleneck Analysis; Mean
Value Analysis and Related Techniques; Analysis of Open Queuing Networks; Mean Value Analysis;
Text Book:
1. Raj Jain: The Art of Computer Systems Performance Analysis, 1st edition, John Wiley and Sons, 2012.
Reference Books:
1. Paul J Fortier, howard E Michel: computer Systems Performance Evaluation and prediction, 1st edition,
Elsevier, 2009.
2. Trivedi K S: Probability and Statistics with Reliability, Queuing and Computer Science Applications, 1st
edition, PHI, 2011.
Course Delivery: The course will be delivered through lectures, presentations, classroom discussions, and case
studies. Questions for CIE and SEE are designed in accordance with the Bloom’s taxonomy
78
Course Outcomes:
At the end of the course the students should be able to
1. Appreciate issues of Performance evaluation, selection of evaluation technique
2. Recognize the metrics for performance evaluation
3. Distinguish and differentiate the computational workloads, considerations for workload selection.
4. Discuss terminology to characterize the workload and analyses
5. Appreciate remote terminal emulation usage , capacity planning and management
6. Evaluate various Queuing models ,Queuing networks, Operational laws
79
Course Title: Software Testing
Course Code: CSPE21
Credits (L:T:P) : 3:0:1
Core/ Elective: Elective
Type of course: Lecture, Practicals
Total Contact Hours:56
Prerequisites:
NIL
Course Contents:
Unit 1
A Perspective on Testing, Examples: Basic definitions, Test cases, Insights from a Venn diagram, Identifying test
cases, Error and fault taxonomies, Levels of testing. Examples: Generalized pseudo code, the triangle problem, The
Next Date function, The commission problem, The SATM (Simple Automatic Teller Machine) problem, The
currency converter, Saturn windshield wiper.
Unit 2
Boundary Value Testing, Equivalence Class Testing, Decision Table-Based Testing: Boundary value analysis,
Robustness testing, Worst-case testing, Special value testing, Examples, Random testing, Equivalence classes,
Equivalence test cases for the triangle problem, Next Date function, and the commission problem, Guidelines and
observations. Decision tables, Test cases for the triangle problem, Next Date function, and the commission problem,
Guidelines and observations. Path Testing, Data Flow Testing:DD paths, Test coverage metrics, Basis path testing,
guidelines and observations, Definition-Use testing, Slice-based testing, Guidelines and observations.
Unit 3
Levels of Testing, Integration Testing: Traditional view of testing levels, Alternative life-cycle models, The SATM
system, Separating integration and system testing. A closer look at the SATM system, Decomposition-based, call
graph-based, Path-based integrations, System Testing, Interaction Testing:Threads, Basic concepts for requirements
specification, Finding threads, Structural strategies and functional strategies for thread testing, SATM test threads,
System testing guidelines, ASF (Atomic System Functions) testing example. Context of interaction, A taxonomy of
interactions, Interaction, composition, and determinism, Client/Server Testing.
Unit 4
Process Framework: Validation and verification, Degrees of freedom, Varieties of software. Basic
principles:Sensitivity, redundancy, restriction, partition, visibility, Feedback. The quality process, Planning and
monitoring, Quality goals,Dependability properties, Analysis, Testing, Improving the process, Organizational
factors, Fault-Based Testing,Test Execution:Overview, Assumptions in fault-based testing, Mutation analysis, Faultbased adequacy criteria,Variations on mutation analysis. Test Execution: Overview, from test case specifications to
test cases, Scaffolding, Generic versus specific scaffolding, Test oracles, Self-checks as oracles, Capture and replay.
Unit 5
Planning and Monitoring the Process, Documenting Analysis and Test: Quality and process, Test and analysis
strategies and plans, Risk planning, Monitoring the process, Improving the process, The quality team, Organizing
documents, Test strategy document, Analysis and test plan, Test design specifications documents, Test and analysis
reports.
Text Books:
1.
2.
Paul C. Jorgensen: Software Testing, A Craftsman’s Approach, 3rdEdition, Auerbach Publications, 2012.
Mauro Pezze, Michal Young: Software Testing and Analysis –Process, Principles and Techniques,
1stEdition, WileyIndia, 2011.
80
Reference Books:
1. Aditya P Mathur:Foundations of Software Testing, 1stEdition,Pearson Education, 2008.
2. Srinivasan Desikan, Gopalaswamy Ramesh: Software testing Principles and Practices, 2ndEdition, Pearson
Education, 2007
Course Delivery:
The course will be delivered through lectures, presentations, classroom discussions, and practical implementations.
Questions for CIE and SEE are designed in accordance with the Revised Bloom’s taxonomy.
Course Outcomes:
At the end of the course, a student should be able to
1. Identify Test cases, Error and fault taxonomies, Levels of testing.
2. Classify different types of testing (Boundary Value Testing, Equivalence Class Testing and Decision TableBased Testing).
3. Recognize Alternative life - cycle models, recognize Basic concepts for requirements specification, assess
context of interaction.
4. Recognize approaches for Test Execution: from test case specifications to test cases, Scaffolding, Generic
versus specific scaffolding.
5. Identify and plan strategies to test design specifications document.
Mapping Course Outcomes with Programme Outcomes:
Course Outcomes
Programme Outcomes
1
2
Identify Test cases, Error and fault taxonomies, Levels
of testing.
x
x
Classify Boundary Value Testing, Equivalence Class
Testing and Decision Table-Based Testing.
x
x
x
x
x
x
x
x
Recognize Alternative life - cycle models, recognize
Basic concepts for requirements specification, assess
context of interaction.
Recognize approaches for Test Execution: from test
case specifications to test cases, Scaffolding, Generic
versus specific scaffolding.
Identify analysis strategies and plans, to Test design
specifications documents, to Test and analysis reports.
81
3
4
5
6
7
8
9
10
11
12
x
x
x
x
x
x
x
x
x
x
x
Course Title: Software Architecture and Design Patterns
Course Code: CSPE22
Credits (L:T:P) : 4:0:0
Core/ Elective: Elective
Type of Course: Lecture
Total Contact Hours: 56
Prerequisites: NIL
Unit 1
Introduction: The Architecture Business Cycle: Where do architectures come from? Software processes and the
architecture business cycle, What makes a “good” architecture? What software architecture is and what it is not,
Other points of view, Architectural patterns, reference models and reference architectures, Importance of software
architecture, Architectural structures and views.
Unit 2
Architectural Styles and Case Studies: Architectural styles, Pipes and filters, Data abstraction and object-oriented
organization, Event-based, implicit invocation, Layered systems, Repositories, Interpreters, Process control, Other
familiar architectures, Heterogeneous architectures. Case Studies: Keyword in Context, Instrumentation software,
Mobile robotics, Cruise control, Three vignettes in mixed style.
Unit 3
Quality: Functionality and architecture, Architecture and quality attributes, System quality attributes, Quality
attribute scenarios in practice, Other system quality attributes, Business qualities, Architecture qualities. Achieving
Quality: Introducing tactics, Availability tactics, Modifiability tactics, Performance tactics, Security tactics,
Testability tactics, Usability tactics, Relationship of tactics to architectural patterns, Architectural patterns and
styles.
Unit 4
Architectural Patterns: Introduction, From mud to structure: Layers, Pipes and Filters, Blackboard. Distributed
Systems: Broker, Interactive Systems: MVC, Presentation-Abstraction- Control. Adaptable Systems: Microkernel,
Reflection.
Unit 5
Some Design Patterns: Structural decomposition: Whole – Part, Organization of work: Master – Slave, Access
Control: Proxy. Designing and Documenting Software Architecture: Architecture in the life cycle, Designing the
architecture, Forming the team structure, Creating a skeletal system, Uses of architectural documentation, Views,
Choosing the relevant views, Documenting a view, Documentation across views.
Text Books:
1. Len Bass, Paul Clements, Rick Kazman: Software Architecture in Practice, 2nd Edition, Pearson
Education, 2011.
2. Frank Buschmann, Regine Meunier, Hans Rohnert, Peter Sommerlad, Michael Stal: Pattern-Oriented
Software Architecture, A System of Patterns, Volume 1, John Wiley and Sons, 2011.
3. Mary Shaw and David Garlan: Software Architecture- Perspectives on an Emerging Discipline, PrenticeHall of India, 2007.
Reference Books:
1. E. Gamma, R. Helm, R. Johnson, J. Vlissides: Design Patterns- Elements of Reusable Object-Oriented
Software, 1st Edition, Pearson Education, 2012.
2. Web site for Patterns: http://www.hillside.net/patterns/
Course Delivery
The course will be delivered through lectures in the classroom.
82
Course Assessment and Evaluation:
To
Whom
Direct & Indirect Assessment Methods
What
CIE
Internal
Assessment
Test
Quiz
SEE
Standard
Examination
Students
End of Course
Survey
When/ Where
(Frequency in
the course)
Thrice
(Average of the
best two will be
computed)
Max
Marks
Evidence
Collected
Contribution
to
Course Outcomes
30
Blue Books
1, 2, 3, 4 & 5
Once
20
Data sheets
1, 2, 3, 4 & 5
End of Course
(Answering
5
of
10
questions)
100
Answer scripts
1,2,3,4 & 5
Questionnaire
1, 2, 3, 4 & 5
Effectiveness
of
Delivery
of
instructions
&
Assessment
Methods
End of
course
the
-
Course Outcomes:
At the end of the course, a student should be able to:
1. Describe the foundational concepts of software architecture and the important principles and techniques of
software architecture.
2. Identify the structure, advantages and disadvantages of various architectural choices using case studies.
3. Recognize the need of Software architecture and quality requirements for a software system.
4. Assess different architecture styles and solutions.
5. Identify different architectural views and various design patterns for software systems.
Mapping Course Outcomes with Programme Outcomes:
Course Outcomes
1
Describe the foundational concepts of software architecture
and the important principles and techniques of software
architecture.
Identify the structure, advantages and disadvantages of
various architectural choices using case studies.
Recognize the need of Software architecture and quality
requirements for a software system.
Assess different architecture styles and solutions.
Identify different architectural views and various design
patterns for software systems.
83
Programme Outcomes
4 5 6 7 8 9 10
2
3
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
11
12
Course Title: Service Oriented Architecture and Web Services
Course Code: CSPE23
Credits (L:T:P) : 4:0:0
Core/ Elective: Elective
Type of course: Lecture
Total Contact Hours: 56
Prerequisites: Internet Technology
Course Contents:
Unit 1
Introduction to SOA, Evolution of SOA: Fundamental SOA; Common Characteristics of contemporary SOA;
Common tangible benefits of SOA; An SOA timeline (from XML to Web services to SOA); The continuing
evolution of SOA (Standards organizations and Contributing vendors); The roots of SOA (comparing SOA to Past
architectures). Web Services and Primitive SOA: The Web services framework; services (as Web services); Service
descriptions (with WSDL); Messaging (with SOAP).
Unit 2
Web Services and Contemporary SOA: Message exchange patterns; Service activity; Coordination; Atomic
Transactions; Business activities; Orchestration; Choreography. Addressing; Reliable messaging; Correlation;
Polices; Metadata exchange; Security; Notification and eventing
Web Services and Contemporary SOA: Message exchange patterns; Service activity; Coordination; Atomic
Transactions; Business activities; Orchestration; Choreography. Addressing; Reliable messaging; Correlation;
Polices; Metadata exchange; Security; Notification and eventing
Unit 3
Principles of Service – Orientation: Services-orientation and the enterprise; Anatomy of a service-oriented
architecture; Common Principles of Service-orientation; How service orientation principles inter relate; Serviceorientation and object-orientation; Native Web service support for service- orientation principles
Unit 4
Service Layers: Service-orientation and contemporary SOA; Service layer abstraction; Application service layer,
Business service layer, Orchestration service layer; Agnostic services; Service layer configuration scenarios.
Business Process Design: WS-BPEL language basics; WS-Coordination overview; Service-oriented business
process design; WS-addressing language basics; WS-Reliable Messaging language basics
Unit 5
SOA Platforms: SOA platform basics; SOA support in J2EE; SOA support in .NET; Integration considerations
Text Book
1. Thomas Erl: Service-Oriented Architecture – Concepts, Technology, and Design, Pearson Education, 2005
Reference Book
2. Eric Newcomer, Greg Lomow: Understanding SOA with Web Services, Pearson education, 2005
Course Delivery: The course will be delivered through lectures, presentations and classroom discussions.
84
Course Assessment and Evaluation:
Direct & Indirect Assessment Methods
What
CIE
SEE
To
Whom
Internal
Assessment
Tests
Announced
quiz
Surprise
Quiz
Standard
Examination
End of Course
Survey
Students
When/ Where
(Frequency in the
course)
Thrice(Average of
the best two will be
computed)
Max
Marks
Evidence
Collected
Contribution to
Course Outcomes
30
Blue Books
1,2,3 & 4
Once
10
Once
10
End of Course
(Answering
5 of 10 questions)
100
End of the course
Quiz
Answers
Quiz
Answers
-
1, 2 ,3 & 4
Recollection
Skills
Answer
scripts
1,2,3 & 4
Questionnaire
1, 2 ,3, 4 & 5
Effectiveness of
Delivery of instructions
& Assessment Methods
Course Outcomes:
At the end of the course students should be able to:
1. Summarize Web Service and Service oriented Architecture.
2. Illustrate the principles of contemporary SOA and web service.
3. Appraise the principles the layers of Service Oriented Architecture.
4. Estimate the service oriented principles
5. Categorize SOA support in J2EE and SOA support in .NET focusing on platform overview.
Mapping Course Outcomes with Programme Outcomes:
Course Outcomes
Programme Outcomes
1
2
Summarize Web Service and Service oriented
Architecture
Illustrate the principles of contemporary SOA and
web service
Appraise the principles the layers of Service
Oriented Architecture
Estimate the service oriented principles
Categorize SOA support in J2EE and SOA
support in .NET focusing on platform overview
3
4
x
x
x
85
x
5
6
7
8
9
x
x
x
x
x
10
11
12
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
Course Title: Object Oriented Modeling and Design
Course Code: CSPE24
Credits (L:T:P) : 4:0:0
Core/ Elective: core
Type of course: Lecture
Total Contact Hours: 56
Prerequisites: Knowledge of Software Engineering. OO Fundamentals
Course Contents
Unit 1
Introduction, Modeling Concepts, class Modeling: What is Object Orientation? What is OO development? OO
themes; Evidence for usefulness of OO development; OO modeling history. Modeling as Design Technique:
Modeling; abstraction; The three models. Class Modeling: Object and class concepts; Link and associations
concepts; Generalization and inheritance; A sample class model; Navigation of class models; Practical tips.
Advanced Class Modeling, State Modeling: Advanced object and class concepts; Association ends; N-ary
associations; Aggregation; Abstract classes; Multiple inheritance; Metadata; Reification; Constraints; Derived data;
Packages; Practical tips. State Modeling: Events, States, Transitions and Conditions; State diagrams; State diagram
behavior; Practical tips.
Unit 2
Advanced State Modeling, Interaction Modeling: Advanced State Modeling: Nested state diagrams; Nested
states; Signal generalization; Concurrency; A sample state model; Relation of class and state models; Practical tips.
Interaction Modeling: Use case models; Sequence models; Activity models. Use case relationships; Procedural
sequence models; Special constructs for activity models. Process Overview, System Conception, Domain
Analysis: Process Overview: Development stages; Development life cycle. System Conception: Devising a system
concept; Elaborating a concept; Preparing a problem statement. Domain Analysis: Overview of analysis; Domain
class model; Domain state model; Domain interaction model; Iterating the analysis.
Unit 3
Application Analysis, System Design: Application Analysis: Application interaction model; Application class
model; Application state model; Adding operations. Overview of system design; Estimating performance; Making a
reuse plan; Breaking a system in to sub-systems; Identifying concurrency; Allocation of sub-systems; Management
of data storage; Handling global resources; Choosing a software control strategy; Handling boundary conditions;
Setting the trade-off priorities; Common architectural styles; Architecture of the ATM system as the example.
Unit 4
Class Design, Implementation Modeling, Legacy Systems: Class Design: Overview of class design; Bridging the
gap; Realizing use cases; Designing algorithms; Recursing downwards, Refactoring; Design optimization;
Reification of behavior; Adjustment of inheritance; Organizing a class design; ATM example. Implementation
Modeling: Overview of implementation; Fine-tuning classes; Fine-tuning generalizations; Realizing associations;
Testing. Legacy Systems: Reverse engineering; Building the class models; Building the interaction model; Building
the state model; Reverse engineering tips; Wrapping; Maintenance.
Unit 5
Design Patterns: What is a pattern and what makes a pattern? Pattern categories; Relationships between patterns;
Pattern description. Communication Patterns: Forwarder-Receiver; Client-Dispatcher-Server; Publisher-Subscriber.
Idioms: Management Patterns: Command processor; View handler. Idioms: Introduction; what can idioms provide?
Idioms and style; Where to find idioms; Counted Pointer example.
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Text Books:
1. Michael Blaha, James Rumbaugh: Object-Oriented Modeling and Design with UML, 2nd Edition, Pearson
Education, 2005.
2. Frank Buschmann, Regine Meunier, Hans Rohnert, Peter Sommerlad, Michael Stal: Pattern-Oriented Software
Architecture, A System of Patterns, Volume 1, John Wiley and Sons, 2007.
Reference Books:
1. Grady Booch et al: Object-Oriented Analysis and Design with Applications, 3rd Edition, Pearson Education,
2007.
2. Brahma Dathan, Sarnath Ramnath: Object-Oriented Analysis, Design, and Implementation, Universities Press,
2009.
Course Delivery: The course will be delivered through lectures, presentations, classroom discussions, and practical
implementations. Questions for CIE and SEE are designed in accordance with the Bloom’s taxonomy.
Course Outcomes:
At the end of the course the student will be able to:
1. Demonstrate an understanding of modeling as a design technique, identify and create class and state models
for a given case study.
2. Create advanced state and interaction models and apply the process of system conception and domain
analysis for any given problem.
3. Understand the process of application analysis and be able to develop a system design to a given case study
using Rational Suite.
4. Create a class design and implementation model and understand the reverse engineering process and its
importance.
5. Demonstrate an understanding of design patters and its implementation to provide solutions to some design
problems.
Mapping Course Outcomes with Programme Outcomes:
Programme Outcomes
Course Outcomes
Demonstrate an understanding of Modeling as a design
technique, identify and create class and state models for a
given case study
Create advanced state and Interaction Models and apply the
process of System Conception and Domain Analysis for any
given problem
Understand the process of application Analysis and be able to
develop a System Design to a given case study using
Rational Suite
Create a Class Design and Implementation Model and
understand the reverse engineering process and its
importance.
Demonstrate an understanding of design patterns and its
implementation to provide solutions to some design
problems.
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Course Exit Survey Form
Dept of CSE, MSRIT, Bangalore
Name & USN of the student:
Contact details:
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Question
Quality of the course content
For the number of credits, the course workload
was
Relevance of the textbook to this course
Ideas/Concepts that you have found difficult to
grasp
Concepts/topics that should be removed from the
syllabus
New inclusions in the syllabus
Were the lectures clear/well organized and
presented at a reasonable pace?
Did the lectures stimulate you intellectually?
What approaches/aids would facilitate your
learning? You can check multiple options.
Did the problems worked out in the classroom
help you to understand how to solve questions on
your own?
Is the grading scheme clearly outlined and
reasonable/fair?
Are the assignment/lab experiment procedures
clearly explained?
Attainment level of CO1
Attainment level of CO2
Attainment level of CO3
Attainment level of CO4
Attainment level of CO5...COn
Excellent
Very Good
Course code:
Course name:
Responses
Good
Satisfactory
Poor
List
List
List
Yes/No
Yes/No
Lectures/ Programming Assignments/ Presentations/ Tutorials/ Demonstrations/ Practical Exercises/
Mini projects/ Group discussions/ Student seminars/ Expert guest lectures
Yes/No
Yes/No
Yes/No
Signature of the student with date
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