B.Tech. Degree Programme Computer Science & Engineering

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B.Tech. Computer Science & Engineering (Regular)3. MORPHOLOGICAL ANALYSIS AND THELEXICON: Brief review of regular expressions andautomata; finite state transducers; parsing withfeatures; augmented transition networks4. GRAMMARS FOR NATURAL LANGUAGE:Auxiliary verbs and verb phrases; movementphenomenon in language; handling questions incontext-free grammars; hold mechanisms in ATNs.5. HUMAN PREFERENCES IN PARSING: Encodinguncertainty; deterministic parser; word levelmorphology and computational phonology; basictext to speech; introduction to HMMs and speechrecognition, parsing with CFGs; probabilisticparsing; representation of meaning.6. AMBIGUITY RESOLUTION: Statistical methods;estimating probabilities; part-of- speech tagging;obtaining lexical probabilities; probabilistic contextfreegrammars; best first parsing.7. SEMANTICS AND LOGICAL FORM: Wordsenses and ambiguity, encoding ambiguity inlogical form, semantic analysis; lexical semantics;word sense; disambiguation; discourseunderstanding; natural language generation, Indianlanguage case studies.TEXT BOOKAllen James, “Natural Language Understanding”, 2ndedition, Pearson Education, 2003.REFERENCE BOOKS1. Winograd Terry, “Language as a CognitiveProcess”, Addison Wesley, 19832. Gazder G., “Natural Language Processing inProlog”, Addison Wesley, 19893. Arbib Mdlj and Kfaury, “Introduction of FormalLanguage Theory”, Springer Verlag, 19884. Jurafsky D. and Martin J. H., “Speech andLanguage Processing”, Pearson Education, 2002.5. Manning Christopher D. and Schütze Hinrich,“Foundations of Statistical Natural LanguageProcessing”, The MIT Press, Cambridge,Massachusetts.1999.WEB REFERENCES1. http://www.cse.unt.edu/~rada/CSCE5290/2. http://www.bowdoin.edu/~allen/nlp/3. http://www.encyclopedia.com/doc/1G1-160760429.htmlCOMPUTER VISION/ IMAGE L T P CrCS-433PROCESSING 5 0 0 3OBJECTIVETo introduce the student to computer vision algorithms,methods and concepts this will enable the student toimplement computer vision systems with emphasis onapplications and problem solving.PRE-REQUISITESIntroduction to image processing1. RECOGNITION METHODOLOGY: Conditioning;labeling; grouping; extracting, matching; edgedetection; gradient based operators; morphologicaloperators; spatial operators for edge detection;thinning, region growing, region shrinking; labelingof connected components.2. BINARY MACHINE VISION: Thresholding;segmentation; connected component labeling,hierarchal segmentation; spatial clustering; splitand merge; rule-based segmentation; motionbasedsegmentation3. AREA EXTRACTION: Concepts; data-structures;edge; line-linking; Hough transform; line fitting;curve fitting (least-square fitting); RegionAnalysis: Region properties, external points,spatial moments; mixed spatial; gray-levelmoments; boundary analysis: signature properties,shape numbers.4. FACET MODEL RECOGNITION: Labelling lines;understanding line drawings; classification ofshapes by labelling of edges; recognition ofshapes; consisting labelling problem; backtracking;perspective projective geometry; inverseperspective projection; photogrammetry – from 2Dto 3D, Image matching: Intensity matching of IDsignals, matching of 2D image, Hierarchical imagematching.5. OBJECT MODELS AND MATCHING: 2Drepresentation, Global vs. Local features. GeneralFrame Works For Matching: Distance relationalapproach, Ordered structural matching, View classmatching, Models database organization6. GENERAL FRAME WORKS: Distance –relationalapproach, Ordered –Structural matching, Viewclass matching, Models database organization.7. KNOWLEDGE BASED VISION: Knowledgerepresentation, Control-strategies, Informationintegration.TEXT BOOKForsyth David A. and Ponce Jean, “Computer Vision: AModern Approach”, Prentice Hall, 2003.REFERENCE BOOKS1. Jain R., Kasturi R. and Schunk B. G., “MachineVision”, McGraw-Hill, 1995.2. Sonka Milan, Hlavac Vaclav and Boyle Roger,“Image Processing, Analysis, and Machine Vision”, Thomson Learning, 20063. Haralick Robert and Shapiro Linda, “Computer andRobot Vision”, Vol. I and II, Addison-Wesley, 1993WEB REFERENCES1. http://www.umiacs.umd.edu/~ramani/cmsc426/2. http://www.cs.rochester.edu/~nelson/courses/vision/notes/notes.html3. http://www.cogs.susx.ac.uk/courses/compvis/index.htmlCS-434EXPERT SYSTEML T P Cr5 0 0 3OBJECTIVETo educate the students about theory behind Expertsystem and how they fit into the scope of computerscience; that is the logic, probability, data structures,AI, and other topic that form the theory of expertsystem.38
Lingaya’s University, FaridabadPREREQUISITESKnowledge of Artificial Intelligence and PROLOG1. INTRODUCTION TO EXPERT SYSTEM:Introduction; characteristics; development of expertsystem technology; applications and domains;languages, shells and tools; elements, productionsystems.2. THE REPRESENTATION OF KNOWLEDGE:Introduction; the meaning of knowledge;productions; semantic nets, object-attribute-valuetriples; frames; logic and sets; propositional logic;the first order predicate logic; quantifiers3. EXPERT SYSTEMS ARCHITECTURES:Introduction; rule based system architecture; nonproduction system architectures; dealing withuncertainty; knowledge acquisition and validation;knowledge system building tools4. METHOD OF INFERENCE: Introduction; trees,lattices and graphs; state and problem spaces;rules of inference; first order predicate logic; logicsystems; resolution; resolution systems anddeductions; forward and backward chaining5. REASONING UNDER UNCERTAINTY:Introduction; uncertainty; types of error; errors andinduction; probabilities; hypothetical reasoning andbackward induction; temporal reasoning andmarkov chains; uncertainty in inference chain6. INEXACT REASONING: Introduction; uncertaintyand rules; certainty factors; Dempster–ShaferTheory; approximate reasoning; the state ofuncertaint7. DESIGN OF EXPERT SYSTEM: Introduction;stages in the development of an expert system;errors in development stages; softwareengineering and expert system; the expert systemlife cycle; a detailed life cycle model.TEXT BOOKGiarratano Riley, “Expert Systems: Principles andProgramming”, 3rd Edition, Thomson Brooks/Cole,1989REFERENCE BOOKS1. Darlington K, “The Essence of Expert Systems”,Prentice Hall, 20002. Patterson Dan W., “Introduction to Artificial andExpert Systems”, Prentice Hall of India, 20023. Jean-Louis Ermine, “Expert Systems: Theory andPractice”, Prentice Hall of India, 20014. Waterman Donald A., “A Guide to ExpertSystems”, 1 st edition, Pearson Education, 1986.CS-435ROBOTICSL T P Cr5 0 0 3OBJECTIVEThe goal of the course is to familiarize the students withthe concepts and techniques in robot anipulator control,enough to evaluate, chose, and incorporate robots inengineering systems.PRE-REQUISITESExposure to linear algebra and matrix operations,programming in a high level language1. ROBOTIC MANIPULATION: Automation and robots;classification; application; specification; notations.2. DIRECT KINEMATICS: Dot and cross products,co-ordinate frames; rotations; homogeneous; coordinates;link co-ordination arm equation, (fiveaxisrobot, four axis robot, six axis robot).3. INVERSE KINEMATICS: General properties ofsolutions tool configuration; five axis robots, threefouraxis; six axis robot (inverse kinematics).4. WORKSPACE ANALYSIS AND TRAJECTORYPLANNING WORK: envelop and examples,workspace fixtures; pick and place operations;continuous path motion; interpolated motion,straight-line motion5. ROBOT VISION: Image representation, templatematching; polyhedral objects; Shane analysis,segmentation (thresholding, region labelling, shrinkoperators, swell operators, Euler numbers,perspective transformation, structured illumination,camera calibration).6. TASK PLANNING: Task level programming;uncertainty; configuration; space; gross motion;planning; grasp planning; fine-motion planning;simulation of planer motion; source and goalscenes; task planner simulation.7. MOMENTS OF INERTIA, PRINCIPLES OF NCAND CNC MACHINES.TEXT BOOKShilling Robert, “Fundamentals of Robotics-Analysisand Control”, Prentice Hall of India, 2009.REFERENCE BOOKS1. Fu,Gonzales and Lee, “Robotics”, McGraw Hill, 2009.2. Craig J.J., “Introduction to Robotics”, Prentice Hallof India, 19893. Ghoshal, “8051 Micro Controller & interfacing”,Pearson Education, 20084. Staughard, “Robotics and Artificial Intelligence”,Prentice Hall of India, 2009.5. Grover, Wiess, Nagel and Oderey, “IndustrialRobotics”, McGraw Hill, 2008.6. Stdder Walfram, “Robotics and Mechatronics”,Tata McGraw Hill7. Niku S. B., “Introduction to Robotics”, PearsonEducation, 20018. Klafter R. D., Chmielewski T. A. and Negin M.,“Robot Engineering”, Prentice Hall of India, 19949. Mittal R. K. and Nagrath I. J., “Robotics andControl”, Tata McGraw Hill, 2003WEB REFERENCES1. http://en.wikipedia.org/wiki/Robotics2. http://www.transitport.net/Lists/Robotics.in.Japan.html3. http://www-formal.stanford.edu/jmc/whatisai/4. http://library.thinkquest.org/2705/CS-436SPEECH RECOGNITION & L T P CrGENERATION 5 0 0 3OBJECTIVEDevelop an understanding of the relationship of vocaltract shapes and physical acoustics to the acousticspeech signal. Use a spectrum analyzer to relate theacoustic speech signal to acoustical processes. Design39
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