Related Posts Plugin for WordPress, Blogger...

Download

Powered by Blogger.

Blogger news

Search This Blog

Loading...

Wednesday, January 22, 2014

 Handbook of Practical Logic and Automated Reasoning  John Harrison pdf download 


This book meets the demand for a self-contained and broad-based account of the concepts, the machinery and the use of automated reasoning. The mathematical logic foundations are described in conjunction with practical application, all with the minimum of prerequisites. The approach is constructive, concrete and algorithmic: a key feature is that methods are described with reference to actual implementations (for which code is supplied) that readers can use, modify and experiment with. This book is ideally suited for those seeking a one-stop source for the general area of automated reasoning. It can be used as a reference, or as a place to learn the fundamentals, either in conjunction with advanced courses or for self study.
Download

Logic for Computer Scientists by Uwe Schoning pdf Download 


This book introduces the notions and methods of formal logic from a computer science standpoint, covering propositional logic, predicate logic, and foundations of logic programming. The classic text is replete with illustrative examples and exercises. It presents applications and themes of computer science research such as resolution, automated deduction, and logic programming in a rigorous but readable way. The style and scope of the work, rounded out by the inclusion of exercises, make this an excellent textbook for an advanced undergraduate course in logic for computer scientists.

DOWNLOAD

 Mathematical Logic for Computer Science by  Mordechai Ben Ari Second Edition pdf download 


Mathematical Logic for Computer Science is a mathematics textbook with theorems and proofs, but the choice of topics has been guided by the needs of students of computer science. The method of semantic tableaux provides an elegant way to teach logic that is both theoretically sound and easy to understand. The uniform use of tableaux-based techniques facilitates learning advanced logical systems based on what the student has learned from elementary systems. The logical systems presented are: propositional logic, first-order logic, resolution and its application to logic programming, Hoare logic for the verification of sequential programs, and linear temporal logic for the verification of concurrent programs.
Download Now

Thursday, September 26, 2013

Computational Geometry: Algorithms and Applications by Mark de Berg, Otfried Cheong, Marc van Kreveld, Mark Overmars Free Download






Computational geometry focuses on algorithms. Motivation is provided from the application areas as all techniques are related to particular applications in robotics, graphics, CAD/CAM, and geographic information systems. Modern insights in computational geometry are used to provide solutions that are both efficient and easy to understand and implement.

"This third edition contains two major additions: In Chapter 7, on Voronoi diagrams, we now also discuss Voronoi diagrams of line segments and farthest-point Voronoi diagrams. In Chapter 13, we have included an extra section on binary space partition trees for low-density scenes, as an introduction to realistic input models. In addition, a large number of small and some larger errors have been corrected (see the list of errata for the second edition on the Web site). We have also updated the notes and comments of every chapter to include references to recent results and recent literature. We have tried not to change the numbering of sections and exercises, so that it should be possible for students in a course to still use the second edition."

Wednesday, September 18, 2013

Anna University M.E. COMPUTER SCIENCE AND ENGINEERING  SYLLABUS 2013 Download


SEMESTER I
COURSE CODE
COURSE TITLE
L
T
P
C
THEORY
MA8154
Advanced Mathematics for Computing
3
1
0
4
CP8151
Advanced Data Structures and Algorithms
3
0
2
4
CP8101
Multi-Core Architecture
3
0
0
3
CP8152
Object Oriented Systems Engineering
2
0
2
3
CP8153
Open Source Systems and Networking
3
0
0
3
PRACTICAL
CP8111
Professional Practice
0
0
2
1
TOTAL
14
1
6
18
SEMESTER II
COURSE CODE
COURSE TITLE
L
T
P
C
THEORY
CP8251
Virtualization Techniques
3
0
2
4
CP8202
Machine Learning Techniques
3
0
2
4
IF8254
Mobile and Pervasive Computing
3
0
0
3
CP8201
Advances in Compiler Design
3
0
0
3
Elective I
3
0
0
3
Elective II
3
0
0
3
PRACTICAL
CP8211
Case Study
0
0
2
1
CP8212
Technical Seminar
0
0
2
1
TOTAL
18
0
10
22
SEMESTER III
COURSE CODE
COURSE TITLE
L
T
P
C
THEORY
CP8351
Security Principles and Practices
3
0
0
3
Elective III
3
0
0
3
Elective IV
3
0
0
3
Elective V
3
0
0
3
PRACTICAL
CP8311
Project Work Phase-I
0
0
12
6
TOTAL
12
0
12
18
A
2
SEMESTER IV
COURSE CODE
COURSE TITLE
L
T
P
C
PRACTICALS
CP8411
Project Work Phase-II
0
0
24
12
TOTAL
0
0
24
12


ANNA UNIVERSITY SYLLABUS 2013


MA8154 ADVANCED MATHEMATICS FOR COMPUTING COURSE OBJECTIVES:
  • UNIT I RANDOM VARIABLES
L T P C 3104
12
Random variables – Bernoulli, Binomial, Geometric, Poisson, Uniform, Exponential, Erlang and Normal distributions – Function of a Random variable - Moments, Moment generating function.
UNIT II QUEUING MODELS 12
Poisson Process – Markovian Queues – Single and Multi-server Models – Little’s formula – Machine Interference Model – Steady State analysis – Self Service Queue.
UNIT III SIMULATION 12
Discrete Event Simulation – Monte – Carlo Simulation – Stochastic Simulation – Applications to Queuing systems.
UNIT IV TESTING OF HYPOTHESIS 12
Sampling distributions – Estimation of parameters - Statistical hypothesis – Tests based on Normal, t, Chi-square and F distributions for mean, variance and proportion.
UNIT V LINEAR PROGRAMMING 12
Formulation – Graphical solution – Simplex method – Two phase method -Transportation and Assignment Problems.
TOTAL :60 PERIODS
COURSE OUTCOMES:
Upon completion of the course, the student will be able to
  • -  Identify the type of random variable and distribution for a given operational conditions/scene
  • -  Study and Design appropriate queuing model for a given problem/system situation
  • -  To understand and simulate appropriate application/distribution problems
  • -  Differentiate/infer the merit of sampling tests.
  • -  Formulate and find optimal solution in the real life optimizing/allocation/assignment problems
    involving conditions and resource constraints.
    REFERENCES:
  1. Johnson, R.A. Miller and Freund’s,” Probability and Statistical for Engineers, Prentice Hall of India Pvt., Ltd., New Delhi, Seventh Edition, 2005.
  2. Hamdy A. Taha, “Operations Research: An Introduction”, Prentice Hall of India Pvt., Ltd. New Delhi, Eighth Edition, 2007.
  3. Jay L. Devore,” Probability and Statistics for Engineering and the Sciences”, Cengage Learning, Seventh Edition, 2009.
  4. Ross. S.M., “Probability Models for Computer Science”, Academic Press, 2002.
  5. Winston, W.L., “Operations Research”, Thomson – Brooks/Cole, Fourth Edition,
    2003.
  6. Gross D. and Harris C.M., “Fundamentals of Queuing Theory”, John Wiley and
Sons, New York, 1998.
7. J.Medhi,” Stochastic models of Queuing Theory”, Academic Press, Elsevier,
Amsterdam, 2003 






CP8151 ADVANCED DATA STRUCTURES AND ALGORITHMS L T P C 3024
COURSE OBJECTIVES:
  •   To extend the students' knowledge of algorithms and data structures, and to enhance their expertise in algorithmic analysis and algorithm design techniques.
  •   Expected to learn a variety of useful algorithms and techniques and extrapolate from them in order to then apply those algorithms and techniques to solve problems
    UNIT I FUNDAMENTALS 9
    Mathematical Proof Techniques: Induction, proof by contradiction, direct proofs - Asymptotic Notations – Properties of Big-oh Notation –Conditional Asymptotic Notation – Algorithm Analysis – Amortized Analysis – Introduction to NP-Completeness/NP-Hard – Recurrence Equations – Solving Recurrence Equations – Time-Space Tradeoff.
    UNIT II HEAP STRUCTURES 9
    Min/Max heaps – Deaps – Leftist Heaps – Binomial Heaps – Fibonacci Heaps – Skew Heaps – Lazy- Binomial Heaps.
    UNIT III SEARCH STRUCTURES 9
    Binary Search Trees – AVL Trees – Red-Black trees – Multi-way Search Trees –B-Trees – Splay Trees – Tries.
    UNIT IV GEOMETRIC ALGORITHMS 9
    Segment Trees – 1-Dimensional Range Searching - k-d Trees – Line Segment Intersection - Convex Hulls - Computing the Overlay of Two Subdivisions - Range Trees - Voronoi Diagram.
    UNIT V PARALLEL ALGORITHMS 9
    Flynn’s Classifications – List Ranking – Prefix computation – Array Max – Sorting on EREW PRAM – Sorting on Mesh and Butterfly – Prefix sum on Mesh and Butterfly – Sum on mesh and butterfly – Matrix Multiplication – Data Distribution on EREW, Mesh and Butterfly.
    TOTAL : 45 +30 : 75 PERIODS
    COURSE OUTCOMES:
  •   Basic ability to analyze algorithms and to determine algorithm correctness and time efficiency class.
  •   Master a variety of advanced data structures and their implementations.
  •   Master different algorithm design techniques in computational geometry and in parallel algorithms.
  •   Ability to apply and implement learned algorithm design techniques and data structures to solve
    problems.
    REFERENCES:
  1. E. Horowitz, S. Sahni and Dinesh Mehta, Fundamentals of Data structures in C++, University Press, 2007.
  2. G. Brassard and P. Bratley, Algorithmics: Theory and Practice, Printice –Hall,1988.
  3. Mark de Berg, Otfried Cheong, Marc van Kreveld, Mark Overmars, Computational Geometry
    Algorithms and Applications, Third Edition, 2008
  4. James A. Storer, An Introduction to Data Structures and Algorithms, Springer, New York, 2002.
  5. Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein
    Introduction to Al,2009.
7
CP8101 MULTI-CORE ARCHITECTURE 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
  •   To understand the different types of multicore architectures
  •   To understand the design of the memory hierarchy
  •   To expose the students to multicore programming
    UNIT I NEED FOR MULTICORE ARCHITECTURES
L T P C 3003
Fundamentals of Computer Design - Measuring and Reporting Performance - Instruction Level Parallelism and its Exploitation - Concepts and Challenges – Limitations of ILP – Multithreading - SMT and CMP Architectures – The Multicore era.
UNIT II MULTIPROCESSOR ISSUES 9
Symmetric and Distributed Shared Memory Architectures – Cache Coherence Issues - Performance Issues – Synchronization Issues – Models of Memory Consistency - Interconnection Networks – Buses, Crossbar and Multi-stage Interconnection Networks.
UNIT III MULTICORE ARCHITECTURES 9
Homogeneous and Heterogeneous Architectures – Intel Multicore Architectures – SUN CMP architecture – IBM Cell Architecture – GPGPU Architectures.
UNIT IV MEMORY HIERARCHY DESIGN 9
Introduction - Optimizations of Cache Performance - Memory Technology and Optimizations - Protection: Virtual Memory and Virtual Machines - Design of Memory Hierarchies - Case Studies.
UNIT V MULTICORE PROGRAMMING 9
Parallel Programming models – Shared Memory Programming – Message Passing Interface – Open MP Program Development and Performance Tuning.
TOTAL: 45 PERIODS
OUTCOMES:
Upon completion of the course, the students will be able to
  •   Identify the limitations of ILP and the need for multicore architectures
  •   Discuss the issues related to multiprocessing and suggest solutions
  •   Point out the salient features of different multicore architectures and how they
    exploit parallelism
  •   Critically analyze the different types of inter connection networks
  •   Design a memory hierarchy and optimize it
  •   Explain the different parallel programming models
  •   Develop programs using Open MP and optimize them
8
9
REFERENCES:
  1. John L. Hennessey and David A. Patterson, “ Computer Architecture – A Quantitative Approach”, Morgan Kaufmann / Elsevier, 5th. edition, 2012.
  2. Peter S. Pacheco, “An Introduction to Parallel Programming”, Morgan Kaufmann / Elsevier, 2011.
  3. Michael J Quinn, Parallel Programming in C with MPI and OpenMP, Tata McGraw Hill, 2003.
  4. Darryl Gove, “Multicore Application Programming: For Windows, Linux, and Oracle Solaris”,
    Pearson, 2011.
  5. David E. Culler, Jaswinder Pal Singh, “Parallel Computing Architecture : A Hardware/ Software
Approach” , Morgan Kaufmann / Elsevier, 1997.
CP8152 OBJECT ORIENTED SYSTEMS ENGINEERING OBJECTIVE:
L T P C 3003
9
9
   
UNIT I
To understand the importance of object oriented software engineering. To study the various lifecycle models for developing software’s.
To analyze and design software using tools.
To develop efficient software, deploy and maintain after production.

CLASSICAL PARADIGM
System Concepts – Project Organization – Communication – Project Management
UNIT II PROCESS MODELS
Life cycle models – Unified Process – Iterative and Incremental – Workflow – Agile Processes
UNIT III ANALYSIS 9
Requirements Elicitation – Use Cases – Unified Modeling Language, Tools – Analysis Object Model (Domain Model) – Analysis Dynamic Models – Non-functional requirements – Analysis Patterns
UNIT IV DESIGN 9
System Design, Architecture – Design Principles - Design Patterns – Dynamic Object Modeling – Static Object Modeling – Interface Specification – Object Constraint Language
UNIT V IMPLEMENTATION, DEPLOYMENT AND MAINTENANCE 9
Mapping Design (Models) to Code – Testing - Usability – Deployment – Configuration Management – Maintenance
TOTAL: 45 PERIODS
OUTCOMES:
  •   To prepare object oriented design for small/ medium scale problem.
  •   To evaluate the appropriate life cycle model for the system under consideration.
  •   To apply the various tools and patterns while developing software
  •   Testing the software against usability, deployment, maintenance.
    REFERENCES:
  1. Bernd Bruegge, Alan H Dutoit, Object-Oriented Software Engineering, 2nd ed, Pearson Education, 2004.
  2. Craig Larman, Applying UML and Patterns 3rd ed, Pearson Education, 2005.
  3. Stephen Schach, Software Engineering 7th ed, McGraw-Hill, 2007.
  4. Ivar Jacobson, Grady Booch, James Rumbaugh, The Unified Software Development Process,
    Pearson Education, 1999.
  5. Alistair Cockburn, Agile Software Development 2nd ed, Pearson Education, 2007.
9
CP8153 OPEN SOURCE SYSTEMS AND NETWORKING COURSE OBJECTIVES:
  •   To understand the basic issues in open source kernels
  •   To appreciate the different aspects of processes
  •   To understand the role played by files and devices
  •   To understand the basic issues in open source networking
  •   To appreciate the different aspects of internetworking
    UNIT I FOUNDATION
L T P C 3003
9
Introduction – Memory addressing – Processes – Interrupts and exceptions – Kernel synchronization – clock and timer circuits.
UNIT II PROCESSES 9
Process scheduling: policy, algorithm, system calls – Memory management: page frame management, memory area management, slab allocator, aligning objects in memory, noncontiguous memory area management, addresses of noncontiguous memory areas – Process address space: process’s address space, foundational aspects of memory regions, page fault exception handler, creation and deletion – System calls – Signals: foundational aspects of the role of signals, generating a signal, delivering a signal and system calls – Implementation aspects of processes.
UNIT III FILES AND DEVICES 9
Virtual File System – I/O architecture and device drivers, block devices handling, the generic block layer, block device drivers – Implementation aspects of files and devices.
UNIT IV NETWORKING 9
Introduction, data structures overview, user space to kernel interface – System initialization: reasons for notification chains, system initialization overview, device registration and initialization, goals of NIC initialization, interaction between devices and kernel, examples of virtual devices, boot time kernel options, when a device is registered and unregistered – Transmission and reception: decisions and traffic direction, notifying drivers, interrupt handlers, reasons for bottom half handlers, bottom halves solutions, concurrency and locking, preemption, overview of network stack – Bridging: concepts, spanning tree protocol – Implementation aspects of networking.
UNIT V INTERNETWORKING 9
IPv4 concepts – Neighbouring subsystem concepts – Routing concepts, advanced features – Implementation aspects of internetworking.
COURSE OUTCOMES:
Upon completion of the course, the students will be able to
  •   Identify the different features of open source kernels
  •   Install and use available open source kernel
  •   Modify existing open source kernels in terms of functionality or features used
  •   Identify different features of open source networking
  •   Modify and use existing open source networking modules
    REFERENCES:
  1. Daniel P Bovet and Marco Cesati, “Understanding the Linux kernel”, 3rd edition, O’Reilly, 2005.
  2. Christian Benvenuti, “Understanding Linux Network Internals”, O’Reilly, 2006.
  3. Y-D Lin, R-H Hwang and Fred Baker, “Computer networks – an open source approach”, McGraw-
    Hill, 2012.
  4. Alessandro Rubini and Jonathan Corbet, “Linux device drivers”, 2nd edition, O’Reilly, 2001.
  5. Maurice J Bach, “The design of the Unix operating system”, Pearson, 1986.
10
TOTAL: 45 PERIODS
CP8111
PROFESSIONAL PRACTICE
L T P C 0021
THE OBJECTIVES OF PROFESSIONAL PRACTICE:
To Facilitate Research, Analysis, and Problem Solving.
To Interview people who know the context of the Problem and the Solution To Explore various possible alternative solutions
To Estimate Risk

THE OUTCOMES OF PROFESSIONAL PRACTICE:
Formulating a Problem
Describing the Background of the Problem Assessing the needs of the People Framing a Policy
Predicting Business Opportunity Understanding System Implications
CP8251
COURSE OBJECTIVES:
VIRTUALIZATION TECHNIQUES
TOTAL: 30 PERIODS
L T P C 3024
      
UNIT I
To understand the need of virtualization
To explore the types of virtualization
To understand the concepts of virtualization and virtual machines
To understand the practical virtualization solutions and enterprise solutions
To understand the concepts of cloud computing
To have an introduction to cloud programming giving emphasis to Hadoop MapReduce To understand the security issues in cloud computing

OVERVIEW OF VIRTUALIZATION 12
Basics
Full Vs Para-virtualization – Virtual Machine Monitor/Hypervisor - Virtual Machine Basics – Taxonomy of Virtual machines – Process Vs System Virtual Machines – Emulation: Interpretation and Binary Translation - HLL Virtual Machines

UNIT II SERVER AND NETWORK VIRTUALIZATION 12 Server Virtualization: Virtual Hardware Overview - Server Consolidation – Partitioning Techniques - Uses of Virtual server Consolidation – Server Virtualization Platforms, Network Virtualization: Design of Scalable Enterprise Networks – Layer2 Virtualization – VLAN - VFI - Layer 3 Virtualization – VRF - Virtual Firewall Contexts - Network Device Virtualization - Data- Path Virtualization - Routing Protocols
UNIT III STORAGE, DESKTOP AND APPLICATION VIRTUALIZATION 12 Storage Virtualization: Hardware Devices – SAN backup and recovery techniques – RAID – Classical Storage Model – SNIA Shared Storage Model – Virtual Storage: File System Level and Block Level, Desktop Virtualization: Concepts - Desktop Management Issues - Potential Desktop Virtualization Scenarios - Desktop Virtualization Infrastructures, Application Virtualization: Concepts - Application Management Issues - Redesign Application Management – Application Migration
of Virtualization – Types of Virtualization Techniques – Merits and demerits of Virtualization –
11
UNIT IV APPLYING VIRTUALIZATION 12 Practical Virtualization Solutions: Comparison of Virtualization Technologies: Guest OS/ Host OS – Hypervisor – Emulation – Kernel Level – Shared Kernel, Enterprise Solutions: VMWare Server – VMWare ESXi – Citrix Xen Server – Microsoft Virtual PC – Microsoft Hyper-V – Virtual Box, Server Virtualization: Configuring Servers with Virtualization – Adjusting and Tuning Virtual servers – VM Backup – VM Migration, Desktop Virtualization: Terminal services – Hosted Desktop – Web-based Solutions – Localized Virtual Desktops, Network and Storage Virtualization: Virtual Private Networks – Virtual LAN – SAN and VSAN – NAS
UNIT V CLOUD COMPUTING 12
Cloud Computing Basics - Cloud Computing Definition – Evolution of Clod Computing - General Cloud Environments – Cloud Services – Service Providers – Google – Amazon – Microsoft – IBM – EMC – NetApp - Salesforce – Tools for building private cloud - Open Issues in Cloud Computing – Cloud security challenges, Cloud Programming: Hadoop - MapReduce – HDFS – Hadoop I/O – Developing a MapReduce Application
TOTAL: 45 + 30 = 75 PERIODS
COURSE OUTCOMES:
Upon completion of the course, the students will be able to
  •   Deploy legacy OSs on virtual machines
  •   Understand the intricacies of server, storage, network, desktop and application virtualizations
  •   Design new models for virtualization
  •   Design and develop cloud applications on virtual machine platforms
  •   Design new models for Bigdata processing in cloud
    REFERENCES:
  1. James E. Smith, Ravi Nair, - Virtual Machines: Versatile Platforms for Systems and Processes, Elsevier/Morgan Kaufmann, 2005.
  2. David Marshall, Wade A. Reynolds, - Advanced Server Virtualization: VMware and Microsoft Platform in the Virtual Data Center, Auerbach Publications, 2006.
  3. Kumar Reddy, Victor Moreno, - Network virtualization, Cisco Press, July, 2006.
  4. Chris Wolf, Erick M. Halter, - Virtualization: From the Desktop to the Enterprise, APress 2005.
  5. Danielle Ruest, Nelson Ruest - Virtualization: A Beginner’s Guide, TMH, 2009
  6. Kenneth Hess , Amy Newman: Practical Virtualization Solutions: Virtualization from the
    Trenches Prentice Hall 2010
  7. John Rittinghouse, James Ransome, Cloud Computing, Implementation, Management and
    Strategy, CRC Press, 2010
  8. Anthony T. Velte, Toby J. Velte, Robert Elsenpeter - Cloud Computing: A Practical Approach,
    TMH, 2010
  9. Lee Badger , Tim Grance , Robert Patt-Corner , Jeff Voas - Cloud Computing Synopsis and
    Recommendations NIST, May 2011
  10. Tom White - Hadoop: The Definitive Guide Storage and Analysis at Internet Scale O'Reilly
    Media Press May 2012
11.Dave Shackleford - Virtualization security- Protecting Virtualized Environments, Sybex
Publishers, First Edition, 2012
12
CP8202 MACHINE LEARNING TECHNIQUES L T P C 3024
COURSE OBJECTIVES:
    
UNIT I
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

INTRODUCTION 12
Machine Learning - Machine Learning Foundations –Overview – applications - 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 12
Linear Models for Classification - Discriminant Functions -Probabilistic Generative Models - Probabilistic Discriminative Models - Bayesian Logistic Regression. Decision Trees - Classification Trees- Regression Trees - Pruning. Neural Networks -Feed-forward Network Functions - Error Back- propagation - Regularization - Mixture Density and Bayesian Neural Networks - Kernel Methods - Dual Representations - Radial Basis Function Networks. Ensemble methods- Bagging- Boosting.
UNIT III UNSUPERVISED LEARNING 12
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- Independent components analysis
UNIT IV PROBABILISTIC GRAPHICAL MODELS 12
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
UNIT V ADVANCED LEARNING 12
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. Semi - Supervised Learning. Computational Learning Theory - Mistake bound analysis, sample complexity analysis, VC dimension. Occam learning, accuracy and confidence boosting
TOTAL: 45 + 30 = 75 PERIODS
COURSE OUTCOMES:
Upon Completion of the course, the students will be able to
  •   To implement a neural network for an application of your choice using an available tool
  •   To implement probabilistic discriminative and generative algorithms for an application of your
    choice and analyze the results
  •   To use a tool to implement typical clustering algorithms for different types of applications
  •   To design and implement an HMM for a sequence model type of application
  •   To identify applications suitable for different types of machine learning with suitable
justification
13
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” (2nd ed)., Springer, 2008
  6. Stephen Marsland, “Machine Learning –An Algorithmic Perspective”, CRC Press, 2009
IF8254 MOBILE AND PERVASIVE COMPUTING L T P C 3003
COURSE OBJECTIVES :
    
UNIT I
To understand the basics of Mobile computing and Personal computing.
To learn the role of wireless networks in Mobile Computing and Pervasive Computing. To study about the underlying wireless networks.
To understand the architectures of mobile and pervasive applications.
To become familiar with the pervasive devices and mobile computing platforms.

INTRODUCTION 9
Differences between Mobile Communication and Mobile Computing – Contexts and Names – Functions – Applications and Services – New Applications – Making Legacy Applications Mobile Enabled – Design Considerations – Integration of Wireless and Wired Networks – Standards Bodies – Pervasive Computing – Basics and Vision – Principles of Pervasive Computing – Categories of Pervasive Devices
UNIT II 3G AND 4G CELLULAR NETWORKS 9
Migration to 3G Networks – IMT 2000 and UMTS – UMTS Architecture – User Equipment – Radio Network Subsystem – UTRAN – Node B – RNC functions – USIM – Protocol Stack – CS and PS Domains – IMS Architecture – Handover – 3.5G and 3.9G a brief discussion – 4G LAN and Cellular Networks – LTE – Control Plane – NAS and RRC – User Plane – PDCP, RLC and MAC – WiMax IEEE 802.16d/e – WiMax Internetworking with 3GPP
UNIT III 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 IV CONTEXT AWARE COMPUTING 9
Adaptability – Mechanisms for Adaptation - Functionality and Data – 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
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: 45 PERIODS
14
COURSE OUTCOMES:
At the end of the course the student should be able to,
  •   To deploy 3G networks.
  •   To develop suitable algorithms for 4G networks.
  •   To use sensor and mesh networks to develop mobile computing environment.
  •   To develop mobile computing applications based on the paradigm of context aware computing.
    REFERENCES:
  1. Asoke K Talukder, Hasan Ahmed, Roopa R Yavagal, “Mobile Computing: Technology, Applications and Service Creation”, Second Edition, Tata McGraw Hill, 2010.
  2. Reto Meier, “Professional Android 2 Application Development”, Wrox Wiley, 2010.
  3. .Pei Zheng and Lionel M Li, ‘Smart Phone & Next Generation Mobile Computing’, Morgan
    Kaufmann Publishers, 2006.
  4. Frank Adelstein, ‘Fundamentals of Mobile and Pervasive Computing’, TMH, 2005
  5. Jochen Burthardt et al, ‘Pervasive Computing: Technology and Architecture of Mobile Internet
    Applications’, Pearson Education, 2003
  6. Feng Zhao and Leonidas Guibas, ‘Wireless Sensor Networks’, Morgan Kaufmann Publishers,
    2004
  7. Uwe Hansmaan et al, ‘Principles of Mobile Computing’, Springer, 2003
  8. Reto Meier, “Professional Android 2 Application Development”, Wrox Wiley, 2010.
  9. Stefan Poslad, “Ubiquitous Computing: Smart Devices, Environments and Interactions”, Wiley,
2009.
CP8201 ADVANCES IN COMPILER DESIGN COURSE OBJECTIVES:
  •   To understand the various optimization techniques
  •   To understand about compiler’s instruction selection and scheduling techniques
  •   To explore how parallelism is handled by compilers
  •   To understand how compilers deal with pipelining architecture
  •   To appreciate the just-in-time compilations
    UNIT I COMPILER PHASE
L T P C 3003
9
Compiler phases - Compiler techniques review - lexical & syntax analysis, intermediate representation (AST), etc. Introduction to compiler analysis & optimization – basic blocks – DAG – control flow analysis - Data flow analysis – Dependency analysis – dependency graphs – alias analysis
UNIT II OPTIMIZATION TECHNIQUES 9
Optimization Techniques – Early optimization – redundancy elimination – loop optimization – Procedure optimization – Procedural analysis
UNIT III REGISTER ALLOCATION 9
Register allocation strategies – issues – local register allocation and assignment – moving beyond single blocks – global register allocation – instruction selection – simple tree walk scheme – Pattern matching – peephole optimization – instruction scheduling – list scheduling
15
UNIT IV INSTRUCTION LEVEL PARALLELISM PROCESSOR
ARCHITECTURES 9

Instruction level parallelism Processor Architectures – code scheduling constraints – basic block scheduling – global code scheduling – software pipelining – optimization for parallelism - Basic concepts and examples – Iteration spaces – Affine array indexes – Data reuse – Array data dependence - Finding synchronization free parallelism – Synchronization between parallel loops,- Pipelining - Locality optimizations.
UNIT V INTERPROCEDURAL ANALYSIS BASIC CONCEPTS 9
Interprocedural analysis Basic concepts – need for inter-procedural analysis – logic representation of data flow – pointer-analysis algorithm – context insensitive and sensitive inter-procedural analysis - binary decision diagrams – Case study – HOT Compilation – Just-in-time compilation.
TOTAL: 45 PERIODS
COURSE OUTCOMES:
Upon Completion of the course,the students will be able to
  •   Identify the different optimization techniques that are possible for a sequence of code
  •   Design Compilers for a programming language
  •   Map the process of Compilation for a programming paradigm and design compiler for the same
  •   Design a system that uses just-in time compilation or HOT compilation
    REFERENCES:
  1. Aho, Lam, Sethi, & Ullman, Compilers: Principles, Techniques, & Tools (Second Edition),
    Addison-Wesley, 2007. – Unit 1, 4, 5
  2. Steven Muchnick, Advanced Compiler Design and Implementation, Morgan Kaufman
    Publishers, 1997 – Unit 2
  3. Andrew W. Appel and Jens Palsberg, Compiler Implementation in Java (2nd Ed.), Cambridge
    University Press, 2002
  4. Keith D. Cooper, Linda Torczon, Engineering a Compiler, Morgan Kaufman Publishers, 2003 –
    Unit 3
  5. Randy Allen and Ken Kennedy, “Optimizing Compilers for Modern Architectures: A Dependence-
    based Approach”, Morgan Kaufman, 2001.
CP8212 TECHNICAL SEMINAR L T P C 0021
THE OBJECTIVES OF TECHNICAL SEMINAR ARE:
  1. To elicit pro-active participation of the students through
  2. To entrust assignment to present
  3. To inculcate presentation and leadership skills among students
  4. To involving students to learn actively
  5. To offer opportunities of interaction with peer students and staff
THE OUTCOMES OF THE TECHNICAL SEMINAR ARE:
  1. Good Communications Skills.
  2. Knowing the Audience.
  3. Choosing the Topic.
  4. Setting the Goals for the Talk.
  5. Talking to the Audience.
  6. Knowing the Content of the Talk.
  7. Preparation of the Slides.
  8. Answering Questions.
  9. Managing Time.
16
TOTAL : 30 PERIODS
CP8351 SECURITY PRINCIPLES AND PRACTICES
COURSE OBJECTIVES:
  •   To understand the mathematical foundations of security principles
  •   To appreciate the different aspects of encryption techniques
  •   To understand the role played by authentication in security
  •   To appreciate the current trends security practices
    UNIT I INTRODUCTION AND MATHEMATICAL FOUNDATION
L T P C 3003
9
An illustrative communication game – safeguard versus attack – Probability and Information Theory - Algebraic foundations – Number theory.
UNIT II ENCRYPTION – SYMMETRIC TECHNIQUES 9
Substitution Ciphers – Transposition Ciphers – Classical Ciphers – DES – AES – Confidentiality Modes of Operation – Key Channel Establishment for symmetric cryptosystems.
UNIT III ENCRYPTION – ASYMMETRIC TECHNIQUES AND DATA TECHNIQUES
9
Diffie-Hellman Key Exchange protocol – Discrete logarithm problem cryptosystems & cryptanalysis – ElGamal cryptosystem – Need for stronger Security Notions for Public key Cryptosystems – Combination of Asymmetric and Symmetric Cryptography – Key Channel Establishment for Public key Cryptosystems - Data Integrity techniques – Symmetric techniques - Asymmetric techniques
UNIT IV AUTHENTICATION 9
Authentication Protocols Principles – Authentication protocols for Internet Security – SSH Remote logic protocol – Kerberos Protocol – SSL & TLS – Authentication frame for public key Cryptography – Directory Based Authentication framework – Non - Directory Based Public-Key Authentication framework .
UNIT V SECURITY PRACTICES 9
Protecting Programs and Data – Information and the Law – Rights of Employees and Employers – Software Failures – Computer Crime – Privacy – Ethical Issues in Computer Security.
COURSE OUTCOMES:
Upon Completion of the course, the students will be able to
  •   Use the mathematical foundations in security principles
  •   Identify the features of encryption and authentication
  •   Use available security practices
    REFERENCES:
  1. William Stallings, “Crpyptography and Network security: Principles and Practices”, Pearson/PHI, 5th Edition, 2010.
  2. Behrouz A. Forouzan, “Cryptography and Network Security”, 2nd Edition, Tata McGraw Hill Education, 2010.
  3. Wade Trappe, Lawrence C Washington, “Introduction to Cryptography with coding theory”, 2nd Edition, Pearson, 2007.
  4. Douglas R. Stinson ,“Cryptography Theory and Practice ”, 3rd Edition, Chapman & Hall/CRC, 2006.
  5. W. Mao, “Modern Cryptography – Theory and Practice”, Pearson Education, 2nd Edition, 2007. 17
TOTAL: 45 PERIODS
– RSA
  1. Charles P. Pfleeger, Shari Lawrence Pfleeger, “Security in computing”, 3rd Edition, Prentice Hall of India, 2006.
  2. Wenbo Mao, “Modern Cryptography – Theory and Practice”, Pearson Education, 2006.
  3. Charlie Kaufman, Radia Perlman and Mike Speciner, “ Network Security Private Communication
    in a Public World”, PHI, Second Edition, 2012
CP8005 PARALLEL SYSTEMS L T P C 3003
COURSE OBJECTIVES:
  •   To understand the concepts of parallel computing and parallel systems' architecture
  •   To understand the two popular parallel programming paradigms (message passing and shared
    memory)
  •   To understand major performance issues for parallel systems and programs;
  •   To reiterate hot topics in research on parallel computing;
    UNIT I ARCHITECTURES 5
    The changing role of parallelism - Basic Principles - Sources of inefficiency - Metrics: Execution time, speedup, etc. – Throughput vs. latency – Scalability: massive parallelism, Amdahl’s Law, Gustafson’s Law - Parallel architectures – Trends in architectures, CMPs, GPUs, and Grids, Multiprocessors, Multicomputers, Multithreading, Pipelining, VLIWs, Superscaling, Vectors, SIMDs, paradigm of shared-memory, distributed-memory, interconnection networks, optical computing, systolic arrays, cache coherence -– Models of parallelism: PRAM, CTA
    UNIT II PROGRAMMING MODELS 5
    Parallel Programming: Low Level Approaches – Threads – Message passing – Issues in scalability and portability – Transactional Memory - Parallel Programming: Higher Level Approaches – ZPL – Automatic Parallelization and HPF – Chapel – MapReduce
    UNIT III OPTIMIZATION 15
    Principles Of Compiler – Compiler Structure – Properties of a Compiler – Optimization –Importance of Code optimization – Structure of Optimizing compilers – placement ofoptimizations in optimizing compilers – ICAN – Introduction and Overview – Symboltable structure – Local and Global Symbol table management - Intermediate representation – Issues – High level, medium level, low level intermediate languages – MIR, HIR, LIR – ICAN for Intermediate code – Optimization – Early optimization – Constant folding – scalar replacement of aggregates – Simplification – value numbering – constant propagation – redundancy elimination – loop optimization
    UNIT IV LOOP PARALLELIZATIONS 12
    Loop transformations, loop parallelizations, Data-Parallel and Data-Flow Systems Connection MachineCM5 - Data-Flow Models -Parallel Programming Concept dependence and loop parallelization - parallelism profiling Data-Path Design Parallel and Pipelined Design - Scheduling Parallel Programs-Optimal Scheduling Algorithms - Scheduling Heuristics -Loop Transformation and Scheduling -Parallelizing Serial Programs -Data Dependency Test -Parallelization Techniques - Instruction-Level Parallelism
    UNIT V SCHEDULING 8
    Task partitioning - data sharing and task distribution/scheduling - static, dynamic, and speculative tasks - shared memory, message passing, check-in/check-out -collaborative real-time editing-version control systems -synchronization and communication fundamentals -synchronous, asynchronous scheduling -semantics -data sharing: race, dependence, the Bernstein condition -progress: lock and wait freedom - message passing: put/get, send/recv, collectives, Service Support, Resource Management, Availability, Reliability, Security, Fault Tolerance, Recovery, Protection, Scaling.
18
TOTAL : 45 PERIODS
COURSE OUTCOMES:
  •   To understand the concepts of parallel computing and parallel systems' architecture
  •   To understand the two popular parallel programming paradigms (message passing and shared
    memory)
  •   To understand major performance issues for parallel systems and programs;
  •   To reiterate hot topics in research on parallel computing;
    REFERENCES:
  1. David Culler and Jaswinder Pal Singh, Parallel Computer Architecture, Morgan Kaufmann, 1999, ISBN 1-55860-343-3.
  2. Wilkinson B. & Allen M., Parallel Programming: Techniques & Applications Using Networked Workstations, Prentice Hall (2nd Ed), 2005, ISBN: 0131918656
  3. T. G. Lewis and H. El-Rewini, Introduction to Parallel Computing,Prentice-Hall, Englewood Cliffs, NJ, 1992.
  4. K. Mani and S. Tayler, An Introduction to Parallel Programming Jones and Barlett Publishers, 1992.
  5. Ian Foster, Designing and Building Parallel Programs, Addison-Wesley, 1994.
    Michael Wolfe, High Performance Compilers for Parallel Computing, Addison-Wesley, 1996, ISBN 0-8053-2730-4.

  6. U. Banerjee, Loop Parallelization, Kluwer, 1994.
  7. Steven S. Muchnick, “Advanced Compiler Design Implementation”, Morgan Koffman – Elsevier
    Science, India, Indian Reprint 2003.
CP8002 COMPUTATIONAL GAME THEORY L T P C 3003
COURSE OBJECTIVES:


 
To introduce the student to the notion of a game, its solutions concepts, and other basic notions and tools of game theory, and the main applications for which they are appropriate, including electronic trading markets;
To formalize the notion of strategic thinking and rational choice by using the tools of game theory, and to provide insights into using game theory in modeling applications;

To draw the connections between game theory, computer science, and economics, especially emphasizing the computational issues;
To introduce contemporary topics in the intersection of game theory, computer science, and economics;
UNIT I
Introduction Making rational choices: basics of Games – strategy - preferences – payoffs – Mathematical basics -Game theory –Rational Choice - Basic solution concepts-non-cooperative versus cooperative games - Basic computational issues - finding equilibria and learning in games- Typical application areas for game theory (e.g. Google's sponsored search, eBay auctions, electricity trading markets).
UNIT II GAMES WITH PERFECT INFORMATION 10
Games with Perfect Information - Strategic games - prisoner's dilemma, matching pennies- Nash equilibria- theory and illustrations - Cournot's and Bertrand's models of oligopoly- auctions- mixed strategy equilibrium- zero-sum games- Extensive Games with Perfect Information-repeated games (prisoner's dilemma)- subgame perfect Nash equilibrium; computational issues.
INTRODUCTION 8
19
UNIT III GAMES WITH IMPERFECT INFORMATION 9
Games with Imperfect Information - Bayesian Games – Motivational Examples – General Definitions –Information aspects – Illustrations - Extensive Games with Imperfect -Information - Strategies- Nash Equilibrium – Beliefs and sequential equilibrium – Illustrations - Repeated Games - The Prisoner's Dilemma - Bargaining
UNIT IV NON-COOPERATIVE GAME THEORY 9
Non-cooperative Game Theory - Self-interested agents- Games in normal form - Analyzing games: from optimality to equilibrium - Computing Solution Concepts of Normal-Form Games - Computing Nash equilibria of two-player, zero-sum games -Computing Nash equilibria of two-player, general-sum games - Identifying dominated strategies
UNIT V MECHANISM DESIGN 9
Aggregating Preferences-Social Choice – Formal Model- Voting - Existence of social functions - Ranking systems - Protocols for Strategic Agents: Mechanism Design - Mechanism design with unrestricted preferences- Efficient mechanisms - Vickrey and VCG mechanisms (shortest paths) - Combinatorial auctions - profit maximization Computational applications of mechanism design - applications in Computer Science - Google's sponsored search - eBay auctions
TOTAL: 45 PERIODS
COURSE OUTCOMES:
Upon Completion of the course,t he students will be able to
  •   Discuss the notion of a strategic game and equilibria, and identify the characteristics of main
    applications of these concepts
  •   Do a literature survey on applications of Game Theory in Computer Science and Engineering
  •   Discuss the use of Nash Equilibrium for other problems
  •   Identify key strategic aspects and based on these be able to connect them to appropriate
    game theoretic concepts given a real world situation
  •   Identify some applications that need aspects of Bayesian Games
  •   Implement a typical Virtual Business scenario using Game theory
    REFERENCES:
  1. M. J. Osborne, An Introduction to Game Theory. Oxford University Press, 2004.
  2. N. Nisan, T. Roughgarden, E. Tardos, and V. V. Vazirani (Editors), Algorithmic Game Theory.
    Cambridge University Press, 2007.
  3. M. J. Osborne and A. Rubinstein, A Course in Game Theory. MIT Press, 1994.
  4. A.Dixit and S. Skeath, Games of Strategy, Second Edition. W W Norton & Co Inc, 2004.
  5. Yoav Shoham, Kevin Leyton-Brown, Multiagent Systems: Algorithmic, Game-Theoretic, and
    Logical Foundations, Cambridge University Press 2008
  6. Zhu Han, Dusit Niyato,Walid Saad,Tamer Basar and Are Hjorungnes, “Game Theory in Wireless
    and Communication Networks”, Cambridge University Press, 2012
CP8004 PARALLEL ALGORITHMS L T P C 3003
COURSE OBJECTIVES:
To learn parallel algorithms development techniques for shared memory and DCM models. To study the main classes of fundamental parallel algorithms.
To study the complexity and correctness models for parallel algorithms
20
UNIT I INTRODUCTION 9
Introduction to Parallel Algorithms – Models of computation – Selection – Mergin on EREW and CREW – Median of two sorted sequence – Fast Merging on EREW – Analyzing Parallel Algorithms.
UNIT II SORTING & SEARCHING 9
Sorting Networks – Sorting on a Linear Array – Sorting on CRCW, CREW, EREW – Searching a sorted sequence – Searching a random sequence – Bitonic Sort.
UNIT III ALGEBRAIC PROBLEMS 9
Permutations and Combinations – Matrix Transpositions – Matrix by Matrix multiplications – Matrix by vector multiplication.
UNIT IV GRAPH & GEOMETRY 9
Connectivity Matrix – Connected Components – All Pair Shortest Paths – Minimum Spanning Trees – Point Inclusion – Intersection, Proximity and Construction Problems.
UNIT V OPTIMIZATION & BIT COMPUTATIONS 9
Prefix Sums – Job Sequencing – Knapsack - Adding two integers – Adding n integers – Multiplying two integers – Selection.
TOTAL: 45 PERIODS
COURSE OUTCOMES:
Familiar with design of parallel algorithms in various models of parallel computation.
Familiar with the efficient parallel algorithms related to many areas of computer science:
expression computation, sorting, graph-theoretic problems, computational geometry, etc. Familiar with the basic issues of implementing parallel algorithms.
REFERENCES:
  1. Selim G. Akl, The Design and Analysis of Paralle Algorithms, Prentice Hall, New Jercy, 1989.
  2. Michael J. Quinn, Parallel Computing : Theory & Practice, Tata McGraw Hill Edition, 2003.
  3. Joseph JaJa, Introduction to Parallel Algorithms, Addison-Wesley, 1992.
CP8001 ADVANCED COMPUTING L T P C 3003
COURSE OBJECTIVES:
To understand the basics of quantum computing, membrane computing, molecular computing, DNA computing and nano computing
To understand the models and the theory involved in the biologically inspired computing techniques
To explore the applications of these computing models
UNIT I INTRODUCTION 9
Strings – notations – Regular languages – Context free languages – Context free Grammar – Context Sensitive Grammar – Type 0 Grammar – Universal Turing machine – Lindenmayer systems – Enumerable languages – Complexity
UNIT II DNA COMPUTING 9
Structure of DNA – Operation on DNA molecules – Adleman’s experiments – Other DNA solutions to NP problems – Two dimensional generalization – Computing by carving – Sticker systems – Extended H systems – Controlled H systems – distributed H systems
21
UNIT III MEMBRANE COMPUTING 9
P systems with labelled membranes – examples – Power of P systems – decidability results – Rewriting P systems – P systems with polarized membranes – Normal forms – P systems on Asymmetric graphs – P systems with active membranes – Splicing P systems – Variants, Problems, Conjectures.
UNIT IV QUANTUM COMPUTING 9
Reversible computation – Copy computers – Quantum world – Bits and Qubits – Quantum calculus – Qubit evolution – Measurements – Zeno machines – Randomness – EPR conundrum and Bell’s theorem – Quantum logic – Quantum computers – Quantum algorithms – Quantum Complexity – Quantum Cryptography
UNIT V NANO AND MOLECULAR COMPUTING 9
Defect tolerant nano computing – error detection – Non-traditional computing models – Reliability trade off for nano architecture – Molecular recognition – storage and processing of molecular information
COURSE OUTCOMES:
Upon Completion of the course, the students will be able to
  •   Comprehend the different computing paradigms
  •   Write Grammar rules for the different models of computing
  •   Design applications to incorporate one or more computing models
  •   Try to solve problems and prove the application of the computing models.
    REFERENCES:
  1. Cris Calude Gheorghe Paun, “ Computing with Cells and Atoms: An Introduction to Quantum, DNA and Membrane Computing”, CRC Press, 2000. Unit 1 – 4
  2. Sandeep kumar Shukla, R Iris Bahar, “ Nano, Quantum and Molecular Computing:
    Implications to High Level Design and Validation”,Kluwer Academic Publishers, 2010. Unit 5

  3. Tanya Sienko, Andrew Adamatzky, Michael Conrad , Nicholas G. Rambidi, “Molecular Computing”, MIT Press, 2005. Unit 5
  4. Kamala Krithivasan and Rama R, “Introduction to Formal languages, automata theory and computation”, Pearson Education India, 2009.
TOTAL:45 PERIODS
CP8003 COMPUTATIONAL GEOMETRY L T P C 3003
COURSE OBJECTIVES:
  •   To understand geometric problems.
  •   To learn the algorithmic solutions for geometric problems.
  •   To map problems in various application domains to a geometric problem.
  •   To learn to solve problems in various application domains
UNIT I INTRODUCTION 9
Introduction –Application Domains – Line Segment Intersection – Intersection of Convex Polygons – Polygon Triangulation
UNIT II GEOMETRIC SEARCHING 9
Geometric Searching – Range Searching – Kd-Trees – Range trees – Point-Location Problems 22
UNIT III CONVEX HULL PROBLEM 9
Convex hull Problem – Preliminaries – Convex hull Algorithms in the Plane – Graham’s scan - Jarvis’s March – Quick Hull – Divide-and-conquer – Dynamic Convex Hull Maintenance – Delaunay Triangulation
UNIT IV PROXIMITY PROBLEMS 9
Proximity Problems – Fundamental Algorithms (Closest Pair – All Nearest Neighbours – Euclidean Minimum Spanning Tree – Nearest Neighbour Search) – Lower bounds – Closest Pair Problem : A Divide-and-Conquer Approach
UNIT V VORONOI DIAGRAM 9
Voronoi Diagram – Proximity Problems Solved by the Voronoi Diagram – Planar Applications
TOTAL: 45 PERIODS
REFERENCES:
4. Herbert Edelsbrunner, Algorithms in Combinatorial Geometry, EATCS Monographs in Computer Science, SpringerVerlag, 1987
COURSE OUTCOMES:
Upon completion of the course the students will be able to
  •   Identify problems that can be mapped to geometric problems
  •   Transform problems in different applications to geometric problems
  •   Use the algorithms learnt for solving the transformed problems
  •   Find solution for the problems
  1. Franco P. Preparata, Michael I. Shamos, Computational Geometry: An Introduction, Springer, 1985.
  2. Mark de Berg, Otfried Cheong, Marc van Kreveld, Mark Overmars, Computational Geometry: Algorithms and Applications, Springer, 3rd edition, 2008.
  3. Satyan L. Devadoss and Joseph O'Rourke, Discrete and Computational Geometry, Princeton University Press, 2011.
IF8083 UNIX INTERNALS
COURSE OBJECTIVES:
To understand the design of the UNIX operating system. To become familiar with the various data structures used. To learn the various low-level algorithms used in UNIX.
UNIT I OVERVIEW
L T P C 3003
9
General Overview of the System: History – System structure – User perspective –Operating System Services – Assumptions about Hardware. Introduction to the Kernel Architecture of the UNIX Operating System – Introduction to System Concept - The Buffer Cache - Buffer headers – Structure of the Buffer Pool – Scenarios for Retrieval of a Buffer– Reading and Writing Disk Blocks – Advantages and Disadvantages of the Buffer Cache.
UNIT II FILE SUBSYSTEM 9
Internal Representation of Files: Inodes – Structure of a Regular File – Directories –Conversion of a Path Name to an Inode – Super Block – Inode Assignment to a New File – Allocation of Disk Blocks.
23
UNIT III SYSTEM CALLS FOR THE FILE SYSTEM 9
Open – Read – Write – File And Record Locking – Adjusting the Position of File I/O –lseek – close – File Creation – Creation of Special Files – Changing Directory – Root – Owner - Mode – stat And fstat – Pipes – dup – Mounting And Unmounting File Systems – link – unlink.
UNIT IV PROCESSES 9
Process States and Transitions – Layout of System Memory – The Context of a Process – Saving the Context of a Process – Manipulation of the Process Address Space – Sleep - Process Control - Process Creation – Signals – Process Termination – Awaiting Process Termination – Invoking other Programs – User Id of a Process – Changing the size of a Process – Shell – System Boot and the INIT Process– Process Scheduling.
UNIT V MEMORY MANAGEMENT AND I/O 9
Memory Management Policies – Paging and Segmentation - Swapping – Demand Paging - The I/O Subsystem: Driver Interface – Disk Drivers – Terminal Drivers.
COURSE OUTCOMES:
Upon completion of the course, the students will be able to
  •   To understand the design of the UNIX operating system.
  •   To become familiar with the various data structures used.
  •   To learn the various low-level algorithms used in UNIX.
    REFERENCES:
1. Maurice J. Bach, “The Design of the Unix Operating System”, First Edition, Pearson Education, 1999.
2. B. Goodheart, J. Cox, “The Magic Garden Explained”, Prentice Hall of India,1986. S. J. Leffler, M. K. Mckusick, M. J. .Karels and J. S. Quarterman., “The Design and
Implementation of the 4.3 BSD Unix Operating System”, Addison Wesley, 1998.
CP8074 REAL TIME SYSTEMS DESIGN L T P C 3003
COURSE OBJECTIVE :
To learn real time operating system concepts and the associated issues & techniques
UNIT I REAL TIME SPECIFICATION AND DESIGN TECHNIQUES 9
Introduction– Structure of a Real Time System –Task classes – Performance Measures for Real Time Systems – Estimating Program Run Times – Issues in Real Time Computing – Task Assignment and Scheduling – Classical uniprocessor scheduling algorithms –Fault Tolerant Scheduling.
UNIT II REAL TIME SPECIFICATION AND DESIGN TECHNIQUES 9
Natural languages – mathematical specification – flow charts – structured charts – pseudocode and programming design languages – finite state automata – data flow diagrams – petri nets – Warnier Orr notation – state charts – polled loop systems – phase / sate driven code – coroutines – interrupt – driven systems – foreground/background system – full featured real time operating systems.
UNIT III INTERTASK COMMUNICATION AND SYNCHRONIZATION 9
Buffering data – mailboxes – critical regions – semaphores – deadlock – process stack management – dynamic allocation – static schemes – response time calculation – interrupt latency – time loading and its measurement – scheduling is NP complete – reducing response times and time loading – analysis of memory requirements – reducing memory loading – I/O performance.
24
TOTAL: 45 PERIODS
UNIT IV REAL TIME DATABASES 9
Real time Databases – Basic Definition, Real time Vs General Purpose Databases, Main Memory Databases, Transaction priorities, Transaction Aborts, Concurrency control issues, Disk Scheduling Algorithms, Two – phase Approach to improve Predictability – Maintaining Serialization Consistency – Databases for Hard Real Time Systems.
UNIT V EVALUATION TECHNIQUES 9
Reliability Evaluation Techniques – Obtaining parameter values, Reliability models for Hardware Redundancy – Software error models. Clock Synchronization – Clock, A Nonfault – Tolerant Synchronization Algorithm – Impact of faults – Fault Tolerant Synchronization in Hardware – Fault Tolerant Synchronization in software.
TOTAL : 45 PERIODS
COURSE OUTCOMES:
Understanding principles of real time systems design; be aware of architectures and behaviors of real time operating systems, database and applications.
REFERENCES:
  1. C.M. Krishna, Kang G. Shin, “Real – Time Systems”, McGraw – Hill International Editions, 1997.
  2. Rajib Mall, ”Real-time systems: theory and practice”, Pearson Education, 2007
  3. Stuart Bennett, “Real Time Computer Control – An Introduction”, Prentice Hall of India, 1998.
  4. R.J.A Buhur, D.L Bailey, “An Introduction to Real – Time Systems”, Prentice – Hall International,
    1999.
  5. Philip.A.Laplante, “Real Time System Design and Analysis”, Prentice Hall of India, 3rd Edition,
    April 2004.
  6. Allen Burns, Andy Wellings, “Real Time Systems and Programming Languages”, Pearson
Education, 2003
CP8007 NETWORK PROTOCOLS COURSE OBJECTIVES:
L T P C 3003
8
   
UNIT I
To understand how routing is done in telephone networks
To learn about the different internet routing protocols
To appreciate the different aspects routing in optical and mobile networks
To understand the issues in ad hoc networks the protocols used for th working of ad hoc networks

INTRODUCTION
ISO OSI Layer Architecture, TCP/IP Layer Architecture, Functions of Network layer, General Classification of routing, Routing in telephone networks, Dynamic Non hierarchical Routing (DNHR), Trunk status map routing (TSMR), real-time network routing (RTNR), Distance vector routing, Link state routing, Hierarchical routing.
UNIT II INTERNET ROUTING PROTOCOLS 9
Interior routing protocols: Routing Information Protocol (RIP), Open Shortest Path First (OSPF) – Exterior Routing Protocols: Exterior Gateway Protocol (EGP) and Border Gateway Protocol (BGP). Multicast Routing: Pros and cons of Multicast and Multiple Unicast Routing, Distance Vector Multicast Routing Protocol (DVMRP), Multicast Open Shortest Path First (MOSPF), MBONE, Core Based Tree Routing.
25
UNIT III ROUTING IN OPTICAL WDM NETWORKS 10
Classification of Routing and Wavelength Assignment algorithms – RWA algorithms, Fairness and Admission Control – Distributed Control Protocols – Permanent Routing and Wavelength Requirements – Wavelength Rerouting – Benefits and Issues – Lightpath Migration – Rerouting Schemes – Algorithms – AG – MWPG
UNIT IV MOBILE - IP NETWORKS 9
Macro-mobility Protocols - Micro-mobility protocols – Tunnel based – Hierarchical Mobile IP – Intra domain Mobility Management – Routing based – Cellular IP - Handoff – Wireless Access Internet Infrastructure (HAWAII)
UNIT V MOBILE AD HOC NETWORKS 9
Issues and challenges in ad hoc networks – MAC Layer Protocols for wireless ad hoc networks – Contention-Based MAC protocols – Routing in Ad hoc Networks – Design Issues – Proactive, Reactive and Hybrid Routing Protocols - Transport protocols for ad hoc networks.
COURSE OUTCOMES:
Upon Completion of the course, the students will be able to
  •   Identify challenges in routing in different kinds of networks
  •   Identify the features of the protocols used in different kinds of networks
  •   Compare the issues in designing protocols for different kinds of networks
    REFERENCES:
  1. William Stallings, ‘High-Speed Networks and Internets, Performance and Quality of Service’, IInd Edition, Pearson Education Asia. Reprint India 2002
  2. Martha Steenstrup, ‘Routing in Communication Networks’, Prentice Hall International, New York, 1995.
  3. S. Keshav, ‘An Engineering Approach to Computer Networking’ Addison Wesley 1999.
  4. William Stallings, ‘High speed Networks TCP/IP and ATM Design Principles, Prentice- Hall, New
    York, 1998.
  5. Deepankar Medhi, Karthikeyan Ramasamy, ‘Network Routing: Algorithms, Protocols, and
    Architectures’, Morgan Kaufmann Publishers, 2007.
6.
  1. C.Siva Ram Murthy and B.S.Manoj, ‘Ad Hoc Wireless Networks – Architectures and Protocols’, Pearson Education, 2004.
  2. Ian F. Akyildiz, Jiang Xie and Shantidev Mohanty, ‘A Survey of mobility Management in Next generation All IP- Based Wireless Systems’, IEEE Wireless Communications Aug.2004, pp 16-27.
  3. A.T Campbell et al., ‘Comparison of IP Micromobility Protocols’, IEEE Wireless Communications Feb.2002, pp 72-82.
  4. C.Siva Rama Murthy and Mohan Gurusamy, “WDM Optical Networks – Concepts, Design and Algorithms”, Prentice Hall of India Pvt. Ltd, New Delhi, 2002.
TOTAL: 45 PERIODS
Subir Kumar Sarkar, T G Basavaraju, C Puttamadappa, ‘Ad Hoc Mobile Wireless Networks’,
Auerbach Publications, 2008.
26
CP8006 FAULT TOLERANT SYSTEMS L T P C 3003
COURSE OBJECTIVES:
  •   To provide a comprehensive view of fault tolerant systems
  •   To appreciate the need for fault tolerance
  •   To expose the students to the methods of hardware fault tolerance
  •   To understand the different ways of providing information redundancy
  •   To understand the need for and the different ways of providing software fault tolerance
  •   To expose the students to concept of check pointing and their role in providing fault tolerance
  •   To understand how to handle security attacks
    UNIT I INTRODUCTION: 9
    Fault Classification, Types of Redundancy, Basic Measures of Fault Tolerance, Hardware Fault Tolerance, The Rate of Hardware Failures, Failure Rate, Reliability, and Mean Time to Failure, Canonical and Resilient Structures, Other Reliability Evaluation Techniques, Processor level Techniques.
    UNIT II INFORMATION REDUNDANCY 9
    Information Redundancy, Coding, Resilient Disk Systems, Data Replication, Voting: Hierarchical Organization, Primary-Backup Approach, Algorithm-Based Fault Tolerance, Fault-Tolerant Networks: Measures of Resilience, Common Network Topologies and Their Resilience, Fault- Tolerant Routing.
    UNIT III SOFTWARE FAULT TOLERANCE: 9
    Acceptance Tests, Single-Version Fault Tolerance, N-Version Programming, Recovery Block Approach, Preconditions, Post conditions, and Assertions, Exception-Handling, Software Reliability Models, Fault-Tolerant Remote Procedure Calls.
    UNIT IV CHECKPOINTING: 9
    Introduction, Checkpoint Level, Optimal Checkpointing-An Analytical Model, Cache-Aided Rollback Error Recovery (CARER), Checkpointing in Distributed Systems, Checkpointing in Shared-Memory Systems, Checkpointing in Real-Time Systems, Case Studies: NonStop Systems, Stratus Systems, Cassini Command and Data Subsystem, IBM G5, IBM Sysplex, Itanium
    UNIT V FAULT DETECTION IN CRYPTOGRAPHIC SYSTEMS 9
    Security Attacks Through Fault Injection – Fault Attacks on Symmetric Key Ciphers – Fault Attacks on Public (Asymmetric) Key Ciphers – Counter Measures – Spatial and Temporal Duplication – Error Detecting Codes.
    TOTAL: 45 PERIODS
    COURSE OUTCOMES:
    Uponcompletionof the course,t hestudentswill be ableto
    •   Define the traditional measures of fault tolerance
    •   Discuss the various hardware fault tolerance techniques used
    •   Point out the processor level fault tolerance techniques
    •   Discuss error detecting and correcting codes
    •   Critically analyze the different types of RAID levels
    •   Discuss the different network topologies and their resilience
    •   Discuss techniques like recovery blocks and N-version programming
    •   Define check pointing and models for optimal check pointing
    •   Identify techniques for check pointing in distributed and shared memory systems
    •   Distinguish between symmetric key ciphers and public key ciphers
    •   Provide techniques to detect injected faults in ciphers
27
REFERENCES:
  1. Israel Koren, Mani Krishna, “Fault Tolerant Systems”, Elsevier Science & Technology, 2007.
  2. Parag K. Lala “Fault Tolerant and Fault Testable Hardware Design”, Prentice-Hall International,
    1985.
  3. LL Pullam, “Software Fault Tolerance Techniques and Implementation”, Artech House Computer
    Security Series, 2001.
  4. Martin L Shooman, Willey, “Reliability of Computer Systems and Networks: Fault Tolerance,
    Analysis and Design”, 2002.
CP8073 DATAMINING TECHNIQUES L T P C 3003
COURSE OBJECTIVES:
  •   To Understand Data mining principles and techniques and Introduce DM as a cutting edge business intelligence
  •   To expose the students to the concepts of Datawarehousing Architecture and Implementation
  •   To study the overview of developing areas – Web mining, Text mining and ethical aspects of Data
    mining
  •   To identify Business applications and Trends of Data mining
    UNIT I INTRODUCTION TO DATA WAREHOUSING 8
    Evolution of Decision Support Systems- Data warehousing Components – Building a Data warehouse, Data Warehouse and DBMS, Data marts, Metadata, Multidimensional data model, OLAP vs OLTP, OLAP operations, Data cubes, Schemas for Multidimensional Database: Stars, Snowflakes and Fact constellations
    UNIT II DATA WAREHOUSE PROCESS AND ARCHITECTURE 9
    Types of OLAP servers, 3–Tier data warehouse architecture, distributed and virtual data warehouses.Data warehouse implementation , tuning and testing of data warehouse. Data Staging (ETL) Design and Development, data warehouse visualization, Data Warehouse Deployment, Maintenance, Growth, Business Intelligence Overview- Data Warehousing and Business Intelligence Trends - Business Applications- tools-SAS
    UNIT III INTRODUCTION TO DATA MINING 9
    Data mining-KDD versus datamining, Stages of the Data Mining Process-task premitives, Data Mining Techniques -Data mining knowledge representation – Data mining query languages, Integration of a Data Mining System with a Data Warehouse – Issues, Data preprocessing – Data cleaning, Data transformation, Feature selection, Dimensionality reduction, Discretization and generating concept hierarchies-Mining frequent patterns- association-correlation
    UNIT IV CLASSIFICATION AND CLUSTERING 10
    Decision Tree Induction - Bayesian Classification – Rule Based Classification – Classification by Back propagation – Support Vector Machines – Associative Classification – Lazy Learners – Other Classification Methods – Clustering techniques – , Partitioning methods- k-means- Hierarchical Methods – distancebased agglomerative and divisible clustering, Density-Based Methods – expectation maximization -Grid Based Methods – Model-Based Clustering Methods – Constraint – Based Cluster Analysis – Outlier Analysis
28
UNIT V PREDICTIVE MODELING OF BIG DATA AND TRENDS IN
DAT AMINING 9

Statistics and Data Analysis – EDA – Small and Big Data –Logistic Regression Model - Ordinary Regression Model-Mining complex data objects – Spatial databases – Temporal databases – Multimedia databases – Time series and sequence data – Text mining – Web mining – Applications in Data mining
COURSE OUTCOMES:
Upon Completion of the course, the students will be able to
  •   Evolve Multidimensional Intelligent model from typical system
  •   Discover the knowledge imbibed in the high dimensional system
  •   Evaluate various mining techniques on complex data objects
    TEXT BOOKS:
  1. Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, third edition2011, ISBN: 1558604898.
  2. Alex Berson and Stephen J. Smith, “ Data Warehousing, Data Mining & OLAP”, Tata McGraw Hill Edition, Tenth Reprint 2007.
  3. G. K. Gupta, “Introduction to Data Min Data Mining with Case Studies”, Easter Economy Edition, Prentice Hall of India, 2006.
  4. Data Mining:Practical Machine Learning Tools and Techniques,Third edition,(Then Morgan Kufmann series in Data Management systems), Ian.H.Witten, Eibe Frank and Mark.A.Hall, 2011
  5. Statistical and Machine learning –Learning Data Mining, techniques for better Predictive Modeling and Analysis to Big Data
REFERENCES:
1. Mehmed kantardzic,“Datamining concepts,models,methods, and algorithms”, Wiley Interscience, 2003.
2. Ian Witten, Eibe Frank, Data Mining; Practical Machine Learning Tools and Techniques, third edition, Morgan Kaufmann, 2011.
3. George M Marakas, Modern Data Warehousing, Mining and Visualization,Prentice Hall, 2003.
CP8011 INFORMATION RETRIEVAL TECHNIQUES L T P C 3003
COURSE OBJECTIVES:
  •   To understand the basics of Information Retrieval with pertinence to modeling, query operations and indexing
  •   To get an understanding of machine learning techniques for text classification and clustering
  •   To understand the various applications of Information Retrieval giving emphasis to Multimedia IR,
    Web Search
  •   To understand the concepts of digital libraries
    UNIT I INTRODUCTION: MOTIVATION 8
    Basic Concepts – Practical Issues - Retrieval Process – Architecture - Boolean Retrieval –Retrieval Evaluation – Open Source IR Systems–History of Web Search – Web Characteristics–The impact of the web on IR ––IR Versus Web Search–Components of a Search engine
29
TOTAL: 45 PERIODS
UNIT II MODELING 10
Taxonomy and Characterization of IR Models – Boolean Model – Vector Model - Term Weighting – Scoring and Ranking –Language Models – Set Theoretic Models - Probabilistic Models – Algebraic Models – Structured Text Retrieval Models – Models for Browsing
UNIT III INDEXING 9
Static and Dynamic Inverted Indices – Index Construction and Index Compression. Searching - Sequential Searching and Pattern Matching. Query Operations -Query Languages – Query Processing - Relevance Feedback and Query Expansion - Automatic Local and Global Analysis – Measuring Effectiveness and Efficiency
UNIT IV TEXT CLASSIFICATION AND NAÏVE BAYES 9
Text Classification and Naïve Bayes – Vector Space Classification – Support vector machines and Machine learning on documents. Flat Clustering – Hierarchical Clustering –Matrix decompositions and latent semantic indexing – Fusion and Meta learning
UNIT V SEARCHING THE WEB 8
Searching the Web –Structure of the Web –IR and web search – Static and Dynamic Ranking - Web Crawling and Indexing – Link Analysis - XML Retrieval Multimedia IR: Models and Languages – Indexing and Searching Parallel and Distributed IR – Digital Libraries
TOTAL : 45 PERIODS
COURSE OUTCOMES:
Upon completion of the course, the students will be able to
  •   Build an Information Retrieval system using the available tools
  •   Identify and design the various components of an Information Retrieval system
  •   Apply machine learning techniques to text classification and clustering which is used for efficient
    Information Retrieval
  •   Analyze the Web content structure
  •   Design an efficient search engine
    REFERENCES:
  1. Ricardo Baeza – Yates, BerthierRibeiro – Neto, Modern Information Retrieval: The concepts and Technology behind Search (ACM Press Books), Second Edition 2011
  2. Ricardo Baeza – Yates, BerthierRibeiro – Neto, Modern Information Retrieval, Pearson Education, Second Edition 2005
  3. Christopher D. Manning, Prabhakar Raghavan, Hinrich Schutze, Introduction to Information Retrieval, Cambridge University Press, First South Asian Edition 2012
  4. Stefan Buttcher, Charles L. A. Clarke, Gordon V. Cormack, Information Retrieval Implementing and Evaluating Search Engines, The MIT Press, Cambridge, Massachusetts London, England, 2010
CP8008 BIO INFORMATICS L T P C 3003
COURSE OBJECTIVE:
  •   To understand the basic concepts.
  •   To search information, visualize it.
  •   To learn various bioinformatics algorithms.
  •   To understand data mining techniques.
  •   To study various pattern matching techniques.
30
UNIT I INTRODUCTORY CONCEPTS 8
The Central Dogma – The Killer Application – Parallel Universes – Watson’s Definition – Top Down Versus Bottom up – Information Flow – Convergence – Databases – Data Management – Data Life Cycle – Database Technology – Interfaces – Implementation – Networks – Geographical Scope – Communication Models – Transmissions Technology – Protocols – Bandwidth – Topology – Hardware – Contents – Security – Ownership – Implementation – Management.
UNIT II SEARCH ENGINES, VISUALIZATION AND ALGORITHMS 10
The search process – Search Engine Technology – Searching and Information Theory – Computational methods – Search Engines and Knowledge Management – Data Visualization – sequence visualization – structure visualization – user Interface –Animation Versus simulation – General Purpose Technologies - Exhaustive search – Greedy – Dynamic programming – divide and conquer – graph algorithms
UNIT III STATISTICS AND DATA MINING 9
Statistical concepts – Microarrays – Imperfect Data – Randomness – Variability – Approximation – Interface Noise – Assumptions – Sampling and Distributions – Hypothesis Testing – Quantifying Randomness – Data Analysis – Tool selectionstatistics of Alignment – Clustering and Classification – Data Mining – Methods –Selection and Sampling – Preprocessing and Cleaning – Transformation and Reduction – Data Mining Methods – Evaluation – Visualization – Designing new queries – Pattern Recognition and Discovery – Machine Learning – Text Mining – Tools.
UNIT IV PATTERN MATCHING 9
Pairwise sequence alignment – Local versus global alignment – Multiple sequence alignment – Computational methods – Dot Matrix analysis – Substitution matrices –Dynamic Programming – Word methods – Bayesian methods – Multiple sequencealignment – Dynamic Programming – Progressive strategies – Iterative strategies –Tools – Nucleotide Pattern Matching – Polypeptide pattern matching – Utilities –Sequence Databases.
UNIT V MODELING AND SIMULATION 9
Drug Discovery – components – process – Perspectives – Numeric considerations – Algorithms – Hardware – Issues – Protein structure – AbInitio Methods – Heuristic methods – Systems Biology – Tools – Collaboration and Communications – standards -Issues – Security – Intellectual property.
COURSE OUTCOMES:
  •   Will able to have basic idea of BioInformatics.
  •   Will able to retrieve information’s using various algorithms and techniques.
  •   Will able to sequence the databases.
  •   Will able to do modeling and simulation.
    REFERENCES:
1. Bryan Bergeron, “Bio Informatics Computing”, Second Edition, Pearson Education, 2003.
2. T.K.Attwood and D.J. Perry Smith, “Introduction to Bio Informatics, Longman Essen,1999.
3. An Introduction to, Bioinformatics Algorithms (Computational Molecular Biology) , “Neil C.Jones,

PaveA. Pevzner”, MIT Press 2004.
31
TOTAL : 45 PERIODS
CP8009 BIO-INSPIRED ARTIFICIAL INTELLIGENCE L T P C 3003
COURSE OBJECTIVES:
  •   To appreciate the use of biological aspects in building intelligent systems
  •   To understand the algorithms, programming and applications of Evolutionary and genetic
    algorithms and neural and fuzzy systems
  •   To appreciate the adaptation of cellular and developmental systems
  •   To focus on the understanding of artificial immune systems and its applications
  •   To understand issues in developing collective and behavioral systems
    UNIT I EVOLUTIONARY SYSTEMS 9
Evolutionary Systems – Artificial Evolution - Genetic Representations - Evolutionary Measures - Types of Evolutionary Algorithms - Schema Theory. Evolutionary Computation- Representation- Selection- Reproduction. Genetic Algorithms - Canonical Genetic Algorithm – Crossover- Mutation - Control Parameters – Applications. Genetic Programming - Tree-Based Representation - Building Block Genetic Programming –Applications. Evolutionary Programming – Basics –Operators -Strategy Parameters -Evolutionary Programming Implementations
UNIT II NEURAL AND FUZZY SYSTEMS 9
Neural Networks - Biological Nervous Systems - Artificial Neural Learning - Architecture. Unsupervised Learning - Self-Organizing Feature Maps. Supervised Learning – Types- Learning Rules. Radial Basis Function Networks. Reinforcement Learning – Model Free - Neural Networks and Reinforcement Learning. Fuzzy Systems- Fuzzy Sets – Logic and Reasoning – Controllers- Rough Sets.
UNIT III CELLULAR AND DEVELOPMENT SYSTEMS 9
Cellular Systems - The Basic Ingredients - Cellular Automata -Modeling - Classic Cellular Automata – Other Cellular Systems – Computation - Artificial Life - Complex Systems - Analysis and Synthesis of Cellular Systems. Developmental Systems - Potential Advantages of a Developmental Representation -Rewriting Systems - Synthesis of Developmental Systems - Evolution and Development - Defining Artificial Evolutionary Developmental Systems -Evolutionary Rewriting Systems -Developmental Programs and Processes
UNIT IV IMMUNE SYSTEMS AND COLLECTIVE SYSTEMS 10
Natural Immune systems - Classical View -Working -Constituents of Biological Immune Systems - Immunity Types - Learning the Antigen Structure - The Network Theory - The Danger Theory -Artificial Immune Systems - Algorithms - Classical View Models - Clonal Selection Theory Models - Network Theory Models - Danger Theory Models - Applications and Other AIS models Applications- Biological Self-Organization - Particle Swarm Optimization - Basics - Social Network Structures – Variations - Basic PSO Parameters - Optimization - Applications. Ant Colony Optimization - Cemetery Organization and Brood Care - Division of Labor –Applications
UNITV BEHAVIORAL SYSTEMS 8 Behavioral Systems - Behavior in Cognitive Science - Behavior in Artificial Intelligence - Behavioral Systems – Behavior Based Robots –Evolution - Co-evolution - Learning and Self Reproduction of Behavioral Systems. Cultural Algorithms - Culture and Artificial Culture - Cultural Algorithm - Belief Space – Fuzzy Cultural Algorithms – Applications. Co-evolution – Types - Competitive and Cooperative Co-evolution.
32
TOTAL: 45 PERIODS
COURSE OUTCOMES:
Upon completion of the course, the students will be able to
  •   Use existing open source tools to build an application using genetic approaches
  •   Identify different applications suitable for different types of neural networks giving justifications
  •   Critically analyze the use of cellular systems
  •   Differentiate the different models of immune systems
  •   Do a literature survey on applications of artificial immune systems
  •   Implement the Particle swarm and Ant colony algorithms within a framework and build applications
    REFERENCES:
  1. Claudio Mattiussi, Dario Floreano "Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies” (Intelligent Robotics and Autonomous Agents series), MIT Press, 2008
  2. Andries P. Engelbrecht, “Computational Intelligence: An Introduction”, 2nd Edition , Wiley; 2007
  3. Russell C. Eberhart, Yuhui Shi Computational Intelligence: Concepts to Implementations, Morgan
Kaufmann; 1 edition 2007
CP8015 TEXT DATA MINING
COURSE OBJECTIVES:
  •   To understand the basic issues and types of text mining
  •   To appreciate the different aspects of text categorization and clustering
  •   To understand the role played by text mining in Information retrieval and extraction
  •   To appreciate the use of probabilistic models for text mining
  •   To appreciate the current trends in text mining
    UNIT I INTRODUCTION
L T P C 3003
8
Overview of text mining- Definition- General Architecture– Algorithms– Core Operations – Pre- processing– Types of Problems- basics of document classification- information retrieval- clustering and organizing documents- information extraction- prediction and evaluation-Textual information to numerical vectors -Collecting documents- document standardization- tokenization- lemmatization- vector generation for prediction- sentence boundary determination -evaluation performance
UNIT II TEXT CATEGORIZATION AND CLUSTERING 10
Text Categorization – Definition – Document Representation –Feature Selection - Decision Tree Classifiers - Rule-based Classifiers - Probabilistic and Naive Bayes Classifiers - Linear Classifiers- Classification of Linked and Web Data - Meta-Algorithms– Clustering –Definition- Vector Space Models - Distance-based Algorithms- Word and Phrase-based Clustering -Semi-Supervised Clustering - Transfer Learning
UNIT III TEXT MINING FOR INFORMATION RETRIEVAL AND INFORMATION
EXTRACTION 10
Information retrieval and text mining- keyword search- nearest-neighbor methods- similarity- web- based document search- matching- inverted lists- evaluation. Information extraction- Architecture - Co-reference - Named Entity and Relation Extraction- Template filling and database construction – Applications. Inductive -Unsupervised Algorithms for Information Extraction. Text Summarization Techniques - Topic Representation - Influence of Context - Indicator Representations - Pattern
Extraction - Apriori Algorithm – FP Tree algorithm
33
UNIT IV PROBABILISTIC MODELS 9
Probabilistic Models for Text Mining -Mixture Models - Stochastic Processes in Bayesian Nonparametric Models - Graphical Models - Relationship Between Clustering, Dimension Reduction and Topic Modeling - Latent Semantic Indexing - Probabilistic Latent Semantic Indexing -Latent Dirichlet Allocation- Interpretation and Evaluation - Probabilistic Document Clustering and Topic Models - Probabilistic Models for Information Extraction - Hidden Markov Models - Stochastic Context- Free Grammars - Maximal Entropy Modeling - Maximal Entropy Markov Models -Conditional Random Fields
UNIT V RECENT TRENDS 8
Visualization Approaches - Architectural Considerations - Visualization Techniques in Link Analysis - Example- Mining Text Streams - Text Mining in Multimedia - Text Analytics in Social Media - Opinion Mining and Sentiment Analysis - Document Sentiment Classification - Opinion Lexicon Expansion - Aspect-Based Sentiment Analysis - Opinion Spam Detection – Text Mining Applications and Case studies
TOTAL: 45 PERIODS
COURSE OUTCOMES:
Upon Completion of the course,the students will be able to
  •   Identify the different features that can be mined from text and web documents
  •   Use available open source classification and clustering tools on some standard text data sets
  •   Modify existing classification/clustering algorithms in terms of functionality or features used
  •   Design a system that uses text mining to improve the functions of an existing open source search
    engine
  •   Implement a text mining system that can be used for an application of your choice
    REFERENCES:
  1. Sholom Weiss, Nitin Indurkhya, Tong Zhang, Fred Damerau “The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data”, Springer, paperback 2010
  2. Ronen Feldman, James Sanger -“ The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data”-Cambridge University press, 2006.
  3. Charu C. Aggarwal ,ChengXiang Zhai, Mining Text Data, Springer; 2012
CP8016 WEB DATA MINING L T P C 3003
COURSE OBJECTIVES:
  •   To focus on a detailed overview of the data mining process and techniques, specifically those that are relevant to Web mining
  •   To Understand the basics of Information retrieval and Web search with special emphasis on web Crawling
  •   To appreciate the use of machine learning approaches for Web Content Mining
  •   To understand the role of hyper links in web structure mining
  •   To appreciate the various aspects of web usage mining
    UNIT I INTRODUCTION 8
    Introduction – Web Mining – Theoretical background –Algorithms and techniques – Association rule mining – Sequential Pattern Mining -Information retrieval and Web search – Information retrieval Models-Relevance Feedback- Text and Web page Pre-processing – Inverted Index – Latent Semantic Indexing – Web Search – Meta-Search – Web Spamming
34
UNIT II WEB CONTENT MINING 10
Web Content Mining – Supervised Learning – Decision tree - Naïve Bayesian Text Classification - Support Vector Machines - Ensemble of Classifiers. Unsupervised Learning - K-means Clustering - Hierarchical Clustering –Partially Supervised Learning – Markov Models - Probability-Based Clustering - Evaluating Classification and Clustering – Vector Space Model – Latent semantic Indexing – Automatic Topic Extraction - Opinion Mining and Sentiment Analysis - Document Sentiment Classification
UNIT III WEB LINK MINING 9
Web Link Mining – Hyperlink based Ranking – Introduction -Social Networks Analysis- Co-Citation and Bibliographic Coupling - Page Rank -Authorities and Hubs -Link-Based Similarity Search - Enhanced Techniques for Page Ranking - Community Discovery – Web Crawling -A Basic Crawler Algorithm- Implementation Issues- Universal Crawlers- Focused Crawlers- Topical Crawlers- Evaluation - Crawler Ethics and Conflicts - New Developments
UNIT IV STRUCTURED DATA EXTRACTION 8
Structured Data Extraction: Wrapper Generation – Preliminaries- Wrapper Induction- Instance-Based Wrapper Learning ·- Automatic Wrapper Generation: Problems - String Matching and Tree Matching -. Multiple Alignment - Building DOM Trees - Extraction Based on a Single List Page and Multiple pages- Introduction to Schema Matching - Schema-Level Match -Domain and Instance-Level Matching – Extracting and Analyzing Web Social Networks.
TOTAL: 45 PERIODS
COURSE OUTCOMES:
Upon Completion of the course,t he students will be able to
  •   Build a sample search engine using available open source tools
  •   Identify the different components of a web page that can be used for mining
  •   Apply machine learning concepts to web content mining
  •   Implement Page Ranking algorithm and modify the algorithm for mining information
  •   Process data using the Map Reduce paradigm
  •   Design a system to harvest information available on the web to build recommender systems
  •   Analyze social media data using appropriate data/web mining techniques
  •   Modify an existing search engine to make it personalized
    REFERENCES:
  1. Bing Liu, “ Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications)”, Springer; 2nd Edition 2009
  2. Guandong Xu ,Yanchun Zhang, Lin Li, “Web Mining and Social Networking: Techniques and Applications”, Springer; 1st Edition.2010
  3. Zdravko Markov, Daniel T. Larose, “Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage”, John Wiley & Sons, Inc., 2007
  4. Soumen Chakrabarti, “Mining the Web: Discovering Knowledge from Hypertext Data”, Morgan Kaufmann; edition 2002
  5. Adam Schenker, “Graph-Theoretic Techniques for Web Content Mining”, World Scientific Pub Co Inc , 2005
  6. Min Song, Yi Fang and Brook Wu, Handbook of research on Text and Web mining technologies, IGI global, information Science Reference – imprint of :IGI publishing, 2008
UNIT V WEB USAGE MINING 10
Web Usage Mining - Click stream Analysis -Web Server Log Files - Data Collection and Pre- Processing - Cleaning and Filtering- Data Modeling for Web Usage Mining - The BIRCH Clustering Algorithm -Affinity Analysis and the A Priori Algorithm – Binning. Discovery and Analysis of Web Usage Patterns – Modeling user interests –Probabilistic Latent Semantic Analysis – Latent Dirichlet Allocation Model– Applications- Collaborative Filtering- Recommender Systems – Web Recommender systems based on User and Item – PLSA and LDA Models
35
CP8010 COGNITIVE SCIENCE L T P C 3003
COURSE OBJECTIVES:
  •   To learn the basics of Cognitive Science with focus on acquisition, representation, and use of knowledge by individual minds, brains, and machines, as well as groups, institutions, and other social entities
  •   To study the mind and intelligence, embracing psychology, artificial intelligence, neuroscience and linguistics
  •   To appreciate the basics of cognitive Psychology
  •   To understand the role of Neuro science in Cognitive field
    UNIT I INTRODUCTION TO COGNITIVE SCIENCE 9
    The Cognitive view –Some Fundamental Concepts – Computers in Cognitive Science – Applied Cognitive Science – The Interdisciplinary Nature of Cognitive Science – Artificial Intelligence: Knowledge representation -The Nature of Artificial Intelligence - Knowledge Representation – Artificial Intelligence: Search, Control, and Learning
    UNIT II COGNITIVE PSYCHOLOGY 10
    Cognitive Psychology – The Architecture of the Mind - The Nature of Cognitive Psychology- A Global View of The Cognitive Architecture- Propositional Representation- Schematic Representation- Cognitive Processes, Working Memory, and Attention- The Acquisition of Skill- The Connectionist Approach to Cognitive Architecture
    UNIT III COGNITIVE NEUROSCIENCE 8
    Brain and Cognition Introduction to the Study of the Nervous System – Neural Representation – Neuropsychology- Computational Neuroscience - The Organization of the mind - Organization of Cognitive systems - Strategies for Brain mapping – A Case study: Exploring mindreading
    UNIT IV LANGUAGE ACQUISITION, SEMANTICS AND PROCESSING MODELS 10
    Language Acquisition: Milestones in Acquisition – Theoretical Perspectives- Semantics and Cognitive Science – Meaning and Entailment – Reference – Sense – Cognitive and Computational Models of Semantic Processing – Information Processing Models of the Mind- Physical symbol systems and language of thought- Applying the Symbolic Paradigm- Neural networks and distributed information processing- Neural network models of Cognitive Processes
    UNIT V HIGHER-LEVEL COGNITION 8
    Reasoning – Decision Making – Computer Science and AI: Foundations & Robotics – New Horizons - Dynamical systems and situated cognition- Challenges – Emotions and Consciousness – Physical and Social Environments - Applications
    TOTAL: 45 PERIODS
    COURSE OUTCOMES:
    Upon Completion of the course,t he students will be able to
  •   Explain, and analyze the major concepts, philosophical and theoretical perspectives, empirical
    findings, and historical trends in cognitive science, related to cultural diversity and living in a
    global community.
  •   Use cognitive science knowledge base to create their own methods for answering novel
    questions of either a theoretical or applied nature, and to critically evaluate the work of others in
    the same domain
  •   Proficient with basic cognitive science research methods, including both theory-driven and
    applied research design, data collection, data analysis, and data interpretation.
36
REFERENCES:
  1. Cognitive Science: An Introduction, Second Edition by Neil Stillings, Steven E. Weisler, Christopher H. Chase and Mark H. Feinstein ,1995
  2. Cognitive Science: An Introduction to the Science of the Mind ,José Luis Bermúdez,Cambridge University Press,New York,2010
  3. Cognitive Psychology, Robert L. Solso, Otto H. MacLin and M. Kimberly MacLin, 2007, Pearson Education
  4. Cognitive Science: An Introduction to the Study of Mind (2006) by J. Friedenberg and G. Silverman
  5. How the mind works,Steven Pinker,2009
  6. Cognitive Science: An Interdisciplinary Approach by Carolyn Panzer Sobel and Paul Li, 2013
  7. Mind: Introduction to Cognitive Science, Paul Thagard, 2nd Edition, MIT Press, 2005
CP8075 SOCIAL NETWORK ANALYSIS L T P C 3003
COURSE OBJECTIVES:
  •   To gain knowledge about the current Web development and emergence of Social Web.
  •   To study about the modeling, aggregating and knowledge representation of Semantic Web.
  •   To learn about the extraction and mining tools for Social networks.
  •   To gain knowledge on Web personalization and Web Visualization of Social networks.
    UNIT I INTRODUCTION TO SOCIAL NETWORK ANALYSIS 8
    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 8
    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 NETWROKS
Extracting e
10
volution 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.
UNIT IV PREDICTING HUMAN BEHAVIOR AND PRIVACY ISSUES 10
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.
37
UNIT V VISUALIZATION AND APPLICATIONS OF SOCIAL NETWORKS 8
Graph Theory- Centrality- Clustering - Node-Edge Diagrams, Matrix representation, Visualizing Online Social Networks, Visualizing Social Networks with Matrix-Based Representations- Matrix + Node-Link Diagrams, Hybrid Representations - Applications - Covert Networks - Community Welfare - Collaboration Networks - Co-Citation Networks.
TOTAL:45 PERIODS
COURSE OUTCOMES:
  •   To apply knowledge for current Web development in the era of Social Web.
  •   To model, aggregate and represent knowledge for Semantic Web.
  •   To design extraction and mining tools for Social networks.
  •   To develop personalized web sites and visualization for Social networks.
    REFERENCES:
1. Peter Mika, “Social networks and the Semantic Web”, Springer, 2007. 2.
  1. Guandong Xu , Yanchun Zhang and Lin Li, “Web Mining and Social Networking Techniques and applications”, Springer, 1st edition, 2011.
  2. Dion Goh and Schubert Foo, “Social information retrieval systems: emerging technologies and applications for searching the Web effectively”, IGI Global snippet, 2008.
  3. Max Chevalier, Christine Julien and Chantal Soulé-Dupuy, “Collaborative and social information retrieval and access: techniques for improved user modelling”, IGI Global snippet, 2009.
  4. John G. Breslin, Alexandre Passant and Stefan Decker, “The Social Semantic Web”, Springer, 2009.
IF8078 IMAGE PROCESSING L T P C 3003
COURSE OBJECTIVES:
  •   To understand the basic concepts of digital image processing and various image transforms.
  •   To familiarize the student with the image processing facilities in Matlab.
  •   To expose the student to a broad range of image processing techniques and their applications, and
    to provide the student with practical experience using them.
  •   To appreciate the use of current technologies those are specific to image processing systems.
  •   To expose the students to real-world applications of image processing.
    UNIT I FUNDAMENTALS OF IMAGE PROCESSING AND IMAGE TRANSFORMS 9
    Introduction – Steps in Digital Image Processing – Image sampling and Quantization – Basic relationships between pixels – Color Fundamentals – File Formats – Image Transforms: DFT, DCT, Haar, SVD and KL- Introduction to Matlab Toolbox.
    UNIT II IMAGE ENHANCEMENT AND IMAGE RESTORATION 9
    Image Enhancement in the Spatial Domain: Basic Gray Level Transformations, Histogram Processing, Enhancement Using Arithmetic/Logic Operations, SpatialFiltering , Fuzzy sets for spatial filters – Image Enhancement in the Frequency Domain: Frequency Domain Filters - Image Restoration: Model of Image Degradation/Restoration Process, Noise Models, Linear and non linear image restoration techniques, Blind Deconvolution
Borko Furht, “Handbook of Social Network Technologies and Applications”, Springer,
1st edition, 2010.
38
1st edition
UNIT III MULTI RESOLUTION ANALYSIS AND IMAGE COMPRESSION 9
Multi Resolution Analysis: Image Pyramids – Multi resolution expansion – Fast Wavelet Transforms, Lifting scheme. Image Compression: Fundamentals – Models – Elements of Information Theory – Error Free Compression – Lossy Compression-wavelet based image compression techniques – Compression standards-JPEG/MPEG, Video compression.
UNIT IV IMAGE SEGMENTATION AND DESCRIPTION 9
Image Segmentation: Detection of Discontinuities, Edge Linking and Boundary Detection, Thresholding, Region Based Segmentation, Basic Morphological Algorithms, Morphological Water Sheds - Description: Boundary Descriptors, Regional Descriptors.
UNIT V CURRENT TRENDS AND APPLICATIONS OF IMAGE PROCESSING 9
Applications: Image Classification, Object Recognition, Image Fusion, Steganography – Current Trends: Color Image Processing, Wavelets in Image Processing.
TOTAL: 45 PERIODS
COURSE OUTCOMES:
UponCompletionof the course,t he students
  •   Should have a clear impression of the breadth and practical scope of digital image processing and
    have arrived at a level of understanding that is the foundation for most of the work currently
    underway in this field.
  •   Implement basic image processing algorithms using MATLAB tools
  •   Explore advanced topics of Digital Image Processing.4
  •   Ability to Apply and develop new techniques in the areas of image enhancement- restoration-
    segmentation- compression-wavelet processing and image morphology.
  •   Make a positive professional contribution in the field of Digital Image
    Processing.
    REFERENCES:
    1. Rafael C.Gonzalez and Richard E.Woods, “Digital Image Processing”, Pearson Education, Third Edition, 2008.
    2. S. Sridhar, “Digital Image Processing”, Oxford University Press, 2011.
    3. Milan Sonka, Vaclav Hlavac and Roger Boyle, “Image Processing, Analysis and Machine Vision”,

    Second Edition, Thomson Learning, 2001.
    4. Anil K.Jain, “Fundamentals of Digital Image Processing”, PHI, 2006.
    5. Sanjit K. Mitra, & Giovanni L. Sicuranza, “Non Linear Image Processing”, Elsevier, 2007.
    6. Rafael C.Gonzalez, Richard E.Woods, and Eddins, “Digital Image Processing Using MATLAB”,

    Tata McGraw-Hill, Second Edition, 2009.
CP8012 INTERNET OF THINGS L T P C 3 003
COURSE OBJECTIVES:
  •   To understand the basics of Internet of Things
  •   To get an idea of some of the application areas where Internet of Things can be applied
  •   To understand the middleware for Internet of Things
  •   To understand the concepts of Web of Things
  •   To understand the concepts of Cloud of Things with emphasis on Mobile cloud computing
  •   To understand the IOT protocols
39
UNIT I INTRODUCTION 10
Definitions and Functional Requirements –Motivation – Architecture - Web 3.0 View of IoT– Ubiquitous IoT Applications – Four Pillars of IoT – DNA of IoT - The Toolkit Approach for End-user Participation in the Internet of Things. Middleware for IoT: Overview – Communication middleware for IoT –IoT Information Security
UNIT II IOT PROTOCOLS 8
Protocol Standardization for IoT – Efforts – M2M and WSN Protocols – SCADA and RFID Protocols – Issues with IoT Standardization – Unified Data Standards – Protocols – IEEE 802.15.4 – BACNet Protocol – Modbus – KNX – Zigbee Architecture – Network layer – APS layer – Security
UNIT III WEB OF THINGS 10
Web of Things versus Internet of Things – Two Pillars of the Web – Architecture Standardization for WoT– Platform Middleware for WoT – Unified Multitier WoT Architecture – WoT Portals and Business Intelligence. Cloud of Things: Grid/SOA and Cloud Computing – Cloud Middleware – Cloud Standards – Cloud Providers and Systems – Mobile Cloud Computing – The Cloud of Things Architecture
UNIT IV INTEGRATED 9
Integrated Billing Solutions in the Internet of Things Business Models for the Internet of Things -
UNIT V APPLICATIONS 8
The Role of the Internet of Things for Increased Autonomy and Agility in Collaborative Production Environments - Resource Management in the Internet of Things: Clustering, Synchronisation and Software Agents. Applications - Smart Grid – Electrical Vehicle Charging
Network Dynamics: Population Models – Information Cascades - Network Effects - Network Dynamics: Structural Models - Cascading Behavior in Networks - The Small-World Phenomenon
COURSE OUTCOMES:
Upon completion of the course, the students will be able to
  •   Identify and design the new models for market strategic interaction
  •   Design business intelligence and information security for WoB
  •   Analyze various protocols for IoT
  •   Design a middleware for IoT
  •   Analyze and design different models for network dynamics
    REFERENCES:
  1. The Internet of Things in the Cloud: A Middleware Perspective - Honbo Zhou – CRC Press – 2012
  2. Architecting the Internet of Things - Dieter Uckelmann; Mark Harrison; Florian Michahelles- (Eds.) – Springer – 2011
  3. Networks, Crowds, and Markets: Reasoning About a Highly Connected World - David Easley and Jon Kleinberg, Cambridge University Press - 2010
  4. The Internet of Things: Applications to the Smart Grid and Building Automation by - Olivier Hersent, Omar Elloumi and David Boswarthick - Wiley -2012
  5. Olivier Hersent, David Boswarthick, Omar Elloumi , “The Internet of Things – Key applications and Protocols”, Wiley, 2012
40
TOTAL: 45 PERIODS
CP8013 NETWORK ON CHIP COURSE OBJECTIVES:
L T P C 3003
9
    
UNIT I
To understand the various classes of Interconnection networks. To learn about different routing techniques for on-chip network. To know the importance of flow control in on-chip network.
To learn the building blocks of routers.

To provide an overview of the current state-of-the-art research.
ICN ARCHITECTURES
Introduction - Classification of ICNs - Topologies - Direct networks - Indirect networks-Performance analysis.
UNIT II SWITCHING TECHNIQUES 9
Basic switching techniques - Virtual channels - Hybrid switching techniques Optimizing switching techniques - Comparison of switching techniques - Deadlock, livelock and Starvation.
UNIT III ROUTING ALGORITHMS 9
Taxonomy of routing algorithms - Deterministic routing algorithms - Partially adaptive algorithms - Fully adaptive algorithms - Routing in MINs - Routing in switch-based networks with irregular topologies - Resource allocation policies- Flow control.
UNIT IV NETWORK-ON-CHIP 9
NoC Architectures - Router architecture - Area, energy and reliability constraints - NoC design lternatives - Quality-of Service (QoS) issues in NoC architectures
UNIT V EMERGING TRENDS 9
Fault-tolerance issues - Emerging on-chip interconnection technologies- 3D NoC- Simulation.
COURSE OUTCOMES:
Upon Completion of the course, the students will be able to
  •   Identify the major components required to design an on-chip network.
  •   Compare different switching techniques.
  •   Evaluate the performance and the cost of the given on-chip network.
  •   Demonstrate deadlock-free and livelock free routing protocols.
  •   Simulate and assess the performance of a given on-chip network.
    REFERENCES:
    1. J. Duato, S. Yalamanchili, and Lionel Ni, "Interconnection Networks: An Engineering Morgan Kaufmann Publishers 2004.
    2. William James Dally and Brian Towles, "Principles and Practices of Interconnection ISBN: 0122007514, Morgan Kaufmann, 2003.
Approach",
Networks",
3. Giovanni De Micheli and Luca Benini, "Networks on Chips: Technology and Tools", ISBN:
0123705215, Morgan Kaufmann, 2006
4. Natalie Enright Jerger and Li-Shiuan Peh, “On-ChipNetworks”, Synthesis lectures on computer

architecture #8, Morgan and Claypool Publishers 2009.
5. Fayez Gebali, Haytham Elmiligi, Mohamed Wathed and El-Kharashi “Networks-on-Chips: Theory

and Practice”, CRC Press, Taylor and Francis Group 2009.
41
TOTAL: 45 PERIODS
CP8014 SECURE NETWORK SYSTEM DESIGN L T P C 3003
COURSE OBJECTIVES:
   

UNIT I
Understand security best practices and how to take advantage of the networking gear that is already available
Learn design considerations for device hardening, Layer 2 and Layer 3 security issues, denial of service, IPSec VPNs, and network identity

Understand security design considerations for common applications such as DNS, mail, and web
Identify the key security roles and placement issues for network security elements such as firewalls, intrusion detection systems, VPN gateways, content filtering, as well as for traditional network infrastructure devices such as routers and switches.

Understand the various testing and optimizations strategies to select the technologies and devices for secure network design.
NETWORK SECURITY FOUNDATIONS 9
Secure network design through modeling and simulation, A fundamental framework for network security, need for user level security on demand, Network Security Axioms, security policies and operations life cycle, security networking threats, network security technologies, general and identity design considerations, network security platform options and best deployment practices, secure network management and network security management.
UNIT II IDENTIFYING SYSTEM DESIGNER’S NEEDS AND GOALS 9
Evolution of network security and lessons learned from history, Analyzing top-down network design methodologies, technical goals and tradeoffs – scalability, reliability, availability, Network performance, security, Characterizing the existing internetwork, characterizing network traffic, developing network security strategies.
UNIT III PHYSICAL SECURITY ISSUES AND LAYER 2 SECURITY
CONSIDERATIONS 9

Control physical access to facilities, Control physical access to data centers, Separate identity mechanisms for insecure locations, Prevent password-recovery mechanisms in insecure locations, awareness about cable plant issues, electromagnetic radiation and physical PC security threats, L2 control protocols, MAC flooding considerations, attack mitigations, VLAN hopping attacks, ARP, DHCP, PVLAN security considerations, L2 best practice policies.
UNIT IV IP ADDRESSING AND ROUTING DESIGN CONSIDERATIONS 9
Route summarizations, ingress and egress filtering, Non routable networks, ICMP traffic management, Routing protocol security, Routing protocol authentication, transport protocol management policies, Network DoS/flooding attacks.
UNIT V TESTING AND OPTIMIZING SYSTEM DESIGN 9
Selecting technologies and devices for network design, testing network design – using industry tests, building a prototype network system, writing and implementing test plan, tools for testing, optimizing network design – network performance to meet quality of service (QoS), Modeling, simulation and behavior analysis of security attacks, future issues in information system security.
TOTAL: 45 PERIODS
42
COURSE OUTCOMES:
Follows the best practices to understand the basic needs to design secure network.
  •   Satisfy the need for user and physical level security on demand for various types of network
    attacks.
  •   Uses best practice policies for different network layers protocols.
  •   Understand the network analysis, simulation, testing and optimizing of security attacks to
    provide Quality of Service.
    REFERENCE BOOKS:
    1. Sumit Ghosh, “Principles of secure network system design”, Springer-Verlag, NY,2002.(UNIT I) 2. Sean Convery, “Network security architecture”, Cisco Press, 2004.(UNIT III & IV)
    3. Priscilla Oppenheimer, “Top-Down network Design”, Thrid edition, Cisco press, 2012.

    (UNIT II & V).
    4. Larry L. Peterson, Bruce S. Davie, “Computer Networks: A Systems Approach”, Fourth Edition,

    Morgan Kauffmann Publishers Inc., 2009, Elsevier.
    5. William Stallings, “Crpyptography and Network security Principles and Practices”, Pearson / PHI,

    4th edition, 2006.
    6. Wade Trappe, Lawrence C Washington, “Introduction to Cryptography with coding theory”, 2nd

    edition, Pearson, 2007.
CP8017 BIG DATA ANALYTICS L T P C 3003
COURSE OBJECTIVES:

     
UNIT I
To understand big data analytics as the next wave for businesses looking for competitive advantage
To understand the financial value of big data analytics
To explore tools and practices for working with big data

To understand how big data analytics can leverage into a key component
To understand how to mine the data
To learn about stream computing
To know about the research that requires the integration of large amounts of data

INTRODUCTION TO BIG DATA 9
Analytics – Nuances of big data – Value – Issues – Case for Big data – Big data options Team challenge – Big data sources – Acquisition – Nuts and Bolts of Big data. Features of Big Data - Security, Compliance, auditing and protection - Evolution of Big data – Best Practices for Big data Analytics - Big data characteristics - Volume, Veracity, Velocity, Variety – Data Appliance and Integration tools – Greenplum – Informatica
UNIT II DATA ANALYSIS 9
Evolution of analytic scalability – Convergence – parallel processing systems – Cloud computing – grid computing – map reduce – enterprise analytic sand box – analytic data sets – Analytic methods – analytic tools – Cognos – Microstrategy - Pentaho. Analysis approaches – Statistical significance – business approaches – Analytic innovation – Traditional approaches – Iterative
UNIT III STREAM COMPUTING 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 - Realtime Analytics Platform(RTAP) applications IBM Infosphere – Big data at rest – Infosphere streams – Data stage – Statistical analysis – Intelligent scheduler – Infosphere Streams
43
UNIT IV PREDICTIVE ANALYTICS AND VISUALIZATION 9
Predictive Analytics – Supervised – Unsupervised learning – Neural networks – Kohonen models – Normal – Deviations from normal patterns – Normal behaviours – Expert options – Variable entry - 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 Visualizations - Visual data analysis techniques, interaction techniques; Systems and applications:
UNIT V FRAMEWORKS AND APPLICATIONS 9
IBM for Big Data – Map Reduce Framework - Hadoop – Hive - – Sharding – NoSQL Databases - S3 - Hadoop Distributed file systems – Hbase – Impala – Analyzing big data with twitter – Big data for E- Commerce – Big data for blogs.
COURSE OUTCOMES:
Upon Completion of the course, the students will be able to Identify the need for big data analytics for a domain
  •   Use Hadoop, Map Reduce Framework
  •   Apply big data analytics for a give problem
  •   Suggest areas to apply big data to increase business outcome
  •   Contextually integrate and correlate large amounts of information automatically to gain faster
    insights.
    REFERENCES:
  1. Frank J Ohlhorst, “Big Data Analytics: Turning Big Data into Big Money”, Wiley and SAS Business Series, 2012.
  2. Colleen Mccue, “Data Mining and Predictive Analysis: Intelligence Gathering and Crime Analysis”, Elsevier, 2007
  3. Michael Berthold, David J. Hand, Intelligent Data Analysis, Springer, 2007.
  4. Anand Rajaraman and Jeffrey David Ullman, Mining of Massive Datasets, Cambridge University
    Press, 2012.
  5. Bill Franks, “Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with
    Advanced Analytics”, Wiley and SAS Business Series, 2012.
  6. Paul Zikopoulos, Chris Eaton, Paul Zikopoulos, “Understanding Big Data: Analytics for Enterprise
    Class Hadoop and Streaming Data”, McGraw Hill, 2011.
  7. Paul Zikopoulos, Dirk deRoos, Krishnan Parasuraman, Thomas Deutsch , James Giles, David
    Corrigan, “Harness the Power of Big data – The big data platform”, McGraw Hill, 2012.
  8. Glenn J. Myatt, Making Sense of Data, John Wiley & Sons, 2007
  9. Pete Warden, Big Data Glossary, O’Reilly, 2011.
  10. Jiawei Han, Micheline Kamber “Data Mining Concepts and Techniques”, Second Edition, Elsevier,
    Reprinted 2008.
CP8019 PARALLEL AND DISTRIBUTED DATABASES L T P C 3 003
COURSE OBJECTIVES :
  •   To realize the need of parallel processing, to cater the applications that require a system capable of sustaining trillions of operations per second on very large data sets
  •   To understand the need of data integration over data centralization 44
TOTAL : 45 PERIODS
UNIT I INTRODUCTION TO PARALLEL DATABASES 9
Need of Parallelism - Forms of parallelism – architecture – Analytical models. Basic Query Parallelism – Parallel Search- Parallel sort and Group By- Parallel Join
UNIT II ADVANCED QUERY PROCESSING IN PARALLEL DATABASES 9
Parallel indexing. Parallel Universal Qualification – Collection Join Queries. Parallel Query Scheduling – Optimization, Applications
UNIT III INTRODUCTION TO DISTRIBUTED DATABASES 9
Overview - Promises of DDB –Design Issues – DDB Design – DDB Integration – Data and Access Control.
UNIT IV QUERY PROCESSING IN DISTRIBUTED DATABASES 9
Overview- of Query Processing – Query Decomposition and Data Localization – Optimization of Distributed Queries, Multi-database Query Processing.
UNIT V TRANSACTION MANAGEMENT AND OTHER ADVANCED SYSTEMS 9
Introduction – Concurrency Control - Distributed DBMS Reliability – Data Replication – DDB Applications, Distributed Object Database Management – Peer -to-Peer Data Management - Web Data Management – Streaming Data and Cloud Computing.
COURSE OUTCOMES:
STUDENTS WILL
  • -  Get good knowledge on the need, issues ,design and application of both parallel and distributed databases.
  • -  Know how to write optimal queries to cater applications of that need these forms of databases
  • -  Be able to fragment, replicate and localize their data as well as their queries to get their work
    done faster
  • -  Get idea on other similar trends of optimal data processing
    TEXT BOOKS :
  1. David Taniar, Clement H.C.Leung, Wenny Rahayu, Sushant Goel , “High performance parallel Database processing and Grid databases” , John Wiley & Sons, Wiley Series in Parallel and Distributed Computing, 2008.
  2. M. Tamer Ozsu and Patrick Valduriez, “Principles of Distributed Database Systems”, Springer Science + Business Media , 3rd Edition, 2011.
CP8071 ADVANCED DATABASE ADMINISTRATION AND TUNING L T P C 3003
COURSE OBJECTIVES:
At the end of the course the students would be able to
  •   Design and implement relational database solutions for general applications.
  •   Develop database scripts for data manipulation and database administration.
  •   Understand and perform common database administration tasks, such as database
    monitoring, performance tuning, data transfer, and security.
  •   To balance the different types of competing resources in the database environment so that the
    most important applications have priority access to the resources
45
TOTAL : 45 PERIODS
UNIT I INTRODUCTION TO DATABASE ADMINISTRATION 9
Database Administration - DBA Tasks- Database Design -Performance Monitoring and Tuning – Availability - Database Security and Authorization - Backup and Recovery - Data Integrity- DBMS Release Migration - Types of DBAs - Creating the Database Environment - Choosing a DBMS - DBMS Architectures - DBMS Clustering -DBMS Proliferation - Hardware Issues -Installing the DBMS - DBMS Installation Basics Hardware Requirements -Storage Requirements Memory Requirements Configuring the DBMS - Connecting the DBMS to Supporting Infrastructure Software -Installation Verification - DBMS Environments - Upgrading DBMS Versions and Releases - Fallback Planning Migration Verification
UNIT II DATABASE SECURITY, BACKUP AND RECOVERY 9
Database Users - Granting and Revoking Authority - Types of Privileges - Granting to PUBLIC- Revoking Privileges - Security Reporting - Authorization Roles and Groups - Using Views for Security - Using Stored Procedures for Security Auditing External Security - Job Scheduling and Security - Image Copy Backups - Full vs. Incremental Backups - Database Objects and Backups - DBMS Control - Concurrent Access Issues Backup Consistency - Log Archiving and Backup - DBMS Instance Backup - Designing the DBMS Environment for Recovery - Alternate Approaches to Database Backup - Recovery - Determining Recovery Options Types of Recovery – DBA Tools – DBA Rules of Thumb.
UNIT III FUNDAMENTALS OF TUNING 9
Review of Relational Databases – Relational Algebra – Locking and Concurrency Control – Correctness Consideration – Lock Tuning – Logging and the Recovery Subsystem – Principles of Recovery – Tuning the Recovery Subsystem – Operating Systems Considerations – Hardware Tuning.
UNIT IV INDEX TUNING AND QUERY OPTIMIZATION 9
Types of Queries – Data Structures – B tree – B+ Tree - Hash Structures – Bit Map Indexes – Clustering Indexes – Non Clustering Indexes – Composite Indexes – Hot Tables – Comparison of Indexing and Hashing Techniques. Optimization Techniques - Tuning Relational Systems –- Normalization – Tuning Denormalization – Clustering Two Tables – Aggregate maintenance – Record Layout – Query Cache – Parameter Cache - Query Tuning – Triggers – Client Server Mechanisms – Objects, Application Tools and Performance –Tuning the Application Interface – Bulk Loading Data – Accessing Multiple Databases.
UNIT V TROUBLESHOOTING 9
Query Plan Explainers – Performance Monitors – Event Monitors – Finding “Suspicious” Queries – Analyzing a Query’s Access Plan – Profiling a Query Execution – DBMS Subsystems.
TOTAL : 45 PERIODS
COURSE OUTCOMES:
  •   advanced features of databases in design, administration, and applications
  •   aspires to improve the performance of a database
  •   optimize the use of existing resources within the database environment.
    REFERENCES:
    1. Craig S. Mullins, Database Administration: The Complete Guide to Practices and Procedures, Addison-Wesley Professional, 2002.
    2. Dennis Shasha and Philippe Bonnet, Database Tuning, Principles, Experiments and Troubleshooting Techniques, Elsevier Reprint 2005.
    3. Silberschatz,Korth,DatabaseSystemConcepts,McGrawhill,6thedition,2010.
    4. Thomas Connoly and Carlolyn Begg, Database Systems, A Practical Approach to Design,

    Implementation and Management, Fourth Edition, Pearson Education 2008. 46
CP8018 ETHICAL HACKING AND DIGITAL FORENSICS
COURSE OBJECTIVES:
To learn various hacking techniques and attacks.
To know how to protect data assets against attacks from the Internet.
Toassessandmeasurethreatstoinformationassets.
Tounderstandthebenefitsofstrategicplanningprocess.
To evaluate where information networks are most vulnerable.
To perform penetration tests into secure networks for evaluation purposes.
To enable students to understand issues associated with the nature of forensics.
UNIT I HACKING WINDOWS
L T P C 3003
Hacking windows – Network hacking – Web hacking – Password hacking. A study on various attacks – Input validation attacks – SQL injection attacks – Buffer overflow attacks - Privacy attacks.
UNIT II TCP/IP 9
TCP / IP – Checksums – IP Spoofing port scanning, DNS Spoofing. Dos attacks – SYN attacks, Smurf attacks, UDP flooding, DDOS – Models. Firewalls – Packet filter firewalls, Packet Inspection firewalls – Application Proxy Firewalls. Batch File Programming.
UNIT III FUNDAMENTALS OF COMPUTER FRAUD 9
Fundamentals of Computer Fraud – Threat concepts – Framework for predicting inside attacks – Managing the threat – Strategic Planning Process.
UNIT IV ARCHITECTURE 9
Architecture strategies for computer fraud prevention – Protection of Web sites – Intrusion detection system – NIDS, HIDS – Penetrating testing process – Web Services– Reducing transaction risks.
UNIT V KEY FRAUD INDICATOR SELECTION PROCESS CUSTOMIZED 9
Forensics – Computer Forensics – Journaling and it requirements – Standardized logging criteria – Journal risk and control matrix – Neural networks – Misuse detection and Novelty detection.
TOTAL: 45 PERIODS
COURSE OUTCOMES:
  •   On completion of this course, a student should be able to:
  •   Defend hacking attacks and protect data assets.
  •   Defend a computer against a variety of different types of security attacks using a number of
    hands-on techniques.
  •   Defend a LAN against a variety of different types of security attacks using a number of hands-
    on techniques.
  •   Practice and use safe techniques on the World Wide Web.
  •   Understand computer Digital forensics.
    REFERENCES:
    1. Kenneth C.Brancik “Insider Computer Fraud” Auerbach Publications Taylor & Francis Group– 2008.
    2. Ankit Fadia “ Ethical Hacking” second edition Macmillan India Ltd, 2006
47
9
CP8021 WEB SERVICES
COURSE OBJECTIVES:
  •   To learn the basics of XML technology.
  •   To understand the background of distributed information system.
  •   To analyze and design a web service based application.
  •   To learn the security features of web services and service composition.
    UNIT I DISTRIBUTED INFORMATION SYSTEM
L T P C 3003
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 II WEB SERVICES BUILDING BLOCK 9
Web Services – Definition – Web Services and EAI – Web Services Technologies – XML basics - web services Architecture – SOAP – WSDL – UDDI –WS – Addressing – WS – Routing – Web service implementation – Java based web services - .NET based web services.
UNIT III WEB SERVICE SECURITY 9
XML signature – XML Encryption – SAML - XKMS – WS- Security –WS Policy –Web service security framework – .NET and passport – UDDI and security - web service security in java – mobile web service security.
UNIT IV SEMANTIC WEB SERVICES 9
Semantic web service – architecture – RDF Data model – RDF schema – OWL – ontology – role of ontology in web services - semantic Web service implementation issues .
UNIT V SERVICE COMPOSITION 9
Service Coordination and Composition coordination protocols – WS – Coordination – WS – transaction – WSCI – Service Composition – Service Composition Models – Dependencies between coordination and composition – BPEL – Current trends.
COURSE OUTCOMES:
Upon Completion of the course, the students should be able to:
  •   Create, validate, parse, and transform XML documents.
  •   Design a middleware solution based application.
  •   Develop web services using different technologies.
  •   Compose set of web services using BPEL.
    REFERENCES:
  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. Jorge Cardoso,”Semantic Web Services”,2006
48
TOTAL: 45 PERIODS
CP8072 CLOUD COMPUTING
COURSE OBJECTIVES:
  •   To understand the current trend and basics of cloud computing.
  •   To learn cloud services from different providers.
  •   To understand the collaboration of cloud services.
  •   To expose various ways to collaborate the clud service online.
    UNIT I UNDERSTANDING CLOUD COMPUTING
L T P C 3003
Cloud Computing – History of Cloud Computing – Cloud Architecture – Cloud Storage – Why Cloud Computing Matters – Advantages of Cloud Computing – Disadvantages of Cloud Computing – Companies in the Cloud Today – Cloud Services
UNIT II DEVELOPING CLOUD SERVICES 9
Web-Based Application – Pros and Cons of Cloud Service Development – Types of Cloud Service Development – Software as a Service – Platform as a Service – Web Services – On-Demand Computing – Discovering Cloud Services Development Services and Tools – Amazon Ec2 – Google App Engine – IBM Clouds
UNIT III CLOUD COMPUTING FOR EVERYONE 9
Centralizing Email Communications – Collaborating on Schedules – Collaborating on To-Do Lists – Collaborating Contact Lists – Cloud Computing for the Community – Collaborating on Group Projects and Events – Cloud Computing for the Corporation
UNIT IV USING CLOUD SERVICES 9
Collaborating on Calendars, Schedules and Task Management – Exploring Online Scheduling Applications – Exploring Online Planning and Task Management – Collaborating on Event Management – Collaborating on Contact Management – Collaborating on Project Management – Collaborating on Word Processing - Collaborating on Databases – Storing and Sharing Files
UNIT V OTHER WAYS TO COLLABORATE ONLINE 9
Collaborating via Web-Based Communication Tools – Evaluating Web Mail Services – Evaluating Web Conference Tools – Collaborating via Social Networks and Groupware – Collaborating via Blogs and Wikis
COURSE OUTCOMES:
Able to collaborate the cloud services to any device.
  •   Exploring the online applications of cloud services.
  •   Implementing cloud computing for the corporation.
  •   Design various applications by integrating the cloud services
    REFERENCES:
  1. Michael Miller, Cloud Computing: Web-Based Applications That Change the Way You Work and Collaborate Online, Que Publishing, August 2008.
  2. Kumar Saurabh, “Cloud Computing – Insights into New Era Infrastructure”, Wiley Indian Edition, 2011.
  3. Haley Beard, Cloud Computing Best Practices for Managing and Measuring Processes for On- demand Computing, Applications and Data Centers in the Cloud with SLAs, Emereo Pty Limited, July 2008.
49
TOTAL: 45 PERIODS
9
CP8020 STATISTICAL NATURAL LANGUAGE PROCESSING COURSE OBJECTIVES:
L T P C 3 003

 
  
UNIT I
To understand the representation and processing of Morphology and Part-of Speech Taggers
To appreciate various techniques used for speech synthesis and recognition
To understand different aspects of natural language syntax and the various methods used for processing syntax

To understand different methods of disambiguating word senses
To appreciate the various representations of semantics and discourse To know about various applications of natural language processing

MORPHOLOGY AND PART-OF SPEECH PROCESSING
9
Introduction –Regular Expressions and Automata- Non-Deterministic FSAs. Tranducers –English Morphology - Finite-State Morphological Parsing - Porter Stemmer - Tokenization- Detection and Correction of Spelling Errors. N-grams – Perplexity - Smoothing - Interpolation - Backoff . Part-of- Speech Tagging – English Word Classes - Tagsets - Rule-Based - HMM - Transformation-Based Tagging - Evaluation and Error Analysis. Hidden Markov and Maximum Entropy Models
UNIT II SPEECH PROCESSING 9
Phonetics – Articulatory Phonetics - Phonological Categories - Acoustic Phonetics and Signals - Speech Synthesis – Text Normalization – Phonetic and Acoustic Analysis - Diphone Waveform synthesis – Evaluation- Automatic Speech Recognition –Architecture - Hidden Markov Model to Speech - MFCC vectors - Acoustic Likelihood Computation - Evaluation. Triphones - Discriminative Training - Modeling Variation. Computational Phonology-Finite-State Phonology - Computational Optimality Theory - Syllabification - Learning Phonology and Morphology
UNIT III SYNTAX ANALYSIS 9
Formal Grammars of English – Constituency - Context-Free Grammars –Grammar Rules - Treebanks - Finite-State and Context-Free Grammars - Dependency Grammars. Syntactic Parsing – Parsing as Search - Ambiguity - Dynamic Programming Parsing Methods –CKY- Earley and Chart Parsing- Partial Parsing-Evaluation. Statistical Parsing – Probabilistic Context-Free Grammars - Probabilistic CKY Parsing of PCFGs –Probabilistic Lexicalized CFGs –Collins Parser. Language and Complexity - The Chomsky Hierarchy -The Pumping Lemma
UNIT IV SEMANTIC AND PRAGMATIC INTERPRETATION 9
Representation of Meaning – Desirable Properties - Computational Semantics -Word Senses - Relations Between Senses – WordNet- Event Participants- Proposition Bank -FrameNet -–Metaphor. Computational Lexical Semantics – Word Sense Disambiguation- Supervised Word Sense Disambiguation - Dictionary and Thesaurus Methods- Word Similarity - Minimally Supervised WSD - Hyponymy and Other Word Relations - Semantic Role Labeling -Unsupervised Sense Disambiguation. Computational Discourse - Discourse Segmentation - Unsupervised Discourse Segmentation - Text Coherence - Reference Resolution –Phenomena – Features and algorithms - Pronominal Anaphora Resolution
UNIT V APPLICATIONS 9
Information Extraction – Named Entity Recognition - Relation Detection and Classification -Temporal and Event Processing - Template-Filling - Biomedical Information Extraction. Question Answering and Summarization -Information Retrieval -Factoid Question Answering - Summarization - Single and Multi-Document Summarization - Focused Summarization - Evaluation. Dialog and Conversational Agents – Properties of Human Conversations - Basic Dialogue Systems - VoiceXML - Information- State and Dialogue Acts - Markov Decision Process Architecture. Machine Translation –Issues in Machine Translation - Classical MT and the Vauquois Triangle -Statistical MT - Phrase-Based Translation Model - Alignment in MT –IBM Models –Evaluation
TOTAL : 45 PERIODS
50
COURSE OUTCOMES:
Upon Completion of the course,t he students will be able to
  •   To identify the different linguistic components of given sentences
  •   To design a morphological analyser for a language of your choice using finite state automata
    concepts
  •   To implement the Earley algorithm for a language of your choice by providing suitable
  •   grammar and words
  •   To use a machine learning algorithm for word sense disambiguation
  •   To build a tagger to semantically tag words using WordNet
  •   To design a business application that uses different aspects of language processing.
    REFERENCES:
1. 2.
3. 4.
5. 6.
IF8084
COURSE OBJECTIVES:
Jurafsky and Martin, “Speech and Language Processing”, Pearson Prentice Hall; 2 edition 2008
Christopher D. Manning and Hinrich Schütze, ‘Foundations of Statistical Natural

Language Processing”, MIT Press, 1999
Stevan Bird, “Natural Language Processing with Python”, O'Reilly Media; 1 edition2009 Natural Language Understanding (2nd Edition) [Paperback], Addison- Wesley; 2 edition, 1994
Nitin Indurkhya, Fred J. Damerau, “Handbook of Natural Language Processing, Second

Edition” (Chapman & Hall/CRC Machine Learning & Pattern Recognition), 2010 Alexander Clark, Chris Fox, Shalom Lappin, “The Handbook of Computational Linguistics
and Natural Language Processing” Wiley-Blackwell; 1 edition, 2010
 
 
UNIT I
AD HOC AND WIRELESS SENSOR NETWORKS L T P C 3003
To learn about the issues in the design of wireless ad hoc networks
To understand the working of protocols in different layers of mobile ad hoc and sensor networks
To expose the students to different aspects in sensor networks
To understand various security issues in ad hoc and sensor networks and solutions to the issues

MAC & ROUTING IN AD HOC NETWORKS 9
Introduction – Issues and challenges in ad hoc networks – MAC Layer Protocols for wireless ad hoc networks – Contention-Based MAC protocols – MAC Protocols Using Directional Antennas – Multiple- Channel MAC Protocols – Power-Aware MAC Protocols – Routing in Ad hoc Networks – Design Issues – Proactive, Reactive and Hybrid Routing Protocols
UNIT II TRANSPORT & QOS IN AD HOC NETWORKS 9
TCP’s challenges and Design Issues in Ad Hoc Networks – Transport protocols for ad hoc networks – Issues and Challenges in providing QoS – MAC Layer QoS solutions – Network Layer QoS solutions – QoS Model
UNIT III MAC & ROUTING IN WIRELESS SENSOR NETWORKS 9
Introduction – Applications – Challenges – Sensor network architecture – MAC Protocols for wireless sensor networks – Low duty cycle protocols and wakeup concepts – Contention-Based protocols – Schedule-Based protocols – IEEE 802.15.4 Zigbee – Topology Control – Routing Protocols
51

UNIT IV TRANSPORT & QOS IN WIRELESS SENSOR NETWORKS 9
Data-Centric and Contention-Based Networking – Transport Layer and QoS in Wireless Sensor Networks – Congestion Control – In-network processing – Operating systems for wireless sensor networks – Examples
UNIT V SECURITY IN AD HOC AND SENSOR NETWORKS 9
Security Attacks – Key Distribution and Management – Intrusion Detection – Software based Anti- tamper techniques – Water marking techniques – Defense against routing attacks - Secure Ad hoc routing protocols – Broadcast authentication WSN protocols – TESLA – Biba – Sensor Network Security Protocols – SPINS
TOTAL : 45 PERIODS
COURSE OUTCOMES:
Upon completion of this course students should be able to
  •   Identify different issues in wireless ad hoc and sensor networks
  •   To analyze protocols developed for ad hoc and sensor networks
  •   To identify and understand security issues in ad hoc and sensor netoworks
    REFERENCES:
    1.
  1. Holger Karl, Andreas Willig, “Protocols and Architectures for Wireless Sensor Networks”, John Wiley & Sons, Inc., 2005.
  2. Erdal Çayırcı , Chunming Rong, “Security in Wireless Ad Hoc and Sensor Networks”, John Wiley and Sons, 2009.
  3. C.Siva Ram Murthy and B.S.Manoj, “Ad Hoc Wireless Networks – Architectures and Protocols”, Pearson Education, 2004.
5.
  1. Waltenegus Dargie, Christian Poellabauer, “Fundamentals of Wireless Sensor Networks Theory and Practice”, John Wiley and Sons, 2010
  2. Adrian Perrig, J. D. Tygar, "Secure Broadcast Communication: In Wired and Wireless Networks", Springer, 2006