CS6701 CRYPTOGRAPHY AND NETWORK SECURITY | syllabus

CS6701 CRYPTOGRAPHY AND NETWORK SECURITY L T P C 3 0 0 3

OBJECTIVES:

The student should be made to:
 Understand OSI security architecture and classical encryption techniques.
 Acquire fundamental knowledge on the concepts of finite fields and number theory.
 Understand various block cipher and stream cipher models.
 Describe the principles of public key cryptosystems, hash functions and digital signature.

UNIT I      INTRODUCTION & NUMBER THEORY    (10)

Services, Mechanisms and attacks-the OSI security architecture-Network security model-Classical
Encryption techniques (Symmetric cipher model, substitution techniques, transposition techniques,
steganography).FINITE FIELDS AND NUMBER THEORY: Groups, Rings, Fields-Modular arithmetic-
Euclid’s algorithm-Finite fields- Polynomial Arithmetic –Prime numbers-Fermat’s and Euler’s theorem-
Testing for primality -The Chinese remainder theorem- Discrete logarithms.

UNIT II      BLOCK CIPHERS & PUBLIC KEY CRYPTOGRAPHY    (10)

Data Encryption Standard-Block cipher principles-block cipher modes of operation-Advanced
Encryption Standard (AES)-Triple DES-Blowfish-RC5 algorithm. Public key cryptography: Principles
of public key cryptosystems-The RSA algorithm-Key management - Diffie Hellman Key exchange-
Elliptic curve arithmetic-Elliptic curve cryptography.

UNIT III      HASH FUNCTIONS AND DIGITAL SIGNATURES    (8)

Authentication requirement – Authentication function – MAC – Hash function – Security of hash
function and MAC –MD5 - SHA - HMAC – CMAC - Digital signature and authentication protocols –
DSS – EI Gamal – Schnorr.

UNIT IV      SECURITY PRACTICE & SYSTEM SECURITY    (8)

Authentication applications – Kerberos – X.509 Authentication services - Internet Firewalls for Trusted
System: Roles of Firewalls – Firewall related terminology- Types of Firewalls - Firewall designs - SET
for E-Commerce Transactions. Intruder – Intrusion detection system – Virus and related threats –
Countermeasures – Firewalls design principles – Trusted systems – Practical implementation of
cryptography and security.

UNIT V       E-MAIL, IP & WEB SECURITY    (9)

E-mail Security: Security Services for E-mail-attacks possible through E-mail - establishing keys
privacy-authentication of the source-Message Integrity-Non-repudiation-Pretty Good Privacy-S/MIME.
IPSecurity: Overview of IPSec - IP and IPv6-Authentication Header-Encapsulation Security Payload
(ESP)-Internet Key Exchange (Phases of IKE, ISAKMP/IKE Encoding). Web Security: SSL/TLS
Basic Protocol-computing the keys- client authentication-PKI as deployed by SSLAttacks fixed in v3-
Exportability-Encoding-Secure Electronic Transaction (SET).

                                                                                                                            TOTAL: 45 PERIODS

OUTCOMES:

Upon Completion of the course, the students should be able to:
 Compare various Cryptographic Techniques
 Design Secure applications
 Inject secure coding in the developed applications

TEXT BOOKS:

1. William Stallings, Cryptography and Network Security, 6th Edition, Pearson Education, March
2013. (UNIT I,II,III,IV).
2. Charlie Kaufman, Radia Perlman and Mike Speciner, “Network Security”, Prentice Hall of India,
2002. (UNIT V).

REFERENCES:

1. Behrouz A. Ferouzan, “Cryptography & Network Security”, Tata Mc Graw Hill, 2007.
2. Man Young Rhee, “Internet Security: Cryptographic Principles”, “Algorithms and Protocols”, Wiley
Publications, 2003.
3. Charles Pfleeger, “Security in Computing”, 4th Edition, Prentice Hall of India, 2006.
4. Ulysess Black, “Internet Security Protocols”, Pearson Education Asia, 2000.
5. Charlie Kaufman and Radia Perlman, Mike Speciner, “Network Security, Second Edition, Private
Communication in Public World”, PHI 2002.
6. Bruce Schneier and Neils Ferguson, “Practical Cryptography”, First Edition, Wiley Dreamtech
India Pvt Ltd, 2003.
7. Douglas R Simson “Cryptography – Theory and practice”, First Edition, CRC Press, 1995.
8. http://nptel.ac.in/.

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CS6702 GRAPH THEORY AND APPLICATIONS | syllabus

CS6702 GRAPH THEORY AND APPLICATIONS L T P C 3 0 0 3

OBJECTIVES:

The student should be made to:
 Be familiar with the most fundamental Graph Theory topics and results.
 Be exposed to the techniques of proofs and analysis.

UNIT I      INTRODUCTION    (9)

Graphs – Introduction – Isomorphism – Sub graphs – Walks, Paths, Circuits –Connectedness –
Components – Euler graphs – Hamiltonian paths and circuits – Trees – Properties of trees – Distance
and centers in tree – Rooted and binary trees.

UNIT II      TREES, CONNECTIVITY & PLANARITY    (9)

Spanning trees – Fundamental circuits – Spanning trees in a weighted graph – cut sets – Properties
of cut set – All cut sets – Fundamental circuits and cut sets – Connectivity and separability – Network
flows – 1-Isomorphism – 2-Isomorphism – Combinational and geometric graphs – Planer graphs –
Different representation of a planer graph.

UNIT III      MATRICES, COLOURING AND DIRECTED GRAPH    (8)

Chromatic number – Chromatic partitioning – Chromatic polynomial – Matching – Covering – Four
color problem – Directed graphs – Types of directed graphs – Digraphs and binary relations –
Directed paths and connectedness – Euler graphs.

UNIT IV      PERMUTATIONS & COMBINATIONS    (9)

Fundamental principles of counting - Permutations and combinations - Binomial theorem -
combinations with repetition - Combinatorial numbers - Principle of inclusion and exclusion -
Derangements - Arrangements with forbidden positions.

UNIT V      GENERATING FUNCTIONS    (10)

Generating functions - Partitions of integers - Exponential generating function – Summation operator -
Recurrence relations - First order and second order – Non-homogeneous recurrence relations -
Method of generating functions.

                                                                                                                         TOTAL: 45 PERIODS

OUTCOMES:

Upon Completion of the course, the students should be able to:
 Write precise and accurate mathematical definitions of objects in graph theory.
 Use mathematical definitions to identify and construct examples and to distinguish examples
from non-examples.
 Validate and critically assess a mathematical proof.
 Use a combination of theoretical knowledge and independent mathematical thinking in creative
investigation of questions in graph theory.
 Reason from definitions to construct mathematical proofs.

TEXT BOOKS:

1. Narsingh Deo, “Graph Theory: With Application to Engineering and Computer Science”,
Prentice Hall of India, 2003.
2. Grimaldi R.P. “Discrete and Combinatorial Mathematics: An Applied Introduction”, Addison
Wesley, 1994.

REFERENCES:

1. Clark J. and Holton D.A, “A First Look at Graph Theory”, Allied Publishers, 1995.
2. Mott J.L., Kandel A. and Baker T.P. “Discrete Mathematics for Computer Scientists and
Mathematicians” , Prentice Hall of India, 1996.
3. Liu C.L., “Elements of Discrete Mathematics”, Mc Graw Hill, 1985.
4. Rosen K.H., “Discrete Mathematics and Its Applications”, Mc Graw Hill, 2007

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CS6703 GRID AND CLOUD COMPUTING | syllabus

CS6703 GRID AND CLOUD COMPUTING L T P C 3 0 0 3

OBJECTIVES:

The student should be made to:
 Understand how Grid computing helps in solving large scale scientific problems.
 Gain knowledge on the concept of virtualization that is fundamental to cloud computing.
 Learn how to program the grid and the cloud.
 Understand the security issues in the grid and the cloud environment.

UNIT I      INTRODUCTION    (9)

Evolution of Distributed computing: Scalable computing over the Internet – Technologies for network
based systems – clusters of cooperative computers - Grid computing Infrastructures – cloud
computing - service oriented architecture – Introduction to Grid Architecture and standards –
Elements of Grid – Overview of Grid Architecture.

UNIT II      GRID SERVICES    (9)

Introduction to Open Grid Services Architecture (OGSA) – Motivation – Functionality Requirements –
Practical & Detailed view of OGSA/OGSI – Data intensive grid service models – OGSA services.

UNIT III      VIRTUALIZATION    (9)

Cloud deployment models: public, private, hybrid, community – Categories of cloud computing:
Everything as a service: Infrastructure, platform, software - Pros and Cons of cloud computing –
Implementation levels of virtualization – virtualization structure – virtualization of CPU, Memory and
I/O devices – virtual clusters and Resource Management – Virtualization for data center automation.

UNIT IV      PROGRAMMING MODEL    (9)

Open source grid middleware packages – Globus Toolkit (GT4) Architecture , Configuration – Usage
of Globus – Main components and Programming model - Introduction to Hadoop Framework -
Mapreduce, Input splitting, map and reduce functions, specifying input and output parameters,
configuring and running a job – Design of Hadoop file system, HDFS concepts, command line and
java interface, dataflow of File read & File write.

UNIT V      SECURITY    (9)

Trust models for Grid security environment – Authentication and Authorization methods – Grid
security infrastructure – Cloud Infrastructure security: network, host and application level – aspects of
data security, provider data and its security, Identity and access management architecture, IAM
practices in the cloud, SaaS, PaaS, IaaS availability in the cloud, Key privacy issues in the cloud.

                                                                                                                       TOTAL: 45 PERIODS

OUTCOMES:

At the end of the course, the student should be able to:
 Apply grid computing techniques to solve large scale scientific problems.
 Apply the concept of virtualization.
 Use the grid and cloud tool kits.
 Apply the security models in the grid and the cloud environment.

TEXT BOOK:

1. Kai Hwang, Geoffery C. Fox and Jack J. Dongarra, “Distributed and Cloud Computing: Clusters,
Grids, Clouds and the Future of Internet”, First Edition, Morgan Kaufman Publisher, an Imprint of
Elsevier, 2012.

REFERENCES:

1. Jason Venner, “Pro Hadoop- Build Scalable, Distributed Applications in the Cloud”, A Press, 2009
2. Tom White, “Hadoop The Definitive Guide”, First Edition. O’Reilly, 2009.
3. Bart Jacob (Editor), “Introduction to Grid Computing”, IBM Red Books, Vervante, 2005
4. Ian Foster, Carl Kesselman, “The Grid: Blueprint for a New Computing Infrastructure”, 2nd Edition,
Morgan Kaufmann.
5. Frederic Magoules and Jie Pan, “Introduction to Grid Computing” CRC Press, 2009.
6. Daniel Minoli, “A Networking Approach to Grid Computing”, John Wiley Publication, 2005.
7. Barry Wilkinson, “Grid Computing: Techniques and Applications”, Chapman and Hall, CRC, Taylor
and Francis Group, 2010.

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CS6704 RESOURCE MANAGEMENT TECHNIQUES |syllabus

CS6704 RESOURCE MANAGEMENT TECHNIQUES L T P C 3 0 0 3

OBJECTIVES:

The student should be made to:
 Be familiar with resource management techniques.
 Learn to solve problems in linear programming and Integer programming.
 Be exposed to CPM and PERT.

UNIT I      LINEAR PROGRAMMING    (9)

Principal components of decision problem – Modeling phases – LP Formulation and graphic solution –
Resource allocation problems – Simplex method – Sensitivity analysis.

UNIT II      DUALITY AND NETWORKS    (9)

Definition of dual problem – Primal – Dual relation ships – Dual simplex methods – Post optimality
analysis – Transportation and assignment model - Shortest route problem.

UNIT III      INTEGER PROGRAMMING    (9)

Cutting plan algorithm – Branch and bound methods, Multistage (Dynamic) programming.

UNIT IV      CLASSICAL OPTIMISATION THEORY:    (9)

Unconstrained external problems, Newton – Ralphson method – Equality constraints – Jacobean
methods – Lagrangian method – Kuhn – Tucker conditions – Simple problems.

UNIT V      OBJECT SCHEDULING:    (9)

Network diagram representation – Critical path method – Time charts and resource leveling – PERT.

                                                                                                                        TOTAL: 45 PERIODS 

OUTCOMES:

Upon Completion of the course, the students should be able to:
 Solve optimization problems using simplex method.
 Apply integer programming and linear programming to solve real-life applications.
 Use PERT and CPM for problems in project management

TEXT BOOK:

1. H.A. Taha, “Operation Research”, Prentice Hall of India, 2002.

REFERENCES:

1. Paneer Selvam, ‘Operations Research’, Prentice Hall of India, 2002
2. Anderson ‘Quantitative Methods for Business’, 8th Edition, Thomson Learning, 2002.
3. Winston ‘Operation Research’, Thomson Learning, 2003.
4. Vohra, ‘Quantitative Techniques in Management’, Tata Mc Graw Hill, 2002.
5. Anand Sarma, ‘Operation Research’, Himalaya Publishing House, 2003.

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CS6711 SECURITY LABORATORY | syllabus

CS6711 SECURITY LABORATORY L T P C 0 0 3 2 

OBJECTIVES:

The student should be made to:
 Be exposed to the different cipher techniques
 Learn to implement the algorithms DES, RSA,MD5,SHA-1
 Learn to use network security tools like GnuPG, KF sensor, Net Strumbler

LIST OF EXPERIMENTS:

1. Implement the following SUBSTITUTION & TRANSPOSITION TECHNIQUES concepts:
a) Caesar Cipher
b) Playfair Cipher
c) Hill Cipher
d) Vigenere Cipher
e) Rail fence – row & Column Transformation
2. Implement the following algorithms
a) DES
b) RSA Algorithm
c) Diffiee-Hellman
d) MD5
e) SHA-1
5 Implement the SIGNATURE SCHEME - Digital Signature Standard 6. Demonstrate how to provide secure data storage, secure data transmission and for creating
digital signatures (GnuPG). 7. Setup a honey pot and monitor the honeypot on network (KF Sensor) 8. Installation of rootkits and study about the variety of options
9. Perform wireless audit on an access point or a router and decrypt WEP and WPA.( Net
Stumbler) 10. Demonstrate intrusion detection system (ids) using any tool (snort or any other s/w)

                                                                                                                           TOTAL: 45 PERIODS

OUTCOMES:

At the end of the course, the student should be able to
 Implement the cipher techniques
 Develop the various security algorithms
 Use different open source tools for network security and analysis

LIST OF EQUIPMENT FOR A BATCH OF 30 STUDENTS:

SOFTWARE:

C / C++ / Java or equivalent compiler
GnuPG, KF Sensor or Equivalent, Snort, Net Stumbler or Equivalent

HARDWARE:

Standalone desktops - 30 Nos.
(or)
Server supporting 30 terminals or more.

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CS6712 GRID AND CLOUD COMPUTING LABORATORY | syllabus

CS6712 GRID AND CLOUD COMPUTING LABORATORY L T P C 0 0 3 2

OBJECTIVES:

The student should be made to:
 Be exposed to tool kits for grid and cloud environment.
 Be familiar with developing web services/Applications in grid framework
 Learn to run virtual machines of different configuration.
 Learn to use Hadoop

LIST OF EXPERIMENTS:

GRID COMPUTING LAB

Use Globus Toolkit or equivalent and do the following:
1. Develop a new Web Service for Calculator.
2. Develop new OGSA-compliant Web Service.
3. Using Apache Axis develop a Grid Service.
4. Develop applications using Java or C/C++ Grid APIs
5. Develop secured applications using basic security mechanisms available in Globus Toolkit.
6. Develop a Grid portal, where user can submit a job and get the result. Implement it with and
without GRAM concept.

CLOUD COMPUTING LAB

Use Eucalyptus or Open Nebula or equivalent to set up the cloud and demonstrate.
1. Find procedure to run the virtual machine of different configuration. Check how many virtual
machines can be utilized at particular time.
2. Find procedure to attach virtual block to the virtual machine and check whether it holds the
data even after the release of the virtual machine.
3. Install a C compiler in the virtual machine and execute a sample program.
4. Show the virtual machine migration based on the certain condition from one node to the other.
5. Find procedure to install storage controller and interact with it.
6. Find procedure to set up the one node Hadoop cluster.
7. Mount the one node Hadoop cluster using FUSE.
8. Write a program to use the API's of Hadoop to interact with it.
9. Write a wordcount program to demonstrate the use of Map and Reduce tasks

TOTAL: 45 PERIODS

OUTCOMES:

At the end of the course, the student should be able to
 Use the grid and cloud tool kits.
 Design and implement applications on the Grid.
 Design and Implement applications on the Cloud.

LIST OF EQUIPMENT FOR A BATCH OF 30 STUDENTS:

SOFTWARE:

Globus Toolkit or equivalent
Eucalyptus or Open Nebula or equivalent

HARDWARE:

Standalone desktops 30 Nos
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CS6004 CYBER FORENSICS | syllabus (ELECTIVE-II)

CS6004  CYBER FORENSICS L T P C  3 0 0 3

                                                              

OBJECTIVES:

The student should be made to:
 Learn the security issues network layer and transport layer
 Be exposed to security issues of the application layer
 Learn computer forensics
 Be familiar with forensics tools
 Learn to analyze and validate forensics data

UNIT I     NETWORK LAYER SECURITY &TRANSPORT LAYER SECURITY    (9)

IPSec Protocol - IP Authentication Header - IP ESP - Key Management Protocol for IPSec .
Transport layer Security: SSL protocol, Cryptographic Computations – TLS Protocol.

UNIT II      E-MAIL SECURITY & FIREWALLS    (9)

PGP - S/MIME - Internet Firewalls for Trusted System: Roles of Firewalls – Firewall related
terminology- Types of Firewalls - Firewall designs - SET for E-Commerce Transactions.

UNIT III      INTRODUCTION TO COMPUTER FORENSICS    (9)

Introduction to Traditional Computer Crime, Traditional problems associated with Computer Crime.
Introduction to Identity Theft & Identity Fraud. Types of CF techniques - Incident and incident
response methodology - Forensic duplication and investigation. Preparation for IR: Creating response
tool kit and IR team. - Forensics Technology and Systems - Understanding Computer Investigation –
Data Acquisition.

UNIT IV      EVIDENCE COLLECTION AND FORENSICS TOOLS    (9)

Processing Crime and Incident Scenes – Working with Windows and DOS Systems. Current
Computer Forensics Tools: Software/ Hardware Tools.

UNIT V      ANALYSIS AND VALIDATION    (9)

Validating Forensics Data – Data Hiding Techniques – Performing Remote Acquisition – Network
Forensics – Email Investigations – Cell Phone and Mobile Devices Forensics

                                                                                                                        TOTAL: 45 PERIODS

OUTCOMES:

Upon completion of the course, the student should be able to:
 Discuss the security issues network layer and transport layer
 Apply security principles in the application layer
 Explain computer forensics
 Use forensics tools
 Analyze and validate forensics data

TEXT BOOKS:

1. Man Young Rhee, “Internet Security: Cryptographic Principles”, “Algorithms and Protocols”,
Wiley Publications, 2003.
2. Nelson, Phillips, Enfinger, Steuart, “Computer Forensics and Investigations”, Cengage Learning,
India Edition, 2008.

REFERENCES:

1. John R.Vacca, “Computer Forensics”, Cengage Learning, 2005
2. Richard E.Smith, “Internet Cryptography”, 3rd Edition Pearson Education, 2008.
3. Marjie T.Britz, “Computer Forensics and Cyber Crime”: An Introduction”, 3rd Edition, Prentice
Hall, 2013.

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CS6003 AD HOC AND SENSOR NETWORKS | syllabus (ELECTIVE-II)

CS6003     AD HOC AND SENSOR NETWORKS L T P C 3 0 0 3

                                                                                       

OBJECTIVES:

The student should be made to:
 Understand the design issues in ad hoc and sensor networks.
 Learn the different types of MAC protocols.
 Be familiar with different types of adhoc routing protocols.
 Be expose to the TCP issues in adhoc networks.
 Learn the architecture and protocols of wireless sensor networks.

UNIT I      INTRODUCTION    (9)

Fundamentals of Wireless Communication Technology – The Electromagnetic Spectrum – Radio
propagation Mechanisms – Characteristics of the Wireless Channel -mobile ad hoc networks
(MANETs) and wireless sensor networks (WSNs) :concepts and architectures. Applications of Ad Hoc
and Sensor networks. Design Challenges in Ad hoc and Sensor Networks.

UNIT II      MAC PROTOCOLS FOR AD HOC WIRELESS NETWORKS    (9)

Issues in designing a MAC Protocol- Classification of MAC Protocols- Contention based protocols-
Contention based protocols with Reservation Mechanisms- Contention based protocols with
Scheduling Mechanisms – Multi channel MAC-IEEE 802.11

UNIT III ROUTING PROTOCOLS AND TRANSPORT LAYER IN

AD HOC WIRELESS NETWORKS 9
Issues in designing a routing and Transport Layer protocol for Ad hoc networks- proactive routing,
reactive routing (on-demand), hybrid routing- Classification of Transport Layer solutions-TCP over
Ad hoc wireless Networks.

UNIT IV      WIRELESS SENSOR NETWORKS (WSNS) AND MAC PROTOCOLS (9)

Single node architecture: hardware and software components of a sensor node - WSN Network
architecture: typical network architectures-data relaying and aggregation strategies -MAC layer
protocols: self-organizing, Hybrid TDMA/FDMA and CSMA based MAC- IEEE 802.15.4.

UNIT V      WSN ROUTING, LOCALIZATION & QOS    (9)

Issues in WSN routing – OLSR- Localization – Indoor and Sensor Network Localization-absolute and
relative localization, triangulation-QOS in WSN-Energy Efficient Design-Synchronization-Transport
Layer issues.

                                                                                                                          TOTAL: 45 PERIODS

OUTCOMES:

Upon completion of the course, the student should be able to:
 Explain the concepts, network architectures and applications of ad hoc and wireless sensor
networks
 Analyze the protocol design issues of ad hoc and sensor networks
 Design routing protocols for ad hoc and wireless sensor networks with respect to some protocol
design issues
 Evaluate the QoS related performance measurements of ad hoc and sensor networks

TEXT BOOK:

1. C. Siva Ram Murthy, and B. S. Manoj, "Ad Hoc Wireless Networks: Architectures and Protocols ",
Prentice Hall Professional Technical Reference, 2008.

REFERENCES:

1. Carlos De Morais Cordeiro, Dharma Prakash Agrawal “Ad Hoc & Sensor Networks:
Theory and Applications”, World Scientific Publishing Company, 2006.
2. Feng Zhao and Leonides Guibas, "Wireless Sensor Networks", Elsevier Publication -
2002.
3. Holger Karl and Andreas Willig “Protocols and Architectures for Wireless Sensor Networks”,
Wiley, 2005
4. Kazem Sohraby, Daniel Minoli, & Taieb Znati, “Wireless Sensor Networks-Technology,
Protocols, and Applications”, John Wiley, 2007.
5. Anna Hac, “Wireless Sensor Network Designs”, John Wiley, 2003.

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BM6005 BIO INFORMATICS | syllabus (ELECTIVE-II)

BM6005    BIO INFORMATICS L T P C  3 0 0 3

                                                         

OBJECTIVES:

The student should be made to:
 Exposed to the need for Bioinformatics technologies
 Be familiar with the modeling techniques
 Learn microarray analysis
 Exposed to Pattern Matching and Visualization

UNIT I      INTRODUCTION    (9)

Need for Bioinformatics technologies – Overview of Bioinformatics technologies Structural
bioinformatics – Data format and processing – Secondary resources and applications – Role of
Structural bioinformatics - Biological Data Integration System.

UNIT II      DATAWAREHOUSING AND DATAMINING IN BIOINFORMATICS    (9)

Bioinformatics data – Data warehousing architecture – data quality – Biomedical data analysis – DNA
data analysis – Protein data analysis – Machine learning – Neural network architecture and
applications in bioinformatics.

UNIT III      MODELING FOR BIOINFORMATICS    (9)

Hidden Markov modeling for biological data analysis – Sequence identification –Sequence
classification – multiple alignment generation – Comparative modeling –Protein modeling – genomic
modeling – Probabilistic modeling – Bayesian networks – Boolean networks - Molecular modeling –
Computer programs for molecular modeling.

UNIT IV      PATTERN MATCHING AND VISUALIZATION    (9)

Gene regulation – motif recognition – motif detection – strategies for motif detection – Visualization –
Fractal analysis – DNA walk models – one dimension – two dimension – higher dimension – Game
representation of Biological sequences – DNA, Protein, Amino acid sequences.

UNIT V      MICROARRAY ANALYSIS    (9)

Microarray technology for genome expression study – image analysis for data extraction –
preprocessing – segmentation – gridding – spot extraction – normalization, filtering – cluster analysis
– gene network analysis – Compared Evaluation of Scientific Data Management Systems – Cost
Matrix – Evaluation model - Benchmark – Tradeoffs.

                                                                                                                           TOTAL: 45 PERIODS

OUTCOMES:

Upon Completion of the course, the students will be able to
 Develop models for biological data.
 Apply pattern matching techniques to bioinformatics data – protein data genomic data.
 Apply micro array technology for genomic expression study.

TEXT BOOK:

1. Yi-Ping Phoebe Chen (Ed), “BioInformatics Technologies”, First Indian Reprint, Springer Verlag,
2007.

REFERENCES:

1. Bryan Bergeron, “Bio Informatics Computing”, Second Edition, Pearson Education, 2003.
2. Arthur M Lesk, “Introduction to Bioinformatics”, Second Edition, Oxford University Press, 2005

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EC6703 EMBEDDED AND REAL TIME SYSTEMS | syllabus (ELECTIVE-III)

EC6703    EMBEDDED AND REAL TIME SYSTEMS L T P C 3 0 0 3

                                                                                                
OBJECTIVES:

The student should be made to:
 Learn the architecture and programming of ARM processor.
 Be familiar with the embedded computing platform design and analysis.
 Be exposed to the basic concepts of real time Operating system.
 Learn the system design techniques and networks for embedded systems

UNIT I      INTRODUCTION TO EMBEDDED COMPUTING AND ARM    
PROCESSORS  (9)

Complex systems and micro processors– Embedded system design process –Design example: Model
train controller- Instruction sets preliminaries - ARM Processor – CPU: programming input and outputsupervisor
mode, exceptions and traps – Co-processors- Memory system mechanisms – CPU
performance- CPU power consumption.

UNIT II      EMBEDDED COMPUTING PLATFORM DESIGN    (9)

The CPU Bus-Memory devices and systems–Designing with computing platforms – consumer
electronics architecture – platform-level performance analysis - Components for embedded programs-
Models of programs- Assembly, linking and loading – compilation techniques- Program level
performance analysis – Software performance optimization – Program level energy and power
analysis and optimization – Analysis and optimization of program size- Program validation and testing.

UNIT III      PROCESSES AND OPERATING SYSTEMS    (9)

Introduction – Multiple tasks and multiple processes – Multirate systems- Preemptive real-time
operating systems- Priority based scheduling- Interprocess communication mechanisms – Evaluating
operating system performance- power optimization strategies for processes – Example Real time
operating systems-POSIX-Windows CE.

UNIT IV      SYSTEM DESIGN TECHNIQUES AND NETWORKS    (9)

Design methodologies- Design flows - Requirement Analysis – Specifications-System analysis and
architecture design – Quality Assurance techniques- Distributed embedded systems – MPSoCs and
shared memory multiprocessors.

UNIT V      CASE STUDY    (9)

Data compressor - Alarm Clock - Audio player - Software modem-Digital still camera - Telephone
answering machine-Engine control unit – Video accelerator.

                                                                                                                         TOTAL: 45 PERIODS

OUTCOMES:

Upon completion of the course, students will be able to:
 Describe the architecture and programming of ARM processor.
 Outline the concepts of embedded systems
 Explain the basic concepts of real time Operating system design.
 Use the system design techniques to develop software for embedded systems
 Differentiate between the general purpose operating system and the real time operating
system
 Model real-time applications using embedded-system concepts

TEXT BOOK:

1. Marilyn Wolf, “Computers as Components - Principles of Embedded Computing System Design”,
Third Edition “Morgan Kaufmann Publisher (An imprint from Elsevier), 2012.


REFERENCES:

1. Jonathan W.Valvano, “Embedded Microcomputer Systems Real Time Interfacing”, Third Edition
Cengage Learning, 2012.
2. David. E. Simon, “An Embedded Software Primer”, 1st Edition, Fifth Impression, Addison-Wesley
Professional, 2007.
3. Raymond J.A. Buhr, Donald L.Bailey, “An Introduction to Real-Time Systems- From Design to
Networking with C/C++”, Prentice Hall,1999.
4. C.M. Krishna, Kang G. Shin, “Real-Time Systems”, International Editions, Mc Graw Hill 1997
5. K.V.K.K.Prasad, “Embedded Real-Time Systems: Concepts, Design & Programming”, Dream
Tech Press, 2005.
6. Sriram V Iyer, Pankaj Gupta, “Embedded Real Time Systems Programming”, Tata Mc Graw Hill,
2004.

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CS6006 GAME PROGRAMMING | syllabus (ELECTIVE-III)

CS6006    GAME PROGRAMMING L T P C 3 0 0 3

                                                             

OBJECTIVES:

The student should be made to:
 Understand the concepts of Game design and development.
 Learn the processes, mechanics and issues in Game Design.
 Be exposed to the Core architectures of Game Programming.
 Know about Game programming platforms, frame works and engines.
 Learn to develop games.

UNIT I     3D GRAPHICS FOR GAME PROGRAMMING    (9)

3D Transformations, Quaternions, 3D Modeling and Rendering, Ray Tracing, Shader Models,
Lighting, Color, Texturing, Camera and Projections, Culling and Clipping, Character Animation,
Physics-based Simulation, Scene Graphs.

UNIT II      GAME ENGINE DESIGN    (9)

Game engine architecture, Engine support systems, Resources and File systems, Game loop and
real-time simulation, Human Interface devices, Collision and rigid body dynamics, Game profiling.

UNIT III      GAME PROGRAMMING    (9)

Application layer, Game logic, Game views, managing memory, controlling the main loop, loading and
caching game data, User Interface management, Game event management.

UNIT IV      GAMING PLATFORMS AND FRAMEWORKS    (9)

2D and 3D Game development using Flash, DirectX, Java, Python, Game engines - DX Studio,
Unity.

UNIT V      GAME DEVELOPMENT    (9)

Developing 2D and 3D interactive games using DirectX or Python – Isometric and Tile Based Games,
Puzzle games, Single Player games, Multi Player games.

                                                                                                                           TOTAL: 45 PERIODS

OUTCOMES:

Upon completion of the course, students will be able to
 Discuss the concepts of Game design and development.
 Design the processes, and use mechanics for game development.
 Explain the Core architectures of Game Programming.
 Use Game programming platforms, frame works and engines.
 Create interactive Games.

TEXT BOOKS:

1. Mike Mc Shaffrfy and David Graham, “Game Coding Complete”, Fourth Edition, Cengage
Learning, PTR, 2012.
2. Jason Gregory, “Game Engine Architecture”, CRC Press / A K Peters, 2009.
3. David H. Eberly, “3D Game Engine Design, Second Edition: A Practical Approach to Real-Time
Computer Graphics” 2nd Editions, Morgan Kaufmann, 2006.

REFERENCES:

1. Ernest Adams and Andrew Rollings, “Fundamentals of Game Design”, 2nd Edition Prentice Hall /
New Riders, 2009.
2. Eric Lengyel, “Mathematics for 3D Game Programming and Computer Graphics”, 3rd Edition,
Course Technology PTR, 2011.
3. Jesse Schell, The Art of Game Design: A book of lenses, 1st Edition, CRC Press, 2008.

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CS6007 INFORMATION RETRIEVAL | syllabus (ELECTIVE-III)

CS6007    INFORMATION RETRIEVAL L T P C 3 0 0 3

                                                                         

OBJECTIVES:

The Student should be made to:
 Learn the information retrieval models.
 Be familiar with Web Search Engine.
 Be exposed to Link Analysis.
 Understand Hadoop and Map Reduce.
 Learn document text mining techniques.

UNIT I      INTRODUCTION    (9)

Introduction -History of IR- Components of IR - Issues –Open source Search engine Frameworks -
The impact of the web on IR - The role of artificial intelligence (AI) in IR – IR Versus Web Search -
Components of a Search engine- Characterizing the web.

UNIT II      INFORMATION RETRIEVAL    (9)

Boolean and vector-space retrieval models- Term weighting - TF-IDF weighting- cosine similarity –
Preprocessing - Inverted indices - efficient processing with sparse vectors – Language Model based
IR - Probabilistic IR –Latent Semantic Indexing - Relevance feedback and query expansion.

UNIT III      WEB SEARCH ENGINE – INTRODUCTION AND CRAWLING    (9)

Web search overview, web structure, the user, paid placement, search engine optimization/ spam.
Web size measurement - search engine optimization/spam – Web Search Architectures - crawling -
meta-crawlers- Focused Crawling - web indexes –- Near-duplicate detection - Index Compression -
XML retrieval.

UNIT IV      WEB SEARCH – LINK ANALYSIS AND SPECIALIZED SEARCH    (9)

Link Analysis –hubs and authorities – Page Rank and HITS algorithms -Searching and Ranking –
Relevance Scoring and ranking for Web – Similarity - Hadoop & Map Reduce - Evaluation -
Personalized search - Collaborative filtering and content-based recommendation of documents and
products – handling “invisible” Web - Snippet generation, Summarization, Question Answering, Cross-
Lingual Retrieval.

UNIT V      DOCUMENT TEXT MINING    (9)

Information filtering; organization and relevance feedback – Text Mining -Text classification and
clustering - Categorization algorithms: naive Bayes; decision trees; and nearest neighbor - Clustering
algorithms: agglomerative clustering; k-means; expectation maximization (EM).

                                                                                                                          TOTAL: 45 PERIODS

OUTCOMES:

Upon completion of the course, students will be able to
 Apply information retrieval models.
 Design Web Search Engine.
 Use Link Analysis.
 Use Hadoop and Map Reduce.
 Apply document text mining techniques.

TEXT BOOKS:

1. C. Manning, P. Raghavan, and H. Schütze, Introduction to Information Retrieval , Cambridge
University Press, 2008.
2. Ricardo Baeza -Yates and Berthier Ribeiro - Neto, Modern Information Retrieval: The Concepts
and Technology behind Search 2nd Edition, ACM Press Books 2011.
3. Bruce Croft, Donald Metzler and Trevor Strohman, Search Engines: Information Retrieval in
Practice, 1st Edition Addison Wesley, 2009.
4. Mark Levene, An Introduction to Search Engines and Web Navigation, 2nd Edition Wiley, 2010.

REFERENCES:

1. Stefan Buettcher, Charles L. A. Clarke, Gordon V. Cormack, Information Retrieval: Implementing
and Evaluating Search Engines, The MIT Press, 2010.
2. Ophir Frieder “Information Retrieval: Algorithms and Heuristics: The Information Retrieval Series “,
2nd Edition, Springer, 2004.
3. Manu Konchady, “Building Search Applications: Lucene, Ling Pipe”, and First Edition, Gate Mustru
Publishing, 2008.

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IT6006 DATA ANALYTICS | syllabus (ELECTIVE-III)

IT6006    DATA ANALYTICS L T P C   3 0 0 3

                                                   

OBJECTIVES:

The Student should be made to:
 Be exposed to big data
 Learn the different ways of Data Analysis
 Be familiar with data streams
 Learn the mining and clustering
 Be familiar with the visualization

UNIT I      INTRODUCTION TO BIG DATA    (8)

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,
Stastical concepts: Sampling distributions, resampling, statistical inference, prediction error.

UNIT II      DATA ANALYSIS    (12)

Regression modeling, Multivariate analysis, Bayesian modeling, inference and Bayesian networks,
Support vector and kernel methods, Analysis of time series: linear systems analysis, nonlinear
dynamics - Rule induction - Neural networks: learning and generalization, competitive learning,
principal component analysis and neural networks; Fuzzy logic: extracting fuzzy models from data,
fuzzy decision trees, Stochastic search methods.

UNIT III      MINING DATA STREAMS    (8)

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 - Realtime Analytics Platform(RTAP)
applications - case studies - real time sentiment analysis, stock market predictions.

UNIT IV      FREQUENT ITEMSETS AND CLUSTERING    (9)

Mining Frequent itemsets - Market based model – Apriori Algorithm – Handling large data sets in Main
memory – Limited Pass algorithm – Counting frequent itemsets in a stream – Clustering Techniques –
Hierarchical – K- Means – Clustering high dimensional data – CLIQUE and PROCLUS – Frequent
pattern based clustering methods – Clustering in non-euclidean space – Clustering for streams and
Parallelism.

UNIT V      FRAMEWORKS AND VISUALIZATION    (8)

MapReduce – Hadoop, Hive, MapR – Sharding – NoSQL Databases - S3 - Hadoop Distributed file
systems – Visualizations - Visual data analysis techniques, interaction techniques; Systems and
applications:

                                                                                                                           TOTAL: 45 PERIODS

OUTCOMES:

The student should be made to:
 Apply the statistical analysis methods.
 Compare and contrast various soft computing frameworks.
 Design distributed file systems.
 Apply Stream data model.
 Use Visualisation techniques

TEXT 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.

REFERENCES:

1. Bill Franks, Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with
advanced analystics, John Wiley & sons, 2012.
2. Glenn J. Myatt, Making Sense of Data, John Wiley & Sons, 2007 Pete Warden, Big Data
Glossary, O’Reilly, 2011.
3. Jiawei Han, Micheline Kamber “Data Mining Concepts and Techniques”, Second Edition, Elsevier,
Reprinted 2008.

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IT6005 DIGITAL IMAGE PROCESSING | syllabus (ELECTIVE-III)

IT6005    DIGITAL IMAGE PROCESSING L T P C  3 0 0 3

                                                                         

OBJECTIVES:

The student should be made to:
 Learn digital image fundamentals.
 Be exposed to simple image processing techniques.
 Be familiar with image compression and segmentation techniques.
 Learn to represent image in form of features.

UNIT I      DIGITAL IMAGE FUNDAMENTALS    (8)

Introduction – Origin – Steps in Digital Image Processing – Components – Elements of Visual
Perception – Image Sensing and Acquisition – Image Sampling and Quantization – Relationships
between pixels - color models.

UNIT II      IMAGE ENHANCEMENT    (10)

Spatial Domain: Gray level transformations – Histogram processing – Basics of Spatial Filtering–
Smoothing and Sharpening Spatial Filtering – Frequency Domain: Introduction to Fourier Transform
– Smoothing and Sharpening frequency domain filters – Ideal, Butterworth and Gaussian filters.

UNIT III      IMAGE RESTORATION AND SEGMENTATION    (9)

Noise models – Mean Filters – Order Statistics – Adaptive filters – Band reject Filters – Band pass
Filters – Notch Filters – Optimum Notch Filtering – Inverse Filtering – Wiener filtering Segmentation:
Detection of Discontinuities–Edge Linking and Boundary detection – Region based segmentation-
Morphological processing- erosion and dilation.

UNIT IV      WAVELETS AND IMAGE COMPRESSION    (9)

Wavelets – Subband coding - Multiresolution expansions - Compression: Fundamentals – Image
Compression models – Error Free Compression – Variable Length Coding – Bit-Plane Coding –
Lossless Predictive Coding – Lossy Compression – Lossy Predictive Coding – Compression
Standards.

UNIT V      IMAGE REPRESENTATION AND RECOGNITION    (9)

Boundary representation – Chain Code – Polygonal approximation, signature, boundary segments –
Boundary description – Shape number – Fourier Descriptor, moments- Regional Descriptors –
Topological feature, Texture - Patterns and Pattern classes - Recognition based on matching.

                                                                                                                         TOTAL: 45 PERIODS

OUTCOMES:

Upon successful completion of this course, students will be able to:
 Discuss digital image fundamentals.
 Apply image enhancement and restoration techniques.
 Use image compression and segmentation Techniques.
 Represent features of images.

TEXT BOOK:

1. Rafael C. Gonzales, Richard E. Woods, “Digital Image Processing”, Third Edition, Pearson
Education, 2010.

REFERENCES:

1. Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins, “Digital Image Processing Using
MATLAB”, Third Edition Tata McGraw Hill Pvt. Ltd., 2011.
2. Anil Jain K. “Fundamentals of Digital Image Processing”, PHI Learning Pvt. Ltd., 2011.
3. Willliam K Pratt, “Digital Image Processing”, John Willey, 2002.
4. Malay K. Pakhira, “Digital Image Processing and Pattern Recognition”, First Edition, PHI Learning
Pvt. Ltd., 2011.
5. http://eeweb.poly.edu/~onur/lectures/lectures.html.
6. http://www.caen.uiowa.edu/~dip/LECTURE/lecture.html.

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