Course Description for Spring – 2015

Course Name Faculty name

Advances in Data Mining Kamal Karlapalem

Advances in Robotics & Control Rambabu Kalla

Artificial Neural Networks Yegnanarayana

Cognitive Neuroscience Kavitha Vemuri

Computational Linguistics 2 Radhika M+Soma Paul

Computer Aided Drug Design Deva Priyakumar

Computer Vision Anoop + PJ Naryanan

Database Systems P.Krishna Reddy

Design for Testability Shubhajit Roy

Distributed Systems Vivekanand Vellanki

e-Governance Harish P Iyer+RK Bagga

Electromagnetism & Optics Prabhakar B

Error Correcting Codes Prasad Krishnan

General & Structural Chemistry Deva Priyakumar +Abdul Rehman

Indian Grammatical Tradition Dipti M Sharma

Information Retrieval and Extraction Vasudeva Varma

Information Security Audit and Assurance Shatrunjay Rawat +Mulualem Teku

Internals of Application Servers Ramesh Loganathan

Intro to Neural & Cognitive Modeling S.Bapi Raju + Dipanjan Roy

Intro to Robotics: Mechanics & Control Suril V Shah

Intro to Systems Biology V.Shridevi +Vinod Palakkad

Intro. to Biology A.Rameshwar

Linear Algebra Rajat Tandon

Linguistic Data 2: Collection & Modeling TBD

Medical Image Processing Jayanthi Sivaswamy

Modeling and Simulations M.Krishnan +Abdul Rehman

NLP Applications Manish Shrivastava

Optical Communication & Networks Jayashree Ratnam

Optimization Methods Kannan Srinathan

Principles of Information Security Ashok Kumar Das

Readings from Hindi Literature Harjinder Singh

Remote Sensing RC Prasad

Science Lab II Tapan Kumar Sau +M.Krishnan

Select topics in Physical Chemistry Tapan Kumar Sau +Prabhakar Bhimalapuram

Signal Detection and Estimation Theory Sachin Chaudhari

Software Engineering Raghu Reddy

Software Foundations Suresh Purini +Venkatesh Ch + K.Viswanath

Speech Systems Anil Kumar V

System and Network Security Sanjay Rawat

Topics in Natural Language Semantics Soma Paul

Topics in Speech Processing: Audio Suryakanth VG

Topology Geeta

Usability Engineering Priyanka Srivastava

VLSI Architectures Rahul Shrestha

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TITLE : Advances in Data MiningCourse Code :Note: Please use course code for previously existing courseCREDITS : 3-0-0-4TYPE-WHEN : CSE electiveFACULTY NAME : Kamal KarlapalemPRE-REQUISITE : Data Warehousing and Data MIningOBJECTIVE : To learn about latest techniques and applications of data mining.COURSE TOPICS :

1. Understanding data and feature determination2. Advanced techniques in Data Clustering and Outlier detection3. Graph mining4. Text Analytics5. Time Series mining

PREFERRED TEXT BOOKS: Han & Kamber Data MIning*REFERENCE BOOKS: Zaki, Data Mining & survey papers and other research papers*PROJECT: A semester long term paper.GRADING: 10 quizzes or assignments 40% marks. Term paper 60% marks.Must get at least 40/60 in Term paper to pass the course. To get 40/60 it should be of a novel, full-fledged work thataddresses a significant problem, with algorithms, implementation and results. The term paper must be submitted byMidterm 2 exams time frame. By the end of 10th week of classes.OUTCOME:Exposure to advanced data mining techniques and training to do research to address a problem.REMARKS:

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TITLE : Advances in Robotics and ControlCourse Code :Note : Please use course code for previously existing courseCREDITS : 4TYPE-WHEN : Level-2 Elective, SpringFACULTY NAME : Rambabu KallaPRE-REQUISITE: Linear Control SystemsOBJECTIVE : With the advent of novel mechanisms in robotics such as quad copters, fixed wing aircrafts, Googlecar, humanoid robots and a renewed interest in manipulators due to emergence of personal robotics the need tomodel and control such robot mechanisms cannot be articulated better. This course strives to fill up the vacuumexisting in the robotic curricula by addressing issues related to above. Simultaneously, the course objective is also tobring to the table advanced concepts in control systems and their application to the popular domain of roboticsCOURSE TOPICS:Actuators in Robotics: Different types of Actuators, Dynamic Model of DC Motor from First Principle, Transferfunction of DC Motor, Speed Control of DC Motor, Position Control of DC Motor, Servos.State Space Analysis: State Space (SS) Models, Linear Transformation of SS Models, Eign Values and Eign Vectors,Computation of State Transition Matrix, Transfer Function and Transfer Matrix, Linearization of Non-linear System,State Controllability, State Observability, Canonical Form, Discrete Time State Space Model, Design of ControlSystems in State Space.Review of Mobile Robot Kinematics: Kinematics of differential drives, Ackerman Steer and Omni Drives.Kinodynamic Planning, Non Holonomic is planning.Control of Non Holonomic Systems: Introduction to Non Holonomic (NH) Systems, Modeling Examples NHSystems, Control Properties of NH Systems, NH Motion Planning, Feedback Control of NH Systems, AsymptoticTracking.Review of Kinematics and Dynamics of Manipulators: DH Parameter, Forward Kinematics

Trajectory Tracking of Manipulators: Feedback Linearization, Trajectory Generation, Tracking and Control.Quad-rotor Modelling and Control: Introduction to Quad-rotors (QR), First Principles Model of QR, NonlinearModel Simulation of QR, Model Linearization, Control Strategies for QRs, Static Position Reference Controller,Dynamic Position and Velocity Reference Controllers, Linear State Feedback Control of QR, Optimal Control of QR.PREFERRED TEXT BOOKS:*REFERENCE BOOKS:

1. Modern Control Theory, Katsuhiko Ogata, Pearson Education, 20022. Modern Control Theory, Richard C. Dorf and Robert H. Bishop, Pearson Education, 2011.3. Robot Modeling and Control Mark W. Spong, Seth Hutchinson, and M. Vidyasagar, John Wiley & Sons, 2006.4. Nonholonomic Mechanics and Control, A. M. Bloch, Springer Science, 2003.5. Modeling and Control of Mini-Flying Machines, Pedro Castillo, Rogelio Lozano, Alejandro E. Dzul, Springer-

Verlag, 2005.6. Modeling and Control Simulation for Autonomous Quadrotor, Idris Eko Putro, Lambert Academic Pub., 2011

*PROJECT:GRADING: 2 Mid Semester Exams, 6 Assignments, and End Semester ExamOUTCOME: Students on successful completion of the course get acquainted with the control schemes applied to thefield of Robotics.REMARKS:

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TITLE : Artificial Neural NetworksCREDITS : 3-1-0-4TYPE-WHEN : Elective to be offered in the next semesterFACULTY NAME : B. YegnanarayanaPRE-REQUISITE: Meant only for 6th semester and above. Preferably PG and research scholarsOBJECTIVE : The course is a first course on the subject, highlighting the basics of ANNCOURSE TOPICS: Background to ANN Developments of computer technology and Artificial Intelligence, need fornew models of computing, biological motivation, PDP models. Basics of ANN -- ANN terminology, models of neurons,topology, basic learning laws. Activation and synaptic dynamics Activation dynamics models, synaptic dynamicsmodels, stability and convergence, and recall in neural networks. Feed forward neural networks Analysis of patternassociation, pattern classification and pattern mapping networks, perception learning and convergence theorem, backpropagation learning. Feedback neural networks Analysis of pattern storage networks (Hopfield model), Stochasticnetworks and simulated annealing, Boltzmann machine. Copetitive learning neural networks Components of CLNN,Analysis of pattern clustering networks, analysis of feature mapping networks. Applications of ANN Optimization,traveling salesman problem, speech and speaker recognitionPREFERRED TEXT BOOKS: B.Yegnanarayana, Artificial Neural Networks, Prentice-Hall India, 1999 J.M.Zurada,Introduction to Artificial Neural Systems, 1992*REFERENCE BOOKS: Simon Haykin, Neural Networks A comprehensive foundation, Pearson and Prentice-Hall,1999*PROJECT:GRADING: Midsem exam 1: 20%, Midsem exam 2: 20%, Assignments: 10% Final Exam (oral or written): 50%OUTCOME: I hope they will get some idea of basics of ANN, and the need of ANN for solving pattern recognitionproblemsREMARKS:

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Title: Cognitive NeuroscienceType When: Spring 2014Faculty Name: Kavita VemuriJoint course: IIITH and University of Hyderabad.

The course will examine how modern cognitive neuroscientists explore the neural underpinnings of sensoryinformation – vision, sound, and touch leading to visual/auditory attention, language processing, memory,empathy/emotion and other higher-order cognitive processes. Investigates the different techniques applied touncover observations of clinical populations & non-clinical human populations and also some specimens from theinsect/ animal kingdom. Data collected from powerful methods like functional magnetic resonance imaging (fMRI)and electroencephalogram (EEG) will be analyzed to examine functional brain connectivity. Equal emphasis is onunderstanding analytical methods and the limitations of each. The third part of the course will cover a part of

computational neurosciences, which involves building computer simulation on models of neurons and dynamic neuralcircuits

Lectures: 70%Lab work: 30%The lab work will cover analysis of fMRI, Difusion Tomography imaging, EEG data from research studies designed toinvestigate the neural responses to a visual, auditory or task stimuli.

Textbooks:1. Cognitive Neuroscience by Gazaniga (copy available in ITH library)2. Fundamentals of Computational Neuroscience by Thomas Trapenberg.3. Required research papers. Evaluation:Assignments (6):20%Class presentation (1): 10%Lab work: 30%Mid-sem I: 20%Final Sem: 20%

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TITLE : Computational Linguistics 2CREDITS : 3-0-0-4TYPE-WHEN :FACULTY NAME : Radhika M + Soma PaulPRE-REQUISITE: NLP-1 or CL-1OBJECTIVE: To introduce the students to the basic concepts of structure of texts, meaning in text and contextualinterpretation of text.COURSE TOPICS:SEMANTICS-Background for studying word meaning and sentence meaning, Sentence meaning and propositional content,Reference and Sense, Entailment, Contrariety, Contradiction, Transitivity, Symmetry, Reflexivity.-Word meaning and sentence meaning, content word and grammatical word, Contextual variation. -Semantic classesfor categorizing words: Verb alternation, Accomplishment, Achievement, Activity, Noun alternation, Mass vs Count,Adjective alternation;-Lexical semantic relations - Synonymy, Antonymy, Hyponymy and lexical inheritance, Meronymy; Lexical ambiguity.-Formal representation of natural language.PRAGMATICS AND DISCOURSE: Pragmatics and Discourse analysis as a study of context dependent aspects ofmeaning context, text and relevance.Discourse analysis: Structure of text and coherence; exchange structure and conversational analysis; turn taking;deixis; anaphora; discourse connectives and relations.Pragmatics: Meaning beyond textual context; entailment and inference; conversational implicative, conventionalimplicative and presupposition; co operative interaction and Gricean maxims; speech act theory; language as action,performatives, direct and indirect speech acts and felicity conditions; Reference;SEMINARS: Students will be expected to read research papers on various topics and present in class.PROJECT: Students will do one term project which will include issues related to semantics, pragmatics anddiscourse.PREFERRED TEXT BOOKS:Alan Cruse (2004). Meaning in Language.John Lyons (1995). Linguistic Semantics.Cruse Alan (2004). Meaning in Language: An Introduction to Semantics and Pragmatics. Part 2 and Part 4.Levinson, Stephen C. (1983). Pragmatics.Brown, G and Yule, G. (1983). Discourse Analysis.Cutting Joan (2002). Pragmatics and Discourse: A resource book for students.GRADING: HA10, Seminar 10, Mid Sem 25, Project 20, End Sem 35OUTCOME: Students will have a good understanding of semantic and contextual analysis of texts which will enablethem in building text processing tools and systems.

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TITLE : Computer Aided Drug Design (CADD)CREDITS : 4

TYPE-WHEN : ElectiveFACULTY NAME : Dr. U. Deva PriyakumarPRE-REQUISITE: Molecular architecture or advanced biomolecular architecture or equivalent. Those who areinterested in taking the course and do not have one of these prerequisites; please talk to the course instructor.OBJECTIVE: This course aims to introduce principal concepts in drug design, and specifically the role ofcomputational models. Various methods that are used in computer aided drug design would be discussed, and thebasic principles to understand these methods will also be taught.COURSE TOPICS:Course overview and Introduction (What to expect for the next 15 weeks)Introduction to few computational methods

- Potential energy surface- Force Field parameters- Conformational analysis- Energy calculations using molecular mechanics- Free energy of binding- Basics of chemical bonding

Drug design- Drug- Traditional drug design- Drug-receptor interactions- Biological activity and various measures- Importance of computational methods in drug design Databases - biomolecules & small molecules

Ligand based drug design- Structure activity relationship- Quantitative structure activity relationship (2D & 3D methods)- QSAR parameters- QSAR validation- Pharmacophore based models

Structure based drug design- Docking- Discussion of various common algorithms- Protein flexibility in docking- Scoring functions- De novo drug design

Molecular similarity analysisADME-T predictionAdvanced topics in drug design (Student Seminars)PREFERRED TEXT BOOKS: Textbook of drug design & discovery (Ed. Krogsgaard-Larsen & Lilijefors and Madsen)2001, CRC; Pharmacophore perception, development, and use in drug design (Ed. Gunter) 2000, IUL; Molecularmodeling: principles and applications (Leach) 2001, Prentice Hall; Molecular modeling and simulations (Schlick) 2002,Springer.*REFERENCE BOOKS: Material will be given from time to time*PROJECT:GRADING: Exams - 40% (10 + 10 + 20); Assignments - 40%; Lab Assignment - 10%; Seminars/Quiz - 10%OUTCOME: Understanding of the basic principles involved in computer aided drug design and, to be able toappreciate the utility of the techniques in pharmaceutical industry.REMARKS:

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TITLE : Computer VisionCREDITS : 3-1-0-4TYPE-WHEN :FACULTY NAME : Dr.Anoop & Prof.P J NarayananPRE-REQUISITE : Computer Graphics or Image processingOBJECTIVE :COURSE TOPICS : Relationship between computer vision, graphics and Image processing.Camera model: Imaging process 3D to 2D projection and loss of information, calibrated and un calibrated visionsystems. Limitations of popular cameras and methods to overcome them. Multiple view geometry and imagingsystems. Algebraic constraints, reconstruction, view synthesis. Recognition of objects from appearance, shape, partialview, occlusion, etc., Analysis of video, motion and recognizing dynamic activities.

PREFERRED TEXT BOOKS: Forsytn and Ponce‘ Computer Vision: a modern approach, PearsenEducation Inc.

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TITLE : CSC 441- Database SystemsCREDITS : 4TYPE-WHEN : Second-level course in database systemsFACULTY NAME : P. Krishna ReddyPRE-REQUISITE : Students should have knowledge of SQL, database design and operating systems, programminglanguage, algorithms.OBJECTIVE : Databases have become essential part of every business. A database system can be used to managelarge amounts of data in a persistent manner. The objective of this course is to study the methods that have beenevolved over several decades to build database systems or database management systems software in a focusedmanner which include storage management, index management, query processing, recovery management andtransaction management.COURSE TOPICSIntroduction (3 hours); Data storage ( 3 hours); Representing data elements (3 hours); Index structures (3 hours);Multidimensional indexes (6 hours); Query execution (6 hours); The query compiler (6 hours); Coping with systemfailures (3 hours); Concurrency control (6 hours); More about transaction management (6 hours).PREFERRED TEXT BOOKS:1. Database System Implementation, Hector Garcia-Molina, Jeffrey D. Ullman and Jennifer Widon, PearsonEducation, 2003OTHER TEXT BOOKS:2. Elmasri & Navathe, Fundamentals of Database Systems, Pearson Education, 5th Education.3. Raghu Ramakrishnan and Johannes Gehrke, Database Management Systems, Third edition, McGraw Hill, 2003.4. Abraham Silberschatz, Henry F.Korth, S.Sudarshan, Database system concepts, fifth edition,Mc Graw Hill, 2006.PROJECT:A practical project on indexing, query optimization, and transaction management will be given. The project will beevaluated.GRADING:PROJECT and Assignments: 30%; MIDSEM: 30%; ENDSEM: 40%OUTCOME:The course will help the students in understanding the fundamental concepts of several database managementsystems like ORACLE, DB2, SYBASE and so on. Also, the students will understand the solutions/options to interestingproblems which have been encountered by the designers of preceding DBMSs. Most important, the students will beexposed to internal design of DBMSs and able to tune the DBMSs to meet the performance demands of diverseapplications.

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TITLE : Design for TestabilityCREDITS : 3-1-0-4TYPE-WHEN : Spring 2012FACULTY NAME : Shubhajit RoyPRE-REQUISITE : A course on Digital Circuits (or) B.TechOBJECTIVE : To expose the students to the various techniques adopted to make thetesting (complicated) of manufactured ICs. To make the students to take care of the testing aspectsinto acount at the design stage itself.COURSE TOPICS :1) Introduction: Testing of electronic gadgets, various types of tests, VLSI design flow, role of modeling andsimulation in testing.2) Faults and fault modeling, detection of faults, fault simulation and its applications, functional testing, exhaustiveand non-exhaustive testing, automatic testing procedures.3) Design for testabilty: Various features are to be incorporated for carying out testing from input & output pins, scanarchitecture, board level testing, signature analysis and testing.4) Built in Self Test (BIST), BIST concepts, text patern generation, BIST architectures.5) Testing of Analog and mixed signal ICs, testing of system on chip.PREFERRED TEXT BOOKS:

1) Miron Abramolici, Melin A Breur, Arthur D. Friedman, Digital systems, testing and testable design, Jaico publishinghouse, 20012) Stanley L. Hurst, VLSI Testing, Digital and Mixed Analog / Digital Techniques, Institution of Electrical Engineers,1998, London, United Kingdom.3) Michael L. Bushnel, Vishwani D. Agarwal, Esentials of Electronic Testing for Digital & Mixed Signal FLSI Circuits,Springer 2000*REFERENCE BOOKS:1. “VLSI Test Principles and Architectures: Design for Testabilty”,Laung-Terng Wang, Cheng-Wen Wu, Xiaoqing Wen2. “VLSI Testing”, Stanley Leonard Hurst3. “Electronic Design Automation”, Laung-Terng Wang, Yao-Wen Chang, Kwang-Ting (Tim) Cheng4. “System-on-Chip Test Architectures: Nanometer Design for Testabilty”, Laung-Terng Wang, Charles5. E. Stroud, Nur A. Touba6. “Testing of Digital Systems”, Jha and Gupta*PROJECT:GRADING: 2 Mid Sem Exams 2 x 20 40 2 Surprise Tests 10 Final Examination 50 ----- Total Marks 100 ----- A > 80 B70 � 79 C 60 � 69 D 50 � 59 E < 50OUTCOME:REMARKS:

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TITLE : Distributed SystemsCREDITS : 4FACULTY NAME : Vivekanand VellankiFoundations: Characterizations of Distributed SystemsSystem ModelsNetworking and InternetworkingInter-process CommunicationLogical Time:A framework for a system of logical clocksScalar time, vector time and efficient implementation of vector clocksSynchronization of physical clocks. NTPGlobal state and snapshot recording algorithms:System model and definitionSnapshot algorithms for FIFO channelsMiddleware:Distributed objects and RMITermination Detection:Termination detection using distributed snapshotsA spanning-tree-based termination detection algorithms

Distributed mutual exclusion algorithms:Lamport‘s algorithm, Ricart-Agarwala AlgoritmSughal‘s dynamic information – Structure AlgorithmQuorum-based mutual exclusion AlogorithmMaekawa‘s AlgorithmDeadlock detection in Distributed Systems:Models of deadlocks, Knapp‘s classification of distributed deadlock detection algorithms. Mitchell and Merrit‘salgorithm for single resource modelConsensus and agreement algorithm:Problem definition. Agreement in a failure-free system (synchronous or asynchronous). Agreement in (message-passing) synchronous system with failures. Agreement in asynchronous message passing systems with failures.

Reference Books1) Ajay D. Kshemkalyani and Mukesh Singhal, ―Distributed Computing Principles, Algorithms and

System‖, Cambridge University Press 2008.2) Sukumar Ghosh, ―Distributed Systems – An Algorithmic Approach‖, Chapman & Hall ICRC,

2007.3) M. L. Liu, ―Distributed Computing Principles and Applications‖, Pearson, 2004.4) George Coulouris, Jean Dollimore, Tim Kindberg and Gordon Blair, ―Distributed Systems Concepts and

Design‖, Fifth Edition, Pearson 2011.

5) Mukesh Singhal and Niranjan G. Shivaratri, ―Advanced Concepts in Operating Systems‖,TMH, 1994, 2010.

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TITLE : e-GovernanceCREDITS : 3-1-0-4TYPE-WHEN : Monsoon 2013 - Being offered for the second time for PG and 3 rd/4th UG Students. Max number ofstudents to be restricted to 35.Course wil not be ofered, if students’ number is les than 10.FACULTY NAME : Dr R K BAGGA, Harish P IyerPRE-REQUISITE : None at presentOBJECTIVE :1. Covers basic concepts of e-Government with scope of applications of Information &Communication Technologies (ICT) in Government sector.2. The role of ICT as enabler for government procesreengineering (GPR) for citizen serviceswil be highlighted.3. Propose to cover implementation isues of e-Governance initiatives in India, Changemanagement, transparency, coruptions, Cyber laws and e-Security.COURSE TOPICS :MDULE I ….ICT FOR DEVELOPMENT (ICT4D)I. Exposure to emerging trends in ICT for development.I. Understanding of design and implementation of e-Government projectsII. Systems Approach and MIS Framework in eGovermentIV. National e-Governance Plan(NeGP) for IndiaV. Technical Paper(individual work)MODULE I….e-GOVERNMENT (in Learning By Doing Methodology)1. Need for Government Proces Reengineering(GPR),2. SMART Governments & Thumb Rules3. Architecture and models of e-Governance, including Public Private Partnership (PPP).4. Focusing on Indian initiatives and their impact on citizens.5. Need for Innovation and Change Management in eGovernanceMODULE II…ISSUES & CASE STUDIES1. Critical Suces Factors for eGovernance2. Major isue including coruption, innovations, e-Security and Cyber laws3. Sharing of case studies to highlight best practices in managing e-Governance projects inIndian context4. Mini Projects by students in Groups as team work5. Presentations by GroupsPREFERRED TEXT BOOKS:1.e-Government - The Science of the Posible , J Satyanarayana, Prentice Hal, India (2004)/latestedition2. “Enablers of Change: Selected e-Governance Initiatives in India” Piyush Gupta ,R K Bagga andAyaluri Sridevi (2010) under Print ICFAI Pres*REFERENCE BOOKS:1. Implementing and managing eGovernment, an International Text; by Richard Heeks,Vistaar Publications, India (2006).2. E-Government: From vision to Implementation; by Subhash Bhatnagar; SagePublications India Pvt Ltd. (2004).3. State, IT and Development: by Kenneth Kenniston, RK Bagga and Rohit Raj Mathur,Sage Publications India Pvt Ltd. (2005).4. e-Governance Case Studies; Ashok Agarwal, University Pres India, (2006)5. Compendium of e-Governance Initiatives in India; Piyush Gupta, R K Bagga, UniversityPres India, (2007).6. Transforming Government: RK Bagga and Piyush Gupta (2008)7. Fostering e-Governance: Piyush Gupta, R K Bagga and A Sridevi (2009)TUTORIALS: Field visits to some typical e-Governance applications in and around Hyderabad e e- Seva/e-Sagu/EMRI/Aarogyasri/INCOIS wil be aranged.LABORATORY: Yes; Module I wil be using Internet and involve Lab work in ISWOP Cel. It is planned to folowLearning By Doing Methodology for Module I

* MINI PROJECT: Participants on this course would be aloted some e-Governance projects analysis work and wouldbe asked to write a paper and make formal presentations.GRADING:QUIZZES : 10%TECH PAPER/HA : 10%MIDTERM EXAMS : 30% (15% EACH)MINI PROJECT : 20%FINAL EXAM : 30%OUTCOME: After taking this course, the student wil be exposed to the major national initiatives in National e-Governance Plan (NeGP) for India .They wil be able to analyze the essential parameters/critical suces factors of e-Government projects. They wil also be able to carry out independent study and undertake critical analysis andevaluation of ongoing E-GOV projects.

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TITLE : Electromagnetism and OpticsCourse Code : ???CREDITS : 4 (3-1-0-0)TYPE-WHEN : Spring 2014FACULTY NAME : Prabhakar BhimalapuramPRE-REQUISITE : NoneOBJECTIVE : Understand Maxwell Equations as the central unifying ``principle'' of electromagneticphenomena. And provide introduction to modern optics.

COURSE TOPICS : One half of the course (~12 lectures and about 5 tutorials) will directly deal withElectromagnetism, starting with Maxwell Equations. The second half (~12 lectures and 5 tutorials) will deal withoptics; after a short overview of geometric optics.Following are the topics in their rough chronological order:

A-1. Maxwell Laws of electromagnetism; Electrostatics and Magnetostatics; scalar and vector potentialsA-2. Poisson & Laplace equations; named laws in electromagnetism.A-3. Dielectrics and their polarisation; use in capacitors.A-4. Electromagnetic radiation: LightA-5. Plane wave solutions to Maxwell Equations in vacuum;A-6. Light polarization. Other effects on radiation propagating in medium.----B-1. Various physical properties of lightB-2. Phenomena of diffraction: modelsB-3. Scalar theory of light: Helmholtz equation. Kirchoff formulation. Rayliegh-Somerfield formulation. Huygens-

Fresnel Principle.B-4. Holography.B-5. Introduction to non-imaging optics, statistical optics.B-6. Theory of lasersB-6. Overview of working of a few optical instruments (telescope, microscope, camera, Atomic Force Microscopes,

compound lenses etc)

PREFERRED TEXT BOOKS:1) Feynman Lecture notes on Physics, Volume II. (for electromagnetism)2) Feyman Lecture notes on Physics, Volume I (for optics)3) Optics, by Ajoy Ghatak. (for Wave optics)

*REFERENCE BOOKS:*PROJECT:GRADING:Every week will have one homework, but need not be submitted. Every week, the tutorial will start with a quiz (30

min), so approximately 10 quizzes will be conducted; of these ~10 quizzes, marks from top 8 quizzes will beconsidered for grading purposes.

1) Quizzes: ~ 10 quizzes of which top 8 will be considered : 30%2) In class work: small problems, participation : 10%3) Exams: Midsem-1 and Midsem-2, 15% each : 30%

4) Endsem exam : 30%

OUTCOME: The student after taking this course will be able to appreciate and use Maxwell Equations as the centraltool for estimating and computing various optical phenomena.

REMARKS:

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TITLE : ERROR CORRECTING CODESCREDITS : 4 creditsTYPE-WHEN : Monsoon 2011FACULTY NAME : Prasad KrishnanPRE-REQUISITE : Basic course on Communication theoryOBJECTIVE : To provide mathematical approach to the theory Of Error Correction CodingCOURSE TOPICS :MINIMAL SYLLABUS:(1) Basic Models of Communication System : Information Theory(2) Common Sense Approach to the Theory of Block Codes(3) Mathematical Preliminaries: Groups and Vector Spaces(4) Linear Block Codes(5) Cyclic Codes, Rings and Polynomials

ADDITIONAL CONTENT ( TIME PERMITTING ):(6) Rudiments of Number Theory and Algebra(7) BCH and Reed Solomon CodesTEXT BOOK: "ERROR CORRECTION CODING: Mathematical Methods and Algorithms"by TODD K. MOON

(Wiley Inter science publishers)

*REFERENCE BOOKS:(1) S.Lin abd D.J.Costello,” Error Control Coding : Fundamentals and Applications,”(2) R.E. Blahut,”Theory and Practise of Error Control Codes,”Addison Wesley Or the more recent title“Algebraic Codes for Data Transmission

*PROJECT: Programming Project will be givenGRADING: 1. Mid Terms : 40% marks

2. Homeworks : 20 % Marks3. Final Exam : 40 % marks

OUTCOME: Good understanding of mathematical concepts utilized in the design of error control codes.Application of the concepts in designing block codes.REMARKS:

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TITLE : General and Structural ChemistryCREDITS : 3-1-0-4TYPE-WHEN : Core for CND /Open elective for others: Spring 2013FACULTY NAME : Deva Priyakumar + Abdul RehmanPRE-REQUISITE: NoneOBJECTIVE : Help students to understand basic principles of chemistry from a cross disciplinary point of view.COURSE TOPICS:

1. Atomic Structure and Periodicity: The importance of chemical principles, introduction to atomic structure andneed for quantum mechanics, periodic classification of elements, outer electronic configuration, periodicityin properties, classification into metals, non-metals and insulators.

2. Chemical Bonding and Shapes of Compounds: Structure and bonding, VSEPR theory, molecular orbitaltheory, shapes of molecules, hybridization, dipole moment, ionic solids and lattice energy.

3. Classification of elements: Main Group Elements (s and p blocks): Chemistry with emphasis on grouprelationship and gradation in properties; structure of electron deficient compounds of main group elementsand application of main group elements.

4. Transition Metals (d block): Characteristics of 3d elements and coordination complexes, color and magneticproperties of metal complexes.

5. Rare gas: Structure and bonding in rare gas compounds.6. Acid-base equilibrium: Hard-Soft Acid Bases (HSAB theory), Chemical and biological buffers.7. Basic Concepts in Organic Chemistry and Stereochemistry: Electronic (resonance and inductive) effects,

Optical isomerism in compounds containing one and two asymmetric centers, designation of absoluteconfiguration, conformations of cyclohexanes, aromaticity and Huckel‘s rule.\

8. Equilibria, rates and mechanism of chemical reactions: Control of equilibria and rate of reactions, enthalpyand entropy, intermediates and transition states, role of solvent and catalyst, how mechanism of reactionsare discovered.

9. Coordination chemistry: Nomenclature, Isomerism in coordination compounds, splitting of orbitals in variousligand fields, Crystal field and ligand field theories, MO theory of coordination compounds.

10. Laws of thermodynamics: Enthalpy and thermochemistry, Entropy and free energy, criterion of spontaneityfor equilibrium processes.

11. Physical and chemical equilibrium: Solutions and phase equilibria: Colligative properties, Electrolytes andnon-electrolytes, Ideal and non-ideal solutions, colloids; Chemical equilibrium in the gas phase – equilibriumconstants and their relation to free energy – temperature dependence; Equilibrium in the aqueous phase –pH, buffers and indicators – complex ions-3; Heterogeneous equilibria – adsorption-1.

12. Electrochemistry – voltage and free energy – standard potentials.

PREFERRED TEXT BOOKS:Ralph H. Petrucci, General Chemistry: Principles & Modern Applications, 8th Edition, Addison Wesley Longman (2003)*REFERENCE BOOKS:

1. J. D. Lee, Concise Inorganic Chemistry, 5th Edition, Wiley-Blackwell2. J. E. Huheey, R. L. Keiter and E. A. Keiter and O. K. Medhi, Inorganic Chemistry: Principles of Structure and

Reactivity, 4th Edition, Pearson Education (2008)3. J. Clayden, N. Greeves, S. Warren, P. Wothers, Organic Chemistry, Oxford University Press4. T. E. Brown, H. E. LeMay, B. E. Bursten, C. Murphy, Chemistry: The Central Science, 11th

Edition, Prentice Hall5. P W Atkins, Elements of Physical Chemistry, 5/E, Oxford University Press (2010)

GRADING:Assignments, Project* and Quizzes - 60%Exams - 40% [2 midterm (10 + 10) + Endsem (20)] Total - 100%*To be decidedOUTCOME: Students would be ‗chemenabled ‘ to appreciate current research in natural(physical and biological) sciences.REMARKS: For CND students this will be a core prerequisite course and hence, need to be fine tuned afterassessing the abilities and the potentials of the CND students. The grading plan may accordingly be modified, after acouple of weeks, to accommodate a project. Non-CND students will be selected on the basis of their interest in CNSas a domain in general, and in ‗chemistry‘ in particular. A personal interview will be conducted before accepting theenrollment of non-CND students.

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Course Name : Indian Grammatical Tradition Applied to NLPObjective : To explore the application of Indian grammatical traditions to modern languages. A major goal of thecourse would be to study how language conveys meaning.Faculty Name : Dipiti M SharmaEvaluation : HA 15

Seminar 15Project 35Endsem 35

Topics to be Covered :The course will be discussion orienteda) The relation between phrase structure grammar and dependency grammar.b) English from Paninian grammatical viewpointc) Concepts to be discusssed :

sphota, pravitti nimitta prakritti – pratyaya vibhag karaka, vibhakti, samas, samarthya tatparya, vivakshaa aakaaMkshaa, yogyataa, sannidhi

d) Tools to implement the theoretical frameworkReading ListOn the Architecture of Panini's Grammar. Three lectures delivered at the Hyderabad Conference on the Architectureof Grammar, Jan. 2002, and at UCLA, March 2002.

Panini, His Work and its Traditions by George Cardona Motilal Banarsidass 1988

Panini : Re-interpreted by Charu Deva Shastri Motilal Banarsidass 1990

Bhartrihari : A Study of the Vakyapadiya in the light of the Ancient Commentaries by K.A> Subramania Iyer, DeccanCollege, Poona 1969

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TITLE : Information Retrieval and ExtractionCREDITS : 3-1-0-4TYPE-WHEN :FACULTY NAME : Vasudeva VarmaPRE-REQUISITE:OBJECTIVE :COURSE TOPICS: Search, Information Retrieval, Information Extraction - An Introducion (Function of an IRsystem, Kinds of IR systems, Components of an IR system, Problems in designing an IR system., The nature ofunstructured and semi-structured text). Role of Language Processing in Search, IR and IE, Role of Machine Learningin Search, IR and IE, Modeling documents for IR purpose - Vector model, term weighing, Similarity measures, textcollections and issues, Text processing and Indexing Techniques (Preliminary stages of text analysis and documentprocessing, tokenization, stemming, lemmatization, stop words, phrases), Data Structures for IR and IE, distributedand Parallel IR (Advanced Indexing, query expansion, Postings size estimation, merge sort, dynamic indexing,positional indexes, n-gram indexes, Index compression, Web Based Search, Page Ranking, LSI, Evaluation of IR andIE Systems, Ontologies and Categorization, Named Entity Recognition, Personalization, Question Answering,Summarization Cross Lingual Information Retrieval, Other applications and Conclusions,PREFERRED TEXT BOOKS:*REFERENCE BOOKS: 1. Modern Information Retrieval, by R. Baeza-Yates and B. Ribeiro-Neto. 2. InformationRetrieval: Algorithms and Heuristics by D. Grossman and O. Frieder*PROJECT: There are no home assignments. This is a project Intensive course. Groups will have projectdeliverables every alternate week. Project Deliverable: Finalize the project, Preliminary study and requirementsSpecification document, Architecture and DGRADING: Project - 80% (Evaluated every alternate week) Take Home Exam - 20%OUTCOME:REMARKS:

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TITLE : Information Security Audit and AssuranceCREDITS : 3-0-0-4TYPE-WHEN : SpringFACULTY NAME : Shatrunjay Rawat & Mulualem TekuPRE-REQUISITE : Basic understanding of Computer Networks and Operating SystemsOBJECTIVE : To learn how to evaluate and enhance information security of ITinfrastructure and organizationsCOURSE TOPICS :

(1) Introduction to Information Security(2) Security weaknesses in various networking protocols – IP, TCP, UDP, SMTP, RIP, OSPF, etc.(3) Network Security Products – Firewall, IDS/IPS, VPN Devices, Content Screening Gateways, etc.(4) Physical Security – Access Control Systems, Video Surveillance, etc.(5) Security Features of Operating Systems(6) PKI

(7) Security Standards – ISO 27001, Indian IT Act, IPR Laws(8) Security Audit procedures(9) Developing Security Policies(10)Disaster Recovery, Disaster Management(11)Business Continuity Management(12)Security considerations while developing software

The course will be primarily driven by class room discussions and assignments.PREFERRED TEXT BOOKS: No single text book. Required study material will be identified as course progresses.REFERENCE BOOKS: RFCs; Various Acts/Laws and Standards; Security Guideline documents of Operating SystemsPROJECT: TBDGRADING: Based on class participation, presentations, assignments, Mid/End Sem exams, Viva, etc. OUTCOME:Understanding of security needs and issues of IT infrastructure. Have basic skills on security audit of networks,operating systems and application software.REMARKS:

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TITLE : Internals of Application ServersCREDITS : 3-1-0-4TYPE-WHEN :FACULTY NAME : Dr. Ramesh LoganathanPRE-REQUISITE:OBJECTIVE :COURSE TOPICS: Understand essence of middlewares and distributed object technology. J2EETechnology and Architecture overview. J2EE App Server architecture. Lifecycle of a J2Eeapplication-deployment thrurunning and unemployment. Web Container internals. EJB Container internals. Essentials of Clustering architecture,Project problems DiscussionsPREFERRED TEXT BOOKS: *REFERENCE BOOKS: *PROJECT:GRADING:OUTCOME:REMARKS:

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TITLE : Introduction to Neural and Cognitive ModelingCourse Code : To be given.CREDITS : 4TYPE-WHEN : Spring semester (Dec 27- May 5)FACULTY NAME: Professor S. Bapi Raju, Dr. Dipanjan Roy, and Dr. John Eric Steephen

PRE-REQUISITE:Interest in Neuroscience and Cognitive Science, Basic background in Calculus, Probability and Statistics, LinearAlgebra, Ordinary Differential Equations and aptitude for programming.

OBJECTIVE:This is an introductory course on computational models used in Neuroscience and Cognitive Science. The emphasis ison multiple scales (three levels) of modeling – Single Neuron-level, Network-level and Abstract (Connectionist andBayesian) models. The course emphasizes the need for and role of theory and computation in Neuroscience andCognitive Science.COURSE TOPICS:Part I: Introduction to Neuroscience; Compartmental models of neuron; Spiking Neuron models.Part II: Neural population codes; information representation; neural encoding and decoding; hierarchy andorganization of sensory systems; Spiking Network models of sensory systems; Neuroplasticity and learning.Part III: Introduction to Hebbian, Competitive and Error-driven learning rules; Neural Network models ofPerception, Attention, Memory, Language and Executive Function.

PREFERRED TEXT BOOKS:REFERENCE BOOKS:

1) J. M. Bower and D. Beeman (2003). The Book of GENESIS: Exploring Realistic Neural Models with theGEneral NEural SImulation System, Internet Edition.

2) Peter Dayan and L. F. Abbott (2005). Theoretical Neuroscience: Computational and Mathematical Modelingof Neural Systems. MIT press.

3) R. O'Reilly & Y. Munakata (2000). Computational Explorations in Cognitive Neuroscience: Understanding theMind by Simulating the Brain. MIT Press.

PROJECT: (see below)GRADING:Mid-term Exam(s): 30%Final Exam: 40%Quiz / Assignment, Project: 30%OUTCOME:At the end of the course, students will have an appreciation of models used in Neuroscience at multiple levels ofresolution and would acquire familiarity with programming environments that implement them. Although the coursestands independently by itself, it adds computational perspective to courses such as Introduction to Cognitive Scienceand Introduction to Cognitive Neuroscience.REMARKS:

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TITLE : Introduction to Robotics: Mechanics & ControlCredits : 3-1-0-4FACULTY NAME : Suril V ShahRequisite : A course in linear control systems and the likeCourse Description:Robotics is an inter-disciplinary subject concerning areas of mechanics, electronics, information theory, control andautomation. This course provides an introduction to robotics and covers fundamental aspects of modeling and controlof robot manipulators. Topics include history and application of robotics in industry, rigid body kinematics,manipulator forward and inverse kinematic solution methods, Jacobians, singularities, redundancies, serial linkmanipulator dynamics, trajectory generation, sensors and actuators, position control and interaction force control.Syllabus & Timetable:

• Overview [w 1]• Introduction to Robotics Manipulators [w 1]• Rigid Motions: Spatial Descriptions and Transformations [w 1-3]• Forward and Inverse Kinematics, Workspace, and Redundancies [w 3-4]• Differential Kinematics and Statics [w 5-6]• Dynamics [w 7-8]• Position Control [w 8-11]• Force Control [w 11]• Trajectory Generation [w12]• Actuators and Sensors [w 7-12] (Time Permitting)

Text Book: "Introduction to Robotics: Mechanics and Control," by John J. Craig, 3rd edition, Pearson Prentice-Hall,2005. (Several copies Available in the Library)Additional References: ―Robotics : Fundamental Concepts and Analysis,‖ by Ashitava Ghosal ,OxfordUniversity Press.(Available in the Library)Lab Experiments: Students will have the opportunity to build robot models with CAD softwares like Solidworks andMSC Visual Nastran and also integrate them using MATLAB and SIMULINK.Grading Scheme:• Assignments 15%• Laboratories 10%• Mid-Term 30%• Final 45%

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TITLE : INTRODUCTION TO SYSTEMS BIOLOGYCourse Code :CREDITS : 4TYPE-WHEN : SPRING

FACULTY NAME : Dr. V. SHRIDEVI + VINOD PALAKKADPRE-REQUISITE:OBJECTIVE :This course provides an overview of systems biology approaches and tools, and will enable students to integrateconcepts from multiple disciplines and understand how advances in biochemistry, cell and molecular biology,genomics, proteomics, computation, and bioinformatics support novel insights into biological complexity.COURSE TOPICS:UNIT -1

SYSTEMS LEVEL REASONING:o Bottom-Up and Top-Down Approaches for Complex Systems

OVERVIEW OF CELL PHYSIOLOGY:o Cell growth, division, motility, differentiation, death, homeostasis, excitability

CELL SIGNALING PATHWAYS:o From molecules to Pathways

SIGNAL FLOW: From Pathways to Networkso Types of networks in cellular systems biology: protein-protein, metabolic, miRNA, gene regulatory

networksUNIT –2

MATHEMATICAL REPRESENTATIONS OF CELL BIOLOGICAL SYSTEMSo Input/output relationshipso Enzyme Kineticso Design principles of biological systemso Deterministic and stochastic modellingo Parameter estimation and sensitivity analysiso Spatial modellingo Modelling of signaling pathwayso Biological Switches and Clockso Metabolic networks and flux balance analysiso Advantages and limitations of various modelling techniques

SIMULATIONS OF CELL BIOLOGICAL SYSTEMS MODELLING STANDARDS AND TOOLS

UNIT-3 NETWORK BIOLOGY:

o Graph theoretic description of networko Scale free networks in biologyo Motifs, modules and hierarchical networkso Network Robustnesso Bayesian networks

NETWORK INFERENCE AND VISUALIZATION:o Introduction to high throughput data analysiso Genes2networks, lists2networks, from expression patterns to regulatory networkso Tracing pathways with chip enrichment analysis and kinases enrichment analysiso Visualization of networks using pajek, cytoscapeo Visualizing large scale dynamics: Grid analysis of time series expression

UNIT-4 ANALYZING LARGE DATA SETS:

o Statistical tests to identify differentially expressed geneso Programs for analyzing genomic datasets: cufflink, cuffdiffo Annotating differentially expressed genes and proteins for sub cellular functionso Gene set enrichment analysis

NETWORK ANALYSIS:o Analysis of network from differentially expressed geneso Analysis of protein-protein interaction networkso Combining annotation of nodes with network topology

UNIT- 5 APPLICATIONS OF SYSTEMS BIOLOGY:

o Systems Biotechnologyo Systems and Synthetic Biologyo Systems Analysis of Complex Diseaseso Systems Pharmacology: Understanding Drug Action from a Systems Perspective

PREFERRED TEXT BOOKS:1. Systems Biology: A Textbook answers to problems By Edda Klipp, Wolfram Liebermeister, Christoph

Wierling, Axel Kowald, Hans Lehrach, Ralf Herwig Wiley-VCH, 1st edition (August 2009).2. Systems Biology: Properties of Reconstructed Networks By Bernhard O.

Palsson Cambridge University Press; 1st edition (January 2006).3. An Introduction to Systems Biology: Design Principles of Biological Circuits

by Uri Alon , Chapman & Hall, 1st edition (July 2006).

GRADING:Mid semester exam 1 – 20%Mid semester exam 2 – 20%End semester exam – 40%Assignments – 10%OUTCOME:After successfully completing the course, students are expected to have a good understanding of the basic concepts,challenges, current research topics, and trends in selected topics of computational systems biology.

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TITLE : Introduction to BiologyCREDITS : 3-1-0-4TYPE-WHEN :FACULTY NAME : Dr. A. RameshwarPRE-REQUISITE:OBJECTIVE :COURSE TOPICS: Characterization of life, Atoms and molecules in living organisms, OrganicMolecules-Monomers, Macromolecules, cells, Chemical reactions, Energy transformations, Photosynthesis, respiration,Heredity, Genes, Flow of Genetic Information, Origin of life, Evolution,Systematics, Growth & Development. Homeostasis, Disease and defense mechanisms, Aging, cell death.PREFERRED TEXT BOOKS: Voet,Voet & Pratt, Fundamentals of Biochemistry, Wiley pp931, MBV Roberts Biology:A Functional Approach., ELBS pp693*REFERENCE BOOKS:*PROJECT:GRADING:OUTCOME:REMARKS:

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Title : Linear AlgebraCredits : 4 (three hours per week plus tutorial)When : SpringFaculty Name : Rajat TandonPrerequisites : Basic 12th class algebra including basic operations on matrices and the definition of a Group.Objective: to give a theoretical justification for matrix theory.Course Topics: Basic Groups, Rings and Fields. Vector spaces, linear span, linear independence, existence of basis.Linear transformations. Solutions of linear equations, row reduced echelon form, complete echelon form, rank.Minimal polynomial of a linear transformation. Jordan canonical form. Determinants. Characteristic polynomial,eigenvalues and eigenvectors. Inner product space. Gram Schmidt orthoganalization. Unitary and Hermitiantransformations. Diagonalization of Hermitian transformations.

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TITLE : Linguistics Data 2: Collection and ModelingCREDITS : 4TYPE-WHEN : SpringFACULTY NAME: TBDPRE-REQUISITE: Linguistic Data I

OBJECTIVE: The objective of Linguistic Data II course is to introduce the students to the necessary concepts andthe methods for analysing linguistic data at different levels of language organization. They will also be given practicaltraining in analyzing data, storing and modeling it for NLP applications.COURSE TOPICS:

1. Discourse structure theory- Informational structure- Attentional structure- Intentional structure

2. Collection and formatting of data from various web resources3. Various discourse annotation schema

- Penn Discourse Tree Bank (PDTB)- Hindi Discourse Relation Bank (HDRB)- Rhetorical Structure Theory (RST)- Indian grammatical tradition

4. Annotation of collected data5. Discourse modelling

GRADING: HA10, Seminar 10, Project 30, MidSem 20, End Semester 30Reference:

- PDTB Guidelines- HDRB Guidelines- RST Manual

Grosz and Sidner. 1986. Attention, intentions, and the structure of discourse. Amba kulkarni and Monali Das. 2012.Discourse Analysis of Sanskrit Texts.

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TITLE : Medical Image processingCREDITS : 3-1-0-4TYPE-WHEN :FACULTY NAME : Dr. Jayanthi SivaswamyPRE-REQUISITE : Digital image processing (preferred)OBJECTIVE : Medical images are a vital and widely used source of diagnostic information. From simple X-rays toSPECT and FMRI such images provide a window into the functioning of human bodies and other organisms.Processing of medical images is needed for various purposes ranging from providing high quality information forvisual inspection and guidance for surgeries, to extracting higher order information about the condition of differenttissues/organs/structures. This course will provide an hands-on introduction to the exciting area of medical imageprocessing, an area of focus for several major international conferences.COURSE TOPICS :1. Physics of medical imaging Optical, X-ray, acoustic, magnetic and nuclear2. Fundamentals Types of images, data formats, tools for medical image processing (ITK, VTK)3. 3D and nD image processing4. Problems in med IP Image conditioning illumination/geometric correction, denoising Segmentation Geometric andother methods Rigid and non-rigid image registration and Fusion Reconstruction5. Validation of results Signal detection theoretic issuesPREFERRED TEXT BOOKS:*REFERENCE BOOKS: Fundamentals of medical imaging by Paul Seutens, Cambridge University Press. Handbookof Medical Imaging, Vol 2: Medical image processing and analysis, by Jacob Beutel and Milan Sonka. Geometricmethods in medical image processing by Ravikanth Malladi. Medical image processing- reconstruction and restorationby Jiri Jan. Medical Imaging Systems, by Albert Macouski, Prentice Hall, New Jersey.*PROJECT:GRADING: 2 midsem exams (40%) + 1 final project (30%)+ assignments using ITK (National Library of MedicineInsight Toolkit) an open source software library (30%).OUTCOME:REMARKS:

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TITLE : Modeling and SimulationsCREDITS : 3-0-1-4TYPE-WHEN : Bouquet core & Open elective, Spring 2011-12FACULTY NAME : M.Krishnan + Abdul RehmanPRE-REQUISITE: None

OBJECTIVE : To introduce the fundamental concepts of molecular modeling and simulation to students (mainlyfor computational natural sciences and bioinformatics students) and motivate/train them to apply theseconcepts/techniques to solve interesting research problems.COURSE TOPICS:

1 Basic Maths: coordinate systems, vector algebra, differential equations, matrices, Taylor expansion (1lecture)

2 Molecular Mechanics: Molecular force fields, energy minimization (2 lectures)(3) Molecular Dynamics: Equations of motion, phase space distribution functions, sampling, integrators,

boundary conditions, electrostatics, molecular constraints (5 lectures)(4) Free energy calculations: Umbrella sampling, thermodynamic integration, replica exchange method (2

lectures)(5) Monte Carlo methods: Pi-value computation, important sampling, Metropolis algorithm, applications (1

lecture)(6) Non-equilibrium molecular dynamics: Jarzynski equality, steered molecular dynamics, shear flow (2 lectures)(7) solvent models: Implicit models, explicit models (1 lectures)(8) Quantum Chemistry: Operators, wavefunctions, postulates, probability density, time-dependent Schrodinger

equation (2 lectures)(9) Translational, rotational, vibrational dynamics of simple quantum systems, hydrogen atom (3 lectures)(10)Molecular quantum mechanics: Born-Oppenheimer approximation, LCAO, Variation theorem, perturbation

theory, Huckel theory, HF, semi-empirical methods, electron correlation, CI (4 lectures)(11)DFT (1 lecture)(12)Force field parameterization using quantum mechanical methods (1 lecture)(13)Students presentations (3 lectures)

PREFERRED TEXT BOOKS:*REFERENCE BOOKS:

1. Computer Simulation of Liquids, by M.P. Allen and D.J. Tildesley2. Understanding Molecular Simulation: From Algorithms to Applications, by D. Frenkel and B. Smit3. Molecular Quantum Mechanics by Atkins

*PROJECT:GRADING: Will be decided later after discussing with studentsOUTCOME:REMARKS:

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TITLE : NLP ApplicationsCREDITS : 3-0-1-4TYPE-WHEN :FACULTY NAME : Manish ShrivastavaPRE-REQUISITE : Intro to NLP

OBJECTIVE : This is the advanced course in Natural Language Processing intended for honors, dual degree, BTP,MTech and PhD students.COURSE TOPICS :In this course, students get an overview of various areas in NLP and the current research trends in each of them.The topics covered include machine translation (rule based & statistical), discourse, statistical parsing, word sensedisambiguation, natural language generation, co reference resolution, semantic role labeling etc.. The course alsocovers two of the most popular machine learning methods (Expectation-Maximization and Maximum Entropy Models)for NLP. Students would be introduced to tools such as NLTK, CoreNLP to aid them in their research.PREFERRED TEXT BOOKS:*REFERENCE BOOKS:*PROJECT: There will be a mini project and research readings once every alternate week.REMARKS:

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TITLE : Optical Communication and NetworksCREDITS : 3-1-0-4TYPE-WHEN : Spring Semester

FACULTY NAME : Jayashree RatnamPRE-REQUISITE : Communication / Computer NetworksOBJECTIVE : Study architectural aspects of current and evolving fiber-based Optical

Networks as well as the underlying lightwave communication technologyCOURSE TOPICS:Optical fiber-based communication:Signal Propagation, Attenuation, Dispersion, Non-linear effects (SPM, XPM, FWM), Crosstalk; Wavelength divisionmultiplexing (WDM)Amplification, Dispersion Compensation, Crosstalk Reduction, Wavelength Stabilization; Modulation/Demodulation-Single/Multilevel, Signal Formats, Spectral Efficiency, Capacity limits, Coherent modulation, Optical CDMAWDM-based Devices and Subsystems: Optical Add Drop Multiplexers, Modulators, Wavelength Converters,Erbium Doped Fiber Amplifier, Optical Cross Connects; Device Impairments and Optical Link DesignWDM-Based Optical Networks:Broadcast and Select networks: Single/ multi hop; Access Control, Scheduling methods; Performance AnalysisWavelength-routed Networks: Network Design: Routing and wavelength assignment; Virtual Topology, Survivability,Dimensioning; Performance AnalysisInternetworking with Optical Layer: IP over WDM, Optical Transport Network, Gigabit & WDM-PONs, MultiProtocol Lambda Switching, Optical Burst Switching, Storage Area NetworksPREFERRED TEXT BOOKS/ PAPERS:

(1) Optical Networks: A Practical Perspective by Rajiv Ramaswami and Kumar Sivarajan, Morgan KaufannPublishers 2000

(2) Broadband Access Networks by L. G. Kazovsky, Ning Cheng, Wei-Tao Shaw, David Gutierrez, Shing-WaWong, Wiley Publishers

(3) Mischa Schwartz, ―Telecommunication Networks, Protocols, Modeling and Analysis‖, Pearson Education2004.

*REFERENCE BOOKS:1. Some Tutorial/Survey Papers from IEEE Journals*PROJECT:GRADING:Periodic Assignments/Quiz: 20 marksMid-term Examinations: 40 marksEnd Semester Examination: 40 marksOUTCOME:REMARKS:

1. This subject partly covers communication-intensive as well as protocol/algorithmintensive topics. Hencemight be suitable as an Elective Course for ECE/EC Dual or CSE/CS Dual students in their 4th years at L1/L2.

2. Tentative limit on number of students is 20. Pre-requisites are as mentioned above.

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TITLE : Optimization MethodsCREDITS : 3-1-0-4TYPE-WHEN : Spring, 4XXX levelFACULTY NAME : Kannan SrinathanPRE-REQUISITE : Strict Prerequisites: NILExpected Background: To follow this course, some level of familiarity with linear algebra (specially, vectors andmatrices) is expected. In addition, student is expected to know the fundamentals of algorithms and some of thepopular problems (eg. shortest path.)

OBJECTIVE: 1.To enable students to formulate and solve problems in an optimization framework.2. To expose a set of powerful tools and techniques to the students. To demonstrate how these tools (i.e.,optimization methods) can be used in practice.3. To visualize the optimization algorithms and know the numerical and practical issues in their implementation.4. To relate the optimization methods to applications in diverse areas.COURSE TOPICS : Linear Equations, Solutions based Matrix Factorization, Singular Value Decomposition, LinearLeast squares, Numerical algorithms, Convergence, Applications.Nonlinear equations, Unconstrained minimization, Gradient, Hessian, Conjugate gradient, Newton's method,Applications and Computational Issues.Linear Programming, Geometric Interpretation, Simplex Method, Duality, primal dual method, Interior point methods,Ellipsoidal methods, Computational Issues.

Integer programming, LP relaxation, Examples from combinatorial optimization. Shortest paths, network flows andmatchingsAdditional topics (if time permits) related to (i) Specific Algorithms (eg. Cutting plane algorithms, Stochasticgradients)(ii) Applications in Approximate Algorithms (iii) Computational issues in large scale optimization (iv) Heuristicmethods for optimizationPREFERRED TEXT BOOKS:*REFERENCE BOOKS:1. M T Heath, ``Scientific Computing'', TMH (Most of First six chapters)2. C H Papadimitriou and K Steiglitz, ‗Combinatorial Optimization: Algorithms and

Complexity'' (Most of First seven chapters), Dover3. S. Boyd and L Vandenberghe, ``Convex Optimization'', Cambridge University Press (Online

Copy available at: http://www.stanford.edu/~boyd/cvxbook/)4. L Vandenberghe, Lecture Notes for Applied Numerical Computing, (Online available at:

http://www.ee.ucla.edu/~vandenbe/103/reader.pdf)5. D Bertisimas and J N Tsitsiklis, ``Introduction to Linear Optimization'', Athena Scientific6. J Matousek and B. Gartner, ``Understanding and Using Linear Programming'', Springer, 2007

GRADING: Evaluation will be more or less as follows:Mid Semester Exams (2) - 30Final Exam - 25Quizes - 10Assignments - 25TermPaper/Project - 10OUTCOME: This course will help in sharpen the problem solving skills of students. Students will have experience informally stating problems with the associated constraints, and solving them with computer friendly algorithms.

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TITLE : Principles of Information SecurityCREDITS : 3-1-04TYPE-WHEN : SpringFACULTY NAME : Ashok Kumar DasPRE-REQUISITE : Algorithms

OBJECTIVE: To discuss on the fundamentals of the state-of-the-art information security protocols

COURSE TOPICS:Classical cryptography and their cryptanalysis, perfect secrecy, Shannon's theorem, pseudorandom generators,stream ciphers, CPA-secure encryption, pseudorandom permutations, practical block ciphers (3-DES, AES), modes ofoperation, MACs, Hash functions, CCA-secure encryption, Diffie-Hellman key exchange, Public key cryptosystems(RSA, El Gamal, Paillier, Rabin, Goldwasser-Micali), PKCSv1.5, digital signatures, DSS, digital certificates and PKI,basic cryptographic protocols, oblivious transfer, secret sharing, Byzantine agreement, secure multipartycomputation, interactive proof systems, cryptography in noisy channels and quantum cryptography.TEXTBOOK:Y. Lindell and J. Katz. Introduction to Modern Cryptography. MIT press.REFERENCE BOOKS:(a) Oded Goldreich. Foundations of Modern cryptography: Parts I and II. Cambridge Press. 2001.(b) A. Menezes, P.C. van Oorschot and S.A. Vanstone. Handbook of Applied Cryptography, CRC Press, 1996.GRADING :Mid-sem exams: [40\% GRADE]End-sem exam: [40\% GRADE]Term-paper/Assignments: [20\% GRADE]OUTCOME:The course will be useful for students who plan to do research/product development/analysis in areas related tosecure computing in their career.

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TITLE : Readings from Hindi LiteratureCREDITS : 4

FACULTY NAME : Harjinder SinghPRE-REQUISITE :OBJECTIVE : This course will expose the students to selected works from modern Hindi literature along withelements of literary criticism and contemporary debates.COURSE TOPICS : Poetry: Ajnyeya, Muktibodh, Nagarjun, Raghuveer Sahay, Nand Kishore Acharya, MangleshDabral, Vinod Kr Shukla, Kumar Vikal.Fiction: Nirmal Varma, Bhishm Sahani, Gyanranjan, Mrinal Pandey, Uday PrakashNon-fiction and criticism: (i) Pleasure readings; (ii) aesthetics versus committed literature; (iii) Sociology of literature;(iv) elements of lit crit developed in the west Derrida, Goldman; (v) Namvar Singh, Manager Pandey. If possible,some plays will also be read. For comparative exercise, works from other languages, will also be usedoccasionally.PREFERRED TEXT BOOKS: Works of writers available in IIIT library. Additional handouts may be given.*REFERENCE BOOKS:*PROJECT: Works of writers available in IIIT library. Additional handouts may be given.GRADING: Mid Sem and Final exams 75% (20+55) Assignments 15% term paperProject: 10%OUTCOME: Appreciation of literature in general and a reasonable amount of knowledge of contemporary writing inHindi.

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TITLE : Remote SensingCREDITS : 3-1-0-4TYPE-WHEN : Open ElectiveFACULTY NAME : RC PrasadPRE-REQUISITE: Open to PG, UG-4 & UG-3OBJECTIVE : Remote sensing techniques are widely used as a primary source of information in a rangeof applications including natural resource management and mitigating disasters. The objective of the course is toimpart knowledge on various techniques of remote sensing, data acquisition, processing, product generation and itsutility for modeling and management purposes.COURSE TOPICS:Course Structure (each of approximately 1-2 week duration):

1. Introduction to Remote sensing2. Physics of Electro Magnetic Radiation (EMR)3. Earth Observation Satellites and Platforms4. EMR interaction with Atmosphere and Earth materials5. Sensors and its characteristics6. Optical Remote sensing7. Hyper-spectral Imaging8. Microwave Remote sensing9. Geo-registration and Map projections10. Image processing techniques11. Image Interpretation12. Digital image classification13. Major applications of Remote sensing in

a. Vegetation / Terrestrial ecologyb. Hydrologyc. Land use / Land cover – Carbon Assessmentd. Urban / Object detection

A few lectures, may be given by Invited Speakers in related areas during the course to provide the students a widerunderstanding of its relevance and application. In addition, there will be a hands-on (lab tutorials) introduction to oneor two RS software and tools at relevant times during the course.PREFERRED TEXT BOOKS:

1. Introduction to Remote Sensing by James B. Campbell2. Remote Sensing and Image Interpretation by Thomas.M.Lillesand3.Remote sensing Digital Image Analysis by J.A Richards and Xiuping Tia

*REFERENCE BOOKS:1.Fundamental of Remote Sensing by CCRS (Online) 2.Principles of Remote sensing by ITC (online)*PROJECT:GRADING:1. Assignments (max. of 4) 30%

2. Project 10%3. Mid-term Exams (2) 30% [15% + 15%]4. End-Semester Exam (1) 30%Details of Assignments/Projects will be announced during the course.OUTCOME: Students after finishing this course are expected to be well versed with the techniques and approachesthat are used to understand and process Satellite imagery and extract meaningful earth/terrestrial surface or sub-surface parameters. Also, they are expected to get a feel of the application gaps and limitations of the currentsatellite imageries & their processing or information extraction techniques with respect to the multiple applicationdomains like urban mapping, agriculture, forestry, water resources, defense, natural resource management anddisaster management.

******************************************************************************TITLE : Science Lab IICREDITS : 4TYPE-WHEN : Spring 2012FACULTY NAME : Tapan Kumar Sau + M.KrishnanPRE-REQUISITE:OBJECTIVE : Main objective of this laboratory course is to understand the concepts of select science topicsthrough lab sessions.List of proposed experiments:UV-Vis Absorption SpectroscopyFT-IR SpectrscopyCyclic VoltametryDetermination of Wavelength of a Source – Diffraction Grating. Newton‘s Rings Radius of Curvature of Plano –Convex lens: Laser –single and double slitsMelde's Expt. - Transverse & Longitudinal Modes:Magnetic Field Expt: Stewart & Gees methodDetermination of Velocity of sound-volume resonatorProject:

1.1.1. Groups of two students1.1.2. Start date Jan 21, 20111.1.3. Submission of working experiment April 10, 20111.1.4. Talk to instructors a few times before the start date; only approved experiments will be

supported by lab infrastructure.Grading: Tentative60 % Lab work (Report + Performance)20 % Viva voce/ quiz20 % ProjectPREFERRED TEXT BOOKS: No preferred textbooks. Hands-out will be provided.OUTCOME: This laboratory course is designed to introduce students to the bachelor level science laboratorytechniques. Students completing this course are expected to learn the following: (i) hands on experience of selectscience topics (ii) measure and report physical quantities with appropriate precision, (iii) convert raw data to aphysically meaningful form, (iv) apply appropriate methods of analysis to raw data, (v) recognize the relevance ofdata, (vi) work safely in the lab, (vii) adhere to instructions on laboratory safety, (viii) recognize hazardous situationsand act appropriately, and (ix) recognize the applicability of scientific principles to real world situations.

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TITLE : Selected Topics in Physical Chemistry: Chemical Kinetics &Reaction Dynamics, Electrochemistry, and Solid StateCREDITS : 4 creditsTYPE-WHEN : SpringCore for CND Students + Elective for BTech StudentsFACULTY NAME : Tapan K. Sau and Prabhakar BhimalapuramPRE-REQUISITE : Science I or Science I courses for BTech (CSE/ECE) students (SelectionBased on CGP)OBJECTIVE : To introduce students to a few selected topics of physical chemistryCOURSE TOPICS :(A) Chemical Kinetics and Reaction Dynamics(1) Measuring Reaction Rates, Determining Rate Constants and Reaction Orders,

Reaction Mechanisms, Consecutive Elementary Reactions: Steady State Aproximations, Parallel Reactions, ReversibleReactions(2) Temperature Dependence of Reaction Rates(3) Molecular Reaction Dynamics: Collision Theory, Difusion Controled Reactions, Activated Complex Theory,Potential Energy Surfaces(4) Unimolecular Reactions(5) Catalysis: Homogeneous and Heterogeneous Catalysis, Michaelis-Menten Mechanism and On-Of Rates in Bio-Nano Interactions(6) Kinetics of Complex Reactions: Chain Reactions, Explosion, Photochemical Reactions, Polymerization Kinetics(B) Electrochemistry(1) Electrolyte Solutions: Thermodynamics Of Ion Formation, Debye-Hueckel Theory, Solubility Product: Salting Inand Salting Out(2) Electrochemical Cels: Electrochemical Potential, Fre Energy and Nernst Equation, Use of Cel Potentials(3)Processes at Electrodes: Electrical Double Layer, Rate of Charge Transfer, Polarization(4) Bateries and Fuel Cels: Thermodynamics and Chemistry(5) Electrochemical Proceses: Electrolysis and Corosion(6) Electrochemistry at he Atomic Scale(C) Solid State(1) Crystal Packing and Latices(2) Difraction analysis of solids (x-ray, neutron, etc.)(3) Overview of mechanical, chemical and electrical properties(4) Surfaces: properties reactivity and electrochemistryPREFERED TEXT BOOKS: (1) Physical Chemistry, by P. W. Atkins.(2) Physical chemistry: a molecular aproach by Donald AlanMcQuarie and John Douglas Simon.(3) Physical Chemistry, by Thomas Engel and Philp Reid(4) Physical Chemistry, by G. W. Catelan*REFERENCE BOOKS: (1) Chemical Kinetics, by K. J. Laidler(2) Modern Electrochemistry v.2A Fundamentals of Electrodics, by J.O.Bockris and A. Redy(3) Modern Electrochemistry v. 1, Ionics, by J. O. Bockris and A. RedyGRADING: 10% Clas Participation + 20% Clas Asignments + 70% Term ExamsREMARKS: -

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TITLE: SIGNAL DETECTION AND ESTIMATION THEORY*Credits: (3-0-0-4)Prerequisites: ECE 230 AND ECE 335 OR INSTRUCTOR’S CONSENTFaculty: Sachin ChaudhariTOPIC OUTLINE (APPROX):1. Introduction to Decision making under uncertainty, Minimax, Bayesian, Maximum likelihood approaches.2. Clasical Binary Hypothesis testing, LRTs, suficient statistic, Detection Performance, Neyman- Pearson approach,Uniformly Most Powerful tests, Generalized LRT.3. M-ary Hypothesis Testing, Performance4. General Gausian Detection problems, Performance Bounds5. Parameter estimation: MSE, MAP, MLE; Cramer-Rao Performance bounds6. Karhunen-Loeve representation of Random signals7. Detection of Known signals in additive white Gausian noise, Optimum receivers, Performance.8. Detection of Known signals in additive colored Gausian noise, Optimum receivers, Performance, Signal designconsiderations.9. Estimation of signals with unknown parameters in additive white gausian noise, estimation error performance10. Detection of Signals with unwanted parameters, Performance1. Estimation of continuous waveforms in modulation systems with/without memory12. Linear estimation: Wiener Filtering, Prediction and smoothing13. Kalman-Bucy Filtering, Prediction and smoothingTEXTS AND REFERENCE BOOKS:1. H.L.Vantrees : Detection, Estimation and Modulation Theory, Part I, Wiley, 19682. Srinath, M.D, Rajasekaran, P.K, Viswanathan, R : Introduction to Statistical Signal Processing with Applications,Prentice-Hal, 19993. Sage, A.P and Melsa, J.L : Estimation Theory with Applications to Communications and Control, McGraw-Hil, 1971

4. McGarty, T.P : Stochastic Systems and State Estimation, Wiley, 19715. Mood, A.N, Graybil, F.A, and Boes, D.C : Introduction to the Theory of Statistics, McGraw-Hil, 1974Examination:First Mid-Semester (20), Second Mid-Semester (20), Term-Paper (20), End-Semester (40)* FORMER COURSE NUMBER: ET4105

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TITLE: Software EngineeringCREDITS: 3-1-0-4TYPE-WHEN : FlexicoreFACULTY NAME : Raghu ReddyPRE-REQUISITE : Programming, some project workOBJECTIVE: To develop in-depth understanding of software engineering principles, practices and ability to applythem in developing large scale software systems.COURSE TOPICS: Process and Product, Software Development Life Cycle, Requirements Engineering,Software Architecture and Design, Code construction, Software V&V, Configuration Management, ProjectManagement, Maintenance, UML, Application Security.PREFERRED TEXT BOOKS: references will be provided in class*REFERENCE BOOKS: Fundamentals of Software Engineering (2e) By Carlo Ghezzi, Mehdi Jazayeri,Dino Mandrioli Prentice Hall.Software Engineering, 4/e, by Pfleeger and Atlee, Pearson Education, web references will be provided*PROJECT: There will be case studies. Each case is like a mini project, with challenges embeddedinside the case. Students learn theory, then apply this theory to challenges given in the case (hencepractice).GRADING: (Tentative) Solutions to Case studies: 50% * Mid Semester Exam: 20% * End SemesterExam: 20% * Class Participation and others: 10%OUTCOME: After taking the course, the student will be able torelate to software development practices in the software industry. The student will be able to employ these practicesin medium-to large scale projects both in research/academic environment as well as in the commercial softwaredevelopment.REMARKS: The course will involve student group presentations and discussions during the course.

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Course Title : Software Foundations: A verification based approachCredits : 4 (2 lectures per week)Instructors : • Venkatesh Choppella <venkatesh.choppella@iiit.ac.in> + Suresh Purini<suresh.purini@iiit.ac.in>Objective : The purpose of this course is to study the mathematical foundations of programs and software systemsso as to be able to prove properties about their behaviour. The approach we take is to understand programs in termsof their static structure (types) and their dynamic structure (state). The former involves building type systems andproving properties about the type system (safety and progress) etc. The latter involves building state machines andthen verifying properties (safety and liveness) on them through model checking. The course will use modernverification tools like the Coq theoremprover and model checkers like Spin.Audience : The course is intended for anyone interested in modern engineering methods for the design of robustand high integrity software systems.Prerequisites : CS415 (Principles of Programming Languages) or prior permission from the instructor(s).Syllabus : Preliminaries Natural deduction [2 lec] Operational Semantics Dynamic (small step) and static semantics.Type soundness: preservation and progress. [4 lec]. Type systems Simply typed lambda calculus. Type Checking andInference. Polymorphism. Sub typing. [5 classes] Co-inductive types Simulation and Bisimulation, Conductivesemantics of type safety. [4 lec] Model Checking [11 lec] Automata and Timed Automata. Temporal Logics. Model

Checking. Specification of safety and liveness properties. Tools: Spin and Uppaal The syllabus may be suitablymodified to include some extra or advanced topics.Text Books: Software Foundations (Pierce et al.) http:// www. cis.upenn.edu/˜bcpierce /sf/current/index.htmlSystems and Software Verification (Berard et al.) Springer, 1998.

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TITLE : Speech SystemsCredits : 3-1-0-4Faculty : Anil Kumar VSemester :SpringType-When : Elective - offered anytime on need basis - for Year 3/4 and PG studentsPre-Requisite : Speech Signal ProcessingOBJECTIVE: This is an advance (Level 2) course which deals with algorithms and models for processing, recognition,synthesizing, enhancement of speech signals. The objective is to provide rigorous theoretical background and handson experience to the students in implementing basic and advanced algorithms in speech signal processing, speechrecognition and speech synthesis.COURSE TOPICS:W1: Review of Speech Signal ProcessingW2-W3: Representation of Speech (Feature extraction)W4-W5: Modeling techniques for speech systemsW6-W8: Speech Recognition- Limited domain (Word recognition, Dynamic time warping)- Large vocabulary (Continuous speech recognition, Hidden Markov Models, Sphinx)W09-W10: Models for Speech Synthesis- Concatenative synthesis (data driven, Festival)- Parametric synthesis (HTS)W11-W12: Speaker Recognition- Text dependent (DTW based, VQ based)- Text independent (GMM based, Neural network based)W13-W14: Speech Enhancement- Spectral subtraction- Harmonic noise decomposition- Enhancement by processing high SNR regionsTEXTBOOK: (1) Spoken Language Processing: A Guide to Theory, Algorithm and SystemDevelopment by Xuedong Huang, Alex Acero, Hsiao-Wuen Hon, Prentice-Hall.(2) Fundamentals of Speech Recognition (Prentics Hall Signal processing Series) (Paperback) byLawrence Rabiner and Biing-Hwang Juang)(3) Documentation associated with Sphinx/HTK and Festival(4) Relevant papers/materials will be providedGRADING:First Mid-Semester Examination:: 15%Second Mid-Semester Examination:: 15%End Mid-Semester Examination:: 30%Assignments: 20%FINAL PROJECT: 20%Assignments:1. Feature extraction (W3)2. Modeling techniques (W5)3. Speech Recognition (W8)4. Speech Synthesis (W10)5. Speaker Recognition (W12)6. Speech Enhancement (W13)FINAL PROJECT: During the last four weeks of the course the students are required to take upa project related to one of the two topics (1) Models for Speech Recognition, (2) Models forSpeech Synthesis,OUTCOME: Upon the completion of this course, the student would gain not only theoreticalknowledge about processing of speech but also understand the implementation issues in speech signal processing,speech recognition and speech synthesis.

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TITLE : System and Network SecurityCourse Code : CS5470CREDITS : 4TYPE-WHEN :FACULTY NAME : Sanjay Rawat

PRE-REQUISITE: programming languages (C/C++, Python), operating systems + architecture (basis), POIS(CSE418) (have taken earlier or enrolled this semester).

OBJECTIVE : This course is intended to introduce students the exciting world of information security research.The main focus of this course would be on non-cryptographic security research (as cryptographic security is coveredin detail in CSE418) i.e. topics related to software vulnerabilities, malware, intrusion detection/prevention systems.The course is divided into two major parts. The first part is about “offensive computing” which is based on thepremise “Know your enemy first”. This part covers techniques that are used for attacking systems, including low-levelvulnerabilities like buffer-overflow, cross-site scripting, format strings. These techniques are used by hackers andmalwares to invade systems (thus know your enemy first). The second part is about “defensive computing”, whichcovers techniques/technologies to defend against above mentioned attacks, including cryptographic protocols,intrusion detection systems, firewalls. At the end of the course, the students should:

1. understand the various issues in software security;2. understand the techniques that are applied in order to address security issues;3. understand the majority of the attacks that hamper the security of the networks, e.g. bug exploitation (aka

hacking);4. learn basics of malware analysis and defensive techniques;5. learn the use of cryptographic primitives for securing networks6. Understand that security is a layered approach.

COURSE TOPICS :PART I- Offensive Computing

A. Introduction to Software vulnerabilities: Non-web software vulnerabilities (low level bug, e.g., buffer overflow, use-after-free etc.) How to find such vulnerabilities and then attack/hack? Web specific vulnerabilities and their analysis (e.g. XSS, CSRF, SQLinjection etc.)

B. Malware Analysis: Introduction to Malwares Analysis techniques Android malwares

PART II – Defensive ComputingC. Operating system and application level defense

Stack overflow prevention Address space layout randomization Input sanitization

D. Firewalls – first layer of defense Introduction to Firewalls and type of firewalls

E. Intrusion Detection System: Introduction to IDS/IPS Types of IDS

F. Network Security with Cryptography IPSec SSL

PREFERRED TEXT BOOKS:Text book: to be announced*REFERENCE BOOKS:

Assembly book for x86 Practical malware analysis, by Sikorski and Honig

GRADING:10%: Class attendance and discussion/participation25%: Hands-on assignments (4-6)25%: mid term (2)40%: End exam

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TITLE : Topics in Natural Language SemanticsCREDITS : 4TYPE-WHEN : Spring-2013FACULTY NAME : Soma PaulPRE-REQUISITE: CL1, CL2OBJECTIVE:Formal Analysis and logical representation of natural language enable computer-based analysis of natural languagesfor a wide range of applications such as Word Sense Disambiguation, Inferencing, and machine translation and soon. This course aims at building a strong theoretical understanding of the semantics of various linguisticconstructions and then developing a computer tractable framework for representing the semantic analysis of naturallanguage.COURSE TOPICS:

1. Overview of Dependency Relations as described in Paninian Dependency Grammara. Karaka relationsb. Non-karaka relations

2. Need for Deeper Semantic Analysis from the perspective of MT and QA3. Semantic analysis of various constructions with examples from Indian Languages

a. Modifiers such as adjective, adverb, Relative clause, genitiveb. Complex Predicatec. Quantifier

4. Formal Semantic Representationa. Integration of Logical and ontological concepts for formal semantic representationb. Semantic Lexicon in terms of feature structure

i. Karaka frame and Lakshana Chartii. Qualia Structure of Pustejovskyiii. Lexical Conceptual Structure of Jackendoff

5. Conversion of Dependency structure of Indian Language text to Formal semantic representation*REFERENCE BOOKS:

- Jackendoff, Ray (1990): Semantic Structures, MIT Press, Cambridge, Massachusetts- Jackendoff, Ray (2011) Conceptual Semantics, in Claudia Maienborn, Klaus von Heusinger, and Paul

Portner (eds.), Semantics: An International Handbook of Natural Language Meaning, Vol. 1, 688-709.- Levin, B. (1993), English Verb Classes and Alterations, The University of Chicago Press- James Pustejovsky (1991): The Generative Lexicon, http://acl.ldc.upenn.edu/J/J91/J91-4003.pdf

Materials on Lakshana chart (To be provided as manuscripts)- Partee, Barbara (2007) Compositionality and coercion in semantics: The dynamics of adjective

meaning. Cognitive Foundations of Interpretation.- Bharati A., Husain S., Ambati B., Jain S., Sharma D. and Sangal R.. (2008). Two Semantic features

make all the difference in Parsing accuracy. Proc. of ICON-08.- Bharati A., Sharma D., Husain S., Bai L., Begam R. and Sangal R. (2009a). AnnCorra: TreeBanks

for Indian Languages, Guidelines for Annotating Hindi TreeBank (version – 2.0).PROJECT:Semantic Annotation on the corpus annotated with dependency relation.GRADING: Assignment 10%Mid-Sem Exam 30%End Sem Exam 30%Term Project 30%OUTCOME:Participants of the course will gain understanding of semantic analysis of natural language. They are expected tolearn various semantic representations with an aim of developing deeper semantic annotated text for Indianlanguages.

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TITLE : TOPICS IN SPEECH PROCESSING - AUDIO INFORMATION RETRIEVALCredits : 4-3-0-0 (= credits - hours per week - tutorials - lab sessions)Type-When : Spring Semester 2013Faculty Name : Suryakanth V. GangashettyPre-Requisite : CS4731 Information retrieval and extraction / ECE 448 Speech signal processing / IEC239 Digitalsignal analysis and applications / ECE341 Digital signal processing

Objective : The objective is to provide fundamentals of handling time series data including speech, audio and musicsignals for the application of information retrieval and analysis, and to provide hands-on experience in building audioinformation retrieval system(s).Course :Topics Fundamentals (Units 1-2): Text retrieval Vs audio/speech/music retrieval, issues in audio/speech/musicretrieval, feature extraction from time series, segmentation, signal to symbol transformations (classifying sounds,speech recognition), indexing mechanisms, statistical and template based matching and search algorithms.Applications (Units 3-4): Audio and music search, speaker diarization, meetings dataPreferred Text Book : There is no single text book. Relevant chapters will be suggested from

1. Huang, X., Acero, A. and Hon, H-W. (2001), Spoken Language Processing: A Guide to Theory, Algorithmand System Development, Prentice Hall.

2. Baeza-Yates, R. and Ribeiro-Neto, B. (2011), Modern Information Retrieval: The Concepts and TechnologyBehind Search, Addison Wesley.

Reference Books : A few relevant research articles and journals will be provided in due course. Project One majorproject involving the development of an audio information retrieval system.Grading : [Assignments = 30% ]+ [Project = 30%] + [One Mid-semester Exam = 20%] + [Final Exams = 20%]Outcome : The students are expected to become well-versed with fundamentals of handling time series data andwith tools and techniques for implementation of audio information systems. RemarksELECTIVITY: This is an elective course and can be taken by 3rd Year or 4th Year undergraduate students inComputer Science (CS) or by any postgraduate students in Computer Science (CS) or Electronics and CommunicationEngineering (ECE).

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TITLE : TopologyCourse Code :Note: Please use course code for previously existing courseCREDITS : 4TYPE-WHEN : Semester Dec- AprilFACULTY NAME : GeetaPRE-REQUISITE : NoneOBJECTIVE : Learning TopologyCOURSE TOPICS : Topological Spaces, Various examples of topological spaces, Limit points and ClosedSets, Continous Functions, Connectedness, Compactness, Countability and Seperation Axioms, Urysohn Lemma,Tietze Extension Theorem, Tychonoff Theorem.

PREFERRED TEXT BOOKS: Topology, James R. Munkers, Computational Topology Herbert Edelsbrunner and JohnHarer*REFERENCE BOOKS: Real Analysis, W. Rudin*PROJECT: N.A.GRADING: Quizzes 10-20 marks + three Exams 80-90 marksOUTCOME: Understanding mathematical topology and some exposure to applications.REMARKS:

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TITLE : Usability EngineeringCourse Code : CSE567Note: Please use course code for previously existing courseCREDITS : 4TYPE-WHEN : Spring 2014FACULTY NAME : Priyanka SrivastavaPRE-REQUISITE : NoneOBJECTIVE : The focus of this course is on role of cognitive and non-cognitive perspectives inergonomic and user-friendly designs. Ergonomics deals with design factors required for equipment designs for theworkplace to maximize the productivity and minimize the fatigue error and discomfort. User-friendly or Usability,deals with the ease, efficiency and effectiveness of any system/ design interface. Since, any human activity entailsphysiological and mental/ cognitive processing, which understanding would play an important role in ergonomics anduser-friendly design. The course will highlight the competencies and limitations of the human being in general (such

as perception, memory, attention and time-sharing, mental-workload, stress, decision making and problem solving),to make better technologies for society and science.Upon completion of course students will be able to identify/ recognize the cognitive and non-cognitive perspective indesigning more effective and better machines such as automated machines and assistive technologies.

COURSE TOPICS :1. Introduction to Ergonomics, Neuroergonomics, and Usability2. History of Human Factors3. Psychology of Everyday Actions4. To Err is Human: Design challenges and User-centered design5. Differently abled people, easy and complex designs6. Identifying Mental Model and its relations with design7. Generation of User Interface8. Human factors in augmented and virtual reality9. Design and Evaluation Methods

PREFERRED TEXT BOOKS:1. Norman, D. (2013). The Design of Everyday Things: Revised and Expanded Edition2. Proctor, R.W., & Zandt, T.V. (2008). Human Factors in Simple and Complex Systems, Second Edition. 24*REFERENCE BOOKS:1. Hale, K.S., & Stanney, K.M. (2014). Handbook of virtual environments: design, implementation, andApplications, Second Edition (Human Factors and Ergonomics)*PROJECT:Each team of 2 will conceptualize, design, the study and highlight the implications.GRADING:Exams – 60Quizzes – 10Assignments – 10Exams – Mid semester II and Final Term – 40Project – 40OUTCOME:The course aims to introduce various emerging issues in ergonomics and usability research. The student will realizethe issues related to cognitive and non-cognitive processing and its implication through conducting psychophysicalexperiments.REMARKS:The course requires reading, field and lab work and further designing a small study, highlighting the various issues ofergonomics and usability in design interface and submitting at the end. Students will be encouraged to use varioustools available in Cognitive Science lab to address their research problems. The students will be allowed to work atany time for experiments and studies during the ideas development and conducting studies.

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TITLE : VLSI ArchitecturesCREDITS : 4TYPE-WHEN :FACULTY NAME : Rahul ShresthaPRE-REQUISITE : Basic Electronic Circuits, Digital Logic

OBJECTIVE : After completing the course a student shall be able to:

1. Design, model and simulate the architecture of a VLSI system subject to specific constraints of performance,accuracy and area.2. Analyze circuit architectures to check whether design goals have been met or not. The course shall cover thearchitectural design of VLSI systems with the notion of optimization for area, speed, power dissipation, cost andreliability. It will encompass within its folds traditional and state of the art analog and digital VLSI architecturesoptimized for specific purposes.

COURSE TOPICS :1. Introduction. Goals of VLSI Design: Optimization of Speed, Area, Power dissipation, cost and reliability. Review ofVLSI design flow 2L2. Algorithmic modeling of system 4L3. Architectural design of VLSI circuits and systems: Resource utilization and time constraints 4L4. Data-path subsystems: Adder and multiplier data-path architectures 6L

5. Array subsystems: Memory arrays and programmable gate arrays 4L6. Logic level design and optimization 2L7. Analog array architectures 4L8. Low power and high speed analog and digital VLSI architectures 4L9. Clock generators and clock routing for VLSI chips 2L10. High speed interconnect design 2L11. Digital Signal Processing using array architectures: Systolic array and Wave-front array 4L12. Dynamically reconfigurable gate arrays 2L13. Inexact computing: Probabilistic CMOS model based design architectures and probabilistic pruning2LPREFERRED TEXT BOOKS: 1. N. Weste and D. Harris, ―CMOS VLSI Design: Circuit and Systems Perspective‖,Pearson Education (2008)2. J.M. Rabaey, A. Chandrakasan, B. Nikolic, ―Digital Integrated Circuits‖, Prentice Hall (2009)*REFERENCE BOOKS:*PROJECT:GRADING:OUTCOME:REMARKS:

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