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UNIVERSITY OF RAJSHAHI Faculty of Science DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING (North Block, 4th Science Building) Tel: 0721-711103 Fax: 0721-750064 E-mail: [email protected] Web Site: http://www.ru.ac.bd/cse Syllabus for M.Sc. Session: 2013–2014 EXAMINATION: 2014 M.Sc. Syllabus, Session: 20010-2011 University of Rajshahi Faculty of Science Department of Computer Science and Engineering Syllabus for M.Sc. Degree Session: 2013 - 2014 M.Sc. Examination: 2014 The Master of Science (M.Sc.) Courses in Computer Science and Engineering (CSE) are of one academic year and is not more than three academic years from the date of first admission. A student will study of 40 Credits with total 1000 Marks. The courses have been designed for two groups: (i) General and (ii) Thesis. The courses for the groups are distributed as follows: (i) Courses for General Group: Course Code CSE 501 CSE 502 CSE 503 CSE 504 CSE 505 Option I (T) CSE 514GT CSE 515GV CSE 516P (Marks:150 Credits:4) CSE 517J Course Title Marks Credits Pattern Recognition Network Design and Management Data Mining Embedded Systems Advanced Web Engineering (One course should be selected from Table-I) Tutorial, Attendance and Continuous assessment General Viva Voce CSE 516P (A): Pattern Recognition Lab. CSE 516P (B): Network Design and Management Lab. CSE 516P (C): Data Mining Lab. CSE 516P (D): Embedded Systems Lab. CSE 516P (E): Advanced Web Engineering Lab. Option I (P): Lab related with option I (T) Project Grand Total 100 100 100 100 100 100 100 4 4 4 4 4 4 4 100 25 25 4 1 1 25 25 25 25 50 1000 1 1 1 1 2 40 1 Dept. of CSE, University of Rajshahi (ii) Courses for Thesis Group: Course Code CSE 501 CSE 502 CSE 503 CSE 504 CSE 505 Option I (T) CSE 514GT CSE 515GV CSE 518TH CSE 519TV Course Title Marks Credits Pattern Recognition Network Design and Management Data Mining Embedded Systems Advanced Web Engineering (One course should be selected from Table-I) Tutorial, Attendance and Continuous assessment General Viva Voce Thesis Thesis Viva Voce Grand Total 100 100 100 100 100 100 100 100 150 50 1000 4 4 4 4 4 4 4 4 6 2 40 Table I: Option I Courses Code CSE 506 CSE 516P(F) CSE 507 CSE 516P(G) CSE 508 CSE 516P(H) CSE 509 CSE 516P(I) CSE 510 CSE 516P(J) CSE 511 CSE 516P(K) CSE 512 CSE 516P(L) CSE 513 CSE 516P(M) Course Title Human Computer Interaction Human Computer Interaction Lab Computer Animation and Virtual Reality Computer Animation and Virtual Reality Lab. Robotics and Intelligent Systems Robotics and Intelligent Systems Lab. Mobile Communication Mobile Communication Lab. Computer Vision Computer Vision Lab. Mathematical Programming Mathematical Programming Lab. Cloud Computing Cloud Computing Natural Language Processing Natural Language Processing Marks Credits 100 25 100 25 4 1 4 1 100 25 100 25 100 25 100 25 100 25 100 25 4 1 4 1 4 1 4 1 4 1 4 1 *Tutorial 50% + Attendance 20% + Continuous assessment 30% =100%. Continuous assessment includes project and thesis progress presentation. *The marks for attendance shall be awarded on the basis of attendance in the classes according to the following table: 2 M.Sc. Syllabus, Session: 20010-2011 Attendance Marks Attendance Marks Attendance Marks 95-100% 20% 90-<95% 18% 85-<90% 16% 80-<85% 14% 75-<80% 12% 70-<75% 10% 65-<70% 8% 60-<65% 6% <60% 0% Brief descriptions of the Ordinance for the Master of Science (M.Sc.) Degree, Faculty of Science, University of Rajshahi Duration of the Course: The M.Sc course consisting of General and Thesis Groups shall extend over a period of one academic year. The degree has to be completed within a minimum of one academic year and in not more than three academic years from the date of first admission. Admission Requirements: For admission to the M.Sc. course in CSE Department a student must have the following qualifications: The Bachelor of Science with Honours Degree of four years duration of this University or of a recognised University in the CSE or similar subject. A maximum of two years’ break of study after passing B.Sc. Honours Examination shall be allowed. Candidates appearing at the Bachelor of Science (B.Sc.) Honours final examination from this university may be admitted provisionally to the Master of Science (M.Sc.) classes pending publication of their examination results: the confirmation of their admission being subject to their passing the examination as and when the results of examination are published. The number of seats in CSE Department will be determined by the CSE Academic Committee based on facilities available in the Department. Admission will be on the basis of merits (and if necessary), through admission test to be decided by the CSE Department. Eligibility for examination: In order to be eligible for taking the M.Sc. Examination, a candidate must have pursued a regular course of study by attending not less than 75% of 3 Dept. of CSE, University of Rajshahi the total number of classes held (theoretical, practical, tutorials etc.) provided that the Academic Committee of the CSE Department on special grounds and on such documentary evidence, as may be necessary, may recommend to the Vice-Chancellor cases of shortage of attendance ordinarily not below 60% for condonation. A candidate appearing in the examination under the benefit of this provision shall have to pay in addition to the examination fees, the requisite fee prescribed by the Syndicate for the purpose. A candidate, who failed to appear at the examination or fails to pass the examination, may on the approval of the relevant Department be readmitted to the following session. Admission to M.Sc Examination: Every candidate for admission to M.Sc. examination shall submit his/her application in the prescribed from together with certificates of attendance and fulfill all other conditions prescribed by the University. The application shall be submitted through the chairman of the Department and Provost of the Hall be submitted through the Controller of Examinations at least six weeks before the date fixed for the commencement of the examination. Medium of Questions and Answers: Questions shall be made in English. The medium of answer in the examination of all courses shall be in English. The Grading Systems: (a) Credit Point (CP): The credit points achieved by an examinee for 1 (one) unit course shall be 4(four). Numerical Grade 80% or its above 75% to less than 80% 70% to less than 75% 65% to less than 70% 60% to less than 65% 55% to less than 60% 50% to less than 55% 45% to less than 50% 40% to less than 45% Less than 40% Incomplete LG A+ (A Plus) A (A Regular) A- (A Minus) B+ (B Plus) B (B Regular) B- (B Minus) C+ (C Plus) C (C Regular) D F I 4 GP 4.00 3.75 3.50 3.25 3.00 2.75 2.50 2.25 2.00 0.00 -- CP/Unit 4 4 4 4 4 4 4 4 4 0 0 M.Sc. Syllabus, Session: 20010-2011 (b) Letter Grade (LG) and Grade Point(GP): Letter Grades, corresponding Grade Points and Credit Points shall be awarded in accordance with provisions shown below: Table of LG, GP and CP for credit courses Absence from the final examination shall be considered incomplete with the letter grade “I”. (c) Grade Point Average (GPA) and Total Credit Point (TCP): The weighted average of the grade points obtained in all the courses by a student and Total Credit Point shall be calculated from the following equations: GPA = Sum of [(CP)i x (GP)i] / Sum of (CP)i and TCP = Sum of (CP)i where (GP)i = grade point obtained in individual course, (CP) i = credit point for respective course, GPA = Grade Pont Average obtained and TCP = Total Credit Point obtained. GPA shall be rounded off up to 2 (two) places after decimal to the advantage of the examinee. For instance, GPA = 2.112 shall be rounded off as GPA = 2.12. An illustration of calculating GPA and CGPA: Suppose a student has completed six courses in M.Sc. examination and obtained the following grades: M.Sc. Course Credits (CP) 501 502 503 504 505 506 4 4 4 4 4 4 GPA Letter Grade (LG) A A+ B+ BC F GP 3.75 4.00 3.25 2.75 2.25 0.00 4(3.75) 4(4.00) 4(3.25) 4(2.75) 4(2.25) 4(0.0) 64.00 2.6667 444444 24 His/her GPA is: 2.67 and LG corresponding to GPA = 2.67 is “B-” Award of Degree, Promotion and Improvement of Results: (a) Award of Degree: The degree of Master of Science in any subject shall be awarded on the basis of GPA obtained by a candidate in M.Sc. In order 5 Dept. of CSE, University of Rajshahi to qualify for the M.Sc. degree a candidate must have to obtain within 3 (three) academic years from the date of first admission: (i) A minimum GPA 2.50 (ii) A minimum GP of 2.00 in the Practical/Thesis, and (iii) A minimum TCP of 36 The result shall be given in GPA with the corresponding LG (Table of LG, GP and CP) in bracket. For instance, in the example cited above the result is “GPA=2.67 (B-)” (b) Publication of Results: The result of a successful candidate shall be declared on the basis of GPA. The transcript in English shall show the course number, course title, credit, grade and grade point of individual courses, GPA and the corresponding LG. (c) Result Improvement: A candidate obtaining a GPA of less than 2.75 at the examination shall be allowed to improve his/her result, only once as an irregular candidate within 3 academic years from the date of first admission. The year of examination, in the case of a result improvement, shall remain same as that of the regular examination. His/ her previous grades for Practical courses, Class assessment/Tutorial/Terminal/Home Assignment, Thesis/Dissertation/Project shall remain valid (except the Theory VivaVoce). If a candidate fails to improve GPA, the previous result shall remain valid. 6 M.Sc. Syllabus, Session: 20010-2011 Detail Syllabus for M.Sc. Program CSE 501: Pattern Recognition Lecture: 60 (Hours), Credit: 4, Full Marks: 100 Basics of pattern recognition: Introduction to pattern recognition, feature extraction, and classification. Bayesian decision theory: Classifiers, Discriminant functions, Decision surfaces, Normal density and discriminant functions, discrete features Parameter estimation methods: Maximum-Likelihood estimation, Gaussian mixture models, Expectation-maximization method, Bayesian estimation Hidden Markov models for sequential pattern classification: Discrete hidden Markov models, Continuous density hidden Markov models, Viterbi algorithm, Baum-Welch algorithm Dimension reduction methods: Principal component, Fisher discriminant analysis Non-parametric techniques for density estimation: Parzen-window method, K-Nearest Neighbour method Linear/non-linear discriminant function based classifiers: Multi-layer Perceptron’s, Support vector machines Non-metric methods for pattern classification: Non-numeric data or nominal data, Decision trees Unsupervised learning and clustering: Criterion functions for clustering, Algorithms for clustering: K-means, Hierarchical and other methods, Cluster validation References: 1. 2. 3. 4. R.O.Duda, P.E.Hart and D.G.Stork S.Theodoridis and K.Koutroumbas C.M.Bishop : E.G. Richard, Johnsonbaugh and S. Jost : : : Pattern Classification, John Wiley & Sons, 2001 Pattern Recognition, Academic Press Pattern Recognition and Machine Learning, Springer Pattern Recognition and Image Analysis, Prentice Hall of India Private Ltd., NewDelhi 7 Dept. of CSE, University of Rajshahi CSE 502: Network Design and Management Lecture: 60 (Hours), Credit: 4, Full Marks: 100 Network Design: Design Principles, Determining Requirements, Analyzing the Existing Network, Preparing the Preliminary Design, Completing the Final Design Development, Deploying the Network, Monitoring and Redesigning, Maintaining, Design Documentation, Modular Network Design, Hierarchical Network Design, The Cisco Enterprise Composite Network Model. Technologies - Switching Design: Switching Types, Spanning, Tree Protocol, Redundancy in Layer 2 Switched Networks, STP Terminology and Operation, Virtual LANs, Trunks, Inter VLAN Routing, Multilayer Switching, Switching Security and Design Considerations, IPv4 Address Design, Private and Public Addresses, NAT, Subnet Masks, Hierarchical IP Address Design, IPv4 Routing Protocols, Classification, Metrics, Routing Protocol Selection. Network Security Design: Hacking, Vulnerabilities, Design Issues, Human Issues, Implementation Issues, Threats, Reconnaissance Attacks, Access Attacks, Information Disclosure Attacks, Denial of Service Attacks, Threat Defense, Secure Communication, Network Security Best Practices, SAFE Campus Design. Wireless LAN Design: Wireless Standards, Wireless Components, Wireless Security, Wireless Security Issues, Wireless Threat Mitigation, Wireless Management, Wireless Design Considerations, Site Survey, WLAN Roaming, Wireless IP Phones, Quality of Service Design, QoS Models, Congestion Avoidance, Congestion Management. Network Management: ISO Network Management Standard, Protocols and Tools, SNMP, MIB, RMON NetFlow, Syslog, Network Management Strategy, SLCs and SLAs, IP Service-Level Agreements, Content Networking Design, Case Study, Venti Systems. References: 1. 2. D. Tiare and C. Paquet Craig Zacker : : Campus Network Design Fundamentals, Pearson Education. The Complete Reference: Upgrading and Troubleshooting Networks, Tata McGraw-Hill. 8 M.Sc. Syllabus, Session: 20010-2011 3 4. D. L. Spohn, T. Brown and S. Grau, William Stallings : 5. T. S. Rappaport : 6. M. L. Liu : 7. R. Orfail, Harkey D. Data Network Design, McGraw-Hill. : Wireless Communications and Networks, Prentice Hall Wireless Communications, Pearson Education Distributed Computing: Principles and Applications, Pearson Education. Client/Server Programming with Java and CORBA, John Wiley and Sons, Inc. CSE 503: Data Mining Lecture: 60 (Hours), Credit: 4, Full Marks: 100 Introduction: Models, methodologies, and processes. The KDD process. Generic tasks, Application, Example: weather data Data Warehouse and OLAP: Data Warehouse and DBMS, Multidimensional data model, OLAP operations, Example: loan data set Data preprocessing: Data cleaning, Data transformation, Data reduction, Discretization and generating concept hierarchies, Experiments with Weka - filters, discretization Data mining knowledge representation: Task relevant data, Background knowledge, Interestingness measures, Representing input data and output knowledge, Visualization techniques, Experiments with Weka visualization Attribute-Value Learning Techniques: Attribute generalization, Attribute relevance, Decision trees. Decision lists. Classification and regression trees. Association rules. Correlations. Rule-based mining. The prediction task, Statistical (Bayesian) classification, Instance-based methods (nearest neighbor), Linear models, Experiments with Weka using filters and statistics,- mining association rules, decision trees, prediction. Evaluating what's been learned: Training and testing, Estimating classifier accuracy (holdout, cross-validation, leave-one-out), Combining multiple models (bagging, boosting, stacking), Experiments with Weka training and testing. Clustering: Basic issues in clustering, First conceptual clustering system: Cluster/2, Partitioning methods: k-means, expectation maximization (EM), Hierarchical methods: distance-based agglomerative and divisible 9 Dept. of CSE, University of Rajshahi clustering, Conceptual clustering: Cobweb, Experiments with Weka - kmeans, EM, Cobweb. References: 1. 2. 3. 4. 5. 6. J. Han and M. Kamber Ian H. Witten and Eibe Frank, Data Mining Tan, Steinbach, Kumar David L. Olson and Dursun Delen Maimon, O. and Last, M. : Mitchell, T.M. : : : : : Concepts and Techniques, Morgan Kaufmann Publishers. Practical Machine Learning Tools and Techniques, Morgan Kaufmann Introduction to Data Mining, AddisonWesley Advancesd Data Mining and Techniques, Springer Knowledge Discovery and Data Mining - The Info-Fuzzy Network (IFN) Methodology, Kluwer Academic Publishers, Massive Computing Series. Machine Learning, McGraw-Hill. CSE 504: Embedded Systems Lecture: 60 (Hours), Credit: 4, Full Marks: 100 Introduction to Embedded System: Components of Embedded System, Classification, Characteristic of embedded system, Microprocessors & Micro controllers, Introduction to embedded processors, Embedded software architectures. Review of Hardware: Advanced hardware, timing diagrams, memory, memory selection for embedded system, DMA, interrupts, interrupt and shared data problem, interrupt latency, The CAN bus, and the USB bus, parallel bus protocol, the PCI Bus and GPIB bus, device drivers, serial and/parallel port device drivers. Software architectures, Round Robin, Function queues scheduling architecture, real time operating system architecture. Embedded program modeling concepts in single and multiprocessor systems, software development process, software engineering practices in the embedded software development process. 10 M.Sc. Syllabus, Session: 20010-2011 Real Time operating System (RTOS): Intercrosses communications and synchronization of process, tasks and thread, shared memory, memory locking, memory allocation, signals, semaphore flag, message queues mailboxes, pipes, virtual Sockets. Task, task state, RTOS task scheduling models, context switching and interrupt handing, priority resonation technique, priority inversion, performance metric in scheduling models. Software Development: Embedded Programming in C and C++, Source Code engineering tools for embedded C/C++. Embedded Programming in Java. Study of Micro C/OS-II Hardware description using VHDL/Verilog HDL: Language fundamentals, Gate level, Dataflow and behavioral model, timing controls, block assignments, description of combinational and sequential logic circuits using HDL. Microcontroller programming: Architecture of microcontroller of 8051 family, programming model, register, instruction set, enhanced 8051 features, architecture – introduction to 8 bit and 16 bit microcontrollers, 32 Bit microcontrollers: ARM 2 TDMI core based 32 Bit microcontrollers, register, memory and data transfer application design. References: 1. Raj Kamal : 2. David E Simon : 3. Samir Palnitkar Douglas Perry Kenneth J. Ayata Myke Predko : Steve Heath Sriram Iyer and Pankaj Gupta Tammy Noergaard : : 4. 5. 6. 3. 4. 5. : : : : Embedded System: Architecture, Programming and Design, Tata McGrawHill An Embedded Software Premier, Pearson Education Asia Verilopg HDL, Pearson VHDL, Tata McGraw Hill Edition The 8051 Microcontroller, Thomson and Delmar Learning Programming and Customizing 8051 Microcontroller, McGraw-Hill Embedded Systems Design, Newnes Embedded Real Time Systems Programming, Tata McGraw-Hill Embedded System Architecture, Elsevier India Private Limited 11 Dept. of CSE, University of Rajshahi CSE 505: Advanced Web Engineering Lecture: 60 (Hours), Credit: 4, Full Marks: 100 Web Engineering: Attributes of Web based system and Application, Web App Engineering Layers, Web Engineering Process Web App Project: Formulation Web based Systems, Planning for Web Engineering Project, Building Web Engineering Team, Web App Project Management, Metrics for web engineering and Apps. Web Apps Analysis: Requirement Analysis, Analysis Model, Web Apps Estimation, Content Model. Web Apps design: Design issues of Web Apps, Interface Design, Typography, Layout design, Aesthetic Design, Content Design, Architecture Design, Navigation Design, Object Oriented Hypermedia Design, Design Metrics for web Apps. Web Apps Implementation: Client side scripting: Java Script, AJAX, JQuery; Server Side Scripting: ASP.NET, PHP; Framework: PHP MVC frameworks (Code Igniter, Symfony, Zend, CakePHP) ASP.NET MVC Framework, Web Service. Web Apps Security: Encryption techniques (digital signatures, certificates, PKI), Security threats, Securing client/server interactions, Vulnerabilities at the client (desktop security, phishing, etc.) and the server (cross-site scripting, SQL injections, etc.), Building Secure Web Apps. Testing Web Apps: Content Testing, User Interface Testing, Navigation Testing, Configuration Testing, Security Testing, Performance Testing. Maintenance of Web Applications: Web Server and Database server load balancing, web apps performance assessment, Application usage monitoring and report generation References: 1. Roger Pressman and David Lowe 2. Dino Esposito 3. Matt J. Crouch : : : Web Engineering, Tata McGraw Hill Edition, 2008 Programming Microsoft ASP.NET 2.0, Microsoft Press, 2005 ASP.NET and VB.NET web programming , Pearson, 1st Edition, 2002 12 M.Sc. Syllabus, Session: 20010-2011 4. 5. J. Castagnetto,H. Rawat, S. Schumann, C. Scollo and D. Veliath Leon Atkinson : Professional PHP Programming , Wrox Publications, 1999 : Core PHP Programming, Prentice Hall Professional, 2004 Optional Courses CSE 506: Human Computer Interaction Lecture: 60 (Hours), Credit: 4, Full Marks: 100 Foundations: The human: introduction, input-output channels, human memory, reasoning and problem solving, Psychology and the design of interactive systems. The computer: introduction, text entry devices, positioning, pointing and drawing devices, display devices, devices for virtual reality and 3D interaction, physical controls, sensors and special devices, paper printing and scanning, Memory. The Interaction: introduction, models of interaction, terms of interaction, the execution evaluation cycle, the interaction framework, ergonomics: arrangement of controls and displays, the physical environment of interaction, health issues, the use of color, different types of interaction styles, element of WIMP interface. Paradigms: introduction, paradigms for interaction. Design Process: Interaction design basics: introduction, what is design, the process of design, user focus, scenarios, navigation design, screen design and layout, iteration and prototyping. HCI in the software process: introduction, the software life cycle, usability engineering, interactive design and prototyping, design rationale. Design rules: introduction, principles to support usability, standards, guidelines, golden rules and heuristics, HCI patterns. Implementation support: introduction, elements of windowing systems, programming the application, using toolkits, user interface management system. 13 Dept. of CSE, University of Rajshahi Universal design: introduction, universal design principles, multi-modal interaction, designing for diversity. Models and Theories: Cognitive models: introduction, goal and task hierarchies, linguistic models, the challenge of display-based systems, physical and device models, and cognitive architectures. Socio-organizational Issues and stakeholders Requirements: introduction, organizational issues, and capturing requirements. Communication and collaboration models: introduction, face to face communication, conversation, text-based communication, group working Task Analysis: introduction, task decomposition, knowledge based analysis, entity-relationship based technique, sources of information and data collection, uses of task analysis. Dialog notation and design: what is dialog, dialog design notations, diagrammatic notations, textual dialog notation, dialog semantics, dialog analysis and design. Application Areas: Groupware: introduction, groupware systems, computer mediated communication, meeting and decision support systems, shared applications and artifacts, framework for groupware, implementing synchronous groupware. CSCW and social issues: introduction, face-to-face communication, conversation, text-based communication, and organizational issues. Hypertext, multimedia and the World Wide Web: introduction, understanding hypertext, finding things, web technology and issues, static web content, dynamic web content. References: 1. : 2. Dix, Finlay, Abowd , and Beale Ben Shneiderman 3. Suchman : : Human Computer Interaction, Prentice Hall Designing the user Interface: Strategies for Effective Human Computer Interaction, ISBN: 0-74840-762-6, Addison-Wesley, 3rd Edition, 1998 Plans and Situated Action: The Problem of Human - Machine Communication, Cambridge University Press, 1987 14 M.Sc. Syllabus, Session: 20010-2011 4. : 5. Newman and Lamming Monk & Wright 6. Jordan, Patrick : : Interactive Systems Design, Addison Wesley, 1995 Improving Your Human-Computer Interface, Prentice Hall, 1993 Introduction to Usability, ISBN: 074840-762-6, Taylor and Francis, Levittown, PA, 1998 (Paperback) CSE 507: Computer Animation and Virtual Reality Lecture: 60 (Hours), Credit: 4, Full Marks: 100 Computer Animation: Introduction: Perception, Early Devices, The Early Days of "Conventional" Animation, Disney, Principles of Animation, Computer Animation Production Tasks, Digital Editing, Digital Video; A Brief History of Computer Animation. Technical Background: The Display Pipeline, Homogeneous Coordinates and the Transformation Matrix, Compound Transformations, Basic Transformations, 3D Geometric Transformation, Representing an Arbitrary Orientation, Round-off error Considerations, Orientation Representation. Interpolation and Basic Techniques: Interpolation, Controlling the motion along a curve, Path following, Animation Languages, Deforming objects, Morphing (2D). Advanced Algorithms: Automatic Camera Control, Hierarchical Kinematics Modeling, Rigid Body Simulation, Enforcing Soft and Hard Constraints, Controlling Groups of Objects, Implicit Surfaces; Virtual Reality: Introduction: Virtual Reality, Goals and Applications of Virtual Reality, Pillars of VR - Presence and 3D Multimodal Interaction, Building a Virtual Reality System. Requirements Engineering and Storyboarding: Example-Ship Simulator Design. Object and Scene Modeling: Object Modeling, Geometric (Form) Modeling/ Implementation, Various Representations for Geometry, Performance-Conscious Form Modeling, Scene Construction, Object 15 Dept. of CSE, University of Rajshahi Placement by Series of Action, Function and Behavior Modeling, Ship Simulator Example Revisited. Output Display: The Human Visual System, Human Depth Perception and Stereoscopy, Visual Display Systems. Sensors and Input Processing: Trackers, Event Generators, Sensor Errors and Calibration. 3D Multimodal Interaction Design: Why Go 3D Multimodal? Structured Approach to Interaction/Interface Design, Metaphors, Interface Design Multimodality, Case Studies-Ship Simulator. References: 1. Rick Parent : 2. Gerard Jounghyun Kim : 3. Alan Watt and Mark Watt : Advanced Animation and Rendering Techniques, Publisher: Addison Wesley Professional, 1992 4. Howard Rheingold : Virtual Reality: The Revolutionary Technology of Computer-Generated Artificial Worlds - and How It Promises to Transform Society, Publisher: Simon & Schuster, 1992 Computer Animation: Algorithms and Techniques, Publisher: MKP (Morgan Kaufmann Publishers) Designing Virtual Reality Systems: The Structured Approach, Publisher: Springer CSE 508: Robotics and Intelligent Systems Lecture: 60 (Hours), Credit: 4, Full Marks: 100 Introduction: History, robot architectures, technical concepts of robotics, computing and robots, actuation and sensing, robotic system design, applications. Coordinate systems: Cartesian coordinates, transformation matrices, reference frames, relative and general transformations, orientation, inverse transformations, graphs. Rigid-Body Dynamics, Mobile Robots, Personal Assistants, and Games 16 M.Sc. Syllabus, Session: 20010-2011 Kinematics: position: Joints, members, reference frames, trigonometric solution, Homogeneous transformations, direct and inverse kinematics, orientation, precision, efficiency/complexity of kinematics solutions. Kinematics: motion: Derivatives, velocity and acceleration of a rigid bodies, differential movement, Jacobian, and singularities. Sensors, measurements and perception: Sensors hierarchy, Dynamic Systems, Sensors and Actuators, interfaces, internal and external sensors, location, computer vision, applications. Structure of robot brain programs. Input statements. Basic repetition structures: timed, forever, and counting. Sensing from within: Proprioception in the Scribbler: battery, stall, and time sensing. Examples of behaviors using proprioception. Loops with conditions: comparison operations and logical connectives in Python. Sensing the world: camera, light, and proximity. Writing reactive behaviors: making decisions in Python. Sensing light and obstacles. Control: Basic concepts in control systems, digital control for position, Behavior-based control. Dynamic Effects of Feedback Control, Analog and Digital Control Systems, Optimal Control, Least-Squares Estimation and Numerical Optimization, Monte Carlo Evaluation and Evolutionary Algorithms, Formal Logic and Computing, Predicate Calculus; 1st-order Logic, and Fuzzy Sets, Probability and Statistics, Multivariate Statistics and Stochastic Control, Stochastic, Robust, and Adaptive Control, Classification of Data Sets, Introduction to Neural Networks, Training Neural Networks, Machine Learning and Knowledge Representation, Task Planning and Multi-Agent Systems System design: System integration: mechanism, actuators and sensors, and software, Designing insect-like behaviors, Braitenberg vehicles, Making decisions, Designing reactive behaviors. Other examples: refrigerator detective, burglar alarm robot, References: 1. Robert F. Stengel : Robotics and Intelligent Systems: A Virtual Textbook, Princeton University, Princeton, NJ, http://www.princeton.edu/~stengel/RISVirText. html, 2012. 17 Dept. of CSE, University of Rajshahi CSE 509: Mobile Communication Lecture: 60 (Hours), Credit: 4, Full Marks: 100 Introduction: Wireless and Mobile Computing Architecture – Limitations of wireless and mobile communication – Wireless Telecommunication Networks: Digital cellular Systems, TDMA - CDMA – Wireless Networking Techniques –Mobility Bandwidth Tradeoffs – Portable Information Appliances. Emerging Wireless Network Standards: 3G Wireless Networks – State of Industry – Mobility support Software – End User Client Application – Mobility Middleware –Middleware for Application Development Adaptation and Agents - Service Discovery Middleware - Finding Needed Services - Interoperability and Standardization. Mobile Networking: Virtual IP Protocols - Loose Source Routing Protocols - Mobile IP – CDPD – GPRS – UMTS - Security and Authentication – Quality of Service – Mobile Access to the World Wide Web. Mobile Data Management: Mobile Transactions - Reporting and Co Transactions –Kangaroo Transaction Model - Clustering Model –Isolation only transaction – 2 Tier Transaction Model – Semantic based nomadic transaction processing. Mobile Computing Models: Client Server model – Client/Proxy/Server Model – Disconnected Operation Model – Mobile Agent Model – Thin Client Model – Tools: Java, Brew, Windows CE, WAP, Sybian, and EPOC. References: 1. Reza B Fat and Roy.T. Fielding 2. Abdelsalam A Helal, Richard Brice, Bert Haskel, Marek Rusinkiewicz, Jeffery L Caster and Darell Woelk 3. Golden Richard, Frank Adelstein, Sandeep KS Gupta, Golden Richard and Loren Schwiebert 4. Uwe Hansmann, Lothar Merk, Martin S. Nicklons and Thomas Stober : : : : Mobile Computing Principles, Cambridge University Press. Anytime, Anywhere Computing, Mobile Computing Concepts and Technology, Springer International Series in Engineering and Computer Science, 2000. Fundamentals of Mobile and Pervasive Computing, McGrawHill Professional Publishing. Principles of Mobile Computing, Springer. 18 M.Sc. Syllabus, Session: 20010-2011 CSE 510: Computer Vision Lecture: 60 (Hours), Credit: 4, Full Marks: 100 Introduction: What is computer vision, why is it difficult, background, human vision, application areas. Image formation: geometry and photometry Geometry, brightness, quantization, camera calibration, photometry (brightness and color) Image segmentation: Region segmentation, Edge and line finding Image processing: Edge detection, corner detection, line and curve detection, SIFT operator, image-based modeling and rendering, mosaics, snakes. Multi-view Geometry: Shape from stereo and motion, feature matching, surface fitting, Active ranging Image classification: Pixel classification, region classification, face detection and identification Object Recognition: Model-based methods, appearance-based methods, invariants Motion analysis: Motion detection and tracking, optical flow, inference of human activity from image sequences References: 1. : 2. D. A. Forsyth, J. Ponce R. Szeliki 3. V. S. Nalwa : 4. R. Hartley and Zisserman : 5. Rafael Gonzalez and Richard Woods : Computer Vision: A Modern Approach, Prentice Hall Computer Vision: Algorithms and Applications, publisher : Springer, 2010, Draft available online (http://szeliski.org/Book) A Guided Tour of Computer Vision, Addison-Wesley,1993 Multiple View Geometry in Computer Vision, Cambridge University Press, ISBN: 0521540518, 2nd edition, 2004 Digital Image Processing, Addison-wesley, 3rd edition 19 Dept. of CSE, University of Rajshahi CSE 511: Mathematical Programming Lecture: 60 (Hours), Credit: 4, Full Marks: 100 Elements of convex analysis: Basic terminology, Convex sets and convex functions, Projection, Separating hyperplanes, Farkas Lemma, Polihedral sets. Linear programming: Introduction to linear programming, Duality, Certificates of optimality and unboundedness, Simplex method and its variants, Sensitivity analysis and parametric programming. Nonlinear programming:Unconstrained optimization: Local optimality conditions, steepest descent method, Newton’s method and its variants. Constrained optimization: Local optimality conditions for equality constrained problems, Karush-Kuhn-Tucker conditions & constraint qualification, Lagrangian duality and saddle point optimality conditions. Discrete optimization: Computational complexity, modeling techniques, network problems and total unimodularity, relaxation and search, dynamic programming, the art and joy of optimization-applications. References: 1. Dimitris Bertsimas and John N. Tsitsiklis Mokhtar S. Bazaraa, C. M. Shetty and Hanif D. Sherali C. H. Papadimitriou and K. Steiglitz : 4. Vasek : 5. Robert Fourer, David M. Gay, and Brian W. Kernighan : 2. 3. : : Introduction to Linear Optimization, Athena Scientific Nonlinear Programming: Theory and Algorithms, Wiley Combinatorial OptimizationAlgorithms and Complexity, Prentice Hall Linear Programming, W. H. Freeman, New York A Modeling Language for Mathematical Programming, Duxbury Press/Brooks/Cole Publishing Company 20 M.Sc. Syllabus, Session: 20010-2011 CSE 512: Cloud Computing Lecture: 60 (Hours), Credit: 4, Full Marks: 100 Introduction to different types of computing: Edge computing, Grid computing, Distributed Computing, Cluster computing, Utility computing, Cloud computing. Cloud computing architecture: Architectural framework; Cloud deployment models; Virtualization in cloud computing; Parallelization in cloud computing; Green cloud. Cloud Bus; Cloud service models: Software as a Service (SaaS); Infrastructure as a Service (IaaS); Platform as a Service (PaaS). Foundational elements of cloud computing: Virtualization; Cloud computing operating System; Browser as a platform; Advanced web technologies (Web 2.0, AJAX and Mashup); Introduction to autonomic systems; Service Level Agreements(SLA); Security/Privacy; Cloud economics; Risks assessment; Current challenges facing cloud computing. Case studies. Practical sessions: Creating Windows servers on the cloud; Creating Linux servers on the cloud; Deploying applications on the cloud; Major cloud solutions. References: 1. J. Lin and C. Dyer, Morgan and Claypool : 2. T. Velte, A. Velte, R. Elsenpeter John W. Rittinghouse and James F. Ransome : 4. George Reese : 5 Andrew S. Tanenbaum, and Maarten van Steen : 6. Abraham Silberschatz, Peter B. Galvin, and Greg Gagne 3. : 21 Data-Intensive Text Processing with Map Reduce, 2010. Cloud Computing, A Practical Approach, McGraw-Hill. Cloud Computing, Implementation, Management, and Security, CRC Press. Cloud Application Architectures, O’Reilly. Distributed Systems: Principles and Paradigms, Prentice Hall. Operating System Concepts, Wiley. Dept. of CSE, University of Rajshahi CSE 513: Natural Language Processing Lecture: 60 (Hours), Credit: 4, Full Marks: 100 Introduction; Word Modeling: Automata and Linguistics, Statistical Approaches and Part of Speech Tagging; Linguistics and Grammars; Parsing Algorithms; Parsing Algorithms and the Lexicon; Semantic; Feature Parsing; Tree Banks and Probabilistic Parsing; Machine Translation; Evolutionary Models of Language Learning and Origins. References: 1. Daniel Jurafsky, and James H. Martin : 2. Christopher D. Manning, and Hinrich Schtze : Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition, Prentice Hall. Foundations of Statistical Natural Language Processing, The MIT Press. CSE 514GT: Tutorial, Attendance and Continuous Assessment Credit: 4, Full Marks: 100 Tutorial 50% + Attendance 20% + Continuous assessment 30% =100%. Continuous assessment includes project and thesis progress presentation. CSE 515GV: General Viva Voce Credit: 4, Full Marks: 100 General viva voce will be conducted by Examination Committee. 22 M.Sc. Syllabus, Session: 20010-2011 CSE 516 P: Practical Credit: 6, Full Marks: 150 Practical course consists of Five (5) mandatory lab courses from CSE 516P (A) – CSE 516P (E) and One (1) Optional I (P) from CSE 516P (F) – CSE 516P (M) based on Option I (T). CSE 516 P (A): Pattern Recognition lab based on CSE501 CSE 516 P (B): Network Design and Management lab based on CSE502 CSE 516 P (C): Data Mining lab based on CSE503 CSE 516 P (D): Embedded Systems lab based on CSE504 CSE 516 P (E): Advanced Web Engineering lab based on CSE505 CSE 516 P (F): Human Computer Interaction lab based on CSE506 CSE 516 P (G): Computer Animation and Virtual Reality lab based on CSE507 CSE 516 P (H): Robotics and Intelligent Systems lab based on CSE508 CSE 516 P (I): Mobile Communication lab based on CSE509 CSE 516 P (J): Computer Vision lab based on CSE510 CSE 516 P (K): Mathematical Programming lab based on CSE511 CSE 516 P (L): Cloud Computing lab based on CSE512 CSE 516 P (M): Natural Language Processing lab based on CSE513 CSE 517J: Project Credit: 2, Full Marks: 50 Project paper evaluation, presentation and oral examination will be conducted by Examination Committee. CSE 518TH: Thesis Credit: 6, Full Marks: 150 Submitted Thesis paper evaluation based on thesis work. CSE 517TV: Thesis Viva Voce Credit: 2, Full Marks: 50 Presentation and oral examination will be conducted by Examination Committee. 23