B.Tech. Degree Programme Computer Science & Engineering

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B.Tech. Computer Science & Engineering (Regular)and implement digital filters to synthesize speech andcode speech at a low bit rate. Implement speechanalysis and speech synthesis modules using objectorientedsoftware programs, using techniques such asclass derivation, the use of software objects ascomponents in a larger software system.PRE-REQUISITESKnowledge of artificial intelligence, natural languageprocessing, digital signal processing, neural networks1. FUNDAMENTALS OF SPEECH RECOGNITION:Introduction, the paradigm for speech, recognition,out line, brief history of speech recognitionresearch.2. SPEECH GENERATION: Formant frequencies inspeech, parametric source-filter synthesis,formant synthesizers, pitch detection, amplitudeanalyzer, vocabulary, text-to-speech conversion,vocoders3. THE SPEECH SIGNAL: Production, reception,and acoustic-phonetic characterization: the speechproduction system, representing speech in timeand frequency domains, speech sounds andfeatures; approaches to automatic speechrecognition by machine.4. SIGNAL PROCESSING AND ANALYSISMETHODS FOR SPEECH RECOGNITION: Thebank-of filters, front-end processor; linear predictivemodel for speech recognition; vector quantization;auditory based spectral analysis model.5. PATTERN COMPARISON TECHNIQUES:Speech detection, distortion measures:mathematical considerations, distortion measuresperceptualconsiderations, spectral-distortionmeasures, incorporation of spectral dynamicfeatures into distortion measures; time alignmentand normalization.6. SPEECH RECOGNITION SYSTEM DESIGN ANDIMPLEMENTATION ISSUES: Application ofsource coding techniques to recognition, templatetraining methods; performance analysis andrecognition enhancements; template adoption tonew talkers; discriminative methods in speechrecognition; speech recognition in adverseenvironment;THEORY AND IMPLEMENTATION OF HIDDENMARKOV MODELS: Discrete time Markovprocesses; extensions to hidden Markov models;the three basic problems for HMMs; types ofHMMs; implementation issues for HMMs; HMMsystem for isolated word recognition7. SPEECH RECOGNITION BASED ONCONNECTED WORDS MODELS: Generalnotations for the connected word-recognitionproblem; two level dynamic programming algorithm;level building algorithm; one pass algorithm; multiplecandidate strings; grammar networks for connecteddigit recognition; segmental k-means trainingprocedure; connected digit recognitionimplementation; task oriented applications ofautomatic speech recognition and generation.TEXT BOOKBernard Gold and Nelson Morgan, “Speech and AudioSignal Processing”, John Wiley & Sons, 2004REFERENCE BOOKS1. Rabiner Lawrence R. and Juang B.,“Fundamentals of Speech Recognition”, PearsonEducation,20042. Rabiner Lawrence R. and Schafer R. W., “DigitalProcessing of Speech Signals”, PearsonEducation, 20043. Rabiner Lawrence R. and Bernard Gold, “Theoryand Application of Digital Signal Processing”,Prentice Hall of India, 19754. Rich Elaine and Knight Kevin, “ArtificialIntelligence”, 3rd Edition, Tata McGraw Hill, 20065. Jurafsky D. and Martin J. H., “Speech andLanguage Processing”, Pearson Education, 2009.WEB REFERENCES1. http://pages.cs.wisc.edu/~dyer/cs540/notes/speech.html2. http://www.patentstorm.us/patents/6708150/claims.html3. http://www.thefreelibrary.com/Speech+Therapy:+A+new+generation+of+voicerecognition+technology+--...-a084072940CS-437SOFT COMPUTINGL T P Cr5 0 0 3OBJECTIVETo introduce about incorporating more mathematicalapproach (beyond conventional logic system) into theartificial intelligence approaches for problem solvingsuch as fuzzy logic, genetic algorithms, etc.PRE-REQUISITESKnowledge of mathematics, statistics and probability1. INTRODUCTION: Comparison of soft computingmethods: neural networks, fuzzy logic, geneticalgorithm with conventional artificial intelligence(hard computing).2. OPTIMIZATION: Least-Square methods forsystem identification, recursive least squareestimator, LSE for nonlinear models; derivativebased optimization: descent methods, Newton’smethod, conjugate gradient methods; nonlinearleast-squares problems: Gauss Newton method,Levenberg- Marquardt method.3. NEURAL NETWORKS: Different architectures;back-propagation algorithm; hybrid learning rule,supervised learning: perceptrons, adaline, backpropagationmultilayer perceptrons, radial basisfunction networks; unsupervised learning:competitive learning network, Kohonen selforganizingnetworks, Hebbian learning, Hopfieldnetwork.4. FUZZY SET THEORY: Basic definition andterminology; basic concepts of fuzzy logic; settheoretic operators; membership functions:formulation and parameterization; fuzzy union,intersection, and complement; fuzzy rules andfuzzy reasoning; fuzzy inference systems:Mamdani and Sugeno fuzzy models; fuzzyassociative memories.5. NEURO-FUZZY MODELLING: Adaptive neurofuzzyinference systems (ANFIS), neuro-fuzzy40
Lingaya’s University, Faridabadcontroller: feedback control, expert control, backpropagation through time and real-time recurrentlearning, reinforcement learning control; gradientfreeoptimisation.6. NEURO-FUZZY CONTROLLER INENGINEERING APPLICATIONS: Fuzzy logic incontrol engineering: Mamdani and Sugenoarchitecture for fuzzy control; analytical issues infuzzy logic control; applications: fuzzy logic inintelligent agents; fuzzy logic in mobile robotnavigation, fuzzy logic in database systems, fuzzylogic in medical image segmentation7. GENETIC ALGORITHMS: Basics of geneticalgorithms; design issues in genetic algorithm;genetic modeling; hybrid approach, GA basedfuzzy model identification; fuzzy logic controlledgenetic algorithm; neuro- genetic hybrids & fuzzy –genetic hybrids.TEXT BOOKRajasekharan S. and Vijayalakshmi Pai S. A., “NeuralNetworks, Fuzzy Logic & Genetic Algorithms”, Prentice-Hall of India, 2003REFERENCE BOOKS1. Goldberg David E., “Genetic Algorithms”, PearsonEducation, 2003.2. Goldberg David. E., “Genetic Algorithms in Search,Optimization & Machine Learning”, Addison-Wesley, 19893. Karray, “Soft Computing & Intelligence System,Pearson Education, 2004.4. Freeman James A. and Skapura David M., “NeuralNetworks”, Pearson Education, 2002.5. Jang J. S. R., Sun C. T. and Mizutani E., “Neurofuzzyand Soft Computing”, Prentice-HallInternational, Inc USA, 1997.6. Yen John and Langari Reza, “Fuzzy Logic,Intelligence, Control, and Information”, PearsonEducation, Delhi, 2003.7. Lin C. T. and Lee C. S. G., “Neural FuzzySystems”, Prentice-Hall, 1996.8. Zurada Jack N., “Introduction to NeuralNetworks”, Jaico Publishers, 5 th Edition, IndiaReprint 2003.9. Haykin Simon, “Neural Networks”, Prentice-Hall,1993/Pearson Education, 1999.10. Kecman Vojislav, “Learning and Soft Computing”,MIT Press, 200111. Koza J, “Genetic Programming”, MIT Press, 1993WEB REFERENCES1. en.wikipedia.org/wiki/Soft_computing2. www.springer.com/engineering/journal/5003. www.soft-computing.de4. www.softcomputing.es5. www-bisc.cs.berkeley.eduCS-441ADVANCED DATABASE L T P CrMANAGEMENT SYSTEMS 5 0 0 3OBJECTIVETo bring out various issues related to advancedcomputing with respect to database managementsystems such as parallelism in implementation, databackup and recovery management, intelligent datamining techniques, standards, etc.PRE-REQUISITES: Knowledge of databasemanagement systems1. DATA MODELS: EER model and relationship tothe OO model; object oriented data model andODMG standard; other data models - NIAM,GOOD, ORM2. QUERY OPTIMISATION: Query executionalgorithms; heuristics in query execution; costestimation in query execution; semantic queryoptimisation; database transactions andrecovery procedures: transaction processingconcepts, transaction and system concepts,desirable properties of a transaction, schedulesand recoverability, serializability of schedules;transaction support in SQL; recoverytechniques; database backup; concurrencycontrol, locking techniques for concurrencycontrol, concurrency control techniques;granularity of data items3. CLIENT/SERVER COMPUTING: Client/Serverconcepts; 2-tier and 3-tier client/server systems;client/server architecture and the internet; client/database server models; technology componentsof client/server systems; application developmentin client/server systems4. DISTRIBUTED DATABASES: Reliability andcommit protocols; fragmentation and distribution;view integration; distributed database design;distributed algorithms for data management;heterogeneous and federated database systems5. DEDUCTIVE DATABASES: Recursive queries;Prolog/Datalog notation; basic inferencemechanism for logic programs; deductive databasesystems; deductive object oriented databasesystems6. DATA WAREHOUSING: Basic concepts; datawarehouse architecture; data characteristics;reconciled data layer data transformations; deriveddata layer user interface.7. COMMERCIAL AND RESEARCH PROTOTYPES:Parallel database; multimedia database, mobiledatabase; digital libraries; temporal databaseTEXT BOOKRamakrishnan Raghu, “Database ManagementSystem”, McGraw Hill, 3rd Edition, 2003REFERENCE BOOKS1. Elmasri R. and Navathe S. B., “Fundamentals ofDatabase Systems”, 3rd Edition, Addison Wesley,Low Priced Edition, 2000.2. Tamer M. and Valduricz, “Principles of DistributedDatabase Systems”, 2nd Edition, LPE PearsonEdition.3. Silbershatz A., Korth H. F. and Sudarshan S.,“Database System Concepts”, 3rd Edition,McGraw-Hill, International Edition, 1997.4. Desai Bipin C., “An Introduction to DatabaseSystems”, Galgotia Publications.5. lioffer Feffray A., Prescotl Mary B.and McFaddenFred R., “Modern Database Management”, 6thEdition, Pearson Education.41
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