Syllabus for M Sc - University of Rajshahidept.ru.ac.bd/cse/wp-content/uploads/2013/09/MSc-2013... · Web viewFor admission to the M.Sc. course in CSE Department a student must have

UNIVERSITY OF RAJSHAHI

Faculty of Science

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING

(North Block, 4th Science Building)

Tel: 0721-711103Fax: 0721-750064

E-mail: [email protected] Site: http://www.ru.ac.bd/cse

Syllabus for M.Sc.Session: 2013–2014

EXAMINATION: 2014

Dept. of CSE, University of Rajshahi

University of RajshahiFaculty of Science

Department of Computer Science and EngineeringSyllabus for M.Sc. Degree

Session: 2013 - 2014M.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

Course Title Marks Credits

CSE 501 CSE 502 CSE 503CSE 504 CSE 505Option I (T)

Pattern RecognitionNetwork Design and ManagementData MiningEmbedded SystemsAdvanced Web Engineering (One course should be selected from Table-I)

100100100100100100

444444

CSE 514GT Tutorial, Attendance and Continuous assessment

100 4

CSE 515GV General Viva Voce 100 4

CSE 516P (Marks:150 Credits:4)

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)

2525

25252525

11

1111

CSE 517J Project 50 2Grand Total 1000 40

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M.Sc. Syllabus, Session: 20010-2011

(ii) Courses for Thesis Group:

Course Code

Course Title Marks Credits

CSE 501 CSE 502 CSE 503CSE 504 CSE 505Option I (T)

Pattern Recognition Network Design and ManagementData MiningEmbedded SystemsAdvanced Web Engineering (One course should be selected from Table-I)

100100100100100100

444444

CSE 514GT Tutorial, Attendance and Continuous assessment 100 4CSE 515GV General Viva Voce 100 4CSE 518TH Thesis 150 6CSE 519TV Thesis Viva Voce 50 2

Grand Total 1000 40

Table I: Option ICourses

CodeCourse Title Marks Credits

CSE 506CSE 516P(F)

Human Computer InteractionHuman Computer Interaction Lab

10025

41

CSE 507CSE 516P(G)

Computer Animation and Virtual RealityComputer Animation and Virtual Reality Lab.

10025

41

CSE 508 CSE 516P(H)

Robotics and Intelligent SystemsRobotics and Intelligent Systems Lab.

10025

41

CSE 509CSE 516P(I)

Mobile CommunicationMobile Communication Lab.

10025

41

CSE 510CSE 516P(J)

Computer VisionComputer Vision Lab.

10025

41

CSE 511 CSE 516P(K)

Mathematical ProgrammingMathematical Programming Lab.

10025

41

CSE 512CSE 516P(L)

Cloud ComputingCloud Computing

10025

41

CSE 513CSE 516P(M)

Natural Language ProcessingNatural Language Processing

10025

41

*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:

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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

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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 LG GP CP/Unit80% or its above A+ (A Plus) 4.00 475% to less than 80% A (A Regular) 3.75 470% to less than 75% A- (A Minus) 3.50 465% to less than 70% B+ (B Plus) 3.25 460% to less than 65% B (B Regular) 3.00 455% to less than 60% B- (B Minus) 2.75 450% to less than 55% C+ (C Plus) 2.50 445% to less than 50% C (C Regular) 2.25 440% to less than 45% D 2.00 4Less than 40% F 0.00 0Incomplete I -- 0

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(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) Letter Grade (LG) GP

501 4 A 3.75502 4 A+ 4.00503 4 B+ 3.25504 4 B- 2.75505 4 C 2.25506 4 F 0.00

His/her GPA is: 2.67

and LG corresponding to GPA = 2.67 is “B-”

Award of Degree, Promotion and Improvement of Results:

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(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 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 36The 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 Viva-Voce). If a candidate fails to improve GPA, the previous result shall remain valid.

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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 featuresParameter estimation methods: Maximum-Likelihood estimation, Gaussian mixture models, Expectation-maximization method, Bayesian estimationHidden Markov models for sequential pattern classification: Discrete hidden Markov models, Continuous density hidden Markov models, Viterbi algorithm, Baum-Welch algorithmDimension reduction methods: Principal component, Fisher discriminant analysisNon-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 machinesNon-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. R.O.Duda, P.E.Hart and D.G.Stork

: Pattern Classification, John Wiley & Sons, 2001

2. S.Theodoridis and K.Koutroumbas

: Pattern Recognition, Academic Press

3. C.M.Bishop : Pattern Recognition and Machine Learning, Springer

4. E.G. Richard, Johnsonbaugh and S. Jost

: Pattern Recognition and Image Analysis, Prentice Hall of India Private Ltd., NewDelhi

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CSE 502: Network Design and ManagementLecture: 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. D. Tiare and C. Paquet

: Campus Network Design Fundamentals, Pearson Education.

2. Craig Zacker : The Complete Reference: Upgrading and Troubleshooting Networks, Tata McGraw-Hill.

3 D. L. Spohn, T. Data Network Design, McGraw-Hill.

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Brown and S. Grau,4. William Stallings : Wireless Communications and

Networks, Prentice Hall5. T. S. Rappaport : Wireless Communications, Pearson

Education6. M. L. Liu : Distributed Computing: Principles and

Applications, Pearson Education.7. R. Orfail, D.

Harkey: Client/Server Programming with Java

and CORBA, John Wiley and Sons, Inc.

CSE 503: Data MiningLecture: 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

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clustering, Conceptual clustering: Cobweb, Experiments with Weka - k-means, EM, Cobweb.

References:

1. J. Han and M. Kamber

: Concepts and Techniques, Morgan Kaufmann Publishers.

2. Ian H. Witten and Eibe Frank, Data Mining

: Practical Machine Learning Tools and Techniques, Morgan Kaufmann

3. Tan, Steinbach, Kumar

: Introduction to Data Mining, Addison-Wesley

4. David L. Olson and Dursun Delen

: Advancesd Data Mining and Techniques, Springer

5. Maimon, O. and Last, M.

: Knowledge Discovery and Data Mining - The Info-Fuzzy Network (IFN) Methodology, Kluwer Academic Publishers, Massive Computing Series.

6. Mitchell, T.M. : Machine Learning, McGraw-Hill.

CSE 504: Embedded SystemsLecture: 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.

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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 : Embedded System: Architecture,

Programming and Design, Tata McGraw-Hill

2. David E Simon : An Embedded Software Premier, Pearson Education Asia

3. Samir Palnitkar

: Verilopg HDL, Pearson

4. Douglas Perry : VHDL, Tata McGraw Hill Edition5. Kenneth J.

Ayata: The 8051 Microcontroller, Thomson and

Delmar Learning6. Myke Predko : Programming and Customizing 8051

Microcontroller, McGraw-Hill

3. Steve Heath : Embedded Systems Design, Newnes4. Sriram Iyer and

Pankaj Gupta: Embedded Real Time Systems

Programming, Tata McGraw-Hill5. Tammy

Noergaard: Embedded System Architecture, Elsevier

India Private Limited

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CSE 505: Advanced Web EngineeringLecture: 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: Web Engineering, Tata McGraw Hill

Edition, 20082. Dino Esposito : Programming Microsoft ASP.NET

2.0, Microsoft Press, 20053. Matt J. Crouch : ASP.NET and VB.NET web

programming , Pearson, 1st Edition,

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2002

4. J. Castagnetto,H. Rawat, S. Schumann, C. Scollo and D. Veliath

: Professional PHP Programming , Wrox Publications, 1999

5. Leon Atkinson : Core PHP Programming, Prentice Hall Professional, 2004

Optional Courses

CSE 506: Human Computer InteractionLecture: 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.

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Implementation support: introduction, elements of windowing systems, programming the application, using toolkits, user interface management system.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 workingTask 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. Dix, Finlay, Abowd , and Beale

: Human Computer Interaction, Prentice Hall

2. Ben Shneiderman : Designing the user Interface: Strategies for Effective Human Computer Interaction, ISBN: 0-74840-762-6, Addison-Wesley, 3rd

Edition, 1998

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3. Suchman : Plans and Situated Action: The Problem of Human - Machine Communication, Cambridge University Press, 1987

4. Newman and Lamming

: Interactive Systems Design, Addison Wesley, 1995

5. Monk & Wright : Improving Your Human-Computer Interface, Prentice Hall, 1993

6. Jordan, Patrick : Introduction to Usability, ISBN: 0-74840-762-6, Taylor and Francis, Levittown, PA, 1998 (Paperback)

CSE 507: Computer Animation and Virtual RealityLecture: 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.

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Object and Scene Modeling: Object Modeling, Geometric (Form) Modeling/ Implementation, Various Representations for Geometry, Performance-Conscious Form Modeling, Scene Construction, Object 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 : Computer Animation: Algorithms

and Techniques, Publisher: MKP (Morgan Kaufmann Publishers)

2. Gerard Jounghyun Kim

: Designing Virtual Reality Systems: The Structured Approach, Publisher: Springer

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

CSE 508: Robotics and Intelligent SystemsLecture: 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.

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http://www.amazon.com/exec/obidos/search-handle-url/002-8594203-1924807?_encoding=UTF8&search-type=ss&index=books&field-author=Howard%20Rheingold

http://www.amazon.com/exec/obidos/search-handle-url/002-8594203-1924807?_encoding=UTF8&search-type=ss&index=books&field-author=Howard%20Rheingold


Rigid-Body Dynamics, Mobile Robots, Personal Assistants, and Games

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.

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html, 2012.

CSE 509: Mobile CommunicationLecture: 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

: Mobile Computing Principles, Cambridge University Press.

2.

Abdelsalam A Helal, Richard Brice, Bert Haskel, Marek Rusinkiewicz, Jeffery L Caster and Darell Woelk

: Anytime, Anywhere Computing, Mobile Computing Concepts and Technology, Springer International Series in Engineering and Computer Science, 2000.

3.

Golden Richard, Frank Adelstein, Sandeep KS Gupta, Golden Richard

: Fundamentals of Mobile and Pervasive Computing, McGraw-Hill Professional Publishing.

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and Loren Schwiebert4.

Uwe Hansmann, Lothar Merk, Martin S. Nicklons and Thomas Stober

: Principles of Mobile Computing, Springer.

CSE 510: Computer VisionLecture: 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 photometryGeometry, 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. D. A. Forsyth, J. Ponce

: Computer Vision: A Modern Approach, Prentice Hall

2. R. Szeliki : Computer Vision: Algorithms and Applications, publisher : Springer, 2010, Draft available online (http://szeliski.org/Book)

3. V. S. Nalwa : A Guided Tour of Computer Vision, Addison-Wesley,1993

4. R. Hartley and Zisserman

: Multiple View Geometry in Computer Vision, Cambridge University Press,

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ISBN: 0521540518, 2nd edition, 20045. Rafael Gonzalez

and Richard Woods

Digital Image Processing, Addison-wesley, 3rd edition

CSE 511: Mathematical ProgrammingLecture: 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

: Introduction to Linear Optimization, Athena Scientific

2. Mokhtar S. Bazaraa, C. M. Shetty and Hanif D. Sherali

: Nonlinear Programming: Theory and Algorithms, Wiley

3. C. H. Papadimitriou and K. Steiglitz

: Combinatorial Optimization-Algorithms and Complexity, Prentice Hall

4. Vasek : Linear Programming, W. H. Freeman, New York

5. Robert Fourer, David : A Modeling Language for

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M. Gay, and Brian W. Kernighan

Mathematical Programming, Duxbury Press/Brooks/Cole Publishing Company

CSE 512: Cloud ComputingLecture: 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

: Data-Intensive Text Processing with Map Reduce, 2010.

2.

T. Velte, A. Velte, R. Elsenpeter

: Cloud Computing, A Practical Approach, McGraw-Hill.

3.

John W. Rittinghouse and James F. Ransome

: Cloud Computing, Implementation, Management, and Security, CRC Press.

4.

George Reese : Cloud Application Architectures, O’Reilly.

5 Andrew S. Tanenbaum, and Maarten van Steen

: Distributed Systems: Principles and Paradigms,

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Prentice Hall.6.

Abraham Silberschatz, Peter B. Galvin, and Greg Gagne

Operating System Concepts, Wiley.

CSE 513: Natural Language ProcessingLecture: 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

: Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition, Prentice Hall.

2.

Christopher D. Manning, and Hinrich Schtze

: 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

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General viva voce will be conducted by Examination Committee.

CSE 516 P: PracticalCredit: 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 CSE501CSE 516 P (B): Network Design and Management lab based on CSE502CSE 516 P (C): Data Mining lab based on CSE503CSE 516 P (D): Embedded Systems lab based on CSE504CSE 516 P (E): Advanced Web Engineering lab based on CSE505CSE 516 P (F): Human Computer Interaction lab based on CSE506CSE 516 P (G): Computer Animation and Virtual Reality lab based on CSE507CSE 516 P (H): Robotics and Intelligent Systems lab based on CSE508CSE 516 P (I): Mobile Communication lab based on CSE509CSE 516 P (J): Computer Vision lab based on CSE510CSE 516 P (K): Mathematical Programming lab based on CSE511CSE 516 P (L): Cloud Computing lab based on CSE512CSE 516 P (M): Natural Language Processing lab based on CSE513

CSE 517J: Project


Project paper evaluation, presentation and oral examination will be conducted by Examination Committee.

CSE 518TH: ThesisCredit: 6, Full Marks: 150

Submitted Thesis paper evaluation based on thesis work.

CSE 517TV: Thesis Viva Voce

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Presentation and oral examination will be conducted by Examination Committee.

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Documents

Syllabus for M Sc - University of Rajshahidept.ru.ac.bd/cse/wp-content/uploads/2013/09/MSc-2013... · Web viewFor admission to the M.Sc. course in CSE Department a student must have