B.Tech. Degree Programme Computer Science & Engineering

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B.Tech. Computer Science & Engineering (Regular)TEXT BOOKFoley James D., van Dam Andeies, Feiner Stevan K.and Hughes Johb F., “Computer Graphics Principlesand Practices”, 2nd Edition, Addision Wesley, 2000REFERENCE BOOKS1. Hearn Donald and Baker M. Pauline, “ComputerGraphics”, 2nd Edition, Prentice Hall of India, 19992. Rogers David F., “Procedural Elements forComputer Graphics”, 2nd Edition, Tata McGrawHill, 20013. Watt Alan, “Fundamentals of 3-DimensionalComputer Graphics”, Addision Wesley, 19994. John Corrign, “Computer Graphics: Secrets andSolutions”, BPB Publications, 19945. Krishanmurthy N., “Introduction to ComputerGraphics”, Tata McGraw Hill, 2002WEB REFERENCES1. http://en.wikipedia.org/wiki/Computer_graphics2. http://www.cgw.com/ME2/Default.asp3. http://www.graphics.cornell.edu/online/tutorial/4. http://graphics.stanford.edu/CS-352OPERATING SYSTEMS L T P CrLAB 0 0 2 1LIST OF EXPERIMENTS1. Study of Windows 2003 Operating System,Various services available in Windows 2003 Serverand Internal/system commands for network andsystem monitoring in Windows 2003 Server,2. Difference between the Windows 2003 Server andWindows 2003 Client software.3. Study of Linux Operating System (Linux kernel,shell, basic commands like make, pipe and filterand Simple programs to display process group Ids:PID, PPID, GID), Internal/system commands fornetwork and system monitoring in Linux.4. Display "Linux Programming Lab" N times usinglibrary function calls and system calls5. Programs using system calls that provides errorchecking6. Programs using Processes.7. Administration of Linux Operating System(connecting users, connectivity across LAN andWAN; Mounting and un-mounting of devices,taking backups, restoring data from backups8. Writing of Shell Scripts9. AWK programming10. Study of MacOS features, Internal/system commandsfor network and system monitoring in MacOS11. Study of differences between Windows 2003Server, Linux and MacOS12. Programs using Command Line Arguments.13. Programs for Simple Shell and Complex Shell withcd command, editor command, etc.14. Programs for Primitive Communications.15. Programs using Pipes: Unnamed Pipes, NamesPipes16. Programs using Message Queues.REFERENCE BOOKS1. Bach Maurich, “Design of the Unix OperatingSystem”, Prentice Hall of India, 19862. Prato Stephen, “Advanced Unix Programmer’sGuide”, BPB Publications, 20063. Das Sumitabha, “Unix- Concept and Applications”,Tata McGraw Hill, 2002CS-353COMPUTER GRAPHICS L T P CrLAB 0 0 2 1LIST OF EXPERIMENTS1. 2D line as raster graphics display usingBresenhem line drawing algorithm2. 2D line drawing as raster graphics display usingDDA line drawing algorithm3. Circle drawing as raster graphics display using midpoint circle drawing algorithm4. Polygon filling as raster graphics display usingBoundary fill algorithm and Flood fill algorithm5. Line clipping6. Polygon clipping7. Display 3D object as 2D raster graphics displayusing perspective transformation8. Rotation for 3D object about arbitrary axis9. Hidden surface removal from a 3D object10. 2D transformations of a given object (triangle,rectangle, pentagon) for translating, scaling,rotating, reflecting, shearing11. Create a screen saver using inbuilt functions ofgraphics12. Zoom an object13. Reverse zooming14. Create a Bezier CurveREFERENCE BOOKS1. Hearn Donald and Baker M. Pauline, “ComputerGraphics”, 2nd Edition, Prentice Hall of India, 19992. Rogers David F., “Procedural Elements forComputer Graphics”, 2nd Edition, Tata McGrawHill, 2001CS-401NEURAL NETWORKSL T P Cr5 0 0 3OBJECTIVEThe goal is to relay the theoretical and practicalfundamental knowledge of neural networks andstudying its analogy to human brain.PRE-REQUISITESKnowledge of mathematics, computer architecture andorganization1. OVERVIEW OF BIOLOGICAL NEURONS:Structure of biological neurons relevant to ANNs.2. FUNDAMENTAL CONCEPTS OF ARTIFICIALNEURAL NETWORKS: Models of ANNs; Feedforward and feedback networks; learning rules:Hebbian learning rule, perception learning rule,delta learning rule, Widrow-Hoff learning rule,correction learning rule, Winner –lake all learningrule, etc.3. SINGLE LAYER PERCEPTION CLASSIFIER:Classification model, features and decisionregions; training and classification using discreteperceptron, algorithm, single layer continuous34
Lingaya’s University, Faridabadperceptron networks for linearly separableclassifications.4. MULTI-LAYER FEED FORWARD NETWORKS:linearly non-separable pattern classification; deltalearning rule for multi-perceptron layer; generalizeddelta learning rule, error back-propagation training;learning factors; examples.5. SINGLE LAYER FEED BACK NETWORKS: Basicconcepts; Hopfield networks; training andexamples.6. ASSOCIATIVE MEMORIES: Linear association,basic concepts of recurrent auto associativememory: retrieval algorithm, storage algorithm; bidirectionalassociative memory, architecture,association encoding and decoding, stability.7. SELF ORGANIZING NETWORKS: Unsupervisedlearning of clusters, winner-take-all learning, recallmode, Initialization of weights, separabilitylimitationsTEXT BOOKZurada Jacek M., “Introduction to Artificial NeuralSystems”, 5 th Edition, India Reprint 2003.REFERENCE BOOKS1. Haykin Simon, “Neural Networks: AComprehensive Formulation”, Addison Wesley,19982. Kosko, “Neural Networks”, Prentice Hall of India,19923. Bose N. K. and Liang P., “Neural NetworkFundamentals”, Tata McGraw Hill, 2002.4. Sivanandan, “Introduction to Neural Networks withMATLAB 6.0”, Tata McGraw Hill, 2005CS-402ARTIFICIAL INTELLIGENCEL T P Cr5 0 0 3OBJECTIVETo introduce about artificial intelligence approaches toproblem solving, various issues involved andapplication areasPRE-REQUISITESKnowledge of neural networks, data structures1. INTRODUCTION TO AI AND SEARCHTECHNIQUES: Foundation and history of AI; data,information and knowledge; AI problems andtechniques – AI programming languages, problemspace representation with examples; blind searchstrategies, breadth first search, depth first search,heuristic search techniques: hill climbing: best firstsearch, A * algorithm AO* algorithm, Means-endsanalysis.2. KNOWLEDGE REPRESENTATION ISSUES:predicate logic; logic programming; constraintpropagation; representing knowledge using rules.3. REASONING UNDER UNCERTAINITY:Reasoning under uncertainty, non monotonicreasoning; review of probability; Bayes’probabilistic interferences and Dempster Shafertheory; heuristic methods; symbolic reasoningunder uncertainty; statistical reasoning, fuzzyreasoning.4. PLANNING & GAME PLAYING: Minimax searchprocedure; goal stack planning; non linearplanning, hierarchical planning, planning insituational calculus; representation for planning;partial order planning algorithm5. LEARNING: Basic concepts; rote learning,learning by taking advices, learning by problemsolving, learning from examples, discovery aslearning, learning by analogy; explanation basedlearning; neural nets; genetic algorithms.6. OTHER KNOWLEDGE STRUCTURES: semanticnets, partitioned nets, parallel implementation ofsemantic nets; frames, common sense reasoningand thematic role frames; architecture ofknowledge based system; rule based systems;forward and backward chaining; frame basedsystems.7. APPLICATIONS OF ARTIFICIAL INTELLIGENCE:Principles of natural language processing; rulebased systems architecture; expert systems,knowledge acquisition concepts; AI application torobotics, and current trends in intelligent systems;parallel and distributed AI: psychological modeling,parallelism in reasoning systems, distributedreasoning systems and algorithmsTEXT BOOKRich Elaine and Knight Kevin, “Artificial Intelligence”,3rd Edition, Tata McGraw Hill, 1991REFERENCE BOOKS1. Nilson Nils J., “Artificial Intelligence”, McGraw-Hill,New York 19712. Russell Stuart and Norvig Peter, “ArtificialIntelligence: A Modern Approach”, Prentice Hall ofIndia, 19983. Negnevitsky, “Artificial Intelligence: A Guide toIntelligent System”, Pearson Education, 2004.4. Patterson O. W., “Introduction to ArtificialIntelligence & Expert Systems”, Prentice Hall ofIndia, 1996.5. Winston Patrick Henry, “Artificial Intelligence”, 3rdEdition, Addition Wesley, 19926. Clockson & Mellish, “Programming PROLOG”, 3rdEdition, Narosa Publications, 2002.WEB REFERENCES1. http://wwwformal.stanford.edu/jmc/whatisai/2. http://library.thinkquest.org/2705/3. www.imdb.com.CS-421COMPILER DESIGNL T P Cr5 0 0 3OBJECTIVETo lay adequate foundation for design anddevelopment of compiler and other system softwaretools such as linkers, debuggers, assemblers, etc.PRE-REQUISITESKnowledge of data structures, basic programmingconcepts, theory of computations and operating systems1. INTRODUCTION: Evolution of ComponentsSystems Programming, Assemblers, Loaders,35
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