ABV
//
DR. A.P.J. ABDUL KALAM TECHNICAL UNIVERSITY
LUCKNOW
Evaluation Scheme & Syllabus
For
B.Tech. Fourth Year
(Information Technology)
On
Choice Based Credit System
(Effective from the Session: 201920)
DR. A.P.J. ABDUL KALAM TECHNICAL UNIVERSITY LUCKNOW
B.Tech. (Information Technology )
VII SEMESTER
SI.
No.
Subject Code
Subject Name
LTP
Th/Lab
Marks
ESE
Sessional
CT
Total Credit
TA
1
Open Elective1
Open Elective Course 1
300
70
20
10
100
3
2
IT Elective3
Deptt Elective Course3
300
70
20
10
100
3
3
IT Elective4
Deptt Elective Course4
310
70
20
10
100
4
4
RIT701
Cryptography & Network
Security
310
70
20
10
100
4
5
RCS702
Artificial Intelligence
300
70
20
10
100
3
6
RIT751
Cryptography & Network
Security Lab
002
50
50
100
1
7
RCS752
Artificial Intelligence Lab
002
50
50
100
1
8
RIT753
Industrial Training
003
100
100
2
9
RIT754
Project
006
200
200
3
450
1000
24
TOTAL
450
100
B.Tech. (Information Technology)
VIII SEMESTER
Subject Name
LTP
Th/Lab
Marks
ESE
1 Open Elective2
Open Elective Course2
300
70
20
2
IT Elective5
Deptt Elective Course5
310
70
3
IT Elective6
Deptt Elective Course6
300
70
4
RIT851
Seminar
003
5
RIT852
Project
0012
SI.
No.
Subject Code
TOTAL
Sessional
Total
Credit
10
100
3
20
10
100
4
20
10
100
3
100
100
2
250
600
12
380
1000
24
CT
350
560
60
DEPARTMENTAL ELECTIVES
ITELECTIVE 3
1.
2.
3.
4.
RIT070 Computer Graphics
RCS071 Application of Soft Computing
RCS072 High Performance Computing
RCS073 Human Computer Interface
TA
ITELECTIVE4
1. RCS075 Cloud Computing
2. RCS076 Blockchain Architecture Design
3. RCS077 Agile Software Development
4. RCS078 Augmented & Virtual Reality
ITELECTIVE5
1. RCS080 Machine Learning (Mapping with MOOCS: https://onlinecourses.nptel.ac.in/noc17_cs17/preview
https://onlinecourses.nptel.ac.in/noc17_cs26/preview)
2. RCS081 Game Programming
3. RCS082 Image Processing (Mapping with MOOCS: https://onlinecourses.nptel.ac.in/noc18_ee40/preview
https://nptel.ac.in/courses/106105032/
4. RCS083 Parallel and Distributed Computing (Mapping with MOOCS: https://nptel.ac.in/courses/106102114/,
https://nptel.ac.in/courses/106104024/)
ITELECTIVE6
1. RCS085 Speech Natural language processing (Mapping with MOOCS: https://nptel.ac.in/courses/106101007/
https://nptel.ac.in/courses/106105158/)
2. RCS086 Deep Learning (Mapping with MOOCS: https://onlinecourses.nptel.ac.in/noc18_cs41/preview )
3. RCS087 Data Compression
4. RCS088 Quantum Computing (Mapping with MOOCS: https://onlinecourses.nptel.ac.in/noc18_cy07)
B.TECH. (INFORMATION TECHNOLOGY)
VII & VIII SEMESTER (DETAILED SYLLABUS)
CRYPTOGRAPHY & NETWORK SECURITY
DETAILED SYLLABUS
Unit
I
II
III
310
Topic
Proposed
Lecture
Introduction to security attacks, services and mechanism, Classical encryption techniquessubstitution ciphers and transposition ciphers, cryptanalysis, steganography, Stream and block
ciphers. Modern Block Ciphers: Block ciphers principles, Shannon’s theory of confusion and
diffusion, fiestal structure, Data encryption standard(DES), Strength of DES, Idea of differential
cryptanalysis, block cipher modes of operations, Triple DES
Introduction to group, field, finite field of the form GF(p), modular arithmetic, prime and relative
prime numbers, Extended Euclidean Algorithm, Advanced Encryption Standard (AES) encryption
and decryptionFermat’s and Euler’s theorem, Primarily testing, Chinese Remainder theorem,
Discrete Logarithmic Problem,Principals of public key crypto systems, RSA algorithm, security of
RSA
Message Authentication Codes: Authentication requirements, authentication functions, message
authentication code, hash functions, birthday attacks, security of hash functions, Secure hash
algorithm (SHA) Digital Signatures: Digital Signatures, Elgamal Digital Signature Techniques, Digital
signature standards (DSS), proof of digital signature algorithm,
Key Management and distribution: Symmetric key distribution, DiffieHellman Key Exchange,
Public key distribution, X.509 Certificates, Public key Infrastructure. Authentication Applications:
Kerberos, Electronic mail security: pretty good privacy (PGP), S/MIME.
IP Security: Architecture, Authentication header, Encapsulating security payloads, combining
security associations, key management. Introduction to Secure Socket Layer, Secure electronic,
V
transaction (SET) System Security: Introductory idea of Intrusion, Intrusion detection, Viruses and
related threats, firewalls
Text books:
IV
1. William Stallings, “Cryptography and Network Security: Principals and Practice”, Pearson Education.
2. Behrouz A. Frouzan: Cryptography and Network Security, Tata McGraw Hill
3. C K Shyamala, N Harini, Dr. T.R.Padmnabhan Cryptography and Security ,Wiley
4. Bruce Schiener, “Applied Cryptography”. John Wiley & Sons
5. Bernard Menezes,” Network Security and Cryptography”, Cengage Learning.
6. AtulKahate, “Cryptography and Network Security”, Tata McGraw Hill
08
08
08
08
08
ARTIFICIAL INTELLIGENCE
DETAILED SYLLABUS
Unit
I
II
III
300
Topic
Proposed
Lecture
Introduction: Introduction to Artificial Intelligence, Foundations and History of Artificial
Intelligence, Applications of Artificial Intelligence, Intelligent Agents, Structure of Intelligent
Agents. Computer vision, Natural Language Possessing.
Introduction to Search : Searching for solutions, Uniformed search strategies, Informed search
strategies, Local search algorithms and optimistic problems, Adversarial Search, Search for games,
Alpha  Beta pruning
Knowledge Representation & Reasoning: Propositional logic, Theory of first order logic, Inference
in First order logic, Forward & Backward chaining, Resolution, Probabilistic reasoning, Utility
theory, Hidden Markov Models (HMM), Bayesian Networks.
Machine Learning : Supervised and unsupervised learning, Decision trees, Statistical learning
models, Learning with complete data  Naive Bayes models, Learning with hidden data  EM
algorithm, Reinforcement learning,
Pattern Recognition : Introduction, Design principles of pattern recognition system, Statistical
Pattern recognition, Parameter estimation methods  Principle Component Analysis (PCA) and
V
Linear Discriminant Analysis (LDA), Classification Techniques – Nearest Neighbor (NN) Rule, Bayes
Classifier, Support Vector Machine (SVM), K – means clustering.
Text books:
IV
08
08
08
08
08
1. Stuart Russell, Peter Norvig, “Artificial Intelligence – A Modern Approach”, Pearson Education
2. Elaine Rich and Kevin Knight, “Artificial Intelligence”, McGrawHill
3. E Charniak and D McDermott, “Introduction to Artificial Intelligence”, Pearson Education
4. Dan W. Patterson, “Artificial Intelligence and Expert Systems”, Prentice Hall of India,
CRYPTOGRAPHY & NETWORK SECURITY LAB
The following programs may be developed 1.Write a C program that contains a string (char pointer) with a value \Hello World’. The program should XOR each
character in this string with 0 and displays the result.
2.Write a C program that contains a string (char pointer) with a value \Hello World’. The program should AND or and
XOR each character in this string with 127 and display the result
3.Write a Java program to perform encryption and decryption using the following algorithms:
a) Ceaser Cipher
b) Substitution Cipher
c) Hill Cipher
4. Write a Java program to implement the DES algorithm logic
5.Write a C/JAVA program to implement the BlowFish algorithm logic
6.Write a C/JAVA program to implement the Rijndael algorithm logic.
7.Using Java Cryptography, encrypt the text “Hello world” using BlowFish. Create your own key using Java keytool.
8. Write a Java program to implement RSA Algoithm
9.Implement the DiffieHellman Key Exchange mechanism using HTML and JavaScript. Consider the end user as one of
the parties (Alice) and the JavaScript application as other party (bob).
10.Calculate the message digest of a text using the SHA1 algorithm in JAVA.
11. Calculate the message digest of a text using the SHA1 algorithm in JAVA.
Artificial Intelligence Lab
The following programs may be developed 1.Study of Prolog.
2 Write simple fact for the statements using PROLOG.
3 Write predicates One converts centigrade temperatures to Fahrenheit, the other checks if a temperature is below
freezing.
4 Write a program to solve the Monkey Banana problem.
5 WAP in turbo prolog for medical diagnosis and show the advantage and disadvantage of green and red cuts.
6 WAP to implement factorial, fibonacci of a given number.
7 Write a program to solve 4Queen problem.
8 Write a program to solve traveling salesman problem.
9 Write a program to solve water jug problem using LISP
COMPUTER GRAPHICS
DETAILED SYLLABUS
Unit
Topic
Introduction and Line Generation: Types of computer graphics, Graphic Displays Random scan
displays, Raster scan displays, Frame buffer and video controller, Points and lines, Line drawing
algorithms, Circle generating algorithms, Midpoint circle generating algorithm, and parallel
version of these algorithms.
Transformations: Basic transformation, Matrix representations and homogenous coordinates,
Composite transformations, Reflections and shearing.
Windowing and Clipping: Viewing pipeline, Viewing transformations, 2D Clipping algorithmsII
Line clipping algorithms such as Cohen Sutherland line clipping algorithm, Liang Barsky
algorithm, Line clipping against non rectangular clip windows; Polygon clipping – Sutherland
Hodgeman polygon clipping, Weiler and Atherton polygon clipping, Curve clipping, Text clipping
Three Dimensional: 3D Geometric Primitives, 3D Object representation, 3D Transformation, 3III
D viewing, projections, 3D Clipping.
Curves and Surfaces: Quadric surfaces, Spheres, Ellipsoid, Blobby objects, Introductory concepts
IV
of Spline, Bspline and Bezier curves and surfaces.
Hidden Lines and Surfaces: Back Face Detection algorithm, Depth buffer method, A buffer
method, Scan line method, basic illumination models– Ambient light, Diffuse reflection, Specular
V
reflection and Phong model, Combined approach, Warn model, Intensity Attenuation, Color
consideration, Transparency and Shadows.
Text books:
I
Donald Hearn and M Pauline Baker, “Computer Graphics C Version”, Pearson Education
2. Foley, Vandam, Feiner, Hughes – “Computer Graphics principle”, Pearson Education.
3. Rogers, “ Procedural Elements of Computer Graphics”, McGraw Hill
4. W. M. Newman, R. F. Sproull – “Principles of Interactive computer Graphics” – Tata MCGraw Hill.
5. Amrendra N Sinha and Arun D Udai,” Computer Graphics”, Tata MCGraw Hill.
6. R.K. Maurya, “Computer Graphics ” Wiley Dreamtech Publication.
7. Mukherjee, Fundamentals of Computer graphics & Multimedia, PHI Learning Private Limited.
8. Donald Hearn and M Pauline Baker, “Computer Graphics with OpenGL”, Pearson education
300
Proposed
Lecture
08
08
08
08
08
APPLICATION OF SOFT COMPUTING
DETAILED SYLLABUS
Unit
Topic
Neural NetworksI (Introduction & Architecture) : Neuron, Nerve structure and synapse,
Artificial Neuron and its model, activation functions, Neural network architecture: single layer and
multilayer feed forward networks, recurrent networks. Various learning techniques; perception and
convergence rule, Autoassociative and hetroassociative memory.
Neural NetworksII (Back propogation networks): Architecture: perceptron model, solution,
single layer artificial neural network, multilayer perception model; back propogation learning
II
methods, effect of learning rule coefficient ;back propagation algorithm, factors affecting
backpropagation training, applications.
Fuzzy LogicI (Introduction): Basic concepts of fuzzy logic, Fuzzy sets and Crisp sets, Fuzzy set
III
theory and operations, Properties of fuzzy sets, Fuzzy and Crisp relations, Fuzzy to Crisp
conversion.
Fuzzy Logic –II (Fuzzy Membership, Rules) : Membership functions, interference in fuzzy logic,
IV
fuzzy ifthen rules, Fuzzy implications and Fuzzy algorithms, Fuzzyfications & Defuzzificataions,
Fuzzy Controller, Industrial applications
Genetic Algorithm(GA): Basic concepts, working principle, procedures of GA, flow chart of GA,
V
Genetic representations, (encoding) Initialization and selection, Genetic operators, Mutation,
Generational Cycle, applications.
Text books:
I
300
Proposed
Lecture
08
08
08
08
08
1. S. Rajsekaran & G.A. Vijayalakshmi Pai, “Neural Networks,Fuzzy Logic and Genetic Algorithm:Synthesis and
Applications” Prentice Hall of India.
2. N.P.Padhy,”Artificial Intelligence and Intelligent Systems” Oxford University Press. Reference Books:
3. Siman Haykin,”Neural Netowrks”Prentice Hall of India
4. Timothy J. Ross, “Fuzzy Logic with Engineering Applications” Wiley India.
5. Kumar Satish, “Neural Networks” Tata Mc Graw Hill
HIGH PERFORMANCE COMPUTING
DETAILED SYLLABUS
300
Unit
Topic
I
Overview of Grid Computing Technology, History of Grid Computing, High Performance
Computing, Cluster Computing. PeertoPeer Computing, Internet Computing, Grid Computing
Model and Protocols, Types of Grids: Desktop Grids, Cluster Grids, Data Grids, HighPerformance Grids, Applications and Architectures of High Performance Grids, High Performance
Application Development Environment.
08
II
Open Grid Services Architecture,
Considerations, GLOBUS Toolkit.
08
III
Overview of Cluster Computing, Cluster Computer and its Architecture, Clusters Classifications,
Components for Clusters, Cluster Middleware and SSI, Resource Management and Scheduling,
Programming, Environments and Tools, Cluster Applications, Cluster Systems,
08
IV
Beowulf Cluster: The Beowulf Model, Application Domains, Beowulf System Architecture,
Software Practices, Parallel Programming with MPL, Parallel Virtual Machine (PVM).
08
V
Overview of Cloud Computing, Types of Cloud, Cyber infrastructure, Service Oriented
Architecture Cloud Computing Components: Infrastructure, Storage, Platform, Application,
Services, Clients, Cloud Computing Architecture.
08
Introduction,
Proposed
Lecture
Requirements,
Capabilities,
Security
Text books:
1. Laurence T.Yang, Minyi Guo – High Performance Computing Paradigm and Infrastructure John Wiley
2. Ahmar Abbas, “Grid Computing: Practical Guide to Technology & Applications”, Firewall Media, 2004.
3. Joshy Joseph and Craig Fellenstein , “Grid Computing” Pearson Education, 2004.
4. lan Foster, et al.,“The Open Grid Services Architecture”, Version 1.5 (GFD.80). Open Grid Forum, 2006.
6. RajkumarBuyya. High Performance Cluster Computing: Architectures and Systems. PrenticeHall India, 1999.
HUMAN COMPUTER INTERFACE
DETAILED SYLLABUS
300
Unit
Topic
Proposed
Lecture
I
Introduction : Importance of user Interface – definition, importance of 8 good design. Benefits of
good design. A brief history of Screen design. The graphical user interface – popularity of graphics,
the concept of direct manipulation, graphical system, Characteristics, Web user – Interface
popularity, characteristics Principles of user interface
08
II
Design process: Human interaction with computers, importance of 8 human characteristics human
consideration, Human interaction speeds, understanding business junctions. III Screen Designing :
Design goals – Scre
08
III
Screen Designing : Design goals – Screen planning and purpose, 8 organizing screen elements,
ordering of screen data and content – screen navigation and flow – Visually pleasing composition –
amount of information – focus and emphasis – presentation information simply and meaningfully –
information retrieval on web – statistical graphics – Technological consideration in interface
design.
08
IV
Windows : New and Navigation schemes selection of window, 8 selection of devices based and
screen based controls. Components – text and messages, Icons and increases – Multimedia, colors,
uses problems, choosing colors
08
V
Software tools : Specification methods, interface – Building Tools. 8 Interaction Devices –
Keyboard and function keys – pointing devices – speech recognition digitization and generation –
image and video displays – drivers.
08
Text books:
1. Alan Dix, Janet Finlay, Gregory Abowd, Russell Beale Human Computer Interaction, 3rd Edition Prentice Hall, 2004.
2. Jonathan Lazar Jinjuan Heidi Feng, Harry Hochheiser, Research Methods in HumanComputer Interaction, Wiley, 2010.
3. Ben Shneiderman and Catherine Plaisant Designing the User Interface: Strategies for Effective HumanComputer
Interaction (5th Edition, pp. 672, ISBN 0 321537351, March 2009), Reading, MA: AddisonWesley Publishing Co.
CLOUD COMPUTING
DETAILED SYLLABUS
Unit
Topic
310
Proposed
Lecture
INTRODUCTION
Introduction to Cloud Computing – Definition of Cloud – Evolution of Cloud Computing –
08
Underlying Principles of Parallel and Distributed Computing – Cloud Characteristics – Elasticity in
Cloud – Ondemand Provisioning.
CLOUD ENABLING TECHNOLOGIES
Service Oriented Architecture – REST and Systems of Systems – Web Services – PublishII
08
Subscribe Model – Basics of Virtualization – Types of Virtualization – Implementation Levels of
Virtualization – Virtualization Structures – Tools and Mechanisms – Virtualization of CPU –
Memory – I/O Devices –Virtualization Support and Disaster Recovery.
CLOUD ARCHITECTURE, SERVICES AND STORAGE
Layered Cloud Architecture Design – NIST Cloud Computing Reference Architecture – Public,
III
08
Private and Hybrid Clouds – laaS – PaaS – SaaS – Architectural Design Challenges – Cloud
Storage – StorageasaService – Advantages of Cloud Storage – Cloud Storage Providers – S3.
RESOURCE MANAGEMENT AND SECURITY IN CLOUD
Inter Cloud Resource Management – Resource Provisioning and Resource Provisioning Methods –
IV
08
Global Exchange of Cloud Resources – Security Overview – Cloud Security Challenges –
SoftwareasaService Security – Security Governance – Virtual Machine Security – IAM –
Security Standards.
CLOUD TECHNOLOGIES AND ADVANCEMENTS
Hadoop – MapReduce – Virtual Box — Google App Engine – Programming Environment for
V
08
Google App Engine –– Open Stack – Federation in the Cloud – Four Levels of Federation –
Federated Services and Applications – Future of Federation.
Text 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. Rittinghouse, John W., and James F. Ransome, ―Cloud Computing: Implementation, Management and Security,
CRC Press, 2017.
3. Rajkumar Buyya, Christian Vecchiola, S. ThamaraiSelvi, ―Mastering Cloud Computing, Tata Mcgraw Hill, 2013.
4. Toby Velte, Anthony Velte, Robert Elsenpeter, “Cloud Computing – A Practical Approach, Tata Mcgraw Hill, 2009.
5. George Reese, “Cloud Application Architectures: Building Applications and Infrastructure in the Cloud:
Transactional Systems for EC2 and Beyond (Theory in Practice), O’Reilly, 2009.
I
BLOCKCHAIN ARCHITECTURE DESIGN
DETAILED SYLLABUS
Unit
310
Topic
Proposed
Lecture
Introduction to Blockchain: Digital Money to Distributed Ledgers , Design Primitives: Protocols,
Security, Consensus, Permissions, Privacy.
Blockchain Architecture and Design: Basic crypto primitives: Hash, Signature,) Hashchain to
Blockchain, Basic consensus mechanisms
Consensus: Requirements for the consensus protocols, Proof of Work (PoW), Scalability aspects
II
of Blockchain consensus protocols
Permissioned Blockchains:Design goals, Consensus protocols for Permissioned Blockchains
Hyperledger Fabric (A): Decomposing the consensus process , Hyperledger fabric components,
Chaincode Design and Implementation
III
Hyperledger Fabric (B): Beyond Chaincode: fabric SDK and Front End (b) Hyperledger
composer tool
Use case 1 : Blockchain in Financial Software and Systems (FSS): (i) Settlements, (ii) KYC, (iii)
Capital markets, (iv) Insurance
IV
Use case 2: Blockchain in trade/supply chain: (i) Provenance of goods, visibility, trade/supply
chain finance, invoice management discounting, etc
Use case 3: Blockchain for Government: (i) Digital identity, land records and other kinds of record
V
keeping between government entities, (ii) public distribution system social welfare systems
Blockchain Cryptography, Privacy and Security on Blockchain
Text books:
1. Mstering Bitcoin: Unlocking Digital Cryptocurrencies, by Andreas Antonopoulos
I
08
08
08
08
08
2. Blockchain by Melanie Swa, O’Reilly
3. Hyperledger Fabric  https://www.hyperledger.org/projects/fabric
4. Zero
to
Blockchain

An
IBM
Redbooks
course,
by
Bob
https://www.redbooks.ibm.com/Redbooks.nsf/RedbookAbstracts/crse0401.html
Dill,
David
Smits

AGILE SOFTWARE DEVELOPMENT
DETAILED SYLLABUS
Unit
Topic
310
Proposed
Lecture
AGILE METHODOLOGY
Theories for Agile Management – Agile Software Development – Traditional Model vs. Agile
08
I
Model – Classification of Agile Methods – Agile Manifesto and Principles – Agile Project
Management – Agile Team Interactions – Ethics in Agile Teams – Agility in Design, Testing –
Agile Documentations – Agile Drivers, Capabilities and Values
AGILE PROCESSES
Lean Production – SCRUM, Crystal, Feature Driven Development Adaptive Software
08
II
Development – Extreme Programming: Method Overview – Lifecycle – Work Products, Roles and
Practices.
AGILITY AND KNOWLEDGE MANAGEMENT
Agile Information Systems – Agile Decision Making – Earl‗S Schools of KM – Institutional
Knowledge Evolution Cycle – Development, Acquisition, Refinement, Distribution, Deployment ,
08
III
Leveraging – KM in Software Engineering – Managing Software Knowledge – Challenges of
Migrating to Agile Methodologies – Agile Knowledge Sharing – Role of StoryCards – StoryCard
Maturity Model (SMM).
AGILITY AND REQUIREMENTS ENGINEERING
Impact of Agile Processes in RE–Current Agile Practices – Variance – Overview of RE Using
Agile – Managing Unstable Requirements – Requirements Elicitation – Agile Requirements
08
IV
Abstraction Model – Requirements Management in Agile Environment, Agile Requirements
Prioritization – Agile Requirements Modeling and Generation – Concurrency in Agile
Requirements Generation.
AGILITY AND QUALITY ASSURANCE
Agile Product Development – Agile Metrics – Feature Driven Development (FDD) – Financial and
08
V
Production Metrics in FDD – Agile Approach to Quality Assurance – Test Driven Development –
Agile Approach in Global Software Development.
Text books:
1.David J. Anderson and Eli Schragenheim, "Agile Management for Software Engineering: Applying the Theory of
Constraints for Business Results", Prentice Hall, 2003.
2.Hazza and Dubinsky, "Agile Software Engineering, Series: Undergraduate Topics in Computer Science", Springer,
2009.
3.Craig Larman, "Agile and Iterative Development: A Managers Guide", AddisonWesley, 2004.
4.Kevin C. Desouza, "Agile Information Systems: Conceptualization, Construction, and Management", ButterworthHeinemann, 2007.
AUGMENTED & VIRTUAL REALITY
DETAILED SYLLABUS
Unit
Topic
I
VIRTUAL REALITY AND VIRTUAL ENVIRONMENTS: The historical development of VR:
Scientific landmarks Computer Graphics, Realtime computer graphics, Flight simulation, Virtual
environments, Requirements for VR, benefits of Virtual reality.
310
Proposed
Lecture
08
HARDWARE TECHNOLOGIES FOR 3D USER INTERFACES: Visual Displays Auditory
Displays, Haptic Displays, Choosing Output Devices for 3D User Interfaces.
II
3D USER INTERFACE INPUT HARDWARE: Input device characteristics, Desktop input
devices, Tracking Devices, 3D Mice, Special Purpose Input Devices, Direct Human Input, Home Brewed Input Devices, Choosing Input Devices for 3D Interfaces.
08
III
SOFTWARE TECHNOLOGIES: Database  World Space, World Coordinate, World
Environment, Objects  Geometry, Position / Orientation, Hierarchy, Bounding Volume, Scripts
and other attributes, VR Environment  VR Database, Tessellated Data, LODs, Cullers and
Occluders, Lights and Cameras, Scripts, Interaction  Simple, Feedback, Graphical User Interface,
Control Panel, 2D Controls, Hardware Controls, Room / Stage / Area Descriptions, World
Authoring and Playback, VR toolkits, Available software in the market
08
IV
3D INTERACTION TECHNIQUES: 3D Manipulation tasks, Manipulation Techniques and
Input Devices, Interaction Techniques for 3D Manipulation, Deign Guidelines  3D Travel Tasks,
Travel Techniques, Design Guidelines  Theoretical Foundations of Wayfinding, User Centered
Wayfinding Support, Environment Centered Wayfinding Support, Evaluating Wayfinding Aids,
Design Guidelines  System Control, Classification, Graphical Menus, Voice Commands, Gestrual
Commands, Tools, Mutimodal System Control Techniques, Design Guidelines, Case Study:
Mixing System Control Methods, Symbolic Input Tasks, symbolic Input Techniques, Design
Guidelines, Beyond Text and Number entry .
08
DESIGNING AND DEVELOPING 3D USER INTERFACES: Strategies for Designing and
Developing Guidelines and Evaluation.
VIRTUAL REALITY APPLICATIONS: Engineering, Architecture, Education, Medicine,
Entertainment, Science, Training.
V
Augmented and Mixed Reality, Taxonomy, technology and features of augmented reality,
difference between AR and VR, Challenges with AR, AR systems and functionality, Augmented
reality methods, visualization techniques for augmented reality, wireless displays in educational
augmented reality applications, mobile projection interfaces, markerless tracking for augmented
reality, enhancing interactivity in AR environments, evaluating AR systems.
08
Text books:
1. Alan B Craig, William R Sherman and Jeffrey D Will, “Developing Virtual Reality Applications: Foundations of
Effective Design”, Morgan Kaufmann, 2009.
2. Gerard Jounghyun Kim, “Designing Virtual Systems: The Structured Approach”, 2005.
3. Doug A Bowman, Ernest Kuijff, Joseph J LaViola, Jr and Ivan Poupyrev, “3D User Interfaces, Theory and Practice”,
Addison Wesley, USA, 2005.
4. Oliver Bimber and Ramesh Raskar, “Spatial Augmented Reality: Meging Real and Virtual Worlds”, 2005.
5. Burdea, Grigore C and Philippe Coiffet, “Virtual Reality Technology”, Wiley Interscience, India, 2003.
6. John Vince, “Virtual Reality Systems”, Addison Wesley, 1995.
7. Howard Rheingold, “Virtual Reality: The Revolutionary Technology and how it Promises to Transform Society”,
Simon and Schuster, 1991.
8. William R Sherman and Alan B Craig, “Understanding Virtual Reality: Interface, Application and Design (The Morgan
Kaufmann Series in Computer Graphics)”. Morgan Kaufmann Publishers, San Francisco, CA, 2002
9. Alan B. Craig, Understanding Augmented Reality, Concepts and Applications, Morgan Kaufmann, 2013.
MACHINE LEARNING
DETAILED SYLLABUS
Unit
Topic
310
Proposed
Lecture
INTRODUCTION – Well defined learning problems, Designing a Learning System, Issues in
Machine Learning; THE CONCEPT LEARNING TASK  Generaltospecific ordering of
hypotheses, FindS, List then eliminate algorithm, Candidate elimination algorithm, Inductive bias
DECISION TREE LEARNING  Decision tree learning algorithmInductive bias Issues in
Decision tree learning;
II
ARTIFICIAL NEURAL NETWORKS – Perceptrons, Gradient descent and the Delta rule, Adaline,
Multilayer networks, Derivation of backpropagation rule Backpropagation AlgorithmConvergence,
Generalization;
Evaluating Hypotheses: Estimating Hypotheses Accuracy, Basics of sampling Theory, Comparing
Learning Algorithms;
III
Bayesian Learning: Bayes theorem, Concept learning, Bayes Optimal Classifier, Naïve Bayes
classifier, Bayesian belief networks, EM algorithm;
Computational Learning Theory: Sample Complexity for Finite Hypothesis spaces, Sample
Complexity for Infinite Hypothesis spaces, The Mistake Bound Model of Learning;
IV
INSTANCEBASED LEARNING – kNearest Neighbour Learning, Locally Weighted Regression,
Radial basis function networks, Casebased learning
Genetic Algorithms: an illustrative example, Hypothesis space search, Genetic Programming,
Models of Evolution and Learning; Learning first order rulessequential covering algorithmsV
General to specific beam searchFOIL; REINFORCEMENT LEARNING  The Learning Task, Q
Learning.
Text books:
1. Tom M. Mitchell, ―Machine Learning, McGrawHill Education (India) Private Limited, 2013.
2. Ethem Alpaydin, ―Introduction to Machine Learning (Adaptive Computation and
Machine Learning), The MIT Press 2004.
3. Stephen Marsland, ―Machine Learning: An Algorithmic Perspective, CRC Press, 2009.
4. Bishop, C., Pattern Recognition and Machine Learning. Berlin: SpringerVerlag.
I
08
08
08
08
08
GAME PROGRAMMING
DETAILED SYLLABUS
Unit
Topic
310
Proposed
Lecture
3D GRAPHICS FOR GAME PROGRAMMING :
3D Transformations, Quaternions, 3D Modeling And Rendering, Ray Tracing, Shader
I
Models, Lighting, Color, Texturing, Camera And Projections, Culling And Clipping, Character
Animation, PhysicsBased Simulation, Scene Graphs.
GAME ENGINE DESIGN:
Game Engine Architecture, Engine Support Systems, Resources And File Systems, Game Loop
II
And RealTime Simulation, Human Interface Devices, Collision And Rigid Body Dynamics, Game
Profiling.
GAME PROGRAMMING :
III
Application Layer, Game Logic, Game Views, Managing Memory, Controlling The Main Loop,
Loading And Caching Game Data, User Interface Management, Game Event Management.
GAMING PLATFORMS AND FRAMEWORKS:
IV
2D And 3D Game Development Using Flash, DirectX, Java, Python, Game Engines – DX
Studio, Unity.
GAME DEVELOPMENT:
V
Developing 2D And 3D Interactive Games Using DirectX Or Python – Isometric And Tile Based
Games, Puzzle Games, Single Player Games, Multi Player Games.
Text books:
08
08
08
08
08
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 RealTime Computer
Graphics” 2nd Editions, Morgan Kaufmann, 2006.
4. Ernest Adams And Andrew Rollings, “Fundamentals Of Game Design”, 2nd Edition Prentice Hall / New Riders,
2009.
5. Eric Lengyel, “Mathematics For 3D Game Programming And Computer Graphics”, 3rd Edition, Course
Technology PTR, 2011.
6. Jesse Schell, The Art Of Game Design: A Book Of Lenses, 1st Edition, CRC Press, 2008.
IMAGE PROCESSING
DETAILED SYLLABUS
Unit
Topic
310
Proposed
Lecture
DIGITAL IMAGE FUNDAMENTALS: Steps in Digital Image Processing – Components –
Elements of Visual Perception – Image Sensing and Acquisition – Image Sampling and
I
08
Quantization – Relationships between pixels – Color image fundamentals – RGB, HSI models,
Twodimensional mathematical preliminaries, 2D transforms – DFT, DCT.
IMAGE ENHANCEMENT :
Spatial Domain: Gray level transformations – Histogram processing – Basics of Spatial Filtering–
Smoothing and Sharpening Spatial Filtering, Frequency Domain: Introduction to Fourier
II
08
Transform– Smoothing and Sharpening frequency domain filters – Ideal, Butterworth and Gaussian
filters, Homomorphic filtering, Color image enhancement.
IMAGE RESTORATION :
Image Restoration – degradation model, Properties, Noise models – Mean Filters – Order Statistics
III
08
– Adaptive filters – Band reject Filters – Band pass Filters – Notch Filters – Optimum Notch
Filtering – Inverse Filtering – Wiener filtering
IMAGE SEGMENTATION:
Edge detection, Edge linking via Hough transform – Thresholding – Region based segmentation –
Region growing – Region splitting and merging – Morphological processing erosion and dilation,
IV
08
Segmentation by morphological watersheds – basic concepts – Dam construction – Watershed
segmentation algorithm.
IMAGE COMPRESSION AND RECOGNITION:
Need for data compression, Huffman, Run Length Encoding, Shift codes, Arithmetic coding, JPEG
standard, MPEG. Boundary representation, Boundary description, Fourier Descriptor, Regional
V
08
Descriptors – Topological feature, Texture – Patterns and Pattern classes – Recognition based on
matching.
Text books:
1.
Rafael C. Gonzalez, Richard E. Woods,Digital Image Processing Pearson, Third Edition, 2010
2.
Anil K. Jain,Fundamentals of Digital Image Processing Pearson, 2002.
3.
Kenneth R. Castleman,Digital Image Processing Pearson, 2006.
4.
Rafael C. Gonzalez, Richard E. Woods, Steven Eddins,Digital Image Processing using MATLAB Pearson
Education, Inc., 2011.
5.
D,E. Dudgeon and RM. Mersereau,Multidimensional Digital Signal Processing Prentice Hall Professional
Technical Reference, 1990.
6.
William K. Pratt,Digital Image Processing John Wiley, New York, 2002
7.
Milan Sonka et al Image processing, analysis and machine vision Brookes/Cole, Vikas Publishing House, 2nd
edition, 1999
PARALLEL AND DISTRIBUTED COMPUTING
DETAILED SYLLABUS
Unit
Topic
I
Introduction: Scope , issues, applications and challenges of Parallel and Distributed Computing
Parallel Programming Platforms: Implicit Parallelism: Trends in Microprocessor
Architectures, Dichotomy of Parallel Computing Platforms, Physical Organization, Communication
Costs in Parallel Machines, Routing Mechanisms for Interconnection Networks, GPU, coprocessing.
Principles of Parallel Algorithm Design: Decomposition Techniques,Characteristics of Tasks
and Interactions,Mapping Techniques for Load Balancing.
II
III
IV
CUDA programming model: Overview of CUDA, Isolating data to be used by parallelized
code, API function to allocate memory on parallel computing device, to transfer data, Concepts of
Threads, Blocks, Grids, Developing a kernel function to be executed by individual threads,
Execution of kernel function by parallel threads, transferring data back to host processor with API
function.
Analytical Modeling of Parallel Programs: Sources of Overhead in Parallel Programs,
Performance Metrics for Parallel Systems, The Effect of Granularity on Performance, Scalability of
Parallel Systems, Minimum Execution Time and Minimum CostOptimal Execution Time
Dense Matrix Algorithms: MatrixVector Multiplication, MatrixMatrix Multiplication, Issues
in Sorting on Parallel Computers, Bubble Sort and Variants, Quick Sort, Other Sorting Algorithms
Graph Algorithms: Minimum Spanning Tree: Prim's Algorithm, SingleSource Shortest Paths:
Dijkstra's Algorithm, AllPairs Shortest Paths, Transitive Closure, Connected Components,
Algorithms for Sparse Graph
310
Proposed
Lecture
08
08
08
08
Search Algorithms for Discrete Optimization Problems: Sequential Search Algorithms,
08
Parallel DepthFirst Search, Parallel BestFirst Search, Speedup Anomalies in Parallel Search
Algorithms
Text books:
1.
A Grama, A Gupra, G Karypis, V Kumar. Introduction to Parallel Computing (2nd ed.). Addison Wesley, 2003.
2.
C Lin, L Snyder. Principles of Parallel Programming. USA: AddisonWesley Publishing Company, 2008.
3.
J Jeffers, J Reinders. Intel Xeon Phi Coprocessor HighPerformance Programming. Morgan Kaufmann Publishing
and Elsevier, 2013.
4.
T Mattson, B Sanders, B Massingill. Patterns for Parallel Programming. AddisonWesley Professional, 2004.
V
SPEECH AND NATURAL LANGUAGE PROCESSING
DETAILED SYLLABUS
Unit
Topic
300
Proposed
Lecture
INTRODUCTION :
Origins and challenges of NLP – Language Modeling: Grammarbased LM, Statistical LM –
Regular Expressions, FiniteState Automata – English Morphology, Transducers for lexicon and
I
rules, Tokenization, Detecting and Correcting Spelling Errors, Minimum Edit Distance
08
WORD LEVEL ANALYSIS
Unsmoothed Ngrams, Evaluating Ngrams, Smoothing, Interpolation and Backoff – Word Classes,
PartofSpeech Tagging, Rulebased, Stochastic and Transformationbased tagging, Issues in PoS
tagging – Hidden Markov and Maximum Entropy models.
SYNTACTIC ANALYSIS
ContextFree Grammars, Grammar rules for English, Treebanks, Normal Forms for grammar –
II
Dependency Grammar – Syntactic Parsing, Ambiguity, Dynamic Programming parsing – Shallow
08
parsing – Probabilistic CFG, Probabilistic CYK, Probabilistic Lexicalized CFGs – Feature
structures, Unification of feature structures.
SEMANTICS AND PRAGMATICS
Requirements for representation, FirstOrder Logic, Description Logics – SyntaxDriven Semantic
III
analysis, Semantic attachments – Word Senses, Relations between Senses, Thematic Roles,
08
selectional restrictions – Word Sense Disambiguation, WSD using Supervised, Dictionary &
Thesaurus, Bootstrapping methods – Word Similarity using Thesaurus and Distributional methods.
BASIC CONCEPTS of Speech Processing :
IV
Speech Fundamentals: Articulatory Phonetics – Production And Classification Of Speech Sounds;
08
Acoustic Phonetics – Acoustics Of Speech Production; Review Of Digital Signal Processing
Concepts; ShortTime Fourier Transform, FilterBank And LPC Methods.
SPEECH ANALYSIS:
Features, Feature Extraction And Pattern Comparison Techniques: Speech Distortion Measures–
Mathematical And Perceptual – Log–Spectral Distance, Cepstral Distances, Weighted Cepstral
Distances And Filtering, Likelihood Distortions, Spectral Distortion Using A Warped Frequency
V
Scale, LPC, PLP And MFCC Coefficients, Time Alignment And Normalization – Dynamic Time
08
Warping, Multiple Time – Alignment Paths.
UNIT III : SPEECH MODELING :
Hidden Markov Models: Markov Processes, HMMs – Evaluation, Optimal State Sequence –
Viterbi Search, BaumWelch Parameter ReEstimation, Implementation Issues.
Text books:
1.
Daniel Jurafsky, James H. Martin―Speech and Language Processing: An Introduction to Natural Language
Processing, Computational Linguistics and Speech, Pearson Publication, 2014.
2.
Steven Bird, Ewan Klein and Edward Loper, ―Natural Language Processing with Python, First Edition, OReilly
Media, 2009.
3.
Lawrence Rabiner And BiingHwang Juang, “Fundamentals Of Speech Recognition”, Pearson Education, 2003.
4.
Daniel Jurafsky And James H Martin, “Speech And Language Processing – An Introduction To Natural Language
Processing, Computational Linguistics, And Speech Recognition”, Pearson Education, 2002.
5.
Frederick Jelinek, “Statistical Methods Of Speech Recognition”, MIT Press, 1997.
6.
1. Breck Baldwin, ―Language Processing with Java and LingPipe Cookbook, Atlantic Publisher, 2015.
7.
Richard M Reese, ―Natural Language Processing with Java, OReilly Media, 2015.
8.
Nitin Indurkhya and Fred J. Damerau, ―Handbook of Natural Language Processing, Second Edition, Chapman
and Hall/CRC Press, 2010.
9.
Tanveer Siddiqui, U.S. Tiwary, ―Natural Language Processing and Information Retrieval, Oxford University
Press, 2008.
DEEP LEARNING
DETAILED SYLLABUS
300
Unit
Topic
I
INTRODUCTION : Introduction to machine learning Linear models (SVMs and Perceptrons,
logistic regression) Intro to Neural Nets: What a shallow network computes Training a network:
loss functions, back propagation and stochastic gradient descent Neural networks as universal
function approximates
08
II
DEEP NETWORKS : History of Deep Learning A Probabilistic Theory of Deep LearningBackpropagation and regularization, batch normalization VC Dimension and Neural NetsDeep Vs
Shallow NetworksConvolutional Networks Generative Adversarial Networks (GAN), Semisupervised Learning
08
III
DIMENTIONALITY REDUCTION 9 Linear (PCA, LDA) and manifolds, metric learning  Auto
encoders and dimensionality reduction in networks  Introduction to Convnet  Architectures –
AlexNet, VGG, Inception, ResNet  Training a Convnet: weights initialization, batch
normalization, hyperparameter optimization
08
IV
OPTIMIZATION AND GENERALIZATION : Optimization in deep learning– Nonconvex
optimization for deep networks Stochastic Optimization Generalization in neural networks Spatial
Transformer Networks Recurrent networks, LSTM  Recurrent Neural Network Language
Models WordLevel RNNs & Deep Reinforcement Learning  Computational & Artificial
Neuroscience
08
V
CASE STUDY AND APPLICATIONS : Imagenet DetectionAudio WaveNetNatural Language
Processing Word2Vec  Joint DetectionBioinformatics Face Recognition Scene UnderstandingGathering Image Captions
08
Text books:
1. Cosma Rohilla Shalizi, Advanced Data Analysis from an Elementary Point of View, 2015.
Proposed
Lecture
2. Deng & Yu, Deep Learning: Methods and Applications, Now Publishers, 2013.
3. Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning, MIT Press, 2016.
4. Michael Nielsen, Neural Networks and Deep Learning, Determination Press, 2015.
DATA COMPRESSION
DETAILED SYLLABUS
Unit
Topic
Compression Techniques: Loss less compression, Lossy Compression, Measures of performance,
Modeling and coding, Mathematical Preliminaries for Lossless compression: A brief introduction
I
to information theory, Models: Physical models, Probability models, Markov models, composite
source model, Coding: uniquely decodable codes, Prefix codes.
The Huffman coding algorithm: Minimum variance Huffman codes, Adaptive Huffman coding:
Update procedure, Encoding procedure, Decoding procedure. Golomb codes, Rice codes, Tunstall
II
codes, Applications of Hoffman coding: Loss less image compression, Text compression, Audio
Compression.
Coding a sequence, Generating a binary code, Comparison of Binary and Huffman coding,
Applications: Bilevel image compressionThe JBIG standard, JBIG2, Image compression.
Dictionary Techniques: Introduction, Static Dictionary: Diagram Coding, Adaptive Dictionary. The
LZ77 Approach, The LZ78 Approach, Applications: File CompressionUNIX compress, Image
III
Compression: The Graphics Interchange Format (GIF), Compression over Modems: V.42 bits,
Predictive Coding: Prediction with Partial match (ppm): The basic algorithm, The ESCAPE
SYMBOL, length of context, The Exclusion Principle, The BurrowsWheeler Transform: Movetofront coding, CALIC, JPEGLS, Multiresolution Approaches, Facsimile Encoding, Dynamic
Markoy Compression.
Distortion criteria, Models, Scalar Ouantization: The Quantization problem, Uniform Quantizer,
IV
Adaptive Quantization, Non uniform Quantization.
Advantages of Vector Quantization over Scalar Quantization, The LindeBuzoGray Algorithm,
V
Tree structured Vector Quantizers. Structured VectorQuantizers.
Text books:
1. Khalid Sayood, Introduction to Data Compression, Morgan Kaufmann Publishers
2. Elements of Data Compression,Drozdek, Cengage Learning
3. Introduction to Data Compression, Second Edition, Khalid Sayood,The Morgan aufmann Series
4.Data Compression: The Complete Reference 4th Edition byDavid Salomon, Springer
5.Text Compression1st Edition by Timothy C. Bell Prentice Hall
300
Proposed
Lecture
08
08
08
08
08
QUANTUM COMPUTING
DETAILED SYLLABUS
300
Unit
Topic
Proposed
Lecture
I
Fundamental Concepts: Global Perspectives, Quantum Bits, Quantum Computation, Quantum
Algorithms, Quantum Information, Postulates of Quantum Mechanisms.
08
II
Quantum Computation: Quantum Circuits – Quantum algorithms, Single Orbit operations,
Control Operations, Measurement, Universal Quantum Gates, Simulation of Quantum Systems,
Quantum Fourier transform, Phase estimation, Applications, Quantum search algorithms –
Quantum counting – Speeding up the solution of NP – complete problems – Quantum Search for an
unstructured database.
08
III
Quantum Computers: Guiding Principles, Conditions for Quantum Computation, Harmonic
Oscillator Quantum Computer, Optical Photon Quantum Computer – Optical cavity Quantum
electrodynamics, Ion traps, Nuclear Magnetic resonance
08
IV
Quantum Information: Quantum noise and Quantum Operations – Classical Noise and Markov
Processes, Quantum Operations, Examples of Quantum noise and Quantum Operations –
Applications of Quantum operations, Limitations of the Quantum operations formalism, Distance
Measures for Quantum information.
08
V
Quantum Error Correction: Introduction, Shor code, Theory of Quantum Error –Correction,
Constructing Quantum Codes, Stabilizer codes, Fault – Tolerant Quantum Computation, Entropy
and information – Shannon Entropy, Basic properties of Entropy, Von Neumann, Strong Sub
Additivity, Data Compression, Entanglement as a physical resource .
08
Text books:
1. Micheal A. Nielsen. &Issac L. Chiang, “Quantum Computation and Quantum Information”, Cambridge University
Press, Fint South Asian edition, 2002.
2. Eleanor G. Rieffel , Wolfgang H. Polak , “Quantum Computing  A Gentle Introduction” (Scientific and Engineering
Computation) Paperback – Import,
3 Oct 2014 3. Computing since Democritus by Scott Aaronson
4. Computer Science: An Introduction by N. DavidMermin 5. Yanofsky's and Mannucci, Quantum Computing for
Computer Scientists.
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