Download Syllabus for M Sc - Rajshahi University Alumni Association

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
444444
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