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UNIVERSITY OF ENGINEERING AND MANAGEMENT, JAIPURCourse Description

Title of Course: Advanced Engineering MathematicsCourse Code: MCSE101L-T Scheme: 3-1 Course Credits: 4

Introduction:The goal of this mathematics course is to provide high school students and college freshmen anintroduction to basic mathematics and especially show how mathematics is applied to solve fundamentalengineering problems. The Topics to be covered (tentatively) include:Numerical Methods

Stochastic process

Advanced linear Equations

Advanced Graph theory

Course Objectives:

In this course, the students will learn differentiation and integration of Complex functions and mappingsin the complex plane. They are introduced to Fourier Transforms to stimulate interest in communications,control and signal processing to prepare them for follow up courses in these areas. They also learn toextend and formalize knowledge of the theory of probability and random variables and get motivated touse of statistical inference in practical data analysis.

Course Contents:

Module I

Numerical Analysis: Introduction to Interpolation formulae: Stirling, Bessel’s And Spline.

Solutions of system of linear and non-linear simultaneous equations: SOR algorithm, Newton’smethod

Module II

Stochastic process: Probability: review, random variables, random processes, Random walk, Brownianmotion, markov process, queues: (M/M/1):(/FIFO),(M/M/1):(N/FIFO).

Module III

Advanced linear algebra: Vector spaces, linear transformations, eigenvalues, Eigenvectors, someapplications of eigen value problems, symmetric, skew-symmetric And orthogonal matrices, similarity ofmatrices, basis of Eigenvectors, diagonalisation

Module IV

Advanced Graph Theory: Connectivity, Matching, Hamiltonian Cycles, Coloring Problems andAlgorithms for searching an element in a data structure (DFS, BFS).


Optional:

Module V

A: Complex Variables: Review of Complex variables, Conformal mapping and transformations,Functions of complex variables, Integration with respect to complex argument, Residues and basictheorems and applications of residues.

B: Combinatorics: Basic Combinatorial Numbers, Generating Functions and Recurrence Relations,Inclusion-Exclusion Principles.

C: Optimization Technique: Calculus of several variables, Implicit function theorem, Nature of singularpoints, Necessary and sufficient conditions for optimization, Elements of calculus of variation,Constrained Optimization, Lagrange multipliers, Gradient method, Dynamic programming.

D: Fourier series and Transform: Revision of Fourier series, integrals and transforms and theirproperties. The2- dimensional Fourier transform, convolution theorem, Parseval’s formula, discreteFourier transform, fast Fourier transform

E: Z-transforms: sequence, representation of sequence, basic operations on Sequences, z-transforms,properties of z-transforms, change on scale, shifting Property, inverse z-transform, solution ofdifference equations, region of Convergence, bilinear (s to z) transform

F: Walsh function and hadamard transform: generating walsh functions of Ordern, characteristic sandapplications of walsh function, hadamard Matrix, properties, fast hadamard transform, applications.Wavelet transform: fundamentals, the Fourier transform and the short term Fourier transform, resolutionproblems, multi-resolution analysis, the Continuous wavelet transform, the discrete wavelet transform.

Referencesbooks:

1. Sen, M.K.andMalik, D.F.-FundamentalofAbstractAlgebra,Mc.GrawHill

2. Khanna,V. K.and Ghamdri, S.K.-CourseofAbstractAlgebra,VikashPub.

3. Halmos, T.R.-NaïveSetTheory,VanNostrand

4. Scarborough,J. B.-Numerical Mathematical Analysis, Oxford University Press

5. Cone, S.D.-Elementary Numerical Analysis, Mc.GrawHill.

6. Mukhopadhyay, P.-Mathematical Statistics, New Central Book Agency

7. Kapoor, V.K andGupta, S.C.-Fundamental of Mathematical Statistics, Sultan Chand and Sons.

8. Uspensky, J.V.-Introduction to Mathematical Probability, Tata Mc.Graw Hill

9. Dreyfus, S.E.-TheArt and Theory of Dynamic Programming–Theory and Applications.


10. Rao, S.S.-Optimisation Theory and Application,Wiley Eastern Ltd., NewDelhi

11. Somasundaram, Discrete Mathematical structures, PHI

12. Kolman, Busby & Ross, Discrete Mathematical structures, PHI

13. V.Krishnamurthy, Combinatorics, Theory and Applications, East-WestPress,1985.

14. N.Alonand J. Spenser, Probabilistic Methods, John Wiley and Sons, 2nd edition, 2000.

15. R.Diestel, Graph Theory, Springer-Verlag, 2nd edition, 2000.

16. I. N.Herstein,"Topics in Algebra",Vani Educational Books, India 1986

17. Kryszig,‘advancedengineeringmathematics’

18. Numerical Methods for Engineers & Scientists by Joe D. Hoffman


Title of Course: Advanced Operating SystemCourse Code: MCSE102L-T Scheme: 3-1 Course Credits: 3

Introduction:This advance course of operating system examines operating system design concepts, data structures andalgorithms, systems programming basics, distributive behavior and real time characteristics. The Topicsto be covered (tentatively) include:

• Comparative study of various operating systems• Overview on modern operating system structure• Process and thread management• Process synchronization and communication• Memory management• Virtual memory• File system• I/O subsystem and device management• Selected examples in networking, protection and security• Real time operating system and its various applications• Distributed operating system• Embedded system

Objectives:In this course we will study the basic components of modern operating system, their functions,mechanisms, policies and techniques used in their implementation and examples from popular operatingsystems. The way different modules in the operating system interact and work together to provide thebasic as well as advance services of an operating system.

Learning Outcomes:Knowledge:1. Understand the theory and logic of traditional and modern operating system.2. You will examine the real time algorithms used for various operations on operating systems.3. You will differentiate between various operating systems functionalities in terms of performance.4. Become aware of the issues in the management of resources like processor, memory and input-output.5. Know the problems in the design of operating system and study the probable solutions.6. Learn to calculate the performance of CPU scheduling and disk scheduling7. Learn about distributive File systems and methods of accessing8. Understanding various security threats9. Get detailed idea on real time operating systems, distributed operating system and their various

applications in real world.Application:1. To develop, implement, and debug various CPU scheduling algorithms2. To develop, implement, and demonstrate the algorithms of synchronizing the processes3. To develop algorithms to find deadlocks4. To develop Disk scheduling algorithms5. To develop event driven scheduling algorithm6. To implement distributive operating system7. To implement real time scheduling algorithm


Course Contents:Unit 1: Operating System Introduction, Structures - Simple Batch, Multi programmed, time-shared,Personal Computer, Parallel, Distributed Systems, Real-Time Systems, System components, Operating-System services, System Calls, Virtual Machines, System Design and

Implementation. Process and CPU Scheduling - Process concepts and scheduling, Operation on processes,Cooperating Processes, Threads, and Interposes Communication Scheduling Criteria, SchedulingAlgorithm, Multiple -Processor Scheduling, Real-Time Scheduling

Unit 2: Memory Management and Virtual Memory - Logical versus Physical Address Space, Swapping,Contiguous Allocation, Paging, Segmentation, Segmentation with Paging. Demand Paging, Performanceof Demanding Paging, Page Replacement, Page Replacement Algorithm, Allocation of Frames,Thrashing. File System Interface and Implementation -Access methods, Directory Structure, Protection,File System Structure, Allocation methods, Free-space Management, Directory Management, DirectoryImplementation, Efficiency and Performance

Unit 3: Deadlocks - System Model, Dead locks Characterization, Methods for Handling Dead locksDeadlock Prevention, Deadlock Avoidance, Deadlock Detection, and Recovery from Deadlock. ProcessManagement and Synchronization - The Critical Section Problem, Synchronization Hardware,Semaphores, and Classical Problems of Synchronization, Critical Regions, Monitors

Unit 4: Operating System Security Issues- Introduction to the topic of Security in Operating Systems,Principles of Information Security, Access Control Fundamentals, Generalized Security Architectures

Unit 5: Introduction to Distributed systems: Goals of distributed system, hardware and softwareConcepts, design issues. Elementary introduction to the terminologies within Modern Oss: Parallel,Distributed, Embedded & Real Time, Mobile, Cloud and Other Operating System Models

Text Books1. Silberschatz, P. Galvin and Greg Gagne, “Operating System Concepts”, Wiley International Company.2. A.S. Tanenbaum, Modern Operating Systems, Prentice Hall India.3. Distributed Operating System - Andrew. S. Tanenbaum, PHI

References1. J. Archer Harris, Operating systems – Schuam’s outlines, Tata Mc Graw Hill.2. Gary Nutt, Operating Systems – A modern perspective, Pearson Education.


Title of Course: Advanced Computer ArchitectureCourse Code: MCSE103L-T Scheme: 4-0 Course Credits: 4

Introduction:

This course focuses on modern advancements in parallel computer architecture, with emphasis onadvanced instruction level parallelism (ILP) and multiprocessor architectures. Topics include: advancedbranch prediction, data speculation, computation reuse, memory dependence prediction, trace caches,dynamic optimizations, checkpoint architectures, latency-tolerant processors, simultaneousmultithreading, speculative multithreading,virtual machines, message passing multiprocessors, UMA, NUMA and COMA shared-memorymultiprocessors, single-chip multiprocessors, wormhole routing techniques, cache coherence, memoryconsistency models, high performance synchronization methods, speculative lock elision andtransactional memory.

Objectives: 1. To enable students to understand the need for parallel processing.2. To give an exposure to the problems related to multiprocessing.3. To get an understanding of the recent trends in the field of Computer Architecture and identifyperformance related parameters.4. To impart understanding on different types of multi core architectures and multithreading.

Learning Outcomes: Upon successful completion of the course student should be able to:1. Modern multi-core processor micro-architectures and interconnect technologies, and be able to explaintheir evolution and be able to critically evaluate their design decisions.2. Be familiar with a variety of parallel architectures including high performance computing architectures.

Course Contents:

Unit 1: The evolution of modern Computer systems – from DEC PDP-11, IBM 360/370 family, CDCCyber 6600, Intel X86 architecture, Performance measurement parameters – MIPS, MFLOPS, SPECratings, CPI etc. Introduction to high performance Computing – Overview, Flynn’s classifications – SISD,SIMD, MISD, MIMD, Examples from Vector & Array Processors, Performance comparison ofalgorithms for Scalar, Vector and Array Processors, Fundamentals of UMA, NUMA, NORMAarchitectures, Performance measurement for parallel architectures – Flynn,s measure, Feng,s measure,Handler’s measure, Amadahl’s law of limitation for parallel processing, Gustafson’s law

Unit 2: Pipelined processor design, Pipeline performance measurement parameters – speedup factor,efficiency, throughput of a linear pipeline, comparing performance of a N stage pipeline with a Nprocessor architecture, Pipeline design principles – Uniform sub computations, Identical computations,Independent computations, Examples from design of Arithmetic pipelines – Floating point Adders,Multipliers, Dividers etc, Classifications of Uni function, Multifunction & Dynamic pipelines, Schedulingin a pipelines with feedback , Pipeline hazards and their solutions.

Unit 3: RISC architecture, characteristics of RISC instruction set & RISC pipeline, its comparisons withCISC, necessity of using optimizing compilers with RISC architecture, Examples from POWER PC andSPARC architectures, Super pipelining (MIPS architecture), Superscalar architecture , Diversifiedpipelines and out of order execution, VLIW architecture, Hardware multithreading (Coarse grained, finegrained & simultaneous multithreading.)


Unit 4: Memory hierarchy – Techniques for improving Cache memory performance parameters,( reducecache miss rate, reduce hit time, reduce miss penalty), Main memory performance enhancement –interleaved memory, improvement of memory bandwidth, use of TLB for performance enhancement

Text Books1. Computer Architecture: A Quantitative Approach – Patterson & Hennessy (Elsevier)2. Computer organization and architecture, designing for performance – Stallings (PHI)3. Computer Architecture & Parallel Processing – Hwang & Briggs(TMH)


Title of Course: Advanced AlgorithmsCourse Code: MCSE104L-T Scheme: 4-0 Course Credits: 4

Introduction:This is a graduate courseon the design and analysis ofalgorithms, covering severaladvanced topics not studiedin typicalintroductory courseson algorithms The Topics to becovered (tentatively) include:

• Complexity Analysis• Advanced data structure• Divide and Conquer• Priority queue• Dynamic Programming• Branch and Bound• Backtracking• Greedy Method• Graph traversal algorithm• Computational geometry• Notion of NP-completeness• Approximation Algorithms• Randomized algorithm• Multithreaded algorithm• Parallel algorithm

Objectives:• Theneedforefficientalgorithmsarisesinnearly everyarea of computerscience.Butthe typeof

problem tobesolved,thenotionofwhatalgorithmsare"efficient,"andeventhemodelofcomputationcanvarywidelyfrom area toarea. Inadvance algorithmscourse,we willsurveymany of thetechniques that applybroadly in thedesign of efficientalgorithms,andstudytheir application inawide rangeof application domainsand computationalmodels.Techniques tobe covered include randomized algorithm,multithreadedalgorithm,parallel algorithm, and approximation algorithms.

Learning Outcomes:Knowledge:1. Understand the different complexity analysis according different problem.You will examine the

algorithms used for various operations on operating systems.2. Understand the advanced data structure like 2-3 tree, red- black tree, B tree, B+ tree, tries, spatial data

representation using k-d tree, quad tree3. Visualize different types of algorithm techniques.Become aware of the issues in the management of

resources like processor, memory and input-output.4. Understand how to traverse a graph and the maximum flow of a network and also pattern matching of a

text.5. Understand the basic principle of different classes of problems like P,NP,NP-complete.6. Understand the basic concepts of randomized algorithm, multithreaded algorithm, parallel algorithm,

and approximation algorithms.

Application:


1. Some familiarity with several ofthemain thrustsofwork in algorithms-sufficient to giveyou some context for formulating and seeking known solutions to an algorithmicproblem.

2. Sufficient background and facility tolet youreadcurrent research publications intheareaofalgorithms.

3. A set oftools for designand analysisofnew algorithms for new problems that youencounter.

Course Contents:

Unit 1: Complexity Analysis of an algorithm, Different Asymptotic notations – their mathematicalsignificance,Amortized Analysis.Unit 2:Advanced data structure-2-3 tree, red- black tree, B tree, B+ tree, tries, spatial data representation using k-dtree, quad tree.Unit 3: Basic method, use, Examples of Divide and Conquer algorithm,Dynamic Programming,GreedyMethod,Backtracking and their complexity,Basic method and example of Graph traversal algorithm.

Unit 4:Computational geometry- robust geometric primitives, convex hull, triangulation, voronoi diagrams, nearestneighbor search, range search, point location, intersection detection, bin packing, medial-axis transform,polygon partitioning, simplifying polygons, shape similarity, motion planning, maintaining line arrangements,minkowski sum.

Unit 5: Set and string problems: set cover, set packing, string matching, approximate string matching, textcompression, cryptography, finite state machine minimization, longest Common substring/subsequence, shortestcommon superstring.

Unit 6:Advanced areas: notion of np-completeness: p class, np-hard class, np-complete class, circuit satisfiabilityproblem. Approximation algorithms, randomized algorithms, multithreaded algorithms, parallel algorithms and itsapplications,

Text Books1. T. H. Cormen, C. E. Leiserson, R. L. Rivest and C. Stein, “Introduction to Algorithms”, 3rd edition, PHI.2. Chvatal, V. Linear Programming. New York, NY: W.H. Freeman and Company, 1983, appendix.

ISBN: 9780716715870. [An easy to read description without all the details.]3. Korte, B. H., and J. Vygen. Combinatorial Optimization. New York, NY: Springer-Verlag, 2002,

chapter 4. ISBN: 9783540431541. [A detailed description.]4. Boyd, Stephen, and LievenVandenberghe. Convex Optimization .Cambridge, UK: Cambridge Univ. Press,

2005. ISBN: 97805218337835. Nemirovski, Arkadi. "Lectures on Modern Convex Optimization." (PDF - 2.7MB)6. Approximation algorithms. Vazirani, V. Approximation Algorithms. NewYork, NY: Springer-Verlag,

2004. ISBN: 9783540653677.7. de Berg, Mark, O. Cheong, M. van Kreveld, and M. Overmars.8. Computational Geometry. 3rd ed. New York, NY: Springer-Verlag, 2008. ISBN:978354077973

References1. E.Horowitz and Shani “Fundamentals of Computer Algorithms”, 2nd edition, Orient Black Swan.2. A. Aho, J.Hopcroftand J.Ullman “The Design and Analysis of computer Algorithms”, Pearson.


Title of Course: Artificial Neural NetworkCourse Code: MCSE105AL-T Scheme: 4-0 Course Credits: 4

Introduction:This course examines Artificial Neural Network concepts and prolog programming basics. The Topics tobe covered (tentatively) include:

• Introduction to artificial neural networks• Linear models for regression and classification• Feed forward neural networks• Radial basis function networks• Kernel methods for pattern analysis• Self-organizing maps• Feedback neural networks

• Kernel methods for pattern analysis

Objectives:In this course we will study the basic components of an Artificial Neural Network, their functions,mechanisms and techniques used in their implementation and examples from Prolog. The way differentmodules in the ANN interact and work together to provide the basic services of an ANN.

Learning Outcomes:Knowledge:1. Understand the theory and logic behind the design and construction of ANN.2. You will examine the algorithms used for various operations on ANN.3. You will differentiate between various ANN functionalities in terms of performance.4. Know the problems in the design of ANN and study the probable solutions.5. Learn to calculate the performance of Kononen.6. An overview of advanced ANN and compare the technical aspects of all the advanced ANN.Application:1. To develop, implement, and debug various algorithms2. To develop, implement, and demonstrate the algorithms of SVM3. To develop algorithms to find RBF.4. To develop Kononen algorithms.

Course Contents:Unit 1: Biological neural networks, Pattern analysis tasks: Classification, Regression, Clustering,Computational models of neurons, Structures of neural networks, Learning principles.

Unit 2: Polynomial curve fitting, Bayesian curve fitting, Linear basis function models, Bias-variancedecomposition, Bayesian linear regression, Least squares for classification, Logistic regression forclassification, Bayesian logistic regression for classification.

Unit 3: Pattern classification using perceptron, Multilayer feed forward neural networks(MLFFNNs),Pattern classification and regression using MLFFNNs, Error back propagation learning, Fast learningmethods: Conjugate gradient method, Auto associative neural networks, Bayesian neural networks.

Unit 4: Regularization theory, RBF networks for function approximation, RBF networks for patternclassification.


Unit 5: Pattern clustering, Topological mapping, Kohonen’s self-organizing map.

Unit 6: Pattern storage and retrieval, Hopfield model, Boltzmann machine, Recurrent neuralnetworks.

Unit 7: Statistical learning theory, Support vector machines for pattern classification, Support vectorregression for function approximation, Relevance vector machines for classification and regression.

Text Books1. B.Yegnanarayana, Artificial Neural Networks, Prentice Hall ofIndia,1999.2. C.M.Bishop, Pattern Recognition and MachineLearning,Springer,2006.

References1. . S.Haykin, Neural Networks–AComprehensiveFoundation,PrenticeHall,1998.


Title of Course: Agent Based Intelligent SystemsCourse Code: MCSE105BL-T Scheme: 4-0 Course Credits: 4

Introduction:The growth of Internet has created new ways for education systems. Learners and teachers realize

their pedagogic activities with less effort, time and money. Agent Based Intelligent System (ABIS) haveproved their worth in multiple ways and in multiple domains in Education. An ABIS is a system thatprovides direct customized instruction or feedback to students without the intervention of human beings.With the explosion of content on the World Wide Web (WWW), the scope of application of Data andWeb Mining to E- Learning applications has increased tremendously. ABIS is a software tool designedinitially to manage user learning processes. ABIS go far beyond conventional training recordsmanagement and reporting. The value-add for ABIS is the extensive range of complementaryfunctionality they offer. Learner self-service (e.g. self-registration on instructor-led training), learningworkflow (e.g. user notification, teacher approval, waitlist management), the provision of on-linelearning, on-line assessment, management of continuous professional education, collaborative learning(e.g. application sharing, discussion threads), and training resource management (e.g. instructors,facilities, equipment), are some of the additional dimensions to leading learning management systems. Inaddition to managing the administrative functions of online learning, some systems also provide tools todeliver and manage instructor-led synchronous and asynchronous online teaching based on learningobject methodology. These systems are also called learning content management systems. An ABISprovides tools for authoring and re-using or content as well as virtual spaces for learner interaction (suchas discussion forums and live chat rooms). Unlike other computer-based training technologies, thesesystems assess each learner's actions within these interactive environments and develop a model of theirknowledge, skills, and expertise. Based on the learner model, ABIS tailor instructional strategies, in termsof both the content and style, and provide explanations, hints, examples, demonstrations, and practiceproblems as needed.

Objectives:1. To introduce the students’ with different issues involved in trying to define and simulate

intelligence.2. To familiarize the students’ with specific, well known Artificial Intelligence methods, algorithms

and knowledge representation schemes.3. To introduce students’ different techniques which will help them build simple intelligent systems

based on AI/IA concepts.

Learning Outcomes:Knowledge:

1. Students will develop a basic understanding of the building blocks of AI as presented in termsof intelligent agents.

2. Students will be able to develop/demonstrate/ build simple intelligent systems or classical toyproblems using different AI techniques.

Application1. Students will be able to choose an appropriate problem-solving method and knowledge

representation scheme.2. Students will develop an ability to analyze and formalize the problem (as a state space, graph,

etc.) and select the appropriate search method.Course Contents:


Unit 1: Definitions, Foundations, History, Intelligent Agents, Problem Solving, Searching,Heuristics, Constraint Satisfaction Problems, Game playing.

Unit 2: Logical Agents, First order logic, First Order Inference, Unification, Chaining,Resolution Strategies, Knowledge Representation, Objects, Actions, Events.

Unit 3: Planning Problem, State Space Search, Partial Order Planning, Graphs, NondeterministicDomains, Conditional Planning, Continuous Planning, Multi-Agent Planning.

Unit 4: Acting under uncertainty – Probability Notation, Bayes Rule and use, BayesianNetworks, Other Approaches, Time and Uncertainty, Temporal Models, Utility Theory,Decision Network – Complex Decisions.

Unit 5: Knowledge in Learning, Relevance Information, Statistical Learning Methods,Reinforcement Learning, Communication, Formal Grammar, Augmented Grammars, Future ofAI.

Text Books1. Stuart Russell and Peter Norvig, "Artificial Intelligence - A Modern Approach",2nd Edition,

Prentice Hall, 2002

References1. Michael Wooldridge, "An Introduction to Multi Agent System", John Wiley, 2002.2. Patrick Henry Winston, Artificial Intelligence, 3rd Edition, AW, 1999.3. Nils.J.Nilsson, Principles of Artificial Intelligence, Narosa Publishing House, 1992.


Title of Course: Advanced Soft ComputingCourse Code: MCSE105CL-T Scheme: 4-0 Course Credits: 4

Introduction:Review of AI techniques and soft computing techniques and their applications in instrumentation

engineering. Soft computing differs from conventional (hard) computing in that, unlike hard computing, itis tolerant of imprecision, uncertainty, partial truth, and approximation. In effect, the role model for softcomputing is the human mind. The principal constituents, i.e., tools, techniques, of Soft Computing (SC)are Fuzzy Logic (FL), Neural Networks (NN), Support Vector Machines (SVM), EvolutionaryComputation (EC), and Machine Learning (ML) and Probabilistic Reasoning (PR).

Objectives:To understand the concepts of advanced soft computing, to enable to develop applications of

advanced soft computing in instrumentation.


1. Use soft computing techniques.2. Handle multi objective optimization problems.

Application1. Use advanced AI techniques of swarm intelligence, particle swarm optimization, antcolony

optimization and petrinets.2. Use rough set theory and granular computing

Course Contents:Unit 1: Introduction to Soft Computing; Difference between Hard and Soft Computing; Introduction toFuzzy Systems, Artificial Neural Network, Evolutionary Algorithms, Rough Set Theory; Hybrid Systems.

Unit 2: Introduction to Fuzzy Sets; Classical and Fuzzy Sets; Fuzzy Sets - Membership Function, BasicOperations, Linguistic Variable, Properties; Fuzzy relations - Cartesian product, Operations on relations;Crisp logic—Laws of propositional logic, Inference; Predicate logic—Interpretations, Inference; Fuzzylogic—Quantifiers, Inference; Fuzzy Rule based system; De-fuzzification methods; Basic Applications ofFuzzy Sets and Logics.Unit 3: Pattern Classification, Pattern Association, Clustering, Simple Clustering algorithm, k-means &k-medoid based algorithm.Unit 4: Neural Networks: Introduction, Mathematical Models, ANN architecture, Learning rules,Supervised, Unsupervised and reinforcement Learning, Multilayer Perceptron, Applications of ArtificialNeural Networks. Competitive learning networks, Kohonen self organizing networks, Hebbian learning;Hopfield Networks, Associative Memories, The boltzman machine; Applications of ANNUnit 5: Other Soft Computing techniques: Simulated Annealing, Tabu search, Ant colony optimization(ACO), Particle Swarm Optimization (PSO).Unit 6: Introduction, Single and Multi-Objective Optimization, Encoding, Fitness Function, GeneticOperations, Genetic Parameters; Schema theorem; Convergence Theory; Multiobjective optimizationusing GA (MOGA); Non-Dominated Sorting Genetic Algorithm; Basic Applications.Unit 7: Hybrid systems, GA based ANN (Optimal Weight Selection); Neuro Fuzzy Systems, fuzzyNeuron, architecture, learning, application.

Text Books


1. “Neuro-Fuzzy and Soft computing”, Jang, Sun, Mizutani, Pearson 2. “Neural networks: acomprehensive foundation”, Haykin, Pearson

2. K. Deb, Multi-Objective Optimization Using Evolutionary Algorithms. Chichester, England:John Wiley, 2001.

3. “Genetic Algorithms”, Goldberg, Pearson4. “Fuzzy Sets & Fuzzy Logic”, G.J. Klir & B. Yuan, PHI

References1. “An Introduction to Neural Networks”, Anderson J.A., PHI, 1999.2. “Introduction to the Theory of Neural Computation”, Hertz J. Krogh, R.G. Palmer, Addison-

Wesley, California, 1991.3. “An Introduction to Genetic Algorithm”, Melanie Mitchell, PHI, 1998.4. “Neural Networks-A Comprehensive Foundations”, Prentice-Hall International, New Jersey,

1999.5. “Neural Networks: Algorithms, Applications and Programming Techniques”, Freeman J.A. &

D.M. Skapura, Addison Wesley, Reading, Mass, (1992).


Title of Course: Object Oriented Information System DesignCourse Code:MCSE105DL-T-P Scheme:4-0-0 Course Credit: 4

Introduction:Object oriented analysis and design is a course that presents an introduction to the design andconstructionof software systems using techniques that view a system as a set of objects that worktogether to realizethe system's functionality. This perspective stands in contrast to more traditional“procedural" or “structured" design techniques that viewed systems as a set of procedures thatmanipulate shared data structures.Objectives:The course presents Proponents of object-oriented techniques point to the exibility and extensibility ofobject-oriented systems along with other such as increased modularity, abstraction, and encapsulation.

Learning Outcomes:After completion of this course, we will examine fundamental object-oriented analysis and designtechniques and show how decisions made during analysis and design impact the implementation ofsoftware systems. This class does not focus on object-oriented programming, however, we willexamine many examples of object-oriented systems written in Java, Python, and Ruby.

Course Contents:Module 1:Data and Information – Types of information: operational, tactical, strategic and statutory – why dowe need information systems – management structure – requirements of information at differentlevels of management – functional allocation of management – requirements of information forvarious functions – qualities of information – small case studyModule 2:Systems Analysis and Design Life Cycle: Requirements determination – requirements specifications –feasibility analysis – final specifications – hardware and software study – system design – systemimplementation – system evaluation – system modification. Role of systems analyst – attributes of asystems analyst – tools used in system analysisModule 3:Information gathering – strategies – methods – case study – documenting study – system requirementsspecification – from narratives of requirements to classification of requirements as strategic, tactical,operational and statutory. Example case studyModule 4:Feasibility analysis – deciding project goals – examining alternative solutions – cost – benefit analysis– quantifications of costs and benefits – payback period – system proposal preparation formanagements – parts and documentation of a proposal – tools for prototype creationModule 5:Tools for systems analysts – data flow diagrams – case study for use of DFD, good conventions –leveling of DFDs – leveling rules – logical and physical DFDs – software tools to create DFDs


Module 6:Structured systems analysis and design – procedure specifications in structured English – examplesand cases – decision tables for complex logical specifications – specification oriented design vsprocedure oriented designModule 7:Data oriented systems design – entity relationship model – E-R diagrams – relationships cardinalityand participation – normalizing relations – various normal forms and their need – some examples ofrelational data base designModule 8:Data input methods – coding techniques – requirements of coding schemes – error detection of codes– validating input data – input data controls interactive data input

Text Books: Design Patterns Explained: A New Perspective on Object Oriented Design (Second Edition)

Alan Shalloway and James R. Trott ISBN 0-321-24714-0 Design Patterns: Elements of Reusable Object-Oriented Software Erich Gamma, Richard

Helm, Ralph Johnson and John Vissides ISBN 0-201-63361-2

References: Refactoring: Improving the Design of Existing Code Martin Fowler, Kent Beck, John Brant,

William Opdyke and Don Roberts ISBN 0-201-48567-2 Refactoring to Patterns Joshua Kerievsky ISBN 0-321-21335-1


Title of Course: Software Engineering & CASE toolsCourse Code: MCSE105EL-T Scheme: 4-0 Course Credits: 4

Introduction:To review and understand the software Process, software engineering models, Software engineering

Practice, data flow diagrams, requirement engineering, object-orientation, understand analysismodeling, design engineering and architectural design, User interface Design and software testingstrategies, learn ethical and social implications of computing and exposure to Professional softwaredevelopment tools and techniques.Appreciate understanding the critical issues involved in software development and accordinglydevelop analysis and design strategies for tackling the core problems across various industrydomains. This would be imparted through hands on exercises and case studies on some real-life andpopular software engineering tools and technologies involving databases, CASE Tools, web serversand other web related tools and technologies (for a N-tier architecture) like Eclipse, Rational Rose,C++ / Java etc. through an Enterprise wide software project implementation in a specific domain areaIn addition, provided that the student has reached an acceptable standard in the assessments andexaminations, the student may then undertake a dissertation / industry project as part of his summertraining module. Work on a dissertation / industry project for this course will normally involve an in-depth study in the area of distributed information systems and computing (e.g., a state-of-the-artreview together with appropriate software development) and provides the student with an excellentopportunity to demonstrate expertise in this area to future employers or as a basis for future MS/PhDstudy.Objectives:1. Case Study based on Software life cycle.2. To develop, implement, and demonstrate the learning through a project that meet stated

specifications.3. You will learn User Interface Design.4. To understand Software Cost Estimation and web engineering.Learning Outcomes:Knowledge:1. You will broaden your knowledge of Software Process Models.

2. You will become aware of the Software Product.3. You will increase your proficiency in Software Project Management.4. You will gain practical experience in Requirements Engineering.5. You will gain practical experience in UML tools.6. You will acquire the background of Software Architecture.7. to understand and be able to explain Software Metrics and Software Reliability.8. You will learn concepts associated with Software Construction.9. You will learn about Software Verification Application:

Course Contents:Unit 1: Software Engineering - Objectives, Definitions, Software Process models - Waterfall Model,Prototype model, RAD, Evolutionary Models, Incremental, Spiral.Software Project Planning - Feasibility Analysis, Technical Feasibility, Cost- Benefit Analysis,COCOMO model.Unit 2: Structured Analysis, Context diagram and DFD, Physical and Logical DFDs, Data Modeling, ERdiagrams, Software Requirements Specification


Unit 3: Design Aspects, Top-Down And Bottom-Up design; Decision tree, decision table and structuredEnglish, Structure chart, Transform analysis Functional vs. Object- Oriented approach.Unit 4: Unified Modeling Language, Class diagram, interaction diagram: collaboration diagram,sequence diagram, state chart diagram, activity diagram, and implementation diagram.Unit 5: Coding & Documentation – Structured Programming, Modular Programming, ModuleRelationship- Coupling, Cohesion, OO Programming, Information Hiding, Reuse, SystemDocumentation.Testing – Levels of Testing, Integration Testing, System Testing.Software Quality, Quality Assurance, Software Maintenance, Software Configuration Management,Software Architecture, Computer Aided Software Engineering (CASE) tool.Unit 6: Object modeling and designClasses, objects, relationships, key abstractions, common mechanisms, diagrams, class diagrams,advanced classes, advanced relationships, interfaces, types, roles, packages, instances, object diagrams,interactions, use cases, use case diagrams, interaction diagrams, activity diagrams, events and signals,state machines, processes, threads, state chart diagrams, components, deployment, collaborations, patternsand frameworks, component diagrams, systems and models, code generation and reverse engineering.Text Books1. .Software Engineering- Rajib Mall (PHI)2. Software Engineering- Pankaj Jalote (Wiley-India)

References1. Software Engineering : A practitioner’s approach– Pressman(TMH)2. Software Engineering- Pankaj Jalote (Wiley-India)3 .Software Engineering- Rajib Mall (PHI)4. Software Engineering –Agarwal and Agarwal (PHI)


Title of Course: Computer Graphics & MultimediaCourse Code: MCSE105FL-T Scheme: 3-1 Course Credits: 4

Introduction:Computer Graphics course presents an introduction to computer graphics designed to give thestudent an overview of fundamental principles. It covers the fundamental concepts in creatinggraphical images on the computer. Computer graphics uses ideas from Art, Mathematics, andComputer Science to create images. Course work stresses the reduction of concepts to practice inthe form of numerous programming assignments. The course will include an overview ofcommon graphics hardware, 2D and 3D transformations and viewing, and basic raster graphicsconcepts such as scan-conversion and clipping. Methods for modeling objects as polygonalmeshes or smooth surfaces, and as rendering such as hidden-surface removal, shading,illumination, and shadows will be investigated.Multimedia course provides mainstreaming the technological media within what is called“Multimedia” is the pattern which led to infinite applications of computer technologies. Theconcept of this technology came into being with the appearance of sound cards, then compactdisks, then came the use of digital camera, then the video which made computer an essentialeducational tool. Nowadays, multimedia expanded to become a field on its own.

Objectives:

This course is designed to provide a comprehensive introduction to computer graphicsleading to the ability to understand contemporary terminology, progress, issues, andtrends. A thorough introduction to computer graphics techniques, focusing on 3Dmodelling, image synthesis, and rendering. We will look at raster scan graphics includingline and circle drawing, polygon filling, anti-aliasing algorithms, clipping, hidden-lineand hidden surface algorithms including ray tracing and, of course, rendering - the art ofmaking photo realistic pictures with local and global illumination models. Theinterdisciplinary nature of computer graphics is emphasized in the wide variety ofexamples and applications. The purpose of multimedia study is to find out the impact ofusing multimedia on students’ academic achievement in the College of Education at KingSaud University. This study’s effort is to answer the following questions like what is theimpact of using multimedia on students’ academic achievement in the “computer & itsuse in education” curriculum and are there any statistically-significant differencesbetween the average marks of the experimental group & that of the control group in thepre & post measurements of students’ academic achievement in the school of Education?


1. To know and be able to understand the core concepts of computer graphics.2. To know and be capable of using OpenGL to create interactive computer graphics.3. To know and be able to understand a typical graphics pipeline.


4. To know and be able to make interactive graphics applications in C++ using one ormore graphics application programming interfaces.

5. To know and be able to demonstrate an understanding of the use of object hierarchy ingraphics applications.

6. To know and be able to write program functions to implement visibility detection.7. To know and be able to make pictures with their computer.8. To know and be able to describe the general software architecture of programs that use

3D computer graphics9. To know the pictorial representation of various points in a image

Application:1. Know and be able to discuss hardware system architecture for computer graphics. This includes,

but is not limited to: graphics pipeline, frame buffers, and graphic accelerators/co-processors.2. Know and be able to use a current 3D graphics API (e.g., OpenGL or DirectX).3. Know and be able to use the underlying algorithms, mathematical concepts, supporting computer

graphics. These include but are not limited to:• Composite 3D homogeneous matrices for translation, rotation, and scaling transformations.• Plane, surface normals, cross and dot products.• Hidden surface detection / removal.• Scene graphs, display lists.

4. Know and be able to select among models for lighting/shading: Color, ambient light; distant andlight with sources; Phong reflection model; and shading (flat, smooth, Gourand, Phong).

5. Know and be able to use and select among current models for surfaces (e.g., geometric;polygonal; hierarchical; mesh; curves, splines, and NURBS; particle.

6. Know and be able to design and implement model and viewing transformations, the graphicspipeline and an interactive render loop with a 3D graphics API.

7. Be able to design and implement models of surfaces, lights, sounds, and textures (with texturetransformations) using a 3D graphics API.

8. Be able to discuss the application of computer graphics concepts in the development of computergames, information visualization, and business applications.

9. Be able to discuss future trends in computer graphics and quickly learn future computer graphicsconcepts and APIs.

Course Contents:Unit 1: Introduction to computer graphics & graphics systemsOverview of computer graphics, representing pictures, preparing, presenting & interacting with picturesfor presentations; Visualization & image processing; RGB color model, direct coding, lookup table;storage tube graphics display, Raster scan display, 3D viewing devices, Plotters, printers, digitizers, Lightpens etc.; Active & Passive graphics devices; Computer graphics software.Scan conversionPoints & lines, Line drawing algorithms; DDA algorithm, Bresenham’s line algorithm, Circle generationalgorithm; Ellipse generating algorithm; scan line polygon, fill algorithm, boundary fill algorithm, floodfill algorithm.

Unit 2: 2D transformation & viewingBasic transformations: translation, rotation, scaling; Matrix representations & homogeneous coordinates,transformations between coordinate systems; reflection shear; Transformation of points, lines , parallellines, intersecting lines. Viewing pipeline, Window to viewport co-ordinate transformation, clippingoperations, point clipping, line clipping, clipping circles, polygons & ellipse.


3D transformation & viewing3D transformations: translation, rotation, scaling & other transformations. Rotation about an arbitrary axisin space, reflection through an arbitrary plane; general parallel projection transformation; clipping,viewport clipping, 3D viewing

Unit 3: CurvesCurve representation, surfaces, designs, Bezier curves, B-spline curves, end conditions for periodic B-spline curves, rational B-spline curves. Hidden surfaces, Depth comparison, Z-buffer algorithm, Backface detection, BSP tree method, the Painter’s algorithm, scan-line algorithm; Hidden line elimination,wire frame methods, fractal - geometry. Color & shading models Light & color model; interpolativeshading model; Texture.

Unit 4: MultimediaIntroduction to Multimedia: Concepts, uses of multimedia, hypertext and hypermedia; Image, video andaudio standards. Audio: digital audio, MIDI, processing sound, sampling, compression. Video: MPEGcompression standards, compression through spatial and temporal redundancy, inter-frame and intra-frame compression. Animation: types, techniques, key frame animation, utility, morphing. Virtual Realityconcepts.

Text Books1. Hearn, Baker – “ Computer Graphics ( C version 2nd Ed.)” – Pearson education2. Z. Xiang, R. Plastock – “ Schaum’s outlines Computer Graphics (2nd Ed.)” – TMH3. D. F. Rogers, J. A. Adams – “ Mathematical Elements for Computer Graphics (2nd Ed.)” – TMH4. Mukherjee, Fundamentals of Computer graphics & Multimedia, PHI5. Sanhker, Multimedia –A Practical Approach, Jaico6. Buford J. K. – “Multimedia Systems” – Pearson Education7. Andleigh & Thakrar, Multimedia, PHI8. Mukherjee Arup, Introduction to Computer Graphics, Vikas9. Hill,Computer Graphics using open GL, Pearson Education


Title of Course: Operating System LabCourse Code: MCSE191L-T-P scheme: 0-0-3 Course Credit: 2

Objectives:1. To learn and understand system calls (remote procedure calls) related to files, processes, threads,

signals, semaphores and implement system programs based on that.2. To provide an understanding of the design aspects of operating system.3. To provide an efficient understanding of the language translation peculiarities by designing a

complete translator for a mini language.

Learning Outcomes: The students will have a detailed knowledge of the concepts of process andshared memory, aware of a variety of approaches to process management and main-memorymanagement, including interference, deadlock, scheduling, fragmentation, thrashing, learn the basicsbehind file systems and input output systems and understand the fundamentals of network anddistributed operating systems. Upon the completion of Operating Systems practical course, the studentwill be able to: Understand and implement basic services and functionalities of the operating system using

system calls. Use modern operating system calls and synchronization libraries in software/ hardware

interfaces. Understand the benefits of thread over process and implement synchronized programs using

multithreading concepts. Analyze and simulate Deadlock Avoidance and Protection algorithm like Bankers. Implement memory management schemes Implement remote procedure call Understand producer and consumer problem.

Course Contents:Exercises that must be done in this course are listed below:Exercise No.1: Simulate Banker’s Algorithm for Dead Lock AvoidanceExercise No.2: Simulate Banker’s Algorithm for Dead Lock PreventionExercise No. 3: Simulate Paging Technique of Memory ManagementExercise No. 4: Thread CreationExercise No. 5: Process CreationExercise No. 6: Producer and Consumer ProblemExercise No. 7: Implementation of Remote Procedure Call

Text Book:1. Maurice J. Bach, Design of the UNIX Operating System, PHI.

Recommended Systems/Software Requirements:1. Intel based desktop PC with minimum of 166 MHZ or faster processor with at least 64 MB RAM

and 100 MB free disk space.2. Turbo C or TC3 complier in Windows XP or Linux Operating System.


Title of Course: Advanced Programming LabCourse Code: MCSE192L-T-P scheme: 0-0-3 Course Credit: 2

Objectives:1. To learn and understand different types artificial neural network algorithm.2. To learnMatLab for the programming of ANN.

Learning Outcomes: The students will have a detailed knowledge of the concepts of matlab.Uponthe completion of Advanced algorithm course, the student will be able to: Understand and implement basic services and functionalities of the ANN using matlab. Use KohonenSelfOrganizingfeaturemaptoClusterthe vectorsusingowninitial weightsandlearningrate.

Understand the benefits of artificial neural network in artificial intelligence.

Course Contents:Exercises that must be done in this course are listed below:Exercise1: Programtogenerate a few activationfunctionthat are being used in neural networkExercise2: Programtoclassifywith a 2-inputperceptron.

Exercise 3: Program for perceptron net for an AND function with bipolar inputs and targets.Exercise 4: Developa MatlabprogramforOR functionwith bipolar inputsandtargetsusingADALINEnetwork.Exercise 5: Developa MatlabprogramtogenerateXOR functionforbipolar inputsandtargetsusingMADALINENetwork.Exercise 6: Developa Matlabprogramtostorethe vector(-1,-1,-1,-1)and(-1,-1,1,1)inanauto-associativenetwork.Findtheweight matrix.Test thenet with (1,1,1,1)asinput.Exercise 7: Considera vector(1,0,1,1)to bestoredinthenet.Test a discreteHopfieldnet witherror inthe1stand4thcomponents (0,0,1,0)ofthestoredvector.Exercise 8: Developa MatlabprogramforXOR function (binaryinputandoutput) withmomentumfactorusingback-propagation algorithm.Exercise 9: DevelopMatlabprogramfordrawingfeature maps(KohonenSelfOrganizingFeaturemaps)in 1-Dimensionalview.Exercise 10: UseKohonenSelfOrganizingfeaturemaptoClusterthevectors(assumefourbinaryvectors)usingowninitial weights(to be assumed)andlearningrate(tobe assumed).

Text Book:1. B.Yegnanarayana, ArtificialNeuralNetworks,PrenticeHall ofIndia,19992. SatishKumar,NeuralNetworks–AClassroomApproach,Tata McGraw-Hill,20033. S.Haykin,NeuralNetworks–AComprehensiveFoundation,PrenticeHall,19984. C.M.Bishop,PatternRecognitionand MachineLearning,Springer,2006

Recommended Systems/Software Requirements:1. Intel based desktop PC with minimum of 166 MHZ or faster processor with at least 64 MB RAM

and 100 MB free disk space.2. Matlab software in Windows XP .


Title of Course: Seminar Based on Literature SurveyCourse Code: MCSE181L-T-P scheme: 0-2-0 Course Credit: 1

The overall aim of the seminar series is to help develop an emerging field at the intersection ofmulti-disciplinary understandings of culture and education. It will build on the existing body ofwork on education and culture, but its aim is explore and develop new perspectives in this area.The objectives of the six exploratory seminars are: to explore new research from a range of academic disciplines which sheds light on the

questions outlined above to showcase cutting edge research on education and culture from outstanding academic

researchers from the UK and internationally to bring together seminar participants from different disciplines such as Sociology,

Philosophy, Psychology, Human Geography, Media Studies as well as Education andCultural Studies

to encourage and financially support the participation of PhD students to actively involve practitioners and users from each venue to engage a core group of policy makers to use the seminars to develop links between academics and stakeholders in the arts,

library, media, community and educational sectors

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