mepco - r2013 (full time)

35

MEPCO SCHLENK ENGINEERING COLLEGE, SIVAKASI

(AUTONOMOUS)

AFFILIATED TO ANNA UNIVERSITY, CHENNAI 600 025

REGULATIONS: MEPCO - R2013 (FULL TIME)

M.TECH INFORMATION TECHNOLOGY

Department Vision Department Mission

To emerge as Realm of Preeminence

that empowers the students to reach

the zenith, as assertive IT professionals

by offering quality technical education

and research environment to best

serve the nation.

To develop dynamic IT

professionals with globally

competitive learning

experience by providing high

class education.

Programme Educational Objectives

After 3 Years of Graduation, our graduates will

1. Flourish as eminent researchers, academicians or IT practitioners.

2. Create, apply and disseminate cognitive ideas related to IT field and

advance in their profession.

3. Nurture continuous self learning to update the latest developments in

IT industries.

Programme Outcomes

The graduates will be able to

1. Conceptualize and apply the basic engineering techniques studied in

their under graduation.

2. Identify and formulate current research problems related to IT

industry.

36

3. Design system, service or component to meet desired economic,

social and ethical needs with in realistic IT constraints.

4. Interpret, analyze and synthesize complex data to provide valid

conclusions.

5. Employ modern tools and techniques to solve research issues related

to Information and Communication Technology.

6. Comprehend and evaluate existing research avenues on their own.

7. Orally communicate ideas clearly in an organized manner.

8. Write concise system documentation, user manual and research

articles.

9. Apply IT principles in the construction of software systems of varying

complexity.

10. Engage in lifelong learning to adapt in their professional work or

graduate studies.

37

CURRICULUM I TO IV SEMESTERS (FULL TIME)

SEMESTER I

SL.

NO

COURSE

CODE

COURSE TITLE L T P C

THEORY

1. 13MA177 Optimization Techniques 3 1 0 4

2.

13MC101

Advanced Data Structures and

Algorithms

(Common to M.E CSE & M.Tech

IT)

3 0 0 3

3. 13MI101 Modern Computer Architecture 3 0 0 3

4. 13MI102 Advanced Database Technology 3 0 0 3

5. 13MI103 Network Programming and

Management

3 0 0 3

6. 13MI104 Soft Computing Techniques 3 1 0 4

PRACTICAL

7. 13MC151 Advanced Data Structures Lab


IT)

0 0 3 2

8. 13MI151 Network Programming Lab 0 0 3 2

9. 13MI152 Comprehension* 0 0 2 1

TOTAL CREDITS 18 2 8 25

38

SEMESTER II

SL.

NO

COURSE

CODE COURSE TITLE L T P C

1. 13MI201 Web Mining and Information

Retrieval

3 0 0 3

2.

13MI202

Cloud Computing Technologies


IT)

3 0 0 3

3. 13MI203 Cyber Security 3 0 0 3

4. 13MI204 Big Data Analytics


IT)

3 0 0 3

5. Elective I 3 0 0 3

6. Elective II 3 0 0 3

PRACTICAL

7. 13MI251 Big Data Analytics Lab 0 0 3 2

8. 13MI252 Cloud Computing Lab (Common to

M.E CSE & M.Tech IT)


IT)

0 0 3 2

9. 13MC252 Technical Seminar * 0 0 2 1


NOTE: * - Internal Assessment Only

39

SEMESTER III

SL.

NO

COURSE

CODE


THEORY

1. Elective III 3 0 0 3

2. Elective IV 3 0 0 3

3. Elective V 3 0 0 3

PRACTICAL

1. 13MI351 Project Work (Phase I) 0 0 12 6


SEMESTER IV

SL.

NO

COURSE

CODE


PRACTICAL

1. 13MI451 Project Work (Phase II) 0 0 24 12


Total Number of Credits : 75

40

CURRICULUM I TO VI SEMESTERS (PART TIME)

SEMESTER I

SL.

NO

COURSE

CODE


THEORY

1. 13MA177 Optimization Techniques 3 1 0 4

2.

13MC101

Advanced Data Structures and

Algorithms


IT)

3 0 0 3

3. 13MI101 Modern Computer Architecture 3 0 0 3

PRACTICAL

4. 13MC151 Advanced Data Structures Lab

(Common to ME CSE & MTech

IT)

0 0 3 2

5. 13MI152 Comprehension* 0 0 2 1


41

SEMESTER II

SL.

NO

COURSE

CODE


THEORY

1.

13MI202 Cloud Computing Technologies


IT)

3 0 0 3

2. 13MI203 Cyber Security 3 0 0 3

3. Elective I 3 0 0 3

PRACTICAL

4.

13MI252 Cloud Computing Lab


IT)

0 0 3 2

5. 13MC252 Technical Seminar* 0 0 2 1


SEMESTER III

SL.

NO

COURSE

CODE


THEORY

1. 13MI102 Advanced Database Technology 3 0 0 3

2. 13MI103 Network Programming and

Management

3 0 0 3

42

3. 13MI104 Soft Computing Techniques 3 1 0 4

PRACTICAL

4. 13MI151 Network Programming Lab 0 0 3 2


SEMESTER IV

SL.

NO

COURSE

CODE


THEORY

1. 13MI201 Web Mining and Information

Retrieval

3 0 0 3

2.

13MI204 Big Data Analytics

(Common to ME CSE & MTech

IT)

3 0 0 3

3. Elective II 3 0 0 3

PRACTICAL

4.

13MI251

Big Data Analytics Lab 0 0 3 2


43

SEMESTER V

SL.

NO

COURSE

CODE


THEORY

1. Elective III 3 0 0 3

2. Elective IV 3 0 0 3

3. Elective V 3 0 0 3

PRACTICAL

4. 13MI351 Project Work (Phase 1) 0 0 12 6


SEMESTER VI

SL.

NO

COURSE

CODE


PRACTICAL

1. 13MI451 Project Work ( Phase 2) 0 0 24 12


NOTE: * - Internal Assessment Only

Total Number of Credits: 75

44

LIST OF ELECTIVES

SL.

NO

COURSE

CODE


1. 13MI401 X- Informatics 3 0 0 3

2. 13MI402 XML and Web Services 3 0 0 3

3. 13MC407 Energy Aware Computing


IT)

3 0 0 3

4. 13MI403 Internet of Things


IT)

3 0 0 3

5. 13MI404 Performance Evaluation and

Reliability of Information System 3 0 0 3

6. 13MI405 Next Generation Wireless Networks 3 0 0 3

7. 13MI406 Pervasive Computing 3 0 0 3

8. 13MI407 Multimedia Technologies 3 0 0 3

9. 13MI408 Video Analytics


IT)

3 0 0 3

10. 13MI409 Wireless Sensor Networks


IT)

3 0 0 3

11. 13MI410 Image Processing and Pattern

Analysis 3 0 0 3

45

12. 13MI411 Machine Learning 3 0 0 3

13. 13MI412 Virtualization Techniques 3 0 0 3

14. 13MI413 Software Agents 3 0 0 3

15. 13MI414 Automata Theory and Compiler

Design 3 0 0 3

16. 13MI415 Social Network Analysis 3 0 0 3

17. 13MI416 Human Computer Interaction and

Human factor 3 0 0 3

18. 13MI417 GPU Architecture and Programming 3 0 0 3

19. 13MI418 Knowledge Engineering 3 0 0 3

20. 13MI419 Parallel Computing 3 0 0 3

21. 13MI420 Ontology and Semantic Web 3 0 0 3

22. 13MI421 Logic Programming 3 0 0 3

23. 13MI422 VLSI Design 3 0 0 3

24. 13MI423 Network Engineering and

Management 3 0 0 3

25. 13MI424 Building Enterprise Application 3 0 0 3

46

SYLLABUS FOR I YEAR M.TECH INFORMAION TECHNOLOGY

SEMESTER I

13MA177 : OPTIMIZATION TECHNIQUES L T P C

3 1 0 4

COURSE OBJECTIVES:

To give the idea about mathematical models related to

Engineering problems

To understand the various methods of solving linear

programming problems

To provide information about optimization techniques related

to constrained and unconstrained non linear programming

Course Outcomes:

Upon completion of the course the students will be able

To formulate mathematical models in engineering applications

To determine the solution of LPP including transportation and

assignment problems

To apply various search methods to identify the optimum

solution for NLPP

To identify the unconstrained and constrained problems in

optimization and their solution

UNIT I LINEAR PROGRAMMING 9

Introduction – Formulation - Graphical solutions – standard and canonical

forms – Simplex method – Revised simplex method – duality in linear

programming – Post optimal analysis.

47

UNIT II TRANSPORTATION AND ASSIGNMENT

PROBLEM

9

Transportation Problem – North-West corner rule – lowest cost minimum

method – Vogel’s approximation method – Modi method for optimization

– Degeneracy in transportation problem – Assignment problem –

Hungarian algorithm – Unbalanced assignment problem –

Maximization problem – Travelling Salesmen Problem.

UNIT III NON-LINEAR PROGRAMMING 9

Introduction – elimination methods : Unrestricted and Exhaustive

search methods, Fibonacci method , golden section method –

Interpolation method: Quadratic and cubic interpolation methods, Direct

root methods – Kuhn-Tucker conditions.

UNIT IV UNCONSTRAINED OPTIMIZATION

TECNIQUES

9

Introduction – Standard form of the problem and basic terminology –

Direct search method: Simplex method, Random search method,

Univariate and pattern search method – Indirect search method:

Steepest Descent (Cauchy) method, Conjugate gradient method,

Newton's method – Application to engineering problems.

UNIT V CONSTRAINED OPTIMIZATION TECNIQUES 9

Introduction – Standard form of the problem and basic terminology –

Direct method: Sequential Linear Programming, Generalised Reduced

gradient method, Methods of feasible direction – Indirect method:

Penalty function method, Interior and exterior penalty function method,

Convex programming problem – Check for convergence – Application

to engineering problems

TUTORIAL :15 PERIODS

TOTAL: 45+15=60 PERIODS

48

REFERENCE BOOKS:

1. Roa, S.S., Engineering Optimization – Theory and Practice, New

Age international Publications, 2010.

2. Deb K., 'Optimisation for Engineering Design-Algorithms and

Example', Prentice Hall

3. Taha, H.A. “Operations Research: An Introduction”, Pearson

Education Inc., (Prentice-Hall of India Pvt. Ltd.), New Delhi, 8th

Edition, 2008.

4. Sinha S.M., “Mathematical Programming: Theory and Methods”,

Elsevier India, 1st Edition, 2006.

5. Gupta P.K. and Hira , D.S., “Operations Research”, S. Chand and

Co. Ltd.,New Delhi, 2001.

6. Manmohan P.K. and Gupta, S.C. , “Operations Research”, Sultan

Chand and Co., New Delhi, 9th Edition, 2001.

7. Ravindran A., Phillips D.T. and Solberg, J.J “Operations Research -

Principles and Practice”, Wiley India Edition, 2007.

13MC101: ADVANCED DATA STRUCTURES AND

ALGORITHMS

L T P C

(Common to M.E CSE / M.Tech IT) 3 0 0 3

COURSE OBJECTIVES:

To recall elementary data structures and the significance of writing

efficient algorithms

To study data structures for concurrency

To study advanced data structures such as search trees, hash

tables, heaps and operations on them

To expose various distributed data structures

49

To understand the principles of efficient algorithm design and learn

various advanced algorithms

Course Outcomes:

Implement and apply concurrent linked lists, stacks, and queues

Perform operations on search trees and hash tables

Perform operations on different types of heap

Implement and apply data structures for strings

Implement advanced concurrent structures

Explain design techniques for algorithms and advanced algorithm

UNIT I DATA STRUCTURES AND CONCURRENCY 9

Review of algorithm design and analysis – review of elementary data

structures – data structures and concurrency – locking linked lists –

coarse-grained synchronization – fine-grained synchronization – lazy

synchronization – non-blocking synchronization – concurrent queues –

bounded partial queues – unbounded lock-free queues – dual data

structures – concurrent stacks – elimination backoff stack

UNIT II SEARCH TREES, HASH TABLES AND STRINGS 9

Search Trees – Weight Balanced trees – Red Black trees – Finger

Trees and level linking – Skip lists – joining and splitting balanced search

trees – Hash trees – extendible hashing- Strings – tries and compressed

tries – dictionaries – suffix trees – suffix arrays.

UNIT III HEAPS 9

Heaps - Array-Based Heaps - Heap-Ordered Trees and Half-Ordered

Trees - Leftist Heaps – Skew Heaps - Binomial Heaps - Changing Keys

in Heaps - Fibonacci Heaps - Double-Ended Heap structures –

multidimensional heaps

50

UNIT IV ADVANCED CONCURRENT STRUCTURES 9

Concurrent hashing – closed-address hash sets – lock-free hash sets –

open-addressed hash sets – lock-based concurrent skip lists – lock-free

concurrent skip lists – concurrent priority queues – bounded priority

queue – unbounded priority queue – concurrent heap – skip list based

unbounded priority queues.

UNIT V ADVANCED ALGORITHMS 9

Introduction to Approximation algorithms – job scheduling on a single

machine – knapsack problem – minimizing weighted sum of completion

time on a single machine – MAX SAT and MAX CUT. Introduction to

Randomized algorithms – min cut. Introduction to Parallel algorithms –

parallel sorting algorithms.

TOTAL: 45 PERIODS

REFERENCE BOOKS:

1. M. Herlihy and N. Shavit, “The Art of Multiprocessor Programming”,

Morgan Kaufmann, 2012.

2. Peter Brass, “Advanced Data Structures”, Cambridge University

Press, 2008.

3. A. V. Aho, J. E. Hopcroft, and J. D. Ullman, “The Design and

Analysis of Computer Algorithms”, Addison-Wesley, 1975.

4. Gavpai, “Data Structures and Algorithms – Concepts, techniques

and Applications”, First Edition, Tata McGraw-Hill, 2008.

5. Edited by S.K. Chang, “Data Structures and Algorithms – Series of

Software Engineering and Knowledge Engineering”, Vol. 13, World

Scientific Publishing, 2003.

6. Jon Kleinberg, "Algorithm Design", Addison-Wesley, 2013.

7. David P. Williamson, David B. Shmoys, “The Design of

51

Approximation Algorithms”, Cambridge University Press, 2011.

8. Rajeev Motwani and Prabhakar Raghavan, “Randomized

Algorithms”, Cambridge University Press, 1995.

9. Michael J. Quinn, “Parallel Computing: Theory & Practice”, Tata

McGraw Hill Edition, 2003.

10. Ananth Grama, Anshul Gupta, et al., “Introduction to Parallel

Computing”, Second Edition, Pearson Education, 2003.

WEB REFERENCES:

1. http://www.geeksforgeeks.org/pattern-searching-set-8-suffix-tree-

introduction/

2. http://iamwww.unibe.ch/~wenger/DA/SkipList/

3. http://www.cs.au.dk/~gerth/slides/soda98.pdf

4. http://www.cs.sunysb.edu/~algorith/files/suffix-trees.shtml

5. http://pages.cs.wisc.edu/~shuchi/courses/880-S07/scribe-

notes/lecture20.pdf

13MI101: MODERN COMPUTER

ARCHITECTURE

L T P C

3 0 0 3

COURSE OBJECTIVES:

To understand the recent trends in the field of Computer

Architecture and identify performance related parameters.

To appreciate the need for parallel processing.

To expose the students to the problems related to multiprocessing.

52

To understand the different types of multicore architectures.

To analyze and use techniques that guarantee cache coherence

and correct sequential memory access across multiprocessor

systems.

COURSE OUTCOMES:

Compare and evaluate the performance of various architectures.

Develop a framework for evaluating design decisions in terms of

application requirements and performance measurements.

Identify the limitations of ILP and the need for multicore

architectures.

Address the issues related to multiprocessing and suggest

solutions.

Utilize the complex architecture involved in multicore processors for

parallel computing.

Analyze design of computer systems, including modern

architectures and suggest the suitable architecture according to the

application.

UNIT I FUNDAMENTALS OF COMPUTER DESIGN AND

ANALYSIS

9

Fundamentals of Computer Design – Classes of Computers – Trends in

Technology, Power, Energy and Cost – Dependability - Measuring and

reporting performance – Quantitative principles of computer design-

Classes of Parallelism - ILP, DLP, TLP and RLP - Instruction set

principles and Examples - Pipelining :Basic & Intermediate concepts

UNIT II INSTRUCTION LEVEL PARALLELISM WITH

DYNAMIC APPROACHES

9

Concepts – Dynamic Scheduling – Dynamic hardware prediction –

Multiple issue – Hardware based speculation – Limitations of ILP – Case

53

studies.

UNIT III INSTRUCTION LEVEL PARALLELISM WITH

SOFTWARE APPROACHES

9

Compiler techniques for exposing ILP – Static branch prediction – VLIW –

Advanced compiler support – Hardware support for exposing more

parallelism – Hardware versus software speculation mechanisms – Case

studies.

UNIT IV MULTIPROCESSORS AND MULTICORE

ARCHITECTURES

9

Symmetric and distributed shared memory architectures – Performance

issues –Synchronisation issues – Models of memory consistency –

Software and hardware multithreading – SMT and CMP architectures –

Design issues – Case studies – CPU Accounting for Multicore

Processors.

UNIT V MEMORY AND I/O 9

Cache performance – Reducing cache miss penalty and miss rate –

Reducing hit time – Main memory and performance – Memory

technology. Types of storage devices – Buses – RAID – Reliability,

availability and dependability – I/O performance measures – Designing

an I/O system – Design, Performance, and Energy Consumption of

eDRAM/SRAM Macrocells for L1 Data Caches.

TOTAL: 45 PERIODS

REFERENCE BOOKS:

1. John L. Hennessey and David A. Patterson, “ Computer

Architecture – A quantitative approach”, Morgan Kaufmann /

Elsevier, 4th. edition, 2007.

2. David E. Culler, Jaswinder Pal Singh, “Parallel Computing

Architecture : A hardware/ software approach” , Morgan Kaufmann

54

/ Elsevier, 1997.

3. William Stallings, “ Computer Organization and Architecture –

Designing for Performance”, Pearson Education, Seventh Edition,

2006.

4. Behrooz Parhami, “Computer Architecture”, Oxford University

Press, 2006.

5. Carlos Luque, Miquel Moreto, Francisco J. Cazorla, Roberto

Gioiosa, Alper Buyuktosunoglu, and Mateo Valero, “CPU

Accounting for Multicore Processors”, IEEE Transactions On

Computers, Vol. 61, No. 2, February 2012.

6. Alejandro Valero, Salvador Petit, Julio Sahuquillo, Pedro López,

José Duato, “Design, Performance, and Energy Consumption of

eDRAM/SRAM Macrocells for L1 Data Caches” IEEE Transactions

On Computers, Vol. 61, No. 9, September 2012.

13MI102 : ADVANCED DATABASE

TECHNOLOGY

L T P C

3 0 0 3

COURSE OBJECTIVES:

To learn the modeling and design of emerging databases.

To acquire knowledge on parallel and distributed databases and its

applications.

To study the usage and applications of Object Oriented and

Intelligent databases.

To understand the usage of advanced data models.

To acquire inquisitive attitude towards research topics in databases.

55

COURSE OUTCOMES:

Design and implement parallel databases using inter and intra

query parallelism concepts

Analyze and apply suitable query and transaction processing

techniques for distributed databases

Develop object oriented and object relational database real time

applications using suitable tools

Expertise and implement intelligent databases using DB2/SQL

Build advanced data models for web, multimedia and mobile

computing services.

UNIT I PARALLEL AND DISTRIBUTED

DATABASES

9

Database System Architectures: Centralized and Client-Server

Architectures – Server System Architectures – Parallel Systems-

Distributed Systems – Parallel Databases: I/O Parallelism – Inter and

Intra Query Parallelism – Inter and Intra operation Parallelism – Design of

Parallel Systems- Distributed Database Concepts - Distributed Data

Storage – Distributed Transactions – Commit Protocols – Concurrency

Control – Distributed Query Processing – Case Studies

UNIT II OBJECT AND OBJECT RELATIONAL

DATABASES

9

Concepts for Object Databases: Object Identity – Object structure – Type

Constructors – Encapsulation of Operations – Methods – Persistence –

Type and Class Hierarchies – Inheritance – Complex Objects – Object

Database Standards, Languages and Design: ODMG Model – ODL –

OQL – Object Relational and Extended – Relational Systems: Object

Relational features in SQL/Oracle – Case Studies.

56

UNIT III INTELLIGENT DATABASES 9

Active Databases: Syntax and Semantics (Starburst, Oracle, DB2)-

Taxonomy- Applications-Design Principles for Active Rules- Temporal

Databases: Overview of Temporal Databases- TSQL2- Deductive

Databases: Logic of Query Languages – Datalog- Recursive Rules-

Syntax and Semantics of Datalog Languages- Implementation of Rules

and Recursion- Recursive Queries in SQL, Data Mining Concepts,

Overview of Data Warehousing and OLAP.

UNIT IV ADVANCED DATA MODELS 9

XML & Internet Databases: XML Data, XML Data Model, XML DTD, XML

Schema, XML Databases, XML Querying, Mobile Databases: Mobile

Computing Architecture, Location and Handoff Management, Mobile

Data Management Issues, Data processing and mobility, Mobile Data

Query Processing, Transaction Management in Mobile databases,

Issues in Information Broadcast- Multimedia Databases – Image

databases, Video databases

UNIT V EMERGING TECHNOLOGIES 9

Geographic Information System, Design and Representation of

Geographic Data, Spatial Databases- Spatial Data Types- Spatial

Indexes - Spatial Relationships- Spatial Data Structures- R-Trees, Spatial

Queries, Genome Databases, Introduction to Big Data, Twig Pattern

Queries, Handling Partial Information in Relational Databases, Domain

Specific Querying. Entity Mining High Utility Dataset Mining.

TOTAL: 45 PERIODS

REFERENCE BOOKS:

1. R. Elmasri, S.B. Navathe, “Fundamentals of Database Systems”,

Fifth Edition, Pearson Education/Addison Wesley, 2007.

2. Thomas Cannolly and Carolyn Begg, “Database Systems, A

57

Practical Approach to Design, Implementation and

Management”, Third Edition, Pearson Education, 2007.

3. Henry F Korth, Abraham Silberschatz, S. Sudharshan, “Database

System Concepts”, Fifth Edition, McGraw Hill, 2006.

4. C.J.Date, A.Kannan and S.Swamynathan, ”An Introduction to

Database Systems”, Eighth Edition, Pearson Education, 2006.

5. Raghu Ramakrishnan, Johannes Gehrke, “Database Management

Systems”, McGraw Hill, Third Edition 2004.

6. Jun Pyo Park, Chang-Sup Park, and Yon Dohn Chung, “Lineage

Encoding: An Efficient Wireless XML Streaming Supporting Twig

Pattern Queries”, IEEE Transactions On Knowledge And Data

Engineering, Vol. 25, No. 7, July 2013

7. Maria Vanina Martinez, Cristian Molinaro, John Grant, and V.S.

Subrahmanian, “Customized Policies for Handling Partial

Information in Relational Databases”, IEEE Transactions on

Knowledge and Data Engineering, Vol. 25, No. 6, June 2013

8. Shasha Li, Chin-Yew Lin, Young-In Song, and Zhoujun Li,

“Comparable Entity Mining from Comparative Questions”, IEEE

Transactions on Knowledge and Data Engineering, Vol. 25, No. 7,

July 2013

13MI103: NETWORK PROGRAMMING AND

MANAGEMENT

L T P C

3 0 0 3

COURSE OBJECTIVES:

To study the design and implementation of a socket based application

using either TCP, UDP and SCTP.

To understand SCTP sockets and its options.

To study the security features in socket programming.

58

To explore the usage of sockets options and the system calls needed

to support unicast, broadcast and multicast applications.

To explore the emerging technologies in network management.

COURSE OUTCOMES:

Design and develop network applications using sockets system calls.

Explore the features of Stream Control Transmission Protocol (SCTP)

Incorporate the security features in the socket programming

Work with various networking tools such as ping, traceroute to

investigate a traffic flow in the network.

Extend network applications for broadcasting and multicasting

Create innovative network design by applying advanced socket

concepts.

Analyze the network management protocols and practical issues

involved in it.

UNIT I APPLICATION DEVELOPMENT 9

Introduction to Socket Programming – Overview of TCP/IP Protocols –

Introduction to Sockets –Iterative TCP programming – Iterative UDP

programming – Concurrent programming – fork and exec - I/O

multiplexing – I/O Models – select function – shutdown function – TCP

echo Server (with multiplexing) – poll function – TCP echoClient (with

Multiplexing) Multiplexing TCP and UDP sockets- Threaded servers –

thread creation and termination – TCP echo server using threads –

Mutexes – condition variables – Ipv6 Socket Programming.

UNIT II ELEMENTARY SCTP SOCKETS AND SOCKET

OPTIONS

9

Introduction to SCTP- Interface Modules – SCTP functions-

sctp_bindx, sctp_connectx, sctp_getpaddrs, sctp_freepaddrs,

59

sctp_getladdrs, sctp_freeladdrs, sctp_sendmsg, sctp_recvmsg,

sctp_opt_info, sctp_peeloff, shutdown – Notifications - Socket options

– getsockopt and setsockopt functions- Socket states – generic socket

options – IP socket options – ICMP socket options – TCP socket

options - SCTP socket options – fcntl functions.

UNIT III ADVANCED SOCKETS I 9

Routing sockets – Datalink socket address structure – Reading and

writing – sysctl operations – get_ifi_info function – Interface name and

index functions- Key Management sockets – Reading and writing –

Dumping Security Association Database – Creating static Security

Association – Dynamically maintaining SA’s – Broadcasting –

Broadcast addresses – Unicast versus Broadcast – (Client) Application

development for broadcasting – Race conditions – Multicasting –

Multicast addresses- Multicasting versus Broadcasting on a LAN –

Multicasting on a WAN – Source specific Multicast – Multicast socket

options – mcast_join, (Client) Application development for multicasting

– Receiving IP multicast infrastructure session announcements –

Sending and receiving.

UNIT IV ADVANCED SOCKETS II 9

Advanced UDP sockets – Receiving flags, Destination IP address and

Interface index – Datagram truncation – Using UDP instead of TCP –

Adding reliability to UDP – Binding interface addresses – Concurrent

UDP servers – SCTP Congestion control performance -Advanced

SCTP sockets – Partial delivery - Notifications – Unordered data –

Binding a subset of addresses – Determining peer and local address

information – Finding an association Id given an IP address –

Heartbeating and Address failure – Peeling off an association –

Controlling timing – Using SCTP instead of TCP – Out of band data –

Signal driven I/O – Design & Implementation of secure socket SCTP.

60

UNIT V NETWORK MANAGEMENT 9

Network Management fundamentals – Issues in SNMP – Basic

concepts in RMON – RMON MIB – statistics group- history group -

alarm group - host group- hostTopN group- matrix group- filter group -

capture group- event group- token Ring group- Issues in RMON1 –

RMON2 – Overview – Protocol Directory group – Protocol Distribution

group – Address Map group – RMON2 host group – RMON2 matrix

group – User History Collection group – Probe Configuration group –

Extensions and practical issues.

TOTAL: 45 PERIODS

REFERENCE BOOKS:

1. W. Richard Stevens, “Unix Network Programming Vol-I”, Second

Edition, Pearson Education, 1998.

2. D.E. Comer, “Internetworking with TCP/IP Vol- III”, (BSD Sockets

Version), Second Edition, Pearson Education, 2003.

3. Michael Donahoo, Kenneth Calvert, “TCP/IP Sockets in C, A

practical guide for programmers”, Second Edition, Elsevier, 2009.

4. Forouzan, “ TCP/IP Protocol Suite” Second Edition, Tata MC Graw

Hill, 2003.

5. Stefan Lindskog, Anna Brunstrom, “Design & Implementation of

secure socket SCTP”, Transactions on Computer Science VI,

Springer, LNCS5730, 2009.

6. Jun-ichiro itojun Hagino, “IPv6 Network Programming”, Elsevier

India Private Limited, NewDelhi, 2006.

7. William Stallings, “SNMP, SNMPv2, SNMPv3 and RMON 1 and

2”,Third Edition, Addison Wesley, 1999.

8. Mani Subramaniam, “Network Management: Principles and

Practice“, Addison Wesley”, First Edition, 2001.

61

WEB REFERENCES:

1. Guanhua Ye, Tarek Saadawi, Myung Lee, “SCTP Congestion

Control Performance In Wireless Multi-Hop Networks”,

Communications and Networks Consortium , January 2011,

http://www.geocities.ws/yegh98/publications/753.pdf

2. Paul Stalvig, “Introduction to the Stream Control Transmission

Protocol (SCTP): The next generation of the Transmission Control

Protocol (TCP)”, F5- Networks Inc, Oct 2007,

www.f5.com/pdf/white-papers/sctp-introduction-wp.pdf‎

13MI104 : SOFT COMPUTING TECHNIQUES L T P C

3 1 0 4

COURSE OBJECTIVES:

To give students knowledge of soft computing theories

fundamentals, i.e. fundamentals of non-traditional technologies and

approaches to solving hard real-world problems, namely

fundamentals of artificial neural networks, fuzzy sets and fuzzy logic

and genetic algorithms.

To introduce the ideas of fuzzy sets, fuzzy logic and use of

heuristics based on human experience

To become familiar with neural networks that can learn from

available examples and generalize to form appropriate rules for

inference systems

To provide the mathematical background for carrying out the

optimization associated with neural network learning

To familiarize with genetic algorithms and other random search

procedures useful while seeking global optimum in self-learning

62

situations

To introduce case studies utilizing the above and illustrate the

intelligent behavior of programs based on soft computing

COURSE OUTCOMES:

Apply the tolerance of imprecision and uncertainty for design of

robust and low-cost intelligent machines.

Acquire knowledge of soft computing theories fundamentals and so

to design program systems using approaches of these theories for

solving various real-world problems.

Implement soft computing techniques in building intelligent

machines

Apply a soft computing methodology for a particular problem

Apply fuzzy logic and reasoning to handle uncertainty and solve

engineering problems

Apply genetic algorithms to combinatorial optimization problems

and apply neural networks to pattern classification and regression

problems

Evaluate and compare solutions by various soft computing

approaches for a given problem.

UNIT I NEURAL NETWORKS 9

Introduction: Soft Computing Constituents – Soft Computing Vs Hard

Computing –Applications - Artificial Neural Network (ANN): Fundamental

Concept – Basic Terminologies – Neural Network Architecture – Learning

Process – Fundamental Models of ANN: McCulloch-Pitts Model –Hebb

Network – Linear Separability. Supervised Learning Networks:

Perceptron Network – Adaline and Madaline Networks – Back

Propagation Network – Radial Basis Function Network

63

UNIT II UNSUPERVISED LEARNING 9

Unsupervised Learning Networks: Kohonen Self Organizing Network –

Counter Propagation Network – ART Network – Hopfield Network -

Special Network– Support Vector Machine- Kernel methods for Pattern

classification- Kernel methods for function optimization.

UNIT III FUZZY LOGIC 9

Introduction- Classic Sets- Fuzzy Sets– crisp relations- Fuzzy Relations-

Fuzzy Equivalence and Tolerance Relation – Value assignments- Fuzzy

Composition- Membership Functions–Fuzzification- Defuzzification.

Fuzzy Arithmetic – Extension Principle – Fuzzy Measures –Fuzzy

Classification

UNIT IV FUZZY MODELLING & APPLICATIONS 9

Fuzzy Rules and Fuzzy Reasoning: Fuzzy Propositions – Formation of

Rules – Decomposition of Rules – Aggregation of Rules – Approximate

Reasoning – Fuzzy Inference and Expert Systems – Fuzzy Decision

Making – Fuzzy Logic Control Systems. Case Studies : Hybrid system-

Neuro Fuzzy system for various applications- Knowledge Leverage

Based TSK Fuzzy System Modeling - Fuzzy C-Means algorithms for very

large Data

UNIT V GENETIC ALGORITHM & APPLICATIONS 9

Genetic Algorithm: Fundamental Concept – Basic Terminologies –

Traditional Vs Genetic Algorithm - Elements of GA - Encoding - Fitness

Function – Genetic Operators: Reproduction – Cross Over - Inversion

and Deletion - Mutation – Simple and General GA - The Schema

Theorem- difference between GA and GP- Applications of GA.

Multiobjective Optimization- Hybrid GA for Feature Selection-

Multiobjective Genetic Fuzzy Clustering for pixel classification- Clustering

Wireless Sensor Network Using Fuzzy Logic and Genetic Algorithm

TUTORIAL :15 PERIODS

TOTAL: 45+15=60 PERIODS

64

REFERENCE BOOKS:

1. J.S.R. Jang, C.T. Sun, E. Mizutani, “Neuro-Fuzzy and Soft

Computing”, Prentice Hall India, 2004

2. S.N. Sivanandam, S.N. Deepa, “Principles of Soft Computing”,

Wiley India, 2007.

3. Timoty J. Ross, “Fuzzy Logic with Engineering Applications”,

McGraw Hill, 1997.

4. Davis E.Goldberg, “Genetic Algorithms: Search, Optimization and

Machine Learning”, Addison Wesley, N.Y., 1989.

5. S. Rajasekaran, G.A.V. Pai, “Neural Networks, Fuzzy Logic,

Genetic Algorithms”, Prentice Hall, India, 2004.

6. Elaine Rich & Kevin Knight, Artificial Intelligence, Second Edition,

Tata Mcgraw Hill

Publishing Comp., 2006, New Delhi.

7. Il-Seok Oh, Jin-Seon Lee and Byung-Ro Moon, “Hybrid Genetic

Algorithms for Feature Selection”, IEEE Transactions On Pattern

Analysis And Machine Intelligence, Vol. 26, No. 11, PP: 1424-1437,

2004.

8. Deng. Z et al, “Knowledge-Leverage-Based TSK Fuzzy System

Modeling” IEEE Transaction on Neural networks and learning

system, Vol.24. Issue:8, PP.1200-1212,2013

9. Sanghamitra Bandyopadhyay,, Ujjwal Maulik, and Anirban

Mukhopadhyay, “Multiobjective Genetic Clustering for pixel

Classification in Remote Sensing Imagery”, IEEE Transactions on

Geoscience and remote sensing, Vol. 51, No. 6, June 2013

10. Havens, T.C et al, “ Fuzzy C-Means algorithms for very large Data”,

IEEE Transaction on Fuzzy system, Vol.20, No.6, PP: 1130-1146,

2012

11. Saeedian, E. et al, “CFGA: Clustering Wireless Sensor Network

65

Using Fuzzy Logic and Genetic Algorithm, IEEE International

Conference on Wireless Communications, Networking and Mobile

Computing(WiCOM), PP: 1:5 2011

13MC151: ADVANCED DATA STRUCTURES

LABORATORY

L T P C


COURSE OBJECTIVES:

To learn implementation of data structures for concurrency

To learn implementation of advanced data structures such as

search trees, hash tables, heaps and operations on them

To learn to implement advanced concurrent data structures

To learn to apply principles of efficient algorithm design and learn

various advanced algorithms

COURSE OUTCOMES:

Implement and apply concurrent linked lists, stacks, and queues

Perform operations on search trees and hash tables

Perform operations on different types of heaps

Implement and apply data structures for strings

Implement advanced concurrent structures

Apply design techniques for algorithms and advanced algorithms

SYLLABUS FOR THE LAB:

Each student has to work individually on assigned lab exercises. Lab

sessions could be scheduled as one contiguous three-hour session per

66

week. The students have to complete a minimum of 12 exercises. It is

recommended that all implementations are carried out in Java. If C or

C++ has to be used, then the threads library will be required for

concurrency.

Implementation and applications of classic linear data structures,

namely, linked lists, queues, and stacks.

Implementation of various locking and synchronization mechanisms

for concurrent linked lists, concurrent queues, and concurrent

stacks.

Implementation of weight balanced search trees and skip lists.

Implantation of suffix trees and pattern matching

Implementation of various heap structures.

Implementation of concurrent hashing, concurrent skip lists, and

concurrent priority queues.

Implementation of approximation and randomized algorithms.

Implementation of parallel sorting algorithms.

Developing an application involving concurrency and data

structures.

TOTAL: 45 PERIODS

REFERENCE BOOKS:

1. M. Herlihy and N. Shavit, “The Art of Multiprocessor Programming”,

Morgan Kaufmann, 2012.

2. Peter Brass, “Advanced Data Structures”, Cambridge University

Press, 2008.

3. A. V. Aho, J. E. Hopcroft, and J. D. Ullman, “The Design and

Analysis of Computer Algorithms”, Addison-Wesley, 1975.

4. Gavpai, “Data Structures and Algorithms – Concepts, techniques

67

and Applications”, First Edition, Tata McGraw-Hill, 2008.

5. Edited by S.K. Chang, “Data Structures and Algorithms – Series of

Software Engineering and Knowledge Engineering”, Vol. 13, World

Scientific Publishing, 2003.

6. Jon Kleinberg, "Algorithm Design", Addison-Wesley, 2013.

7. David P. Williamson, David B. Shmoys, “The Design of

Approximation Algorithms”, Cambridge University Press, 2011.

8. Rajeev Motwani and PrabhakarRaghavan, “Randomized

Algorithms”, Cambridge University Press, 1995.

9. Michael J. Quinn, “Parallel Computing: Theory & Practice”, Tata

McGraw Hill Edition, 2003.

10. AnanthGrama, Anshul Gupta, et al., “Introduction to Parallel

Computing”, Second Edition, Pearson Education, 2003.

WEB REFERENCES:

1. http://www.w3schools.in/c-programming-language

2. http://www.geeksforgeeks.org/pattern-searching-set-8-suffix-tree-

introduction/

3. http://iamwww.unibe.ch/~wenger/DA/SkipList/

4. http://www.cs.au.dk/~gerth/slides/soda98.pdf

5. http://www.cs.sunysb.edu/~algorith/files/suffix-trees.shtml

6. http://pages.cs.wisc.edu/~shuchi/courses/880-S07/scribe-

notes/lecture20.pdf

List of Sample Exercises

1. A file consists of a list of CD titles with information such as

category(in alphanumeric form maximum of 25 characters)and

http://pages.cs.wisc.edu/~shuchi/courses/880-S07/scribe-notes/lecture20.pdf

http://pages.cs.wisc.edu/~shuchi/courses/880-S07/scribe-notes/lecture20.pdf

68

title(in alphanumeric form maximum of 25 characters

characters).Duplicate entries are allowed. Example Categories

are: Education, Entertainment, Examinations, Soft skill, Games

etc.

Design a system to get new entries to add, search for an entry,

delete the existing entries and view the titles. The system does

not know the number of titles in advance. The system may keep

the information either in order or unordered. Compare the

efficiency of the above two approaches.

2. An electronics goods dealer has 50 different types of item and for

each item he has a maximum of 5 branded company products for

sale. Read and store the monthly sales (day wise) of the shop in a

multi list and produce the following reports.

List the day wise total sales amount of all goods

List the weekly sale details of refrigerator.

List the monthly sale details of all LG brand electronic

good

Make the list as empty at the end of seventh day of the

week after taking appropriate back up.

3. Construct concurrent bi-stack in a single array and perform the

following operation for string manipulation such as:

i) Search for a character and Replace it by a new one if

available

ii) Reverse a String

iii) Test for palindrome

iv) Count the occurrences of the given character

4. A deque is a data structure consisting of a list of items, on which

the following operations are possible:

69

Push(X,D): Insert item X on the front end of the deque D.

Pop(D): Remove the front item from the deque D and return it

Inject(X,D): Insert an item X on the rear end of the deque D.

Eject(D): Remove the rear item from the deque D and return it.

Implement the above concurrent double ended queue.

5. Suppose that an advertising company maintain a database and

needs to generate mailing labels for certain constituencies. A

typical request might require sending out a mailing to people who

are between the ages of 34 and 49 and whose annual income is

between $100000 and $200000.This problem is known as a two

dimensional range query. To solve such range queries a two

dimensional search tree namely 2-D tree can be used which has

the simple property that branching on odd levels is done with

respect to the first key and branching on even levels is done with

respect to the second key. Implement a 2-D tree with company

database consist of the following fields such mail-ID, Age, Gender,

Annual Income and Occupation.

6. Using weight balanced search tree construct a Telephone

directory with the Information such as: Phone Number, Name and

address then perform the following

a)Search for a phone number and print the customer name and

address

b) Remove a Phone number from the directory

c) Change the address of the customer whose phone number is

given.

d) Print the content of the directory

70

7. Construct a Red-Black tree with the database containing Book

detail such access Number, Title, Author name, Department and

price. Perform the following operations

i) Search for a book based on Book title

ii) Add a new book entry into the database

iii)Remove the lost book entry from the database

Note: Arrange the list of records based an book title to improve the

performance of the frequent search operations.

8. The details of employees (Emp.Id, Name, Department and Total

Years of experience) of a company are to be maintained. The list

indicates both alphabetical ordering of names, ascending order of

Emp.Id Number and alphabetical ordering of department names.

Perform the following by using Skip List structure:

Insert a new employee detail in the appropriate position

Remove an employee detail where Emp.Id is given

Find an employee detail whose ID is given.

Find employee information whose name in a particular

department is given.

List all employee detail in order of their name.

9. Construct a Suffix Tree data structure to construct a Telephone

directory Telephone directory with the Information such as: Phone

Number, Name and address then perform the following operation

Insert a new customer information into the directory

Disable a connection and Delete that phone number from the

directory

Print all customer addresses that starts with name “AN*”.

Print all customer addresses and phone numbers whose name

71

starts with alphabets A-E.

10. Construct double ended priority queue using Min-Max heaps and

perform the following

a) Find 3rd minimum

b) Delete an item(with random priority)

c) Delete maximum element

d) Heap sort in descending order

e) Modify the priority of an item

11. Online dictionary implementation using Hashing

Implement a dictionary which contains the meaning of different

words. Both the word and the meaning can be in the same

language. Your program should read a word and should give the

meaning. If the word is a new one (not available in the dictionary)

then include the word into its correct position with its meaning.

Implement the same problem using concurrent Hashing technique.

12. Implementation of Approximation algorithm : Solve the 0-1

Knapsack problem

Given a knapsack with maximum weight capacity C and a set S

consisting of n items, each item i with weight wi and profit pi (all wi,

pi and W are integer values). The problem is to pack the knapsack

to achieve maximum total profit of packed items with items total

weight less than or equal to C.

13. Implementation of randomized algorithm: Find the solution for the

Project selection problem.

In the project selection problem, there are n projects and m

equipments. Each project pi yields revenue r(pi)and each

equipment qi costs c(qi) to purchase. Each project requires a

number of equipments and each equipment can be shared by

72

several projects. The problem is to determine which projects and

equipments should be selected and purchased respectively, so

that the profit is maximized.

14. Implement the following parallel sorting and compare the

performance of those algorithms.

i) Parallel Quick sort

ii) Parallel merge sort

iii) Batcher’s Bitonic sort

Note:

Batcher’s Bitonic sort is a parallel sorting algorithm whose main

operation is a technique for merging two bitonic sequences. A

bitonic sequence is the concatenation of an ascending and a

descending sequence. For example 2, 4, 6, 8, 23, 8, 5, 3, 0 is a

bitonic sequences.

13MI151: NETWORK PROGRAMMING

LABORATORY

L T P C

0 0 3 2

COURSE OUTCOMES:

Design network applications using TCP and UDP

Demonstrate the usage of various networking tools.

Analyze network traffic

Analyze packets transmitted over the network.

Simulate the given scenario and analyze the happenings.

73

List of Exercises

1. Write a socket program to implement the following for desired

application.

a. Iterative and Reliable client Servers

b. Iterative and Unreliable Client Servers

c. Concurrent and Reliable Client Servers using fork and

thread

d. Concurrent and Unreliable Client Servers using select

and poll.

e. Add reliability to UDP protocol.

f. Multiplexing both TCP and UDP.

g. SCTP protocol

2. Use the software like wireshark in a LAN to capture the packet

and do a statistical analysis such as: the number of packets

(bits) flowing in/out of a designated system, a pair wise packet

flow among the given IP addresses

3. Use the packet capturing tool and measure the traffic from

each node in a application wise, and pair wise traffic

application

4. Repeat exercise 8 for protocol wise traffic analysis.

5. Simulate the transmission of ping messages over a network

topology and do the following.

a. Find the number of packets dropped due to congestion.

b. Analyze the performance of the different congestion

control algorithms (Old Tahoe, Tahoe, and Reno).

6. Simulate a network using n nodes and set multiple traffic

nodes and do the following.

a. Plot congestion window for different source / destination.

74

SEMESTER II

13MI201 WEB MINING AND INFORMATION

RETRIEVAL

L T P C

3 0 0 3

COURSE OBJECTIVES:

To understand the different knowledge discovery issues in data

mining from the world wide web.

To analyze the different algorithms commonly used by Web

application.

To apply the role played by Web mining in Information retrieval

and extraction

To analyze the use of probabilistic models for web mining

To analyze the current trends in Web mining

To understand the various applications of Information Retrieval

giving emphasis to Multimedia IR, Web Search

COURSE OUTCOMES:

Upon Completion of the course, the students will be able to

Identify and differentiate between application areas for web

content mining, web structure mining and web usage mining.

Analyze various key concepts such as deep web, surface web,

semantic web, web log, hypertext, social network, information

synthesis, corpora and evaluation measures such as precision

and recall.

Use different methods and techniques such as word frequency

and co-occurrence statistics, normalization of data, machine

learning, clustering, vector space models and lexical semantics.

75

Analyze various algorithms commonly used by web mining

applications.

Apply different approaches and techniques of web mining for e.g.

sentiment analysis, targeted marketing, linguistic forensics,

topic/trend-detection-tracking and multi-document summarization

(information aggregation).

Design an efficient search engine.

UNIT I INTRODUCTION 9

Overview of Data mining – Data mining from a Business Perspective –

Data types, Input and output of data mining algorithms- Decision Tree-

Classification and Regression Trees – Preprocessing and Post

processing in Data mining

UNIT II WEB SEARCH 9

Client Side Design: HTML5 – Syntax and Semantics – Markups –

Forms – Audio – Video - Microdata and Custom Data - Canvas.Server

Side Scripting: PHP – Introduction – Creating PHP Pages – PHP with

MySQL – Tables to display data – Form elements.

UNIT III REPRESENTING WEB DATA 9

XML Basics – XML Namespaces - XML Schema - DOM – SAX –

XPath - XSL: Extensible Stylesheet Language – Extensible Stylesheet

Language Transformations (XSLT) - JAXP

UNIT IV LEARNING 9

Similarity and Clustering – Formulations and approaches- Bottom up

and Top down Partitioning Paradigms – Clustering and Visualization

via Embeddings – Probabilistic Approaches to clustering –

Collaborative Filtering – Supervised Learning – Semi Supervised

Learning

76

UNIT V INFORMATION RETRIEVAL 9

Information Retrieval and Text Mining - Keyword Search - Nearest-

Neighbor Methods - Measuring Similarity - Web-Based Document

Search - Document–Matching - Inverted Lists - Evaluation of

Performance - Structure in a Document Collection - Clustering

Documents by Similarity- Evaluation of Performance - Information

Extraction - Patterns and Entities from Text- Coreference and

Relationship Extraction - Template Filling and Database Construction-

Survey on web mining-Personalized Concept-Based Clustering of

Search Engine Queries-Searching the Web Using Composed Pages-A

Collaborative Decentralized Approach to Web Search.

TOTAL: 45 PERIODS

REFERENCE BOOKS:

1. Sholom Weiss, “Text Mining: Predictive Methods for Analyzing

Unstructured Information”, Springer, 2005

2. Hercules Antonio do Prado, Edilson Fernada, “ Emerging

Technologies of Text Mining: Techniques and Applications”,

Information Science Reference (IGI), 2008

3. Min Song, Yi-fang Brrok Wu, “Handbook of Research on Text

and Web Mining Technologies”, Vol I & II, Information Science

Reference (IGI),2009

4. Soumen Chakrabarti “ Mining the Web : Discovery Knowledge

from Hypertext Data “ Elsevier Science 2003

5. K.P.Soman,Shyam Diwakar, V.Ajay “ Insight into Data Mining

Theory and Practice “ Prentice Hall of India Private Ltd 2006

6. Deitel & Deitel, Neito, Lin, Sadhu, “XML How to Program”,

Pearson Education,2008

7. Christopher D. Manning, Prabhakar Raghavan and Hinrich

77

Schütze, “Introduction to Information Retrieval”, Cambridge

University Press, 2008

8. Web Mining Research: A Survey- SIGKDD Explorations ACM

SIGKDD, July 2000 IEEE Transactions on Knowledge and

Data Engineering, Vol. 20, No. 11, November 2008

9. SIGIR’06, August 6–11, 2006, Seattle, Washington, USA.

ACM 1-59593-369-7/06/0008

10. IEEE Transactions on Systems, Man, and Cybernetics—

Part A: Systems and Humans, Vol. 42, No. 5, September

2012

13MI202 CLOUD COMPUTING

TECHNOLOGIES

L T P C


COURSE OBJECTIVES:

To understand the concept of cloud and utility computing.

To understand the various issues in cloud computing.

To familiarize themselves with the types of virtualization.

To familiarize themselves with the lead players in cloud.

To appreciate the emergence of cloud as the next generation

computing paradigm.

To be able to set up a private cloud.

COURSE OUTCOMES:

Recognize the strengths and limitations of cloud computing

Discuss on various virtual machine products

78

Identify the architecture, infrastructure and delivery models of

cloud computing Applications.

Suggest solutions for the core issues of cloud computing such as

security, privacy and interoperability

Decide the appropriate technologies, algorithms and approaches

for the related issues

UNIT I OVERVIEW OF VIRTUALIZATION 8

Basics of Virtualization - Virtualization Types – Desktop

Virtualization – Network Virtualization – Server and Machine

Virtualization – Storage Virtualization – System-level of Operating

Virtualization – Application Virtualization- Virtualization Advantages -

Virtual Machine Basics – Taxonomy of Virtual Machines - Process

Virtual Machines - System Virtual Machines – Hypervisor –

Interpretation and Binary translation.

UNIT II VIRTUALIZATION STRUCTURES 8

Implementation Levels of Virtualization - Virtualization Structures -

Tools and Mechanisms - Virtualization of CPU, Memory, I/O Devices

- Virtual Clusters and Resource Management – Virtualization for

Data-Center Automation.

UNIT III CLOUD INFRASTRUCTURE 9

Scalable Computing over the Internet – Technologies for Network

based Systems - System Models for Distributed and Cloud

Computing – Service Oriented Architecture – NIST Cloud

Computing Reference Architecture. Cloud Computing and Services

Model – Public, Private and Hybrid Clouds – Cloud Eco System -

IaaS - PaaS – SaaS. Architectural Design of Compute and

Storage Clouds – Layered Cloud Architecture Development –

Design Challenges - Inter Cloud Resource Management – Resource

Provisioning and Platform Deployment – Global Exchange of Cloud

79

Resources.

Case Study : Amazon Web Service reference , GoGrid, Rackspace.

UNIT IV PROGRAMMING MODEL 10

Parallel and Distributed Programming Paradigms – MapReduce ,

Twister and Iterative MapReduce – Hadoop Library from Apache –

Mapping Applications - Programming Support - Google App Engine,

Amazon AWS - Cloud Software Environments -Eucalyptus, Open

Nebula, OpenStack.

CloudSim – Architecture - Cloudlets – VM creation – Broker – VM

allocation – Hosts – Data Center.

UNIT V SECURITY IN THE CLOUD AND

RESOURCE MANAGEMENT AND RESOURCE MANAGEMENT

10

Cloud Computing Risk Issues – Cloud Computing Security

Challenges – Cloud Computing Security Architecture – Trusted

cloud Computing – Identity Management and Access Control –

Autonomic Security. Dynamic Resource Allocation Using Virtual

Machines for Cloud Computing Environment - Optimization of

Resource Provisioning Cost in Cloud Computing

TOTAL: 45 PERIODS

REFERENCE BOOKS:

1. Kai Hwang, Geoffrey C Fox, Jack G Dongarra, “Distributed

and Cloud Computing, From Parallel Processing to the

Internet of Things”, Morgan Kaufmann Publishers, 2012.

2. Ronald L. Krutz, Russell Dean Vines, “Cloud Security – A

comprehensive Guide to Secure Cloud Computing”, Wiley –

India, 2010.

3. John W.Rittinghouse and James F.Ransome, “Cloud

Computing: Implementation, Management, and Security”,

80

CRC Press, 2010.

4. George Reese, “Cloud Application Architectures: Building

Applications and Infrastructure in the Cloud” O'Reilly

5. Sivadon Chaisiri, Bu-Sung Lee, and Dusit Niyato,

“Optimization Of Resource Provisioning Cost In Cloud

Computing”, IEEE TRANSACTIONS ON SERVICES

COMPUTING, VOL. 5, NO. 2, APRIL-JUNE 2012.

6. Zhen Xiao, Weijia Song, And Qi Chen, ”Dynamic Resource

Allocation Using Virtual Machines For Cloud Computing

Environment”, IEEE TRANSACTIONS ON PARALLEL AND

DISTRIBUTED SYSTEMS, VOL. 24, NO. 6, JUNE 2013.

7. Rajkumar Buyya, Christian Vecchiola, S.Tamarai Selvi,

‘Mastering Cloud Computing”, TMGH,2013.

8. James E. Smith, Ravi Nair, “Virtual Machines: Versatile

Platforms for Systems and Processes”, Elsevier/Morgan

Kaufmann, 2005.

9. William von Hagen, “Professional Xen Virtualization”, Wrox

Publications, January, 2008.

10. Dimitrios Zissis, Dimitrios Lekkas, “Addressing Cloud

Computing Security Issues”, Future Generation Computer

Systems 28 (2012) 583–592,ELSEVIER

11. Rodrigo N.Calheiros, Rajiv Ranjan, Anton Beloglazov, César

A. F. De Rose, and Rajkumar Buyya, “CloudSim: A Toolkit

for Modeling and Simulation of Cloud Computing

Environments and Evaluation of Resource Provisioning

Algorithms “, Cloud Computing and Distributed Systems

(CLOUDS) Laboratory. http://www.buyya.com/papers/

CloudSim2010.pdf

81

13MI203 CYBER SECURITY L T P C

3 0 0 3

COURSE OBJECTIVES:

To know the concepts of Cyber security and create awareness

about this field in the society.

To familiarize with the fundamental concepts Cyber security and

Hacking

To understand the concept of Forensic science, Ethical Hacking,

Mobile hacking and Cryptography

COURSE OUTCOMES:

Able to use tools for preserving the privacy of confidential data

Analyze security threats, vulnerabilities, and attacks

Demonstrate exploits of systems and networks

Select proper risk analysis methods for protecting infrastructure.

Explain major advantages and disadvantages of different security

methods

Design small and large-scale protection mechanisms

UNIT I CYBER SECURITY FUNDAMENTALS 8

Network and Security Concepts - Information Assurance Fundamentals-

Basic Cryptography- Symmetric Encryption- Public Key Encryption-The

Domain Name System (DNS) –Firewalls- Virtualization- Radio-

Frequency Identification-Microsoft Windows Security Principles-Windows

Tokens- Window Messaging- Windows Program Execution- The

Windows Firewall

UNIT II ATTACKER TECHNIQUES AND MOTIVATIONS 8

How Hackers Cover Their Tracks (Anti-forensics) - How and Why

82

Attackers Use Proxies -Tunneling Techniques-Fraud Techniques-

Phishing, Smishing, Vishing and Mobile Malicious Code- Rogue Anti-

Virus- Click Fraud -Threat Infrastructure - Botnets - Fast-Flux -Advanced

Fast-Flux

UNIT III EXPLOITATION 9

Techniques to Gain a Foothold - Shellcode - Integer Overflow

Vulnerabilities - Stack-Based Buffer Overflows - Format-String

Vulnerabilities- SQL Injection - Malicious PDF Files - Race Conditions -

Web Exploit Tools -DoS Conditions - Brute-Force and Dictionary Attacks

-Misdirection, Reconnaissance and Disruption Methods - Cross-Site

Scripting (XSS) - Social Engineering - WarXing - DNS Amplification

Attacks-Fault Injection Attacks on Cryptographic Devices

UNIT IV MALICIOUS CODE 10

Self-Replicating Malicious Code - Worms - Viruses - Evading Detection

and Elevating Privileges - Obfuscation - Virtual Machine Obfuscation -

Persistent Software Techniques -Rootkits - Spyware - Attacks against

Privileged User Accounts and Escalation of Privileges

Token Kidnapping - Virtual Machine Detection -Stealing Information and

Exploitation

Form Grabbing - Man-in-the-Middle Attacks - DLL Injection - Browser

Helper Objects, Automatic Malicious Code Analysis System

UNIT V DEFENSE AND ANALYSIS TECHNIQUES AND RESOURCE MANAGEMENT 10

Memory Forensics - Why Memory Forensics Is Important - Capabilities of

Memory Forensics - Memory Analysis Frameworks - Dumping Physical

Memory - Installing and Using Volatility - Finding Hidden Processes -

Volatility Analyst Pack - Honeypots -Malicious Code Naming -Automated

Malicious Code Analysis Systems - Passive Analysis - Active Analysis -

Physical or Virtual Machines-Intrusion Detection Systems –Wireless

Intrusion detection system.

TOTAL: 45 PERIODS

83

REFERENCE BOOKS:

1. Cyber Security Essentials, James Graham, Ryan Olson, Rick Howard

Auerbach Publications

2. Computer Security Handbook, Seymour Bosworth, M. E. Kabay, Eric

Whyne, John Wiley & Sons, 2009

3. Cybersecurity: The Essential Body of Knowledge , Dan Shoemaker,

Cengage Learning 2011

4. Security in Computing, Charles B. Pfleeger, Shari Lawrence Pfleeger,

Third Edition, Pearson Education, 2003

5. AMCAS: An Automatic Malicious Code Analysis System , Jia Zhang ;

Yuntao Guan ; Xiaoxin Jiang ; Haixin Duan , WAIM '08. The Ninth

International Conference on Web-Age Information Management, 2008.

6. Fault Injection Attacks on Cryptographic Devices: Theory, Practice,

and Countermeasures , Barenghi, A. ; Politec. di Milano, Milan, Italy ;

Breveglieri, L. ; Koren, I. ; Naccache, D. Proceedings of the IEEE,Nov.

2012

7. Wireless Intrusion Detection System Using a Lightweight Agent ,

Haddadi, F. ; Sarram, M.A. Second International Conference on

Computer and Network Technology (ICCNT), 2010

13MI204: BIG DATA ANALYTICS L T P C


COURSE OBJECTIVES:

To Explore the fundamental concepts of big data and analytics

To apply various techniques for mining data stream.

To analyze the big data using intelligent techniques.

http://www.goodreads.com/author/show/951820.Seymour_Bosworth

http://www.goodreads.com/author/show/3020641.M_E_Kabay

http://www.goodreads.com/author/show/3020642.Eric_Whyne

http://www.goodreads.com/author/show/3020642.Eric_Whyne

http://www.goodreads.com/author/show/1823711.Dan_Shoemaker

http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Jia%20Zhang.QT.&searchWithin=p_Author_Ids:37540258300&newsearch=true

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84

To apply search methods and Visualization.

To design applications using Map Reduce Concepts.

COURSE OUTCOMES:

Work with big data platform and its analysis techniques.

Design efficient algorithms for mining the data from large volumes.

Model a framework for Human Activity Recognition.

Analyze the big data for useful business applications.

Implement search methods and Visualization

UNIT I INTRODUCTION TO BIG DATA 9

Introduction to Big Data Platform – Challenges of Conventional Systems -

Intelligent data analysis – Nature of Data - Analytic Processes and Tools

- Analysis Vs Reporting - Modern Data Analytic Tools - Statistical

Concepts: Sampling Distributions - Re-Sampling - Statistical Inference -

Prediction Error.

UNIT II DATA ANALYSIS 9

Regression Modeling - Multivariate Analysis – Bayesian Methods –

Bayesian Paradigm - Bayesian Modeling - Inference and Bayesian

Networks - Support Vector and Kernel Methods - Analysis of Time Series:

Linear Systems Analysis - Nonlinear Dynamics - Rule Induction - Fuzzy

Logic: Extracting Fuzzy Models from Data - Fuzzy Decision Trees

UNIT III SEARCH METHODS AND VISUALIZATION 9

Search by simulated Annealing – Stochastic, Adaptive search by

Evaluation – Evalution Strategies – Genetic Algorithm – Genetic

Programming – Visualization – Classification of Visual Data Analysis

Techniques – Data Types – Visualization Techniques – Interaction

techniques – Specific Visual data analysis Techniques.

85

UNIT IV MINING DATA STREAMS 9

Introduction To Streams Concepts – Stream Data Model and Architecture

- Stream Computing - Sampling Data in a Stream – Filtering Streams –

Counting Distinct Elements in a Stream – Estimating Moments –

Counting Oneness in a Window – Decaying Window - Real time Analytics

Platform(RTAP) Applications - Case Studies - Real Time Sentiment

Analysis, Stock Market Predictions.

UNIT V FRAMEWORKS 9

MapReduce – Hadoop, Hive, MapR – Sharding – NoSQL Databases - S3

- Hadoop Distributed File Systems – Case Study- Preventing Private

Information Inference Attacks on Social Networks- Grand Challenge:

Applying Regulatory Science and Big Data to Improve Medical Device

Innovation

TOTAL: 45 PERIODS

REFERENCE BOOKS:

1. Michael Berthold, David J. Hand, “Intelligent Data Analysis”,

Springer, 2007.

2. Anand Rajaraman and Jeffrey David Ullman, “Mining of Massive

Datasets”, Cambridge University Press, 2012.

3. Bill Franks, “Taming the Big Data Tidal Wave: Finding Opportunities

in Huge Data Streams with Advanced Analytics”, John Wiley &

sons, 2012.

4. Glenn J. Myatt, “Making Sense of Data”, John Wiley & Sons, 2007

5. Pete Warden, “Big Data Glossary”, O’Reilly, 2011.

6. Jiawei Han, Micheline Kamber “Data Mining Concepts and

Techniques”, Second Edition, Elsevier, Reprinted 2008.

7. Raymond Heatherly , Murat Kantarcioglu and Bhavani

http://www.oreilly.com/catalog/9781449317133/

86

Thuraisingham “Preventing Private Information Inference Attacks on

Social Networks” IEEE Transaction on Knowledge and Data

Engineering, Vol 25, No.8 ,August 2013.

8. Arthur G. Erdman‎ , Daniel F. Keefe‎ , Senior Member, IEEE, and

Randall Schiest‎Grand Challenge: Applying Regulatory Science and‎

Big Data to Improve Medical Device Innovation‎ IEEE Transactions

on Biomedical Engineering, Vol. 60, No. 3, March 2013

13MI251: BIG DATA ANALYTICS LABORATORY L T P C

0 0 3 2

COURSE OBJECTIVES:

To Explore the fundamental concepts of big data and analytics

To apply various programming techniques for mining data stream.

To apply search methods and Visualization.

To design applications using Map Reduce Concepts

To apply various data mining concepts in Weka tool.

COURSE OUTCOMES

Work with big data platform and its analysis techniques.

Design efficient algorithms for mining the data from large volumes.

Analyze the big data for useful business applications.

Implement search methods and Visualization.

Implement various application using Map Reduce concepts.

Implement various data mining concepts in Weka tool.

87

List of Exercises

1. Creating an interactive Hadoop MapReduce job flow.

2. Querying Hadoop MapReduce jobs using Hive.

3. Loading unstructured data into Hadoop Distributed File System

(HDFS).

4. Simplifying Big Data processing and communicating with Pig Latin.

5. Creating and customizing applications to analyze data.

6. Implementing a targeted Big Data strategy.

7. Using weka tool to exploring the data.

8. Preprocess the given data using weka tool.

9. Apply different classification techniques to classify the given data

set.

10. Apply various clustering techniques to cluster the data.

11. Apply various association rule mining algorithms.

13MI252: CLOUD COMPUTING LABORATORY L T P C

(Common to M.E CSE & M.Tech IT) 0 0 3 2

COURSE OBJECTIVES:

To learn how to use Cloud Services.

To implement Virtualization

To implement Task Scheduling algorithms.

To implement Energy-conscious model.

To build Private Cloud.

88

COURSE OUTCOMES:

Analyze the use of Cloud Applications

Apply resource allocation, scheduling algorithms.

Implement Energy-conscious model.

Create virtual machines from available physical resources.

Setup a private cloud.

Familiarize with Open Source Cloud computing Software.

SYLLABUS FOR THE LAB:

List Of Exercises:

1. Study and Usage of Google Apps.

2. Implement Virtual OS using virtual box.

3. Simulate VM allocation algorithm using cloudSim.

4. Simulate Task Scheduling algorithm using CloudSim.

5. Simulate Energy-conscious mode006C using CloudSim.

6. Setup a Private Cloud Using Open Stack or Eucalyptus.

7. Install and configure Open Stack Object Storage - Swift in Ubuntu.

8. Implement Open Stack Nova-Compute.

9. Implement Open Stack Image services – Glance.

89

Syllabus for the Electives

13MI401: X – INFORMATICS L T P C

3 0 0 3

COURSE OBJECTIVES:

To present the state-of-the art developments in medical informatics,

public health informatics and bioinformatics

To demonstrate a thorough understanding of creation,

interpretation, storage and usage of medical information

To understand the case study of computerized patient record

To study and use different tools for clinical information system

To apply the knowledge of bio informatics for systems

COURSE OUTCOMES:

Design and develop clinical and hospital management system on

his own

Design computerized information systems for use in health care

Assure confidentiality of protected patient health information when

using health information system

Conceive and design effective user-centered systems to support

medical work and decision-making

Work with different medical imaging techniques

Apply the knowledge of bio informatics for biological databases

Evaluate outcomes of the use of information in clinical practice

UNIT I MEDICAL INFORMATICS 9

Introduction - Structure of Medical Informatics –Internet and Medicine -

Security Issues Computer based Medical Information Retrieval, Hospital

Management and Information System - Functional Capabilities of a

90

Computerized HIS - E-Health Services - Health Informatics – Medical

Informatics – Bioinformatics

UNIT II HEALTHCARE INFORMATICS 9

Strategic Planning - Selecting a Health Care Information System -

Systems Integration and Maintenance - Systems Integration - Regulatory

and Accreditation Issues - Contingency Planning and Disaster Recovery

UNIT III COMPUTERISED PATIENT RECORD 9

Introduction - History taking by Computer, Dialogue with the Computer -

Components and Functionality of CPR - Development Tools – Intranet -

CPR in Radiology - Application Server Provider - Clinical Information

System - Computerized Prescriptions for Patients

UNIT IV COMPUTERS IN CLINICAL LABORATORY AND

MEDICAL IMAGING

9

Automated Clinical Laboratories - Automated Methods in Haematology -

Cytology and Histology - Intelligent Laboratory Information System -

Computerized ECG, EEG And EMG - Computer Assisted Medical

Imaging - Nuclear Medicine - Ultrasound Imaging Ultrasonography -

Computed X-Ray Tomography - Radiation Therapy and Planning,

Nuclear Magnetic Resonance

UNIT V BIO-INFORMATICS 9

Pair wise Sequence Alignment – Local Versus Global Alignment –

Multiple Sequence Alignment – Computational Methods – Dot Matrix

Analysis – Substitution Matrices – Dynamic Programming – Word

Methods – Bayesian Methods – Multiple Sequence Alignment – Dynamic

Programming – Progressive Strategies – Iterative Strategies – Tools –

Nucleotide Pattern Matching – Polypeptide Pattern Matching – Utilities –

Sequence Databases -Improved Algorithms for Matching r-Separated

Sets with Applications to Protein Structure Alignment - DALIX: Optimal

DALI Protein Structure Alignment

TOTAL: 45 PERIODS

91

REFERENCE BOOKS:

1. R.D.Lele, “Computers in Medicine Progress in Medical Informatics”,

Tata Mcgraw Hill Publishing Computers, 2005

2. Mohan Bansal, “Medical informatics”, Tata Mcgraw Hill Publishing,

2003

3. Burke, Lillian; Well, Barbara, “Information Technology for the Health

Professions”, Prentice Hall, 2006

4. Bryan Bergeron, “Bio Informatics Computing”, Pearson Education,

Second Edition, 2003

5. Karen Wager, Frances Lee and John Glaser, “Managing Health Care

Information Systems”, Josey-Bass Publishers, Second Edition, 2009

6. Aleksandar Poleksic, “Improved Algorithms for Matching r-Separated

Sets with Applications to Protein Structure Alignment”, IEEE/ACM

Transactions On Computational Biology And Bioinformatics, Vol. 10,

No. 1, January/February 2013

7. Inken Wohlers, Rumen Andonov, and Gunnar W. Klau, “DALIX:

Optimal DALI Protein Structure Alignment”, IEEE/ACM Transactions

On Computational Biology And Bioinformatics, Vol. 10, No. 1,

January/February 2013

13MI402: XML AND WEB SERVICES L T P C

3 0 0 3

COURSE OBJECTIVES:

To explore the basics of XML technology

To analyze the background of distributed information system

To analyze and design a web service based application

To apply the security features of web services and service

composition

92

COURSE OUTCOMES:

Create, validate, parse, and transform XML documents

Design a middleware solution based application

Develop web services using different technologies

Design secured web data transmission

Compose set of web services using BPEL

UNIT I XML FUNDAMENTALS 9

XML – structuring with schema DTD – XML Schema – XML Processing

DOM – SAX – Presental XSL – Transformation XSLT – XPath –XQuery

UNIT II DISTRIBUTED INFORMATION SYSTEM 9

Distributed information system – Design of IB – Architecture of IB –

Communication in an IS – Middleware RPC – TP monitors – Object

brokers – Message oriented middleware – EAI – EAI Middleware –

Workflow –Management – benefits and limitations – Web technologies for

Application Integration

UNIT III WEB SERVICES 9

Web Services – Definition – Web Services and EAI – Web Services

Technologies – web services Architecture – SOAP – WSDL – UDDI –WS

– Addressing – WS – Routing WS- Security –WS –Policy –Web Service

invocation framework web services using java – WS using .NET mobile

web service

UNIT IV XML SECURITY 9

XML Security and meta framework - XML signature – XML Encryption –

SAML – XKMS – WS – Security – RDF – semantic Web service

93

UNIT V SERVICE COMPOSITION 9

Service Coordination and Composition coordination protocols – WS –

Coordination – WS – transaction – RosettaNet – WebXML –WSCI –

Service Composition – Service Composition Models – Dependencies

between coordination and composition – BPEL – Current trends - QoS-

Aware Web Service Recommendation by Collaborative Filtering

Case Study : Service-Centric Framework for a Digital Government

Application

TOTAL: 45 PERIODS

REFERENCE BOOKS:

1. Gystavo Alonso, Fabio Casasi, Hareemi Kuno, Vijay Machiraju, “web

Services – concepts, Architecture and Applications”, Springer, 2004

2. Ron Schmelzer etal “XML and Web Services”, Pearson Education,

2002

3. Sandeep Chatterjee and James Webber,” Developing Enterprise web

services: An Architect’s and Guide”, Practice Hall, 2004

4. Freunk P.Coyle, ”XML,web Services and the Data Revolution”,

Pearson, 2002

5. Zibin Zheng, Hao Ma, Michael R. Lyu, and Irwin King, “QoS-Aware

Web Service Recommendation by Collaborative Filtering”, IEEE

Transactions on Services Computing, Vol. 4, No. 2, April-June 2011

6. Athman Bouguettaya, Qi Yu, Xumin Liu and Zaki Malik, “Service-

Centric Framework for a Digital Government Application”, IEEE

Transactions on Services Computing, Vol. 4, No. 1, January-March

2011

94

13MC407:ENERGY-AWARE COMPUTING L T P C


COURSE OBJECTIVES:

To know the fundamental principles energy efficient devices

To study the concepts of Energy efficient storage

To introduce energy efficient algorithms

To enable the students to know energy efficient techniques involved

to support real-time systems

To study energy aware applications

COURSE OUTCOMES:

Design Power efficient architecture Hardware and Software

Analyze power and performance trade off between various energy

aware storage devices

Implement various energy aware algorithms

Restructure the software and Hardware for energy aware

applications

Explore the energy aware applications


Energy efficient network on chip architecture for multi core system-

Energy efficient MIPS CPU core with fine grained run time power gating –

Low power design of Emerging memory technologies

UNIT II ENERGY EFFICIENT STORAGE 9

Disk Energy Management-Power efficient strategies for storage system-

Dynamic thermal management for high performance storage systems-

Energy saving technique for Disk storage systems

95

UNIT III ENERGY EFFICIENT ALGORITHMS 9

Scheduling of Parallel Tasks – Task level Dynamic voltage scaling –

Speed Scaling – Processor optimization- Memetic Algorithms – Online

job scheduling Algorithms

UNIT IV REAL TIME SYSTEMS 9

Multi processor system – Real Time tasks- Energy Minimization – Energy

aware scheduling- Dynamic Reconfiguration- Adaptive power

management-Energy Harvesting Embedded system

UNIT V ENERGY AWARE APPLICATIONS 9

On chip network – Video codec Design – Surveillance camera- Low

power mobile storage

TOTAL: 45 PERIODS

REFERENCE BOOKS:

1. Ishfaq Ah mad, Sanjay Ranka, Handbook of Energy Aware and Green

Computing, Chapman and Hall/CRC, 2012

2. Chong-Min Kyung, Sungioo yoo, Energy Aware system design

Algorithms and Architecture, Springer, 2011

3. Bob steiger wald ,Chris:Luero, Energy Aware computing, Intel

Press,2012

4. Ramesh Karri, David Goodman Eds, System Level Power

Optimization for Wireless Multimedia Communication Power Aware

Computing , Kluwer Academic Publishers, 2002

WEB REFERENCES:

1. http://www.inf.ed.ac.uk/teaching/courses/eac/

96

13MI403 : INTERNET OF THINGS L T P C

(Common to M.E CSE & M.Tech IT) 3 0 0 3

COURSE OBJECTIVES:

To learn the basic issues, policy and challenges in the Internet

To understand the components and the protocols in Internet

To build a small low cost embedded system with the internet

To understand the various modes of communications with

internet

To learn to manage the resources in the Internet

To deploy the resources into business

To understand the cloud and internet environment

COURSE OUTCOMES:

At the end of this course the students will be able to:

Identify the components of IOT

Design a portable IOT using appropriate boards

Program the sensors and controller as part of IOT

Develop schemes for the applications of IOT in real time

scenarios

Establish the communication to the cloud through Wi-Fi /

Bluetooth

Manage the internet resources

Model the Internet of things to business


Definition – phases – Foundations – Policy– Challenges and Issues -

identification - security – privacy. Components in internet of things:

97

Control Units – Sensors – Communication modules – Power Sources

– Communication Technologies – RFID – Bluetooth – Zigbee – Wifi –

RF links – Mobile Internet – Wired Communication

UNIT II PROGRAMMING THE MICROCONTROLLER FOR

IOT

9

Basics of Sensors and actuators – examples and working principles of

sensors and actuators – Cloud computing and IOT –

Arduino/Equivalent Microcontroller platform – Setting up the board -

Programming for IOT – Reading from Sensors - Communication-

Connecting microcontroller with mobile devices – communication

through Bluetooth and USB – connection with the internet using WiFi /

Ethernet

UNIT III RESOURCE MANAGEMENT IN THE INTERNET

OF THINGS

9

Clustering - Software Agents - Data Synchronization - Clustering

Principles in an Internet of Things Architecture - The Role of Context -

Design Guidelines -Software Agents for Object - Data

Synchronization- Types of Network Architectures - Fundamental

Concepts of Agility and Autonomy-Enabling Autonomy and Agility by

the Internet of Things-Technical Requirements for Satisfying the New

Demands in Production - The Evolution from the RFID-based EPC

Network to an Agent based Internet of Things- Agents for the

Behaviour of Objects

UNIT IV BUSINESS MODELS FOR THE INTERNET OF

THINGS

9

The Meaning of DiY in the Network Society- Sensor-actuator

Technologies and Middleware as a Basis for a DiY Service Creation

Framework - Device Integration - Middleware Technologies Needed

for a DiY Internet of Things - Semantic Interoperability as a

98

Requirement for DiY Creation - Ontology- Value Creation in the

Internet of Things-Application of Ontology Engineering in the Internet

of Things-Semantic Web-Ontology - The Internet of Things in Context

of EURIDICE - Business Impact

UNIT V FROM THE INTERNET OF THINGS TO THE

WEB OF THINGS:

9

Resource-oriented Architecture and Best Practices- Designing

RESTful Smart Things - Web-enabling Constrained Devices - The

Future Web of Things - Set up cloud environment – send data from

microcontroller to cloud – Case study –CAM:cloud Assisted Privacy–

Other recent projects

TOTAL: 45 PERIODS

REFERENCE BOOKS:

1. Charalampos Doukas , “Building Internet of Things with the

Arduino”, Create space, April 2002

2. Dieter Uckelmann et.al, “Architecting the Internet of Things”,

Springer, 2011

3. Luigi Atzor et.al, “The Internet of Things: A survey”, Journal on

Networks, Elsevier Publications, October, 2010

4. Huang Lin, Gainesville, Jun Shao, Chi Zhang, Yuguang Fang,

“CAM: Cloud-Assisted Privacy Preserving Mobile Health

Monitoring”, IEEE Transactions on Information Forensics and

Security, 2013

5. Pengwei Hu; Fangxia Hu, “An optimized strategy for cloud

computing architecture”, 3rd IEEE Transactions on Computer

Science and Information Technology (ICCSIT), 2010

99

WEB REFERENCES:

1. http://postscapes.com/

2. http://www.theinternetofthings.eu/what-is-the-internet-of-things

13MI404: PERFORMANCE EVALUATION AND

RELIABILITY OF INFORMATION SYSTEMS

L T P C

3 0 0 3

COURSE OBJECTIVES:

Gain a basic understanding of probability theory and its

applications to networks

Gain an understanding of Markov chains, including Multi-

dimensional chains and their applications in the analysis of

computer networks

Gain an understanding of queuing system models such as M/M/1,

M/M/m/m and M/G/1 and their applications in the analysis of

computer networks

Gain an appreciation for the challenges in the analysis of network

of queues and some of the fundamental results in the field

including Burke’s theorem, Jackson’s theorem etc

Gain an understanding of the reliability basics, modeling and

analysis

COURSE OUTCOMES:

Apply the probability concepts in Networks

Demonstrate the usage of Markov Chains for the analysis of

networks

http://www.theinternetofthings.eu/what-is-the-internet-of-things

100

Able to select opt Queuing discipline for the real time network

applications

Appreciate the importance of Reliability concepts for Network

modeling

Solve research problems related to Computer networks and

evaluate its performance

Design of network of queues and analyze using Burke’s theorem,

Jackson’s theorem

UNIT I 9

Performance Characteristics – Requirement Analysis: Concepts –User,

Device, Network Requirements – Process –Developing RMA ,Delay,

Capacity Requirements – Flow Analysis –Identifying and Developing

Flows –Flow Models –Flow Prioritization –Specification

UNIT II 9

Random variables - Stochastic process –Link Delay components –

Queuing Models – Little’s Theorem – Birth & Death process – Queuing

Disciplines

UNIT III 9

Markovian FIFO Queuing Systems – M/M/1 – M/M/a – M/M/∞ - M/G/1 –

M/M/m/m and other Markov- Non-Markovian and self-similar models –

Network of Queues –Burke’s Theorem – Jackson’s Theorem

UNIT IV 9

Reliability and Availability concepts-failure Containment and

redundancy-Robust Design principles-Error detection-Analysing and

modelling reliability and robustness

101

UNIT V 9

Intelligent Packet Dropping for Optimal Energy-Delay Tradeoffs in

Wireless Downlinks- An Interacting Stochastic Models Approach for the

Performance Evaluation of DSRC Vehicular Safety Communication-

Performance modeling of epidemic routing

TOTAL: 45 PERIODS

REFERENCE BOOKS:

1. Eric Bauer, “Design for Reliability: Information and Computer-Based

Systems” ,Wiley-IEEE Press, 2011

2. James D.McCabe , “Network Analysis ,Architecture and Design” ,

Elsevier, Second Edition, 2003

3. Raj Jain,”The Art of Computer Systems Performance Analysis:

Techniques for Experimental Design, Measurement, Simulation, and

Modeling”,Wiley - Interscience, 1991

4. Bertsekas & Gallager , “Data Networks” , Pearson Education, second

edition, 2003

5. Sheldon Ross, “Introduction to Probability Models” , Academic

Press, New York, Eighth Edition, 2003

6. Nader F.Mir, “Computer and Communication Networks”, Pearson

Education, 2007

7. Paul J.Fortier, Howard E.Michel, “Computer Systems Performance

Evaluation and Prediction”, Elsevier,2003

8. Neely, M.J, “Intelligent Packet Dropping for Optimal Energy-Delay

Tradeoffs in Wireless Downlinks”, IEEE Transactions On Automatic

Control, VOL. 54, NO. 3, March 2009

9. Xiaoyan Yin, Xiaomin Ma, and Kishor S. Trivedi, “An Interacting

Stochastic Models Approach for the Performance Evaluation of

DSRC Vehicular Safety Communication”, IEEE Transactions On

102

Computers, Vol. 62, No. 5, May 2013

10. Yin-Ki LP, Wing-Cheong Lau ; On-Ching Yue, “Performance

modeling of epidemic routing with Heterogeneous Node Types”,

IEEE International Conference on Communications, 2008

13MI405: NEXT GENERATION WIRELESS

NETWORKS

L T P C

3 0 0 3

COURSE OBJECTIVES:

To learn various generations of wireless and cellular networks

To study about fundamentals of 3G Services, its protocols and

applications

To study about evolution of 4G Networks, its architecture and

applications

To study about WiMAX networks, protocol stack and standards

To Study about Spectrum characteristics & Performance evaluation

COURSE OUTCOMES:

Acquaint with the latest 3G/4G and WiMAX networks and its

architecture

Illustrate the implications of various layers in Wireless networks

Able to design and implement wireless network environment for any

application using latest wireless protocols and standards

Analyze the performance of networks

Exploit various diversity schemes in LTE

http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Yin-Ki%20Ip.QT.&searchWithin=p_Author_Ids:37690635100&newsearch=true

http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Wing-Cheong%20Lau.QT.&searchWithin=p_Author_Ids:37347515600&newsearch=true

http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.On-Ching%20Yue.QT.&searchWithin=p_Author_Ids:37294255400&newsearch=true



103


Introduction- History of mobile cellular systems-First Generation, Second

Generation- Generation 2.5, Overview of 3G & 4G, 3GPP and 3GPP2

standards

UNIT II 3G NETWORKS 9

3G Networks- Evolution from GSM- 3G Services & Applications- UMTS

network structure- Core network- UMTS Radio access- HSPA – HSUPA-

HSDPA- CDMA 1X - EVDO Rev 0- Rev A- Rev B- Rev C Architecture-

protocol stack

UNIT III 4G LTE NETWORKS 9

LTE- Introduction- Radio interface architecture- Physical layer- Access

procedures- System Architecture Evolution(SAE) - Algorithms for

Enhanced Inter-Cell Interference Coordination

UNIT IV WIMAX NETWORKS 9

WiMax- Introduction – IEEE 802.16- OFDM- MIMO- IEEE 802.20- Burst

Construction Algorithm for IEEE 802.16

UNIT V SPECTRUM & PERFORMANCE 9

Spectrum for LTE-Flexibility-Carrier Aggregation-Multi standard Radio

base stations-RF requirements for LTE-Power level requirements-

Emission requirements-Sensitivity and Dynamic range-Receiver

susceptibility. Performance Assessment-Performance Evaluation-

Resource Scheduling scheme for Career aggregation

TOTAL: 45 PERIODS

REFERENCE BOOKS:

1. Juha Korhonen, “Introduction to 3G Mobile Communication”, Artech

House, (www.artechhouse.com), Jan 2003

104

2. Erik Dahlman, Stefan Parkvall, Johan Skold, “4G LTE/LTE – Advanced

for Mobile Broadband”, Academic Press 2011

3. Erik Dahlman, Stefan Parkvall, Johan Skold and Per Beming, “3G

Evolution HSPA and LTE for Mobile Broadband”, Academic Press,

Oct 2008

4. Flavio Muratore, “UMTS Mobile Communication for the Future”, John

Wiley & Sons Ltd, Jan 2001

5. Joo-Young Baek, Young-Joo Suh, “Heuristic Burst Construction

Algorithm for Improving Downlink Capacity in IEEE 802.16 OFDMA

Systems”, IEEE Transactions on Mobile Computing 2012 ,Volume:

11, Issue: 1

6. Monogioudis, P., Miernik, J., Seymour, J.P., “Algorithms for Enhanced

Inter-Cell Interference Coordination (eICIC) in LTE HetNets”, Deb, S.;

IEEE/ACM Transactions on Networking, (Volume:PP , Issue: 99 )

March 2013

7. Na Lei; Caili Guo ; Chunyan Feng ; Yu Chen, “A multi-user MIMO

resource scheduling scheme for carrier aggregation scenario”,

International Conference on Wireless Communications and Signal

Processing (WCSP), 2011

13MI406 : PERVASIVE COMPUTING L T P C

3 0 0 3

COURSE OBJECTIVES:

To understand the basics of Pervasive Computing

To learn the role of Sensor & Mesh networks in Pervasive

Computing

To realize the Scope of Context Aware & Wearable Computing

105

To Address the Security issues in Pervasive networks

To develop Mobile applications using Programming

COURSE OUTCOMES:

Exploit the usage of Pervasive Computing in real time applications

Analyze the implications of various layers in Sensor & Mesh

networks

Develop Pervasive computing environment using sensor and mesh

networks

Develop applications based on the paradigm of context aware

computing & wearable computing

Master the security violations and issues in Pervasive Networks

Design applications using android & iOS

UNIT I PERVASIVE COMPUTING AND SYSTEMS 9

Introduction- Tools and Techniques for Dynamic Reconfiguration and

Interoperability of Pervasive Systems-Models for Service and Resource

Discovery-Pervasive Learning Tools and Technologies- Service

Management in Pervasive Computing Environments

UNIT II SENSOR AND MESH NETWORKS 9

Sensor Networks – Role in Pervasive Computing – In Network

Processing and Data Dissemination – Sensor Databases – Data

Management in Wireless Mobile Environments – Wireless Mesh

Networks – Architecture – Mesh Routers – Mesh Clients – Routing –

Cross Layer Approach – Security Aspects of Various Layers in WMN –

Applications of Sensor and Mesh networks

UNIT III CONTEXT AWARE COMPUTING

& WEARABLE COMPUTING

9

Adaptability – Mechanisms for Adaptation - Functionality and Data –

106

Transcoding – Location Aware Computing – Location Representation –

Localization Techniques – Triangulation and Scene Analysis – Delaunay

Triangulation and Voronoi graphs – Types of Context – Role of Mobile

Middleware – Adaptation and Agents – Service Discovery Middleware-

Health BAN- Medical and Technological Requirements-Wearable

Sensors-Intra-BAN communications, Body area network for wireless

patient monitoring

UNIT IV PERVASIVE NETWORKING SECURITY 9

Security and Privacy in Pervasive Networks – Wormhole attacks in

Pervasive Networks- Defense strategies for Network security – Smart

Devices, Systems and Intelligent Environments-Autonomic and Pervasive

Networking – Adaptive architecture of Service Component –Probabilistic

k-Coverage in Pervasive Wireless Sensor Networks-Performance

Evaluation of Pervasive Networks Based on WiMAX Networks-

lmplementation Framework for Mobile and Pervasive Frameworks

UNIT V APPLICATION DEVELOPMENT 9

Three tier architecture - Model View Controller Architecture - Memory

Management – Information Access Devices – PDAs and Smart Phones –

Smart Cards and Embedded Controls – J2ME – Programming for CLDC

– GUI in MIDP – Application Development on Android and iPhone

TOTAL: 45PERIODS

REFERENCE BOOKS:

1. Asoke K Talukder, Hasan Ahmed, Roopa R Yavagal, “Mobile

Computing: Technology, Applications and Service Creation”, Tata

McGraw Hill, Second Edition, 2010

2. Mohammad S. Obaidat et al, “Pervasive Computing and

Networking”, John wiley 2011

3. Reto Meier, “Professional Android 2 Application Development”,

Wrox Wiley,2010

107

4. Pei Zheng and Lionel M Li, “Smart Phone & Next Generation Mobile

Computing”, Morgan Kaufmann Publishers, 2006

5. Frank Adelstein, “Fundamentals of Mobile and Pervasive

Computing”, TMH, 2005

6. Jochen Burthardt et al, “Pervasive Computing: Technology and

Architecture of Mobile Internet Applications”, Pearson Education,

2003

7. Feng Zhao and Leonidas Guibas, “Wireless Sensor Networks”,

Morgan Kaufmann Publishers, 2004

8. Reto Meier, “Professional Android 2 Application Development”,

Wrox Wiley,2010

9. Stefan Poslad, “Ubiquitous Computing: Smart Devices,

Environments and Interactions”, Wiley, 2009

10. Monton, E. ; Hernandez, J.F. ; Blasco, J.M. ; Herve, T. ;

Micallef, J. ; Grech, I. ; Brincat, A. ; Traver, V., “Body area network

for wireless patient monitoring”, IET Communications, Volume: 2 ,

Issue: 2 , 2008

13MI407: MULTIMEDIA TECHNOLOGIES L T P C

3 0 0 3

COURSE OBJECTIVES:

To familiarize with various elements of multimedia

To analyse various multimedia systems

To use various tools for developing multimedia

To develop a multimedia application

108

COURSE OUTCOMES:

Apply MPEG and CD standards in multimedia creation

Use various authoring and editing tools

Develop animation, images, Sound using Multimedia Tools

Develop a multimedia application


Introduction to Multimedia – Characteristics – Utilities – Creation -Uses –

Promotion – Digital Representation – Media and Data streams –

Multimedia Architecture – Multimedia Documents

UNIT II ELEMENTS OF MULTIMEDIA 11

Multimedia Building Blocks: Text - Graphics - Video Capturing - Sound

Capturing - Editing - Introduction to 2D & 3D Graphics -surface

characteristics and texture - lights - Animation: key frames & Tweening,

techniques - principles of animation - 3D animation - file formats

UNIT III MULTIMEDIA SYSTEMS 9

Visual Display Systems – CRT - video adapter card - video adapter cable

– LCD – PDP - optical storage media - CD technology - DVD Technology

- Compression Types and Techniques – CODEC - GIF coding standards

– lossy and lossless – JPEG - MPEG-1 - MPEG-2 - MP3 - Fractals –

MMDBS

UNIT IV MULTIMEDIA TOOLS 9

Authoring tools – features and types - card and page based tools - icon

and object based tools - time based tools - cross platform authoring tools

– Editing tools - text editing and word processing tools - OCR software -

painting and drawing tools - 3D modelling and animation tools - image

editing tools –sound editing tools - digital movie tools – plug -ins and

delivery vehicles for www

109

UNIT V MULTIMEDIA APPLICATION DEVELOPMENT 9

Software life cycle – ADDIE Model – conceptualization – content

collection and processing – story – flowline – script - storyboard -

implementation - multiplatform issues – authoring – Sketch-Based

Annotation and Visualization in Video Authoring - metaphors – testing –

report writing - documentation - case study -Web Application – Console

Application – Distributed Application – Mobile Application - games

consoles – iTV – kiosks –education

TOTAL: 45PERIODS

REFERENCE BOOKS:

1. Parekh R, “Principles Of Multimedia” Tata McGraw-Hill, 2006

2. Ralf Steinmetz, Klara Nahrstedt, “Multimedia:Computing,

Communications and Applications” Prentice Hall, 1995

3. John Villamil, Louis Molina “Multimedia -An Introduction”, Prentice

Hall, New Delhi,1998

4. Tay Vaughan, “Multimedia: Making It Work” McGraw-Hill Professional,

2006

5. Deitel & Deitel “Internet & World Wide Web How to Program”, Fourth

Edition – Prentice Hall, 2008

6. David Pizzi, Jean-Luc Lugrin, Alex Whittaker, and Marc Cavazza ,

”Automatic Generation of Game Level Solutions as Storyboards” IEEE

Transactions on Computational Intelligence and AI in Games, Vol. 2,

No. 3, September 2010

7. Bo-Wei Chen, Jia-Ching Wang, and Jhing-Fa Wang, Fellow, IEEE ”A

Novel Video Summarization Based on Mining the Story-Structure and

Semantic Relations Among Concept Entities” IEEE Transactions on

Multimedia, Vol. 11, NO. 2, February 2009

8. 7. Cui-Xia Ma, Yong-Jin Liu, Hong-An Wang, Dong-Xing Teng, and

Guo-Zhong Dai “Sketch-Based Annotation and Visualization in Video

110

Authoring” IEEE Transactions on Multimedia, Vol. 14, No. 4, August

2012

13MI408: VIDEO ANALYTICS

(Common to M.E CSE / M.Tech IT)

L T P C

3 0 0 3

COURSE OBJECTIVES:

To know the fundamental concepts of big data and analytics

To learn various techniques for mining data streams

To acquire the knowledge of extracting information from surveillance

videos

To learn Event Modelling for different applications

To understand the models used for recognition of objects in videos

COURSE OUTCOMES:

1. Work with big data platform and its analysis techniques

2. Design efficient algorithms for mining the data from large volumes

3. Work with surveillance videos for analytics

4. Design optimization algorithms for better analysis and recognition of

objects in a scene

5. Model a framework for Human Activity Recognition

UNIT I INTRODUCTION TO BIG DATA & DATA ANALYSIS 9

Introduction to Big Data Platform – Challenges of Conventional systems–

Web data- Evolution of Analytic scalability- analytic processes and tools-

Analysis Vs Reporting- Modern data analytic tools- Data Analysis:

Regression Modeling- Bayesian Modeling- Rule induction

111

UNIT II MINING DATA STREAMS 9

Introduction to Stream concepts- Stream data model and architecture –

Stream Computing- Sampling data in a Stream- Filtering Streams-

Counting distinct elements in a Stream- Estimating moments- Counting

oneness in a window- Decaying window- Real time Analytics

platform(RTAP) applications- case studies

UNIT III VIDEO ANALYTICS 9

Introduction- Video Basics - Fundamentals for Video Surveillance- Scene

Artifacts- Object Detection and Tracking: Adaptive Background Modelling

and Subtraction- Pedestrian Detection and Tracking-Vehicle Detection and

Tracking- Articulated Human Motion Tracking in Low-Dimensional Latent

Spaces

UNIT IV BEHAVIOURAL ANALYSIS & ACTIVITY

RECOGNITION

9

Event Modelling- Behavioural Analysis- Human Activity Recognition-

Complex Activity Recognition- Activity modelling using 3D shape, Video

summarization, shape based activity models- Suspicious Activity Detection

UNIT V HUMAN FACE RECOGNITION & GAIT ANALYSIS 9

Introduction- Overview of Recognition algorithms – Human Recognition

using Face-Face Recognition from still images, Face Recognition from

video, Evaluation of Face Recognition Technologies- Human Recognition

using gait- HMM Framework for Gait Recognition, View Invariant Gait

Recognition, Role of Shape and Dynamics in Gait Recognition - Factorial

HMM and Parallel HMM for Gait Recognition- Face Recognition

Performance- Role of Demographic Information

TOTAL: 45 PERIODS

112

REFERENCE BOOKS:

1. Michael Berthold, David J.Hand, “Intelligent Data Analysis”, Springer,

2007

2. Anand Rajaraman and Jeffrey David Ullman, “Mining of Massive

Datasets”, Cambridge University Press, 2012

3. Yunqian Ma, Gang Qian, “Intelligent Video Surveillance: Systems and

Technology”, CRC Press (Taylor and Francis Group), 2009

4. Rama Chellappa, Amit K.Roy-Chowdhury, Kevin Zhou.S, “Recognition

of Humans and their Activities using Video”, Morgan&Claypool

Publishers, 2005

5. YiHuang,DongXu and Tat-Jen Cham, “Face and Human Gait

Recognition Using Image-to-Class Distance” IEEE Transactions On

Circuits And Systems For Video Technology, Vol. 20, No. 3, March 2010

6. Changhong Chen, Jimin Liang, Heng Zhao, Haihong Hu, and Jie Tian,

“Factorial HMM and Parallel HMM for Gait Recognition”, IEEE

Transactions On Systems, Man, And Cybernetics—Part C: Applications

And Reviews, Vol. 39, No. 1, January 2009

7. Changhong Chen, Jimin Liang, Haihong Hu, Licheng Jiao, Xin

Yang, “Factorial Hidden Markov Models for Gait Recognition”, Advances

in Biometrics Lecture Notes in Computer Science Volume

4642, 2007, pp 124-133

8. Brendan F. Klare, Mark J. Burge, Joshua C. Klontz, Richard W. Vorder

Bruegge, and Anil K. Jain, “Face Recognition Performance: Role of

Demographic Information” IEEE Transactions On Information Forensics

And Security, Vol. 7, No. 6, December 2012

http://link.springer.com/search?facet-author=%22Haihong+Hu%22

113

13MI409 : WIRELESS SENSOR NETWORKS

(Common to M.E CSE / M.Tech IT)

L T P C

3 0 0 3

COURSE OBJECTIVES:

To understand the basics of Sensor Networks

To learn various fundamental and emerging protocols of all layers

To study about the issues pertaining to major obstacles in

establishment and efficient management of sensor networks

To understand the nature and applications of sensor networks

To understand various security practices and protocols of Sensor

Networks

COURSE OUTCOMES:

Analyze various protocols and its issues

Implement various routing protocols for Sensor networks

Use various security techniques in WSN

Create a Sensor network environment for different type of

applications

UNIT I SENSOR NETWORKS FUNDAMENTALS AND

ARCHITECTURE

9

Introduction and Overview of WSN’s - Application of WSN’s - Challenges

for Wireless Sensor Networks - Enabling Technologies for Wireless

Sensor Networks - Node Architecture - Sensing Subsystem - Processing

Subsystem - Communication Interfaces - Prototypes

UNIT II NETWORKING SENSORS 9

Fundamentals of (Wireless) MAC Protocols - Low duty cycle protocols

and wakeup concepts - Contention based Protocols Naming and

Addressing – Fundamentals - Address and Name Management in WSN-

114

Assignment of MAC Addresses - Content based and geographic

addressing

UNIT III SENSOR NETWORK MANAGEMENT AND

PROGRAMMING

9

Sensor Management - Topology Control Protocols and Sensing Mode

Selection Protocols - Time Synchronization - Sender/sender

synchronization - Sender/receiver synchronization - Localization and

Positioning – Operating Systems and Sensor Network Programming –

Sensor Network Simulators - A Lightweight and Energy-Efficient

Architecture for WSN’s

UNIT IV SENSOR NETWORK DATABASES, PLATFORMS

AND TOOLS

9

Sensor Database Challenges – Querying – Aggregation - Sensor Node

Hardware – Berkeley Motes - Programming Challenges - Node-level

software platforms - Node-level Simulators - State-centric programming.

UNIT V SENSOR NETWORK SECURITY 9

Security in Sensor Networks - Challenges of Security in WSNs - Security

Attacks in sensor networks - Detecting and Localizing Identity - Based

Attacks in WSN - Protocols and Mechanisms for security - Symmetric and

Public key Cryptography - Key Management - Low-Energy Symmetric

Key Distribution in Wireless Sensor Networks - Defenses against attacks

- Secure Protocols- TinySec – SPINS - Localized Encryption and

Authentication Protocol - IEEE 802.15.4 and Zigbee security

TOTAL: 45 PERIODS

REFERENCE BOOKS:

1. Kazem Sohraby, Daniel Minoli,Taieb Znati, “Wireless Sensor

Networks: Technology, Protocols, and Applications” John Wiley &

115

Sons, Inc .2007

2. Holger Karl, Andreas willig, “Protocols and Architectures for Wireless

Sensor Networks”, John Wiley & Sons, Inc .2005

3. Erdal Çayırcı , Chunming Rong, “Security in Wireless Ad Hoc and

Sensor Networks”, John Wiley and Sons, 2009

4. Waltenegus Dargie, Christian Poellabauer, “Fundamentals of Wireless

Sensor Networks Theory and Practice”, John Wiley and Sons, 2010

5. Miguel A. Lopez-Gomez, Juan C. Tejero-Calado, “A Lightweight and

Energy-Efficient Architecture for Wireless Sensor Networks “IEEE

Transactions on Consumer Electronics, Vol. 55, No. 3, August 2009

6. Yingying Chen, Jie Yang, Wade Trappe, Richard P. Martin, “Detecting

and Localizing Identity-Based Attacks in Wireless and Sensor

Networks “, IEEE transactions on vehicular technology, Vol. 59, No.

5,June 2010

7. Kealan McCusker, Noel E. O’Connor, ”Low-Energy Symmetric Key

Distribution in Wireless Sensor Networks” , IEEE Transactions On

Dependable and Secure Computing, Vol. 8, No. 3, May/June 2011

13MI410: IMAGE PROCESSING AND PATTERN

ANALYSIS

L T PC

3 0 0 3

COURSE OBJECTIVES:

To introduce the student to various Image processing and Pattern

recognition techniques

To study the Image fundamentals

To study the mathematical morphology necessary for Image

processing and Image segmentation

To study the Image Representation and description and feature

extraction

116

To study the principles of Pattern Recognition

To know the various applications of Image processing

COURSE OUTCOMES:

Enhance the image for better appearance

Segment the image using different techniques

Represent the images in different forms

Develop algorithms for Pattern Recognition

Extract and deploy the features in various Image processing

applications


Elements of an Image Processing System - Mathematical Preliminaries-

Image Enhancement - Gray scale Transformation - Piecewise Linear

Transformation-Bit Plane Slicing- Histogram Equalization - Histogram

Specification - Enhancement by Arithmetic Operations - Smoothing

Filter -Sharpening Filter- Image Blur Types and Quality Measures

UNIT II MATHEMATICAL MORPHOLOGY and IMAGE

SEGMENTATION

9

Binary Morphology - Opening and Closing - Hit-or-Miss Transform- Gray

scale Morphology - Basic morphological Algorithms - Morphological

Filters- Thresholding - Object (Component) Labeling - Locating Object

Contours by the Snake Model - Edge Operators - Edge Linking by

Adaptive Mathematical morphology - Automatic Seeded Region Growing-

A Top-Down Region Dividing Approach

UNIT III IMAGE REPRESENTATION AND DESCRIPTION

and FEATURE EXTRACTION

9

Run-Length Coding - Binary Tree and Quadtree - Contour

Representation- Skeletonization by Thinning- Medial Axis

117

Transformation-Object Representation and Tolerance- Fourier Descriptor

and Moment Invariants-Shape Number and Hierarchical Features-Corner

Detection- Hough Transform-Principal Component Analysis-Linear

Discriminate Analysis- Feature Reduction in Input and Feature Spaces

UNIT IV PATTERN RECOGNITION 9

The Unsupervised Clustering Algorithm - Bayes Classifier- Support

Vector Machine - Neural Networks - The Adaptive Resonance Theory

Network - Fuzzy Sets in Image Analysis - Document image processing

and classification - Block Segmentation and Classification - Rule-Based

Character Recognition system - Logo Identification - Fuzzy Typographical

analysis for Character Pre-classification - Fuzzy Model for Character

Classification

UNIT V APPLICATIONS 9

Face and Facial Feature Extraction - Extraction of Head and Face

Boundaries and Facial Features - Recognizing Facial Action Units -

Facial Expression Recognition in JAFFE Database - Image

Steganography - Types of Steganography - Applications of

Steganography - Embedding Security and Imperceptibility - Examples of

Steganography Software - Genetic Algorithm Based Steganography -

Robust Face Recognition for Uncontrolled Pose and Illumination

Changes

TOTAL: 45 PERIODS

REFERENCE BOOKS:

1. Frank Y Shih, “Image Processing and Pattern Recognition:

Fundamentals and Techniques”, Willey IEEE Press, April 2010

2. Rafael C. Gonzalez, Richard E. Woods, Steven Eddins, ”Digital

Image Processing using MATLAB”, Pearson Education, Inc., 2004

3. D.E. Dudgeon and R.M. Mersereau, “Multidimensional Digital

118

Signal Processing”, Prentice Hall Professional Technical Reference,

1990

4. William K. Pratt, “Digital Image Processing”, John Wiley, New York,

2002

5. Milan Sonka et al, “Image Processing, Analysis and Machine

Vision”, Brookes/Cole, Vikas Publishing House, 2nd edition, 1999;

6. Sid Ahmed, M.A., “Image Processing Theory, Algorithms and

Architectures”, McGrawHill, 1995

7. Maria De Marsico, Michele Nappi, Daniel Riccio, and Harry

Wechsler “Robust Face Recognition for Uncontrolled Pose and

Illumination Changes” IEEE transactions on Systems, Man, and

Cybernetics: Systems, Vol. 43, no. 1, January 2013

13MI411 : MACHINE LEARNING

L T P C

3 0 0 3

COURSE OBJECTIVES :

To understand the concepts of machine learning

To appreciate supervised and unsupervised learning and their

applications

To understand the theoretical and practical aspects of Probabilistic

Graphical Models

To appreciate the concepts and algorithms of reinforcement

learning

To learn aspects of computational learning theory

119

COURSE OUTCOMES :

Examine the basic concepts of machine learning

Apply supervised learning algorithms for given application

Implement unsupervised learning algorithms for any application

Use probabilistic graphical model for a sequence type of

application

Analyse different types of emerging machine learning algorithms


Machine Learning - Machine Learning Foundations –Overview – Types of

Machine Learning - Basic Concepts in Machine Learning - Examples of

Machine Learning - Applications - Linear Models for Regression -

Linear Basis Function Models - The Bias-Variance Decomposition -

Bayesian Linear Regression - Bayesian Model Comparison

UNIT II SUPERVISED LEARNING 9

Linear Models for Classification – Discriminant Functions - Probabilistic

Generative Models - Probabilistic Discriminative Models - Bayesian

Logistic Regression - Neural Networks - Feed-Forward Network

Functions - Error Back-Propagation - Regularization - Mixture Density

and Bayesian Neural Networks - Kernel Methods - Dual Representations

- Radial Basis Function Networks - Gaussian Processes

UNIT III UNSUPERVISED LEARNING 9

Clustering- K-means - EM - Mixtures of Gaussians - The EM Algorithm in

General -Model Selection for Latent Variable Models - High-Dimensional

Spaces - The Curse of Dimensionality -Dimensionality Reduction - Factor

Analysis - Principal Component Analysis - Probabilistic PCA- Kernel PCA

120

UNIT IV PROBABILISTIC GRAPHICAL MODELS 9

Directed Graphical Models - Bayesian Networks - Exploiting

Independence Properties - From Distributions to Graphs - Examples -

Markov Random Fields - Inference in Graphical Models - Learning –Naive

Bayes Classifiers - Markov Models – Hidden Markov Models – Inference

– Learning- Generalization – Undirected graphical models - Markov

Random Fields- Conditional Independence Properties - Parameterization

of MRFs - Examples - Learning - Conditional Random Fields (CRFs) -

Structural SVMs- Pedestrian Detection for Advanced Driver Assistance

UNIT V ADVANCED LEARNING 9

Sampling – Basic sampling methods – Monte Carlo - Reinforcement

Learning - K-Armed Bandit- Elements - Model-Based Learning - Value

Iteration- Policy Iteration - Temporal Difference Learning- Exploration

Strategies- Deterministic and Non-deterministic Rewards and Actions-

Eligibility Traces- Generalization- Partially Observable States- The

Setting- Example- Stochastic Competitive Learning in Complex Network

TOTAL: 45 PERIODS

REFERENCES:

1. Christopher Bishop, “Pattern Recognition and Machine Learning”

Springer, 2006

2. Kevin P. Murphy, “Machine Learning: A Probabilistic Perspective”,

MIT Press, 2012

3. Ethem Alpaydin, “Introduction to Machine Learning”, Prentice Hall

of India, 2005

4. Tom Mitchell, "Machine Learning", McGraw-Hill, 1997

5. Hastie, Tibshirani, Friedman, “The Elements of Statistical Learning”

Springer, Second Edition, 2008

6. Stephen Marsland, “Machine Learning – An Algorithmic

121

Perspective”, CRC Press, 2009

7. Silva T.C & Liang Zhango,” Stochastic Competitive Learning in

Complex Network”, IEEE Transactions on Neural Networks and

Learning Systems, Vol.23, Issue 3 pp: 385-398, 2012

8. Geronimo et al, “Survey of pedestrian Detection for Advanced

Driver Assistance Systems”, IEEE Transactions on Pattern Analysis

and Machine Intelligence, Vol. 32, Issue 7 , pp 1239-1258, 2010

WEB REFERENCES :

1. http://see.stanford.edu/see/materials/aimlcs229/handouts.aspx

2. http://www.holehouse.org/mlclass/

3. http://ciml.info/

4. http://ocw.mit.edu/courses/electrical-engineering-and-computer-

science/6-867-machine-learning-fall-2006/lecture-notes/

5. http://cs229.stanford.edu/materials.html

13MI412 :VIRTUALIZATION TECHNIQUES

L T P C

3 0 0 3

COURSE OBJECTIVES :

To understand the concept of virtualization

To understand the various issues in virtualization

To familiarize themselves with the types of virtualization

To compare and analyze various virtual machines products

COURSE OUTCOMES :

Identify different VM types and enumerate on Virtual File

Systems

http://see.stanford.edu/see/materials/aimlcs229/handouts.aspx

http://www.holehouse.org/mlclass/

http://ciml.info/

http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/lecture-notes/

http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/lecture-notes/

http://cs229.stanford.edu/materials.html

122

Apply Dynamic Binary Optimization

Identify network virtualization techniques

Apply virtualization for storage

Use various virtual machine products

UNIT I VIRTUAL MACHINES 9

Anatomy of virtual machines – VM types – virtual disk types –

networking – hardware – VM Products – Installing VM Applications

on Desktop – Virtual File Systems – DFS – Windows DFS - Linux

DFS – Kerberos Authentication – Samba DFS share – AFS

Implementation – Creating load balanced clusters – Round robin

DNS – Planning load balanced clusters – Windows NLB clusters –

Linux virtual server Clusters - Building Virtual machine clusters

UNIT II BINARY TRANSLATION AND

OPTIMIZATION

9

Virtual Machine basics – Interpretation – Interpreting Complex

Instruction Set – Binary Translation – Dynamic Translation –

Instruction Set issues – case Study

Dynamic Binary Optimization: Program behaviour – profiling –

optimizing translation blocks – framework – code reordering –

optimization – ISA optimization system – VM Architecture: Object

oriented high level language virtual machines – JVM architecture –

Microsoft Common Language Infrastructure

UNIT III NETWORK VIRTUALIZATION 10

Design of Scalable Enterprise Networks - Virtualizing the Campus

WAN Design – WAN Architecture - WAN Virtualization - Virtual

Enterprise Transport Virtualization–VLANs and Scalability -

Theory Network Device Virtualization Layer 2 - VLANs Layer 3

VRF Instances Layer 2 - VFIs Virtual Firewall Contexts Network

123

Device Virtualization – DataPath Virtualization Layer 2: 802.1q -

Trunking Generic Routing Encapsulation - IPsec L2TPv3 Label

Switched Paths - Control-Plane Virtualization–Routing Protocols-

VRF - Aware Routing Multi-Topology Routing

UNIT IV VIRTUALIZING STORAGE 8

SCSI- Speaking SCSI- Using SCSI buses – Fiber Channel – Fiber

Channel Cables –Fiber Channel Hardware Devices – iSCSI

Architecture – Securing iSCSI – SAN backup and recovery

techniques – RAID – SNIA Shared Storage Model – Classical

Storage Model – SNIA Shared Storage Model – Host based

Architecture – Storage based architecture – Network based

Architecture – Fault tolerance to SAN – Performing Backups –

Virtual tape libraries

UNIT V VIRTUAL MACHINES PRODUCTS 9

Xen Virtual machine monitors- Xen API – VMware – VMware

products - VMware Features – Microsoft Virtual Server –

Features of Microsoft Virtual Server

TOTAL: 45Periods

REFERENCES:

1. William von Hagen, “Professional Xen Virtualization”, Wrox

Publications, January, 2008

2. Chris Wolf , Erick M. Halter, “Virtualization: From the Desktop

to the Enterprise”, APress, 2005

3. Kumar Reddy, Victor Moreno, “Network virtualization”, Cisco

Press, July, 2006

4. James E. Smith, Ravi Nair, “Virtual Machines: Versatile

Platforms for Systems and Processes”, Elsevier/Morgan

Kaufmann, 2005

124

5. David Marshall, Wade A. Reynolds, “Advanced Server

Virtualization: VMware and Microsoft Platform in the Virtual

Data Center”, Auerbach Publications, 2006

13MI413: SOFTWARE AGENTS L T P C

3 0 0 3

COURSE OBJECTIVES :

To understand the basic concepts of software agents

To learn the software agents for cooperative learning

To have an understanding of multi agent systems

To know how software agents communicates and collaborates with

each other

To learn the mobile agents and its security

COURSE OUTCOMES :

Analyze how the transformation occurs from direct manipulation to

delegation

Apply the software agents for cooperative learning

Analyze the interaction between various agents

Develop a Intelligent agent-based system using a contemporary

agent development platform

Apply black box security to authenticate the agents

UNIT I AGENT AND USER EXPERIENCE 9

Interacting with Agents - Agent from Direct Manipulation to Delegation -

Interface Agent Metaphor with Character - Designing Agents - Direct

125

Manipulation versus Agent Path to Predictable

UNIT II AGENTS FOR LEARNING IN INTELLIGENT

ASSISTANCE

9

Agents for Information Sharing and Coordination - Agents that Reduce

Work Information Overhead - Agents without Programming Language -

Life like Computer character - S/W Agents for cooperative Learning -

Architecture of Intelligent Agents - Agents for Information Gathering -

Open Agent Architecture - Communicative Action for Artificial Agent

UNIT III MULTIAGENT SYSTEMS 9

Interaction between agents – Reactive Agents – Cognitive Agents –

Interaction protocols – Agent coordination – Agent negotiation – Agent

Cooperation – Agent Organization – Self-Interested agents in Electronic

Commerce Applications

UNIT IV INTELLIGENT SOFTWARE AGENTS 9

Interface Agents – Agent Communication and Collaboration - Overview of

Agent Oriented Programming - Agent Communication Language - Agent

Based Framework of Interoperability – Agent Knowledge Representation

– Agent Adaptability – Belief Desire Intention

UNIT V MOBILE AGENTS AND SECURITY 9

Mobile Agent Paradigm - Mobile Agent Concepts -Mobile Agent

Technology-– Mobile Agent Applications - Case Study -Tele Script -

Agent Tel -Agent Security Issues – Mobile Agents Security – Protecting

Agents against Malicious Hosts – Untrusted Agent – Black Box Security –

Authentication for agents - Hierarchical semantic processing architecture

for smart sensors in surveillance networks - Transparent synchronization

protocols for compositional real-time systems

TOTAL: 45 PERIODS

126

REFERENCES

1. Jeffrey M.Bradshaw," Software Agents ", MIT Press, 2000

2. William R. Cockayne, Michael Zyda, "Mobile Agents", Prentice Hall,

1998

3. Joseph P.Bigus & Jennifer Bigus, "Constructing Intelligent agents

with Java: A Programmer's Guide to Smarter Applications ", Wiley,

1997

4. Russel, Norvig, "Artificial Intelligence: A Modern Approach",

Pearson Education, Second Edition, 2003.

5. Richard Murch, Tony Johnson, "Intelligent Software Agents",

Prentice Hall, 2000.

6. Gerhard Weiss, “Multi Agent Systems – A Modern Approach to

Distributed Artificial Intelligence”, MIT Press, 2000.

7. D. Bruckner , C. Picus , R. Velik , W. Herzner and G. Zucker

"Hierarchical semantic processing architecture for smart sensors in

surveillance networks", IEEE Trans. Ind. Inf., vol. 8, no. 2, pp.291

-301 2012

8. M. M. H. P. vanden Heuvel , R. J. Bril and J. J. Lukkien ,

"Transparent synchronization protocols for compositional real-time

systems", IEEE Trans. Ind. Inf., vol. 8, no. 2, pp.322 -336 2012

WEB REFERENCES

1. www.csdl.tamu.edu

2. www.csc.ncsu.edu

3. www.cs.cmu.edu

4. www.cse.fau.edu

127

13MI414: AUTOMATA THEORY AND

COMPILER DESIGN

L T P C

3 0 0 3

COURSE OBJECTIVES :

To understand the basic properties of formal languages and formal

grammars

To understand the relation between types of languages and types

of finite automata

To understand basic properties of Turing machines and computing

with Turing machines

To enrich the knowledge in various phases of compiler and its use

To extend the knowledge of parser

COURSE OUTCOMES :

Design finite automata for a given language

Construct a Turing machine for a given input

Construct the LR and SLR parser for the given grammar

Analyze type checking system and run time environments

Apply code optimization techniques to improve the program

performance

UNIT I AUTOMATA 9

Introduction to formal proof – Additional forms of proof – Inductive proofs

–Finite Automata (FA) – Deterministic Finite Automata (DFA) – Non-

deterministic Finite Automata (NFA) – Finite Automata with Epsilon

transitions- Equivalence and minimization of Automata

128

UNIT II CONTEXT-FREE GRAMMARS AND

LANGUAGES

9

Context-Free Grammar (CFG) – Parse Trees – Ambiguity in grammars

and languages – Definition of the Pushdown automata – Languages of a

Pushdown Automata – Equivalence of Pushdown automata and CFG–

Deterministic Pushdown Automata- Normal forms for CFG – Pumping

Lemma for CFL – Closure Properties of CFL – Turing Machines –

Programming Techniques for TM

UNIT III BASICS OF COMPILATION 9

Compilers – Analysis of source program – Phases of a compiler –

Grouping of phases – Compiler construction tools – Lexical Analyzer :

Token Specification – Token Recognition – A language for Specifying

lexical analyzer – Top down parser : Table implementation of Predictive

Parser – Bottom up Parser : SLR(1) Parser – Parser generators

UNIT IV TYPE CHECKING AND RUNTIME

ENVIRONMENTS

9

Syntax directed definitions – Construction of syntax trees – Type systems

– Specification of a simple type checker- Equivalence of type expressions

– Type conversions – Attribute grammar for a simple type checking

system – Runtime Environments - Source language issues – Storage

organization – Storage allocation strategies – Parameter parsing

UNIT V CODE GENERATION AND OPTIMIZATION 9

Issues in the design of a code generator- -A simple code generator-The

DAG representation of basic blocks - Generating code from DAG –

Dynamic programming code generation algorithm – Code generators -

The principle sources of optimization-Peephole optimization- Optimization

of basic blocks-Loops in flow graphs - Code improving transformations-

Kachroo Formal Language Modelling and Simulations of Incident

Management

TOTAL: 45 PERIODS

129

REFERENCES:

1. J.E. Hopcroft, R. Motwani and J.D. Ullman, “Introduction to

Automata Theory,Languages and Computations”, Pearson

Education, Second Edition, 2007

2. Alfred V. Aho, Monica S.Lam, Ravi Sethi, Jeffrey D.Ullman,

“Compilers :Principles, Techniques and Tools”, Pearson Education,

Second Edition, 2008

3. Neveen Shlayan and Pushkin “KachrooFormal Language Modeling

and Simulations of Incident Management” IEEE Transactions on

Intelligent Transportation Systems, Vol. 13, No. 3, September 2012

4. H.R. Lewis and C.H. Papadimitriou, “Elements of the theory of

Computation”, Pearson Education, Second Edition, 2003

5. Thomas A. Sudkamp,” An Introduction to the Theory of Computer

Science, Languages and Machines”, Pearson Education, Third

Edition, 2007

6. Raymond Greenlaw an H.James Hoover, “Fundamentals of Theory

of Computation, Principles and Practice”, Morgan Kaufmann

Publishers, 1998

7. Micheal Sipser, “Introduction of the Theory and Computation”,

Thomson Brokecole, 1997

8. J. Martin, “Introduction to Languages and the Theory of

computation”, Tata Mc Graw Hill, Third Edition, 2007

9. Randy Allen, Ken Kennedy, “Optimizing Compilers for Modern

Architectures: A Dependence-based Approach”, Morgan Kaufmann

Publishers, 2002

10. Steven S. Muchnick, “Advanced Compiler Design and

Implementation”, Morgan Kaufmann Publishers - Elsevier Science,

India, Indian Reprint 2003.

130

WEB REFERENCES:

1. http://www.onesmartclick.com/engineering/compiler-design.html

2. http://citeseer.ist.psu.edu/Programming/CompilerDesign/hubs.html

3. http://www1.cs.columbia.edu/~aho

4. http://infolab.stanford.edu/~ullman/

5. http://dinosaur.compilertools.net/

6. http://epaperpress.com/lexandyacc/

13MI415 : SOCIAL NETWORK ANALYSIS L T P C

3 0 0 3

COURSE OBJECTIVES :

To gain knowledge about the current Web development and

emergence of Social Web

To study about the modelling, aggregating and knowledge

representation of Semantic Web

To learn about the extraction and mining tools for Social networks

To understand and predict human behaviour for social communities

To learn how text mining can be done in social networks

COURSE OUTCOMES :

Acquire knowledge to analyze social networks

Model, aggregate and represent knowledge for Semantic Web

Use extraction and mining tools for Social networks

Apply reality mining to predict human behaviour for social communities

Apply various algorithms for text mining in social networks

http://www.onesmartclick.com/engineering/compiler-design.html

131

UNIT I INTRODUCTION TO SOCIAL NETWORK

ANALYSIS

9

Introduction to Web - Limitations of current Web – Development of

Semantic Web – Emergence of the Social Web - Network analysis -

Development of Social Network Analysis - Key concepts and measures in

network analysis - Electronic sources for network analysis - Electronic

discussion networks, Blogs and online communities, Web-based

networks - Applications of Social Network Analysis

UNIT II MODELLING, AGGREGATING AND

KNOWLEDGE REPRESENTATION

9

Ontology and their role in the Semantic Web - Ontology-based

Knowledge Representation - Ontology languages for the Semantic Web –

RDF and OWL - Modelling and aggregating social network data - State-

of-the-art in network data representation, Ontological representation of

social individuals, Ontological representation of social relationships,

Aggregating and reasoning with social network data, Advanced

Representations

UNIT III EXTRACTION AND MINING COMMUNITITES

IN WEB SOCIAL NETWORKS

9

Extracting evolution of Web Community from a Series of Web Archive -

Detecting Communities in Social Networks - Definition of Community -

Evaluating Communities - Methods for Community Detection & Mining -

Applications of Community Mining Algorithms - Tools for Detecting

Communities Social Network Infrastructures and Communities -

Decentralized Online Social Networks- Multi-Relational Characterization

of Dynamic Social Network Communities

132

UNIT IV PREDICTING HUMAN BEHAVIOR AND

PRIVACY ISSUES

9

Understanding and Predicting Human Behaviour for Social Communities

- User Data Management, Inference and Distribution - Enabling New

Human Experiences - Reality Mining - Context-Awareness - Privacy in

Online Social Networks - Trust in Online Environment - Trust Models

Based on Subjective Logic - Trust Network Analysis - Trust Transitivity

Analysis - Combining Trust and Reputation - Trust Derivation Based on

Trust Comparisons - Attack Spectrum and Countermeasures

UNIT V TEXT MINING IN SOCIAL NETWORKS 9

Introduction – Keyword Search – Query Semantics and Answer Ranking

- Keyword search over XML and relational data - Keyword search over

graph data - Classification Algorithms - Clustering Algorithms - Transfer

Learning in Heterogeneous Networks - Application – Gephi Tool

TOTAL: 45 Periods

REFERENCES:

1. Charu C. Aggarwal, “Social Network Data Analytics”, Springer,

2011

2. Guandong Xu , Yanchun Zhang and Lin Li, “Web Mining and Social

Networking Techniques and applications”, Springer, first

edition, 2011.

3. Peter Mika, “Social networks and the Semantic Web”, Springer, first

edition 2007.

4. Borko Furht, “Handbook of Social Network Technologies and

Applications”, Springer, first edition, 2010.

5. Dion Goh and Schubert Foo, “Social information retrieval systems:

emerging technologies and applications for searching the Web

effectively”, IGI Global snippet, 2008.

http://www.google.com/search?tbo=p&tbm=bks&q=inauthor:%22Dion+Goh%22

http://www.google.com/search?tbo=p&tbm=bks&q=inauthor:%22Schubert+Foo%22

133

6. Max Chevalier, Christine Julien and Chantal Soulé-Dupuy,

“Collaborative and social information retrieval and access:

techniques for improved user modelling”, IGI Global snippet, 2009.

7. John G. Breslin, Alexandre Passant and Stefan Decker, “The Social

Semantic Web”, Springer, 2009

WEB REFERENCES:

1. www.utdallas.edu

2. ibook.ics.uci.edu

3. www.ebmtools.org

13MI416: HUMAN COMPUTER INTERACTION AND

HUMAN FACTOR

L T P C

3 0 0 3

COURSE OBJECTIVES :

To learn the principles, fundamentals and developments of human

computer interaction (HCI)

To analyze HCI theories, as they relate to collaborative or social

software.

To establish target users, functional requirements, and interface

requirements for a given computer application

To think and understand user interface design principles to design and

evaluate interactive technologies

COURSE OUTCOMES :

Interpret the contributions of human factors and technical constraints on

human-computer interaction

http://www.google.com/search?tbo=p&tbm=bks&q=inauthor:%22Max+Chevalier%22

http://www.google.com/search?tbo=p&tbm=bks&q=inauthor:%22Christine+Julien%22

http://www.google.com/search?tbo=p&tbm=bks&q=inauthor:%22Chantal+Soul%C3%A9-Dupuy%22

http://www.google.com/search?tbo=p&tbm=bks&q=inauthor:%22John+G.+Breslin%22

http://www.google.com/search?tbo=p&tbm=bks&q=inauthor:%22Alexandre+Passant%22

http://www.google.com/search?tbo=p&tbm=bks&q=inauthor:%22Stefan+Decker%22

134

Evaluate the usability of human computer interaction system

Analyze various models of HCI

Describe how a development system can include dialog and rich

interaction

Acquire knowledge on recent issues in HCI for real time applications

UNIT I INTRODUCTION TO HCI & HUMAN FACTOR 9

Introduction to HCI - Humans – Information Process – Computer – Information

Process – Differences and Similarities – Need for Interaction – Models –

Ergonomics – Style – Context – Paradigms for interaction – Designing of

Interactive Systems - User Focus – Navigation and screen design – Human

factors – Ergonomics design and criteria – methods – models of human

performance – Design to fit tasks and people

UNIT II DESIGN, IMPLEMENTATION AND EVALUATION 9

Software Process – Usability Engineering – Iterative Design Practices –

Design Rules – Usability – Principles – Standards and Guidelines – Design

Patterns – Implementation Tool support – Windowing Systems – Interaction

Tool Kit – User Interface Management System – Evaluation Techniques –

Evaluation Design – Evaluating Implementations – Observational Methods

UNIT III MODELS 9

Universal Design Principles – Multimodal Systems – User Support –

Requirements – Approaches – Cognitive Model – Goal and tasks – Linguistic

Model – Physical and Device Models - Architecture – Socio organizational

issues – Communication and Collaboration Models – Task Analysis –

Knowledge

UNIT IV DIALOG AND RICH INTERACTION 9

Introduction To Dialog – Design Notations – Graphical – Textual Dialog –

Formal Descriptions – Dialog Semantics - Analysis – System Models –

135

Standard Formalism - Interaction Models - Behaviour – Modelling Rich

Interaction – Status Event Analysis – Properties – Rich Contexts – Sensor-

based Systems – Groupware – Applications – Ubiquitous Computing – Virtual

Reality

UNIT V EXPERIMENTAL DESIGN AND RESEARCH ISSUES

IN HCI

9

Multimodal HCI – Framework – Meta-model – Interaction modalities – software

framework for multimodal HCI – Need for design - Overview of existing tools –

Proposed HCI^2 – Design and Implementation – Demonstration of usage –

Semi-supervised classifier – Bayesian network classifier algorithm – Classifier

for HCI Applications

TOTAL: 45 PERIODS

REFERENCES:

1. Alan Dix, Janet Finlay, Gregory Abowd, Russell Beale, “Human

Computer Interaction”, Prentice Hall, Third Edition, 2004

2. Mark R. Lehto, Steven J. Landry, ”Introduction to Human Factors and

Ergonomics for Engineers”, CRC Press Taylor & Francis Group,

Second edition, 2013

3. Jonathan Lazar Jinjuan Heidi Feng, Harry Hochheiser, “Research

Methods in Human-Computer Interaction”, Wiley, 2010

4. Ben Shneiderman and Catherine Plaisant, “Designing the User

Interface: Strategies for Effective Human-Computer Interaction”,

Addison-Wesley Publishing Co, Fifth Edition, 2009

5. Jenny Preece, Yvonne Rogers, and Helen Sharp, “Interaction Design:

Beyond Human-Computer Interaction ”, Wiley, Third edition, 2011.

6. Obrenovic, Starcevic, “Modeling Multimodal Human-Computer

Interaction “, IEEE Computer, Volume: 37, Issue: 9,Pages: 65 – 72,

2004

http://www.amazon.de/Interaction-Design-Beyond-Human-Computer/dp/0470665769/

http://www.amazon.de/Interaction-Design-Beyond-Human-Computer/dp/0470665769/

http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Obrenovic,%20Z..QT.&searchWithin=p_Author_Ids:37363449500&newsearch=true

http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Starcevic,%20D..QT.&searchWithin=p_Author_Ids:37332672000&newsearch=true

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=1333009&queryText%3DModeling+Multimodal+Human-+Computer+Interactio

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=1333009&queryText%3DModeling+Multimodal+Human-+Computer+Interactio

http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=29430

136

7. Jie Shen and Maja Pantic, “HCI^2 Framework: A Software Framework

for Multimodal Human-Computer Interaction Systems “ ,IEEE

Transactions On Cybernetics, Vol. 43, Issue: 6, Pages 1593-1606, ,

December 2013

8. Cohen, Cozman, Sebe, Cirelo, Huang, “Semisupervised learning of

classifiers: theory, algorithms, and their application to human-

computer interaction” , IEEE Transactions on Pattern Analysis and

Machine Intelligence, Volume: 26 , Issue: 12, Pages: 1553 – 1566,

2004

Web References:

1. http://en.wikipedia.org/wiki/Human%E2%80%93computer_interaction

2. http://www.interaction-

design.org/encyclopedia/human_computer_interaction_hci.html

3. http://courses.iicm.tugraz.at/hci/

4. http://tochi.acm.org/

13MIT417: GPU ARCHITECTURE AND

PROGRAMMING

L T P C

3 0 0 3

COURSE OBJECTIVES :

To study the evolution and importance of GPUs

To program using GPU programming frameworks such as CUDA C and

OpenCL

To analyze the impact of the hardware architecture on the execution of

the CUDA application, and implement solutions that will optimize

performance

http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Cohen,%20I..QT.&searchWithin=p_Author_Ids:37274584500&newsearch=true

http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Cozman,%20F.G..QT.&searchWithin=p_Author_Ids:37271230600&newsearch=true

http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Cozman,%20F.G..QT.&searchWithin=p_Author_Ids:37271230600&newsearch=true

http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Cirelo,%20M.C..QT.&searchWithin=p_Author_Ids:37271229900&newsearch=true

http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Cirelo,%20M.C..QT.&searchWithin=p_Author_Ids:37271229900&newsearch=true

http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=34



http://tochi.acm.org/

137

To implement a substantial parallel program exhibiting significant

parallel speedup

COURSE OUTCOMES :

Examine the architecture and capabilities of modern GPUs

Write programs using CUDA

Develop simple OpenCL software applications for GPU

Implement concurrency using OpenCL

Analyze the performance of GPU Vs CPU

UNIT I GPU ARCHITECTURES 9

GPU-Parallel Computers –Architecture of GPU-Speed Vs Parallelism-parallel

Programming languages and Models-GPU Computing - Evolution of Graphics

pipelines-GPGPU-scalable GPU-Recent developments

UNIT II CUDA 9

Introduction – CUDA Program Structure – Device memories – Data Transfer –

Kernel Functions –Threading- CUDA Threads – Thread Organization –

Synchronization & Transparent Scalability –Thread Assignment-Thread

Scheduling and latency Tolerance- CUDA memories

UNIT III OPENCL BASICS 9

OpenCL Standard –Kernel and OpenCL Execution Model-Platform and

Devices-Host Device Interaction-The Execution Environment-Contexts-

Command Queues-Events – Memory Model –OpenCL Device Architecture -

Superscalar Execution-VLIW-SIMD and Vector Processing-hardware

Multithreading-Multi Core-Architectures- Basic OpenCL Examples.

UNIT IV OPENCL CONCURRENCY AND EXECUTION

MODEL

9

Introduction – Kernels - Work items – Work groups and Execution domain-

138

OpenCL Synchronization– Kernels – Fences – Barriers – Queuing and Global

Synchronization – Memory Consistency in OpenCL – Events –Command

Queues to multiple devices-User Events-Event Callbacks – Host side memory

model – Device Side memory Model-Device side relaxed consistency-Global

Memory-Local Memory-Constant memory-Private Memory

UNIT V PERFORMANCE AND CASE STUDY 9

CPU / GPU performance and design challenges-GPU Computing– Parallel

GPU Architecture and Performance – Memory Performance Consideration –

Case Studies

TOTAL: 45 PERIODS

REFERENCES:

1. David B. Kirk, Wen-mei W. Hwu, “Programming massively parallel

processors”,Morgan Kauffman, 2010

2. B.R. Gaster, L. Howes, D.R. Kaeli, P. Mistry, D. Schaa, “

Heterogeneous computing with OpenCL”, Elsevier, 2012

3. John L. Hennessey and David A. Patterson, “Computer Architecture – A

quantitative approach”, Morgan Kaufmann / Elsevier, fifth edition, 2012.

4. H.Bidgoli, “CUDA by Example: An Introduction to General-Purpose GPU

Programming”, Addison Wesley, ISBN-10: 0131387685.

5. Striemer, G.M.Akoglu, A. “Sequence alignment with GPU: Performance

and design challenges” Parallel & Distributed Processing, IPDPS 2009.

6. Wei Yi , Yuhua Tang , Guibin Wang , Xudong Fang “A Case Study of

SWIM: Optimization of Memory Intensive Application on GPU” Parallel

Architectures, Algorithms and Programming (PAAP), 2010

7. Cope, B. , Cheung, P.Y.K. Luk, W. Howes, L.,”Performance

Comparison of Graphics Processors to Reconfigurable Logic: A Case

Study “IEEE Transactions on Computers, (Volume:59 , Issue: 4 )

8. Sangpil Lee, Seoul, Won Woo Ro “Parallel GPU architecture simulation

http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Striemer,%20G.M..QT.&searchWithin=p_Author_Ids:37546053900&newsearch=true

http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Striemer,%20G.M..QT.&searchWithin=p_Author_Ids:37546053900&newsearch=true

http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Wei%20Yi.QT.&searchWithin=p_Author_Ids:37852900100&newsearch=true

http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Yuhua%20Tang.QT.&searchWithin=p_Author_Ids:37277295600&newsearch=true

http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Guibin%20Wang.QT.&searchWithin=p_Author_Ids:37404554400&newsearch=true

http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Xudong%20Fang.QT.&searchWithin=p_Author_Ids:37403352800&newsearch=true

http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Cope,%20B..QT.&newsearch=true

http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Cheung,%20P.Y.K..QT.&searchWithin=p_Author_Ids:37272281900&newsearch=true

http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Luk,%20W..QT.&searchWithin=p_Author_Ids:37272281200&newsearch=true

http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Howes,%20L..QT.&searchWithin=p_Author_Ids:37299256600&newsearch=true



http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Sangpil%20Lee.QT.&newsearch=true

http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Won%20Woo%20Ro.QT.&newsearch=true

139

framework exploiting work allocation unit parallelism”, Performance

Analysis of Systems and Software,IEEE,2013

9. Owens, J.D. , Houston, M., Luebke, D. , Green, S. ,”GPU Computing”

Proceedings of the IEEE (Volume:96 , Issue: 5 )

WEB REFERENCES:

1. http://docs.nvidia.com/cuda/cuda-c-programming-guide/

2. https://www.khronos.org/opencl/

3. http://www.nvidia.in/object/cuda_home_new.html

4. http://s08.idav.ucdavis.edu/luebke-nvidia-gpu-architecture.pdf

13MI418:KNOWLEDGE ENGINEERING L T P C

3 0 0 3

COURSE OBJECTIVES

To learn about first order logics

To acquire knowledge about reasoning

To apply object oriented concepts for various expert systems

To assess uncertainty using non monotonic logic

To understand various action and planning strategies for problem

solving

COURSE OUTCOMES

Formulate problem in first order logic and ontologies

Improve resolution and reasoning with horn clauses

Apply object oriented abstractions for knowledge representation



http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Owens,%20J.D..QT.&searchWithin=p_Author_Ids:37365541300&newsearch=true

http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Houston,%20M..QT.&searchWithin=p_Author_Ids:37563167000&newsearch=true

http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Luebke,%20D..QT.&searchWithin=p_Author_Ids:37421437700&newsearch=true

http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Green,%20S..QT.&searchWithin=p_Author_Ids:38123093100&newsearch=true



https://www.khronos.org/opencl/

http://www.nvidia.in/object/cuda_home_new.html

http://s08.idav.ucdavis.edu/luebke-nvidia-gpu-architecture.pdf

140

Solve problems with uncertainty using fuzzy rules

Design and develop applications with action and planning


Knowledge Representation and Reasoning – First order Logic – Syntax

- Semantics Pragmatics – Expressing Knowledge – Levels of

Representation – Knowledge Acquisition and Sharing –

Sharing Ontologies – Language Ontologies –Language Patterns –

Tools for Knowledge Acquisition

UNIT II RESOLUTION AND REASONING 9

Proportional Case – Handling Variables and Quantifiers – Dealing with

Intractability – Reasoning with Horn Clauses - Procedural Control of

Reasoning – Rules in Production– Description Logic - Issues in

Engineering

.Vivid Knowledge – Beyond Vivid

UNIT III REPRESENTATION 9

Object Oriented Representations – Frame Formalism – Structured

Descriptions – Meaning and Entailment - Taxonomies and

Classification – Inheritance – Networks – Strategies for Defeasible

Inheritance – Formal Account of Inheritance Networks

UNIT IV DEFAULTS, UNCERTAINTY AND

EXPRESSIVENESS

9

Defaults – Introduction – Closed World Reasoning – Circumscription –

Default Logic Limitations of Logic – Fuzzy Logic – Nonmontonic

Logic – Theories and World – Semiotics – Auto epistemic Logic -

Vagueness – Uncertainty and Degrees of Belief – Noncategorical

Reasoning – Objective and Subjective Probability- linguistic fuzzy rule

based classification system - fuzzy cognitive maps- fuzzy for large data

141

UNIT V ACTIONS AND PLANNING 9

Explanation and Diagnosis – Purpose – Syntax, Semantics of Context –

First Order Reasoning – Modal Reasoning in Context –

Encapsulating Objects in Context – Agents – Actions – Situational

Calculus – Frame Problem – Complex Actions – Planning – Strips

– Planning as Reasoning – Hierarchical and Conditional Planning

TOTAL: 45 PERIODS

REFERENCES:

1. Ronald Brachman, Hector Levesque, “Knowledge

Representation and Reasoning “, The Morgan Kaufmann Series,

First Edition, 2004.

2. John F. Sowa, “Knowledge Representation: Logical,

Philosophical, and Computational Foundations”,

B r o k e s / C o l e , F i r s t E d i t i o n , 2000.

3. Arthur B. Markman, “Knowledge Representation”, Lawrence

Erlbaum Associates, 1998.

4. Jose Antonio Sanz, Alberto Fern ´ andez, Humberto Bustince ´ ,

and Francisco Herrera, “IVTURS: A Linguistic Fuzzy Rule-Based

Classification System Based On a New Interval-Valued Fuzzy

Reasoning Method With Tuning and Rule Selection”, IEEE

Transactions On Fuzzy Systems, Vol. 21, No. 3, June 2013

5. Elpiniki I. Papageorgiou, and Jose L. Salmeron, “A Review of

Fuzzy Cognitive Maps Research During the Last Decade”, IEEE

Transactions on Fuzzy Systems, Vol. 21, No. 1, February 2013

6. Timothy C. Havens, James C. Bezdek, Christopher Leckie,

Lawrence O. Hall, and Marimuthu Palaniswami, “Fuzzy c-Means

Algorithms for Very Large Data”, IEEE Transactions On Fuzzy

Systems, Vol. 20, No. 6, December 2012.

142

WEB REFERENCES

1. http://pages.cs.wisc.edu/~dyer/cs540/notes/fopc.html

2. http://wiki.visualprolog.com/?title=Fundamental_Prolog_Part_1#H

orn_Clause_Logic

3. http://www.jfsowa.com/pubs/semnet.htm

4. http://www.imamu.edu.sa/Scientific_selections/abstracts/Math/Tut

orial%20On%20Fuzzy%20Logic.pdf

5. http://artint.info/html/ArtInt_335.html

13MI419: PARALLEL COMPUTING L T P C

3 0 0 3

COURSE OBJECTIVES :

To understand the basic concepts in parallel computing

architecture

To be familiar with the taxonomies and parallel programming

models

To be able to identify promising applications of parallel computing

To develop parallel algorithms and implement prototype parallel

programs using MPI and OpenMP

To evaluate the performance metrics of parallel programs with

various measures

COURSE OUTCOMES :

Express the need for parallel computing with its issues

Acquire knowledge to design a parallel algorithm using

decomposition and mapping techniques

http://pages.cs.wisc.edu/~dyer/cs540/notes/fopc.html

http://artint.info/html/ArtInt_335.html

143

Interpret message passing paradigm for a parallel algorithm

Design a parallel algorithm for an existing sequential problem

Analyze the complexity and performance metrics of code when

parallelization is done

UNIT I INTRODUCTION TO PARALLEL COMPUTING

AND ARCHITECTURES

9

Parallel computing – parallelism – parallel architecture - scope of parallel

computing – parallel programming platform – implicit parallelism –

limitations of system memory - physical organization of parallel platforms

– communication cost in parallel machines – analytical modelling of

parallel programs.

UNIT II PARALLEL ALGORITHM DESIGN 9

Decomposition Techniques – Recursive – Data – Explorative –

Speculative – Hybrid - Tasks and interaction – characteristics – Mapping

techniques – Load Balancing – Static Mapping – dynamic – Mapping –

Interaction Overhead – algorithm models - Foster’s design methodology

UNIT III MESSAGE PASSING PARADIGM 9

Principles of programming – Basic building block– send and receive –

MPI – Library – Communicators – Examples - circuit satisfiability –

functions – compile and run –topologies and embedding – collective

communication – shared memory programming – parallel loops – data

parallelism – critical section – functional parallelism

UNIT IV PARALLEL PROGRAMMING 9

Sieve of Eratosthenes – sequential algorithm – Data Decomposition –

parallel algorithm– analysis - Floyd's Algorithm – Design parallelism –

analysis – Matrix Multiplication - Sorting - parallel quicksort – hyper

quicksort – regular sampling – Combinatorial search – parallel

Backtracking – parallel branch and bound- parallel alpha-beta search –

analysis

144

UNIT V PERFORMANCE ANALYSIS AND

APPLICATIONS

9

Sources of overhead – Performance Metrics – Parallel overhead – speed

up – efficiency – cost – Amdahl's law – Asymptotic analysis – GPU

computing – Introduction to Parallel Search - Met heuristic Algorithm –

Principles – Parallel Models – Design of GPU based algorithm –

Parallelisation control – Memory management – Application to TSP –

Comparison – Execution time approximation – Overview – EMMA

method – Comparison – Case Study

TOTAL: 45 PERIODS

REFERENCES:

1. Ananth Grama, George Karypis, Vipin Kumar, and Anshul Gupta,

“Introduction to Parallel Computing”, Addison Wesley, Second

Edition ,2003

2. M J Quinn, “Parallel Programming in C with MPI and OpenMP

“,McGraw-Hill Higher Education, first edition, 2004

3. D. Kirk and W. Hwu, “Programming Massively Parallel

Processors”, Snir, Otto, Huss-Lederman, Walker, and Dongarra,

MPI The Complete Reference, The MIT Press, 1994

4. Ted G. Lewis and H. El-Rewini, “Introduction to Parallel

Computing'', Prentice-Hall, 1992

5. Ian Foster, ”Designing and Building Parallel Programs'', Addison

Wesley, 1995

6. Van Luong, Nouredine Melab, and El-Ghazali Talbi, “ GPU

Computing for Parallel Local Search Metaheuristic

Algorithms”,IEEE Transactions on Computers, vol. 62, no. 1, pages

173-185, January 2013.

7. Junqing Sun and Gregory D. Peterson,“An Effective Execution

145

Time Approximation Method for Parallel Computing” IEEE

Transactions ON Parallel and Distributed Systems, vol. 23, no. 11,

Pages 2024-2032, November 2012.

WEB REFERENCES:

1. https://computing.llnl.gov/tutorials/parallel_comp/

2. http://www-users.cs.umn.edu/~karypis/parbook/

3. http://mitpress.mit.edu/books/using-openmp

4. http://www.journals.elsevier.com/parallel-computing/

13MI420 : ONTOLOGY AND SEMANTIC WEB L T P C

3 0 0 3

COURSE OBJECTIVES :

To learn the importance of semantic web

To apply various semantic knowledge representation strategies

To analyze various ontology techniques

COURSE OUTCOMES :

Articulate the concepts of ontology

Represent semantic knowledge using RDF

Generate semantic rules using OWL

Analyze different ontology development methods

Familiarize different tools for ontology construction

146


The Future of the Internet - The Syntactic Web - The Semantic Web -

Ontology in Computer Science - Term Ontology - Taxonomies - Thesauri

and Ontologies - Classifying Ontologies - Web Ontologies -

Web Ontology Description Languages- Ontology – Categories - Intelligence

UNIT II SEMANTIC KNOWLEDGE REPRESENTATION 9

Knowledge Representation in Description Logic - An Informal Example- The

Family of Attributive Languages- Inference Problems- RDF and RDF

Schema-- XML Essentials- RDF- RDF Schema- Schema Vocabulary-OWL-

Requirements for Web Ontology Description Languages- Header

Information- Versioning- Annotation Properties- Classes- Individuals- Data

types- OWL Vocabulary

UNIT III RULE LANGUAGES 9

Rule Languages - Usage Scenarios for Rule Languages- Datalog- RuleML-

SWRL- TRIPLE - Semantic Web Services: Introduction- Web Service

Essentials- OWL-S Service Ontology- An OWL-S Example

UNIT IV ONTOLOGY DEVELOPMENT 9

Methods for Ontology Development - Uschold and King

Ontology Development Method- Toronto Virtual Enterprise Method-

Methontology- KACTUS Project Ontology Development Method- Lexicon-

Based Ontology Development Method- Simplified Methods - Ontology

Sources - Introduction- Metadata- Upper Ontologies- Other Ontologic of

Interest- Ontology Libraries

147

UNIT V SOFTWARE TOOLS 9

Semantic Web Software Tools - Metadata and Ontology Editors - Reasoners-

Other tools - Software Agents - Introduction- Agent Forms- Agent

Architecture- Agents in the Semantic web Context - Semantic Desktop –

Introduction - Semantic Desktop Metadata - Semantic Desktop Ontologies -

Semantic Desktop Architecture - Semantic Desktop Related Applications -

Ontology Application in Art - Ontologies for the Description of Works of Art -

Metadata Schemas for The Description of Works of Art - Semantic

Annotation of Art Images - Improving Web Image Search by Bag - Based

Reranking - Supporting Object - Oriented Programming of Semantic - Web

Software

TOTAL: 45 PERIODS

REFERENCE BOOKS:

1. Karin K. Breitman, Marco Antonio Casanova and Walter

Truszowski, “Semantic Web Concepts: Technologies

and Applications”, Springer, 2007.

2. Heiner Stucken schmidt- Frank van Harmelen,” Information Sharing on

the Semanting Web” , Springer, 2010.

3. Grigoris Antoniou, Frank Van, ”Semantic Web Primer”, Springer, 2012

4. Rudi Studer, Stephan Grimm, Andrees Abeker, ”Semantic

Web Services: Concepts- Technologies and Applications”, Springer,

2007

5. Mohammeid Ali Elijini “Towards the Semantic Web”, Springer, 2012

6. John Davis, Dieter Fensal, Frank Van Harmelen, J. Wiley ,”Towards

the Semantic Web: Ontology Driven Knowledge Management”,

Springer, 2007.

7. Lixin Duan, Wen Li, Ivor Wai,Hung Tsang, Dong Xu, Member, IEEE

“Improving Web Image Search by Bag-Based Reranking”, IEEE

148

Transactions On Image Processing, Vol. 20, No.11, November 2011

8. Matthias Quasthoff and Christoph Meinel, “Supporting Object-Oriented

Programming of Semantic-Web Software” IEEE Transactions On

Systems- Man- And Cybernetics Part C: Applications And Reviews,

Vol. 42, No. 1, January 2012

WEB REFERENCES:

1. http://nrl.sourceforge.net/

2. http://www.princeton.edu

3. http://www.cs.vu.nl/~frankh/postscript/OntoHandbook03OWL.pdf

4. http://scn.sap.com/docs/DOC-18520

13MI421: LOGIC PROGRAMMING L T P C

3 0 0 3

COURSE OBJECTIVES :

To learn the basics and advanced concepts of Prolog

To explain the basic concepts of knowledge representation

To explain the fundamentals of expert systems and knowledge

representation with uncertainty

To represent a problem using constraint and inductive logic

programming

To understand the relation between prolog, modal and

temporal logic

COURSE OUTCOMES :

Write simple program using Prolog language

http://nrl.sourceforge.net/

http://www.cs.vu.nl/~frankh/postscript/OntoHandbook03OWL.pdf

149

Construct Prolog programs using different data structures and

databases

Use Prolog for problem solving

Develop prolog programs for an expert system shell

Extrapolate the concepts of modal and temporal logic

UNIT I THE PROLOG LANGUAGE 9

Introduction to Prolog – Defining Relations by facts – Defining

relations by rules – Recursive Rules - Syntax and Meaning of Prolog

Programs – Data Objects – Matching – Declarative meaning of

Prolog programs – Procedural Meaning – Example – Order of

clauses and goals – Relation between Prolog and logic - Lists –

Operators - Arithmetic – Using Structures - Eight Queen Problems

UNIT II PROGRAMMING STYLE AND

TECHNIQUE

9

Input and Output – Communication with files – Processing files of

terms – Manipulating characters – Constructing and decomposing

atoms – Reading programs - More Built-in Predicates – Testing the

type of terms – Constructing and decomposing terms – Equality and

comparison – Database manipulation – control facilities -

Operations on Data Structures – Sorting lists – Representing sets by

binary trees – Insertion and deletion in a binary dictionary –

Displaying trees - Graphs

UNIT III PROLOG IN ARTIFICIAL INTELLIGENCE 9

Basic Problem-Solving Strategies – Depth first search – Breadth

first search – Analysis of basic search techniques - Best First

Heuristic Search –Best first search – Eight Puzzle – Scheduling –

Space saving techniques for best first search- Problem

Decomposition and AND/OR Graphs

150

UNIT IV CONSTRAINT AND INDUCTIVE LOGIC

PROGRAMMING

9

Constraint satisfaction and logic programming – CLP over real

numbers – Scheduling with CLP – A simulation programs with

constraints – CLP over finite domains - Knowledge Representation

and Expert Systems – Functions and structure of an expert system –

Representing knowledge with if then rules – Forward and backward

chaining in rule based system- An Expert System Shell- Knowledge

representation format - Designing the inference engine - Inductive

Logic Programming – Introduction – Constructing Prolog programs

from examples – Program Hyper

UNIT V MODAL AND TEMPORAL LOGIC 9

Modal logic – Basic Concepts – Relational Structures – Modal

Languages –Models and frames – General Frames – Modal

Consequence Relations – Normal Modal Logics - Temporal Logic –

Basic concepts and notion of logics– Logical Languages –

Semantics – Formal System – Basic Proportional Linear Temporal

Logic – Extensions of LTL - A fully automated framework for control

of linear systems from temporal logic specifications - Optimization

based dynamic reconfiguration of real-time schedulers with support

for stochastic processor consumption

TOTAL: 45 PERIODS

REFERENCES

1. Ivan Bratko, “PROLOG Programming for Artificial Intelligence”,

Addison -Wesley, Pearson Education, Third Edition, 2001

2. Patrick Blackburn, Maarten de Rijke, Yde Venema, “Modal

Logic “,Cambridge University Press 2001

3. Fred Kroger, Stephen Merz,“Temporal Logic and State

151

Systems”, Springer 2008

4. I.Kononenko and N. Lavrac,”Prolog Through Examples”,

Sigma press,1989

5. Ulf Nilsson and Jan Maluszynski, ”Logic Programming and

Prolog “, John Wiley & Sons Ltd,Second Edition, 2000

6. Stuart Russell and Peter Norvig, “Artificial Intelligence A

Modern Approach”, Pearson Education, Third Edition, 2010

7. Antoni Niederlinski,” A Quick and Gentle Guide to Constraint

Logic Programming via Eclipse” ,Gliwice 2011

8. E. Camponogara , A. de Oliveira and G. Lima "Optimization-

based dynamic reconfiguration of real-time schedulers with

support for stochastic processor consumption", IEEE Trans.

Ind. Inf., vol. 57, no. 4, pp.594 -609 2010

9. M. Kloetzer and C. Belta "A fully automated framework for

control of linear systems from temporal logic

specifications", IEEE Trans. Autom. Control, vol. 53, no. 1,

pp.287 -297, 2008

WEB REFERENCES

1. www.csie.ntnu.edu.tw

2. www.cs.tau.ac.il

3. www.cse.msu.edu

4. www.cs.jhu.edu

http://www.cs.jhu.edu/

152

13MI422: VLSI DESIGN L T P C

3 0 0 3

COURSE OBJECTIVES :

To understand and experience VLSI Design Flow

To study the Transistor-Level CMOS Logic Design

To understand VLSI Fabrication and Experience CMOS Physical

Design

To learn Gate Function and Timing Characteristics

COURSE OUTCOMES :

Illustrate the characteristics of MOS circuits

Explore the various steps involved in VLSI Fabrication

Techniques

Investigate the Layout Design Rules

Design various logic devices like Inverter, NAND gate, NOR gate,

combinational logic design

Design various system devices like 4bit shifter, ALU subsystem,

Carry look ahead adders, Multipliers, etc

UNIT I OVERVIEW OF VLSI DESIGN

METHODOLOGY

13

VLSI design process - Architectural design - Logical design - Physical

design - Layout styles - Full custom - Semicustom approaches - MOS

transistor - Threshold voltage - Threshold voltage equations - MOS

device equations - Basic DC equations - Second order effects - MOS

models - Small signal AC characteristics - NMOS inverter - Depletion

mode and enhancement mode pull ups – CMOS inverter - DC

characteristics - Inverter delay - Pass transistor - Transmission gate –

Power consumption in CMOS gates – Static dissipation – Dynamic

Dissipation

153

UNIT II VLSI FABRICATION TECHNIQUES 7

An overview of wafer fabrication – Wafer processing - Oxidation -

Patterning - Diffusion - Ion implantation - Deposition – Silicon gate

NMOS process - CMOS processes - NWell - PWell - Twintub - Silicon

on insulator - CMOS process enhancements - Interconnect - Circuit

elements- Latch up - Latchup prevention techniques

UNIT III LAYOUT DESIGN RULES 7

Need for design rules - Mead Conway design rules for the silicon gate

NMOS process - CMOS based design rules -Simple layout examples -

Sheet resistance - Area capacitance - Wiring capacitance - Driving

large capacitive loads

UNIT IV LOGIC DESIGN 9

Switch logic - Pass transistor and transmission gate based design -

Gate logic - Inverter - Two input NAND gate - NOR gate - Other forms

of CMOS logic – Dynamic CMOS logic - Clocked CMOS logic -

Precharged domino CMOS logic - Structured design - Simple

combinational logic design examples - Parity generator - Multiplexers –

Clocked sequential circuits - Two phase clocking - Charge storage -

Dynamic register element - NMOS and CMOS - Dynamic shift register

- Semistatic register - JK flip flop circuit

UNIT V SUBSYSTEM DESIGN PROCESS 9

General arrangement of a 4-bit arithmetic processor - Design of a 4bit

shifter - Design of a ALU subsystem - Implementing ALU functions

with an adder - Carry look ahead adders - Multipliers - Serial parallel

multipliers – Pipelined multiplier array – Modified Booth's algorithm -

Incrementer / Decrementer -Two phase non-overlapping clock

generator- Low Latency Polynomial Basis Multiplier- Low-Complexity

Multiplier for Based on All-One Polynomials- A Knowledge-Based

154

Expert System for Automatic Visual VLSI Reverse-Engineering: VLSI

Layout Version

TOTAL: 45 PERIODS

REFERENCES:

1. Kamran Eshraghian, Douglas A Pucknell and Sholeh

Eshraghian, “Essentials of VLSI Circuits and Systems,” Prentice

Hall of India, New Delhi, 2005

2. Neil H E West and Kamran Eshranghian, "Principles of CMOS

VLSI Design: A system Perspective", Addision-Wesley, Second

Edition, 2004

3. Sung-Mo Kang and Yusuf Leblebici,” CMOS Digital Integrated

Circuits”,Tata McGraw-Hill, New Delhi, Third edition, 2008.

4. Jan M Rabaey, Chandrasekaran A and Nikolic B, “Digital

Integrated Circuits,” Pearson Education, Third Edition, 2004

5. Amar Mukherjee, "Introduction to nMOS and CMOS VLSI System

Design", Prentice Hall, USA, 1986

6. WayneWolf," Modern VLSI Design: Systems on Chip Design" ,

Pearson Education Inc., 3nd Edition , Indian Reprint, 2007

7. Ima a, J.L. "Low Latency Polynomial Basis Multiplier", Circuits

and Systems I: Regular Papers, IEEE Transactions on, On

page(s): 935 - 946 Volume: 58, Issue: 5, May 2011

8. Jiafeng Xie; Meher, P.K.; Jianjun He "Low-Complexity Multiplier

for Based on All-One Polynomials", Very Large Scale Integration

(VLSI) Systems, IEEE Transactions on, On page(s): 168 - 173

Volume: 21, Issue: 1, Jan. 2013

9. G. Bourbakis, A. Mogzadeh, S. Mertoguno, and C.

Koutsougeras“A Knowledge-Based Expert System for Automatic

Visual VLSI Reverse-Engineering: VLSI Layout Version” , IEEE

Transactions on Systems, Man, and Cybernetics - Part A:

155

Systems and Humans, vol. 32, no. 3, May 2002

WEB REFERENCES:

1. http://www3.hmc.edu/~harris/cmosvlsi/4e/index.html

2. http://ai.eecs.umich.edu/people/conway/VLSI/VLSIText/PP-

V2/V2.pdf

3. http://www.ee.ncu.edu.tw/~jfli/vlsi21/lecture/ch01.pdf

4. http://people.ee.duke.edu/~jmorizio/ece261/261.html

13MI423:NETWORK ENGINEERING AND

MANAGEMENT

L T P C

3 0 0 3

COURSE OBJECTIVES :

To understand the fundamental concepts of network engineering with

IPv6 issues

To explore knowledge on Quality of service

To analyze, design and document computer network specifications to

meet client needs

To apply problem solving approaches to work challenges and make

decisions using network engineering methodologies and its

management

COURSE OUTCOMES :

Develop an basic understanding in the fundamental concepts of

network engineering

Expertise knowledge on Quality of Service of networks

156

Analyze the infrastructures in high performance networks

Explore the features of high speed networks

Manage the operation, administration, maintenance, and provisioning

of networked systems

UNIT I INTRODUCTION TO NETWORKING & IPv6

ISSUES

9

Communication Networks – Network Elements – Switching - Error and

Flow Control – Congestion Control – Layered Architecture – Network

Externalities – Service Integration - IPv6 Introduction – IPv4 addressing &

routing crisis – Patching IPv4 – IPv6 Transition Issues – IPv6 Security

protocols – Security issues in IPv6 – IPv6 Protocol Basics – IPv6

Addressing – Multicast – Routing – IPv6 QoS – Current Issues to Deploy

IPv6

UNIT II QUALITY OF SERVICE 9

Traffic Characteristics and Descriptors – Quality of Service and Metrics –

Best Effort model and Guaranteed Service Model – Limitations of IP

networks – Scheduling and Dropping policies for BE and GS models –

Traffic Shaping algorithms – End to End solutions – Laissez Faire

Approach – Possible improvements in TCP – Significance of UDP in

inelastic traffic

UNIT III HIGH PERFORMANCE NETWORKS 9

Integrated Services Architecture – Components and Services –

Differentiated Services Networks – Per Hop Behaviour – Admission Control

– MPLS Networks – Principles and Mechanisms – Label Stacking – RSVP

– RTP/RTCP

157

UNIT IV HIGH SPEED NETWORKS 9

Optical links – WDM systems – Optical Cross Connects – Optical paths

and Networks – Principles of ATM Networks – B-ISDN/ATM Reference

Model – ATM Header Structure – ATM Adaptation Layer – Management

and Control – Service Categories and Traffic descriptors in ATM

UNIT V NETWORK MANAGEMENT 9

ICMP the Forerunner – Monitoring and Control – Network Management

Systems – Abstract Syntax Notation – CMIP – SNMP Communication

Model – SNMP MIB Group – Functional Model – Major changes in

SNMPv2 and SNMPv3 – Remote monitoring – RMON SMI and MIB

TOTAL: 45 Periods

REFERENCES:

1. Mahbub Hassan and Raj Jain, “High Performance TCP/IP

Networking”, Pearson Education, 2004

2. Larry L Peterson and Bruce S Davie, “Computer Networks: A

Systems Approach”, Fourth Edition, Morgan Kaufman Publishers,

2007

3. Jean Warland and Pravin Vareya, “High Performance Networks”,

Morgan Kauffman Publishers, 2002

4. William Stallings, “High Speed Networks: Performance and Quality of

Service” , Pearson Education, Second edition , 2002

5. Mani Subramaniam, “Network Management: Principles and

Practices”, PearsonEducation, 2000

6. Kasera and Seth, “ATM Networks: Concepts and Protocols”, Tata

McGraw Hill, 2002

7. Pete Loshin, “IPv6 : Theory, Protocol and Practice”, ELSEVIER,

Morgan Kauffmann Publishers Inc., Second edition , 2004

158

WEB REFERENCES:

1. http://docs.oracle.com/cd/E23824_01/html/821-1453/ipv6-

troubleshoot-2.html

2. http://searchsecurity.techtarget.com/tip/Get-ready-for-IPv6-Five-

security-issues-to-consider

3. http://nptel.ac.in/courses/IIT-

MADRAS/Computer_Networks/index.php

4. http://compnetworking.about.com/od/networkdesign/g/bldef_qos.htm

5. http://en.wikipedia.org/wiki/Quality_of_service

6. http://www.highperformancenet.com/

7. http://www.itcom.itd.umich.edu/atm/

8. http://en.wikipedia.org/wiki/Network_management

13MI434: BUILDING ENTERPRISE APPLICATION L T P C

3 0 0 3

COURSE OBJECTIVES:

To explore the fundamental concepts of Enterprise application

To develop skills that will enable them to construct application of high

quality

To understand the process of developing new technology and the

role of experimentation

To introduce ethical and professional issues in developing application

To understand the concepts of different testing strategies

COURSE OUTCOMES:

Identify functional and non-functional requirements for the given

scenario

http://en.wikipedia.org/wiki/Network_management

159

Analyze different concepts of software architectures

Architect the software product as per the requirements

Construct different solution layers for an enterprise application

Apply different testing strategies while developing enterprise

application

UNIT I INTRODUCTION TO ENTERPRISE APPLICATIONS

AND REQUIREMENTS

9

Introduction to enterprise applications and their types- software engineering

methodologies- life cycle of raising an enterprise application- introduction to

skills required to build an enterprise application- key determinants of

successful enterprise applications- measuring the success of enterprise

applications- Inception of enterprise applications- enterprise analysis-

business modelling- requirements elicitation- use case modelling-

prototyping- non functional requirements- requirements validation- planning

and estimation

UNIT II ANALYSIS, DESIGN CONCEPTS AND

PRINCIPLES

9

Concept of architecture- views and viewpoints- enterprise architecture-

logical architecture- technical architecture- design- different technical

layers- best practices- data architecture and design – relational- XML- and

other structured data representations

UNIT III ARCHITECTURAL DESIGN CONCEPTS 9

Infrastructure architecture and design elements - Networking-

Internetworking- and Communication Protocols- IT Hardware and

Software- Middleware- Policies for Infrastructure Management-

Deployment Strategy- Documentation of application architecture and

design

UNIT IV CONSTRUCTION 9

Construction readiness of enterprise applications - defining a construction

plan- defining a package structure- setting up a configuration management

160

plan- setting up a development environment- introduction to the concept of

Software Construction Maps- construction of technical solutions layers-

methodologies of code review- static code analysis- build and testing-

dynamic code analysis – code profiling and code coverage.

UNIT V TESTING 9

Types and methods of testing an enterprise application- testing levels and

approaches- testing environments- integration testing- performance tests-

penetration testing- usability testing- globalization testing and interface

testing- user acceptance testing- rolling out an enterprise application.

TOTAL: 45 Periods

REFERENCE BOOKS:

1. Anubhav Pradhan ,Satheesha, B. Nanjappa, Senthil, K. Nallasamy,

Veera kumar esakimuthu “Raising Enterprise Applications “, Wiley

India Pvt. Ltd, 2012

2. Brett Mclaughlin “Building Java Enterprise Applications”, O'reilly

Media, 2002

3. Soren Lauesen “Software Requirements: Styles & Techniques”,

Addison-Wesley Professional, 2002

4. Brain Berenbach, Daniel, J.Paulish-Juergen, Kazmeier “Software

Systems Requirements Engineering: In Practice”, Mcgraw-

Hill/Osborne Media, 2002

5. Dean Leffingwell “Managing Software Requirements: A Use Case

6. Approach”, Pearson Education ,Second Edition ,2003

7. Vasudeva Varma “Software Architecture: A Case Based Approach”,

Pearson Education, 2009

8. Ron Patton “Software Testing”, Pearson Education ,Second Edition,

2005

9. Naresh Chauhan “Software Testing Principles And Practices”,

Oxford University Press -2010

161

WEB REFERENCES:

1. http://Java.Sun.Com/Blueprints/Guidelines/Designing_Enterprise_Ap

plications_2e/

2. http://msdn.microsoft.com/en-us/library/aa267045(v=vs.60).aspx

3. http://www.infosys.com/IT-services/application-

services/Documents/designing-global-applications.pdf

4. http://www.sparxsystems.com/downloads/whitepapers/enterprise_arc

hitecture_framework_design.pdf

5. https://www.sans.org/readingroom/whitepapers/casestudies/architecti

ng-designing-building-secure-information-technology-infrastructure-

case-study-1261

6. http://www.dell.com/downloads/global/power/ps1q06-20050111-

Lovin.pdf

7. http://www.cs.ucl.ac.uk/staff/ucacwxe/lectures/3C05-04-05/EAI-

Essay.pdf

8. http://www.sciencedirect.com/science/article/pii/S2211381911001056

9. http://cmap.ihmc.us/docs/ConstructingAConceptMap.html

http://msdn.microsoft.com/en-us/library/aa267045(v=vs.60).aspx

http://www.infosys.com/IT-services/application-services/Documents/designing-global-applications.pdf

http://www.infosys.com/IT-services/application-services/Documents/designing-global-applications.pdf

http://www.sparxsystems.com/downloads/whitepapers/enterprise_architecture_framework_design.pdf

http://www.sparxsystems.com/downloads/whitepapers/enterprise_architecture_framework_design.pdf