Post on 12-Feb-2017
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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.
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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.
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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
(Common to M.E CSE & M.Tech
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
(Common to M.E CSE & M.Tech
IT)
3 0 0 3
3. 13MI203 Cyber Security 3 0 0 3
4. 13MI204 Big Data Analytics
(Common to M.E CSE & M.Tech
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)
(Common to M.E CSE & M.Tech
IT)
0 0 3 2
9. 13MC252 Technical Seminar * 0 0 2 1
TOTAL CREDITS 18 0 8 23
NOTE: * - Internal Assessment Only
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SEMESTER III
SL.
NO
COURSE
CODE
COURSE TITLE L T P C
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
TOTAL CREDITS 9 0 12 15
SEMESTER IV
SL.
NO
COURSE
CODE
COURSE TITLE L T P C
PRACTICAL
1. 13MI451 Project Work (Phase II) 0 0 24 12
TOTAL CREDITS 0 0 24 12
Total Number of Credits : 75
40
CURRICULUM I TO VI SEMESTERS (PART 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
PRACTICAL
4. 13MC151 Advanced Data Structures Lab
(Common to ME CSE & MTech
IT)
0 0 3 2
5. 13MI152 Comprehension* 0 0 2 1
TOTAL CREDITS 9 1 5 13
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SEMESTER II
SL.
NO
COURSE
CODE
COURSE TITLE L T P C
THEORY
1.
13MI202 Cloud Computing Technologies
(Common to M.E CSE & M.Tech
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
(Common to M.E CSE & M.Tech
IT)
0 0 3 2
5. 13MC252 Technical Seminar* 0 0 2 1
TOTAL CREDITS 9 0 5 12
SEMESTER III
SL.
NO
COURSE
CODE
COURSE TITLE L T P C
THEORY
1. 13MI102 Advanced Database Technology 3 0 0 3
2. 13MI103 Network Programming and
Management
3 0 0 3
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3. 13MI104 Soft Computing Techniques 3 1 0 4
PRACTICAL
4. 13MI151 Network Programming Lab 0 0 3 2
TOTAL CREDITS 9 1 3 12
SEMESTER IV
SL.
NO
COURSE
CODE
COURSE TITLE L T P C
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
TOTAL CREDITS 9 0 5 11
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SEMESTER V
SL.
NO
COURSE
CODE
COURSE TITLE L T P C
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
TOTAL CREDITS 9 0 12 15
SEMESTER VI
SL.
NO
COURSE
CODE
COURSE TITLE L T P C
PRACTICAL
1. 13MI451 Project Work ( Phase 2) 0 0 24 12
TOTAL CREDITS 0 0 24 12
NOTE: * - Internal Assessment Only
Total Number of Credits: 75
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LIST OF ELECTIVES
SL.
NO
COURSE
CODE
COURSE TITLE L T P C
1. 13MI401 X- Informatics 3 0 0 3
2. 13MI402 XML and Web Services 3 0 0 3
3. 13MC407 Energy Aware Computing
(Common to M.E CSE & M.Tech
IT)
3 0 0 3
4. 13MI403 Internet of Things
(Common to M.E CSE & M.Tech
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
(Common to M.E CSE & M.Tech
IT)
3 0 0 3
10. 13MI409 Wireless Sensor Networks
(Common to M.E CSE & M.Tech
IT)
3 0 0 3
11. 13MI410 Image Processing and Pattern
Analysis 3 0 0 3
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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
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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.
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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
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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
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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
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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.
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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
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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.
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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.
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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
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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.
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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
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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
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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
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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
(Common to M.E CSE / M.Tech IT) 0 0 3 2
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
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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:
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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
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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
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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
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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
(Common to M.E CSE / M.Tech IT) 3 0 0 3
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
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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
(Common to M.E CSE / M.Tech IT) 3 0 0 3
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.
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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.
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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
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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.
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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.
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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
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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
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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
(Common to M.E CSE / M.Tech IT) 3 0 0 3
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
UNIT I INTRODUCTION 9
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
UNIT I INTRODUCTION 9
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
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
103
UNIT I INTRODUCTION 9
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
UNIT I INTRODUCTION 7
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
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
UNIT I INTRODUCTION 9
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
UNIT I INTRODUCTION 9
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
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
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.
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
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
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
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
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
140
Solve problems with uncertainty using fuzzy rules
Design and develop applications with action and planning
UNIT I INTRODUCTION 9
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
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
UNIT I INTRODUCTION 9
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
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
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
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