Department of Computer Science and Engineering


Graduate Catalog 2006-2007

Note: This Catalog was published in August 2006 and supersedes the 2005-2006 Catalog.

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Department of Computer Science and Engineering

department web page: www.cse.uta.edu/
department contact: info@cse.uta.edu
graduate web page: www.cse.uta.edu/graduate/
graduate contact: www.cse.uta.edu/graduate/

Chair

Roger Walker
300 Nedderman Hall
817.272.3605

Admission | Continuation | Degree Requirements | Courses: CSE

Areas of Study and Degrees

Computer Science

M.S., Ph.D.

Computer Science and Engineering

M.S., Ph.D.

Software Engineering

M.SW.Engr.

Master's Degree Plans

Thesis (M.S.) and Non-thesis (M.S., M.SW.Engr.)

Graduate Advisors

Ramesh Yerraballi
341 Nedderman Hall, 817.272.3785
phd@cse.uta.edu

Mike O'Dell
342 Nedderman Hall, 817.272.3988
ms@cse.uta.edu

Bahram Khalili
343 Nedderman Hall, 817.272.5407
ms@cse.uta.edu

Graduate Faculty

Professors

Ahmad, Carroll, Chakravarthy, G. Das, S. Das, Elmasri, Kamangar, Kumar, Kung, Peterson, Walker

Associate Professors

Fegaras, Huber, Weems

Assistant Professors

Aslandogan, Che, Chen, Gao, Lei, D. Liu, Y. Liu, Stojanovic, Wright, Zaruba, Zhang

Objective

The purpose of the graduate programs in Computer Science and Computer Science and Engineering is to facilitate the student's continued professional and scholarly development. The Master of Science (M.S.) programs are designed to extend the student's knowledge and emphasize a particular area of concentration. The Master of Software Engineering (M.SW.Engr.) program is designed to provide the student with the opportunity for professional development in the software engineering field. Students who have completed a bachelor's degree in CS, CSE wishing to pursue a doctoral degree may apply for admission in the B.S. to Ph.D. track. The admission requirements to this highly competitive track are the same as those for “advanced admission” (see Special Admissions Programs). The Doctor of Philosophy (Ph.D.) programs are designed to prepare the student to conduct research and development in an area of concentration.

Typical areas of concentration include

Computer Systems: parallel processing, quality-of-service and resource management in distributed systems, scheduling and load balancing for parallel and distributed systems, tools for parallel programming, performance evaluation, fault-tolerant computing, interconnection networks, multimedia systems, real-time systems, memory system design;
Intelligent Systems: neural networks, machine learning, planning, scientific visualization, pattern recognition, natural language processing, multi-agent environments, decision support;
Software Engineering: requirements engineering, incremental delivery, conceptual modeling, scenario-based techniques, formal specifications, object-oriented software engineering, design methodologies, software testing, software maintenance, software re-engineering, software processes, real-time systems;
Database: temporal databases, object-oriented databases, database models and languages, distributed database systems, indexing and hashing techniques, conceptual modeling, data security, logic and databases, query optimization, relational design theory, user interfaces, data repositories.
Communications: networks, wireless communication, distributed computing, mobile computing, multimedia systems.

Admission

The CSE graduate admission committee bases its decision for graduate admission on the following criteria (in no specific order):

An overall GPA of 3.0 or higher in undergraduate coursework.
A GPA of 3.2 or higher on CS/CSE related coursework in the last two years of undergraduate degree.
Relevance of the student's degree (background) to the CSE curriculum.
Rigor of the student's bachelor's degree. A four-year degree is considered more rigorous than a three-year degree.
Reputation of the university/college that the student has received his/her previous degrees from.
GRE General Test:
1. GRE quantitative score of at least 700
2. GRE verbal score of at least 400
3. A sum of verbal and quantitative GRE scores (i.e. scores from parts 6a and 6b combined) of at least 1150 for MS and 1250 for Ph.D. applicants. An applicant can have a minimum score of 700 on the quantitative GRE or a minimum score of 400 on the verbal GRE, but not both. A passing score on the Fundamentals of Engineering (FE) exam is also given consideration.
Applicants for the MS degree with (or completing in the near future) a BSCSE from UTA and a GPA of at least 3.2 should contact the graduate advisor regarding a GRE waiver. Those with a GPA of at least 3.5 should contact the graduate advisor regarding nomination for Advanced Admission (i.e. admission without application and fee). The GRE waiver may be extended to include non-UTA candidates that have undergraduate degrees in CS or CSE (with GPA of 3.2 or above) from reputable universities with an ABET accredited program or other select universities subject to graduate advisor's approval.
For Ph.D. students, the following are optional. Meeting these criteria will improve both a student's chances of securing admission and receiving financial support.
1. Publication in scholarly conferences/journals.
2. A percentile of 80 score or higher on the Computer Science subject GRE.

The above criteria are used as follows in relevance to the three possible admission decisions, i.e., Unconditional Status; Probationary Status; and Denied.

Unconditional Status: Applies to an applicant who meets the first six criteria above to a degree satisfactory to the graduate admissions committee.
Probationary Status: Applies to an applicant who meets the first six criteria above to a degree satisfactory to the graduate admissions committee but does not fulfill all the deficiency course requirements. It also applies to a student who is accepted with conditions placed on improving one or more of the first six criteria.
Denied: Applies to an applicant who does not meet the first six criteria to a degree satisfactory to the graduate admissions committee. However, an applicant's performance on the GRE test will not be used as the sole criteria for denial.

Requirements for BS to PhD accelerated program

An undergraduate degree in CS or CSE or Equivalent.
An overall GPA of 3.0 or higher in undergraduate coursework.
A 3.2 grade point average (on a 4.0 scale) on the last two years of undergraduate course-work. In particular, performance on CS/CSE related courses are emphasized.
Rigor of the student's Bachelors degree. A three-year degree is not considered rigorous enough.
Reputation of the University/College that the student has received his/her previous degrees from.
A sum of verbal and quantitative scores of 1150 or more on the GRE* :

GRE quantitative score - 700
GRE verbal score - 400
The department does not require the advanced computer science test. A passing score on the Engineering-in-Training (EIT) exam is also given consideration for admission decisions.

(International Applicants)
A Test of English as a Foreign Language (TOEFL) score - 230

Waiver of the Graduate Record Examination

Upon recommendation of the Graduate Advisor, outstanding U.T. Arlington graduates may qualify for waiver of the requirements for the Graduate Record Examination (GRE). To qualify, the applicant must meet the following minimum requirements:

The student must have graduated from a commensurate bachelor's degree program at U.T. Arlington no more than three academic years prior to admission to the graduate program (as measured from the start of the semester for which admission is sought). A commensurate bachelor's degree program is one that is a normal feeder program for the master's degree program to which the student seeks admission. Undergraduate students in their final year of study are also eligible; in such cases, admission with the GRE waiver is contingent upon successful completion of the bachelor's degree.
The student's U.T. Arlington grade-point average must equal or exceed 3.0 in the following calculations:

as calculated for admission to the Graduate School ;
overall;
in the major field; and
in all upper-division work.

Applicants qualifying for waiver of GRE who do not qualify for advanced admission, must comply with all other requirements for admission, i.e., submitting the application for admission, paying fees, providing official transcripts from other institutions, and meeting any requirements established by the admitting graduate program. The GRE waiver must be recommended by the Graduate Advisor at the time of admission. The waiver of GRE program applies to applicants for master's degree programs only. Some programs may require higher grade-point averages to qualify and some will not waive the GRE under any circumstances.

Additionally, some programs may waive the GRE requirement for non-UTA graduates who seek admission as a master's student and meet qualifications listed in those programs' specific admission requirements. Such waivers are not offered by all graduate programs.

Fellowships

The basis for granting a Fellowship to a student will be as follows:

The student is admitted without provisional requirements.
Relative standing with respect to other qualified applicants.

Continuation

To fulfill its responsibility to graduate highly qualified professionals, the Department has established certain requirements that must be met by students continuing in the graduate programs. In addition to the requirements of the Graduate School listed elsewhere in the catalog, the Computer Science and Engineering Department has established additional requirements detailed in its Guide to Graduate Programs.

Degree Requirements

Master of Science in Computer Science - Thesis

The Master of Science in Computer Science degree program is designed to develop the scholarship and research skills of the student. Thirty-one credit hours, which include one orientation seminar credit and six thesis credits, are required.

Master of Science in Computer Science and Engineering - Thesis

The Master of Science in Computer Science and Engineering, which is intended for students with a baccalaureate degree in engineering, requires 31 credit hours of which one is orientation seminar and six are thesis credits, and is designed to develop the scholarship and research skills of the student.

Master of Science in Computer Science - Non Thesis

The Master of Science in Computer Science non-thesis options provide professional development in computer science. The structured option requires 37 credit hours of which one is orientation seminar.

Master of Science in Computer Science and Engineering

The Master of Science in Computer Science and Engineering non-thesis options are intended for students with an engineering baccalaureate degree. The structured option requires 37 credit hours of which one is orientation seminar.

B.S. to Ph.D. Track

The B.S. to Ph.D. track in Computer Science/Computer Science Engineering requires 30 credit hours with 21 hours of diagnostic requirements and nine hours of advanced research-oriented coursework. This is in addition to the Ph.D. requirements.

Ph.D. (Computer Science)

The Ph.D. in Computer Science continues the development of the student's research capability. Coursework selection in each student's program is designed to support the dissertation area selected by the student.

A minimum of two semesters of full-time study is required during the dissertation phase. There is no foreign language requirement.

Ph.D. (Computer Science and Engineering)

The Ph.D. in Computer Science and Engineering is available to students with a prior degree in engineering. It contains essentially the same requirements as the Ph.D. (Computer Science) degree except that it permits interdisciplinary research between Computer Science and one or more of the various engineering disciplines.

Courses in Computer Science and Engineering (CSE)

* Denotes offered online as part of the CSE/EE Online degree program.

CSE5191- INDIVIDUAL STUDY IN COMPUTER SCIENCE (1 - 0)
Topics dealing with special problems in Computer Science on an individual instruction basis. May be repeated for credit.

CSE5194- ORIENTATION SEMINAR (1 - 0)
Presentation of computer science research by CSE faculty, students, and invited speakers. Preparation of program of work.

CSE5306- OPERATING SYSTEMS II (3 - 0)
Hardware and software issues in modern operating systems, distributed and networked operating systems, and real time operating systems. Topics may include multithreading, distributed systems, device drivers, object oriented operating systems, advanced file systems, parallel virtual machines, and load balancing. Examples from current popular modern systems and research operating systems will be analyzed.

CSE5307- PROGRAMMING LANGUAGE CONCEPTS (3 - 0)
Study and evaluation of concepts in programming language for modern computer systems. Programming projects are selected from string-based, symbolic, algorithmic, and object-oriented languages.

CSE5311- DESIGN AND ANALYSIS OF ALGORITHMS (3 - 0)
Techniques for analyzing upper bounds for algorithms and lower bounds for problems. Problem areas include: sorting, data structures, graphs, dynamic programming, combinatorial algorithms, introduction to parallel models.

CSE5314- COMPUTATIONAL COMPLEXITY (3 - 0)
Sequential and parallel complexity classes (e.g., NP-complete and P-complete) and representative problems in languages, logic and graphs. Reduction techniques. Approximate solutions. Complexity hierarchies.

CSE5315- NUMERICAL METHODS (3 - 0)
Selected topics from the theory and practice of using automatic digital computers for approximating arithmetic operations, approximating functions, solving systems of linear and non-linear equations, and solving ordinary and partial differential equations.

CSE5316- MODELING, ANALYSIS, AND SIMULATION OF COMPUTER SYSTEMS (3 - 0)
Mathematical formalism and techniques used for computer system modeling and analysis. Reviews probability, transform theory, coding theory, and Petri nets. Topics may include knowledge based modeling, validation procedures, various simulation techniques for stochastic process and real-time distributed systems.

CSE5317- DESIGN AND CONSTRUCTION OF COMPILERS (3 - 0)
Review of programming language structures, translation, and storage allocation. Introduction to context-free grammars and their description. Design and construction of compilers including lexical analysis, parsing and code generation techniques. Error analysis and simple code optimizations will be introduced.

CSE5318- APPLIED GRAPH THEORY AND COMBINATORICS (3 - 0)
Connected and disconnected graphs; trees; graph planarity; Hamiltonian circuits and Euler tours; coloring; flow and graph optimization algorithms, fundamentals of combinatorics; generating functions and recurrence relations; inclusion-exclusion principle; applications in telecommunications; mobile computing, parallel processing and multiprocessor architectures.

CSE5321- SOFTWARE TESTING (3 - 0)
Study of software quality assurance, software testing process, methods, techniques and tools. Topics include formal review techniques, black box testing, white box testing, integration testing, acceptance testing, regression testing, performance testing, stress testings, and testing of object-oriented software.

CSE5322- SOFTWARE DESIGN PATTERNS (3 - 0)
Study and application of object-oriented software design patterns to software development and maintenance in the object-oriented paradigm.

CSE5323- SOFTWARE ENGINEERING PROCESSES (3 - 0)
Introduces software lifecycle models, process disciplines, project management concepts, and applies them by mastering the Personal Software Process (PSP).

CSE5324- SOFTWARE ENGINEERING: ANALYSIS, DESIGN, AND TESTING (3 - 0)
Motivations, principles, and goals of software engineering; technical aspects of software projects, including: review of structured analysis and structured design, emphasis on object-oriented methods of requirements analysis and specification, design, and implementation; software testing concepts; team project.

CSE5325- SOFTWARE ENGINEERING: MANAGEMENT, MAINTENANCE, AND QUALITY ASSURANCE (3 - 0)
Issues and principles for software management; managerial and support aspects of software projects, including: processes, estimation techniques, planning and scheduling, risk analysis, metrics, and quality assurance. Other topics include: configuration management, verification and validation, and maintenance; team project.

CSE5326- REAL-TIME SOFTWARE DESIGN (3 - 0)
Specification, design, and analysis of real-time systems including real-time logics and decidability of real-time conditions; real-time scheduling approaches, and schedulability analysis, system requirement specifications and languages; procedural and object-oriented methods; specialized analysis techniques for distributed and for control applications; team project.

CSE5327- TELECOMMUNICATIONS SOFTWARE DEVELOPMENT (3 - 0)
General understanding and classification of telecommunications systems and applications. Issues relating to the analysis, design, implementation, and testing of telecommunications software. Prerequisite: CSE 5324 and 5344.

CSE5328- SOFTWARE ENGINEERING TEAM PROJECT I (1 - 2)
Apply the knowledge and skills gained in other software engineering courses to synthesize a solution to a significant and realistic problem. Participate in team project activities, including: proposal writing, problem analysis, software requirements specification, software project planning, and preliminary software design. Prerequisite: CSE 5325 (or concurrent enrollment). Open to Master of Software Engineering candidates only.

CSE5329- SOFTWARE ENGINEERING TEAM PROJECT II (1 - 2)
Continuation of CSE 5328. Team project activities include: detailed software design, implementation, software quality assurance, software testing, integration, and demonstration. Prerequisite: CSE 5328. Open to Master of Software Engineering candidates only.

CSE5330- DATABASE SYSTEMS (3 - 0)
Database system architecture; management and analysis of files, indexing, hashing, and B+-trees; the relational model and algebra; the SQL database language; database programming techniques, database design using Entry-Relationship, extended E-R, and UML modeling; basics of normalization. Introduction to database security, query processing and transaction management. Prerequisite: CSE 2320.

CSE5331- DBMS MODELS AND IMPLEMENTATION TECHNIQUES (3 - 0)
DBMS system implementation techniques, including query optimization, transaction processing, concurrency control, buffer management and recovery. Object-oriented, object-relational and XML databases. Introduction to advanced database models, such as active, distributed, temporal, spatial and data warehousing. Prerequisite: CSE 3330/CSE 5330, or consent of instructor.

CSE5333- DISTRIBUTED AND PARALLEL DATABASES (3 - 0)
Distributed database system architecture and design, distributed transaction management and database interoperability; distributed query processing; parallel database architectures and techniques; and parallel algorithms for database operations. Prerequisite: CSE 5330 or consent of instructor.

CSE5334- DATA MINING (3 - 0)
Preparing data for mining, using preprocessing, data warehouses and OLAP; data mining primitives, languages and system architecture; data mining techniques including association rule mining, classification/prediction and cluster analysis. Prerequisite: CSE 5330 or consent of instructor.

CSE5343- REAL-TIME DATA ACQUISITION AND CONTROL SYSTEMS (2 - 3)
Advanced course in design of microcomputer-based systems. Emphasis is on the application of state-of-the-art microprocessors, microcomputers, and other LSI and VLSI components to real-time, interactive, and/or embedded systems. Prerequisite: CSE 5442 or consent of instructor.

CSE5344- COMPUTER NETWORKS (3 - 0)
Study of computer network architectures, protocols, and interfaces. The OSI reference model and the Internet architecture will be discussed. Networking techniques such as multiple access, packet/cell switching, and internetworking will be studied. Discussion will also include end-to-end protocols, congestion control, high-speed networking, and network management. Emphasis will be on Internet and ATM. Prerequisite: CSE 3320 or consent of instructor.

CSE5346- NETWORKS II (3 - 0)
This course provides an in depth study and comparison of the two primary networking paradigms, Internet/broadcast and switched, using two technologies, IPv6 and ATM, as representative examples. The course is implementation-oriented, focusing on issues such as routing, broadcast, multicast, mobility, network configuration, and quality of service. Prerequisite: CSE 5344.

CSE5347- TELECOMMUNICATION NETWORKS DESIGN (3 - 0)
Design and analysis of telecommunication systems and networks, fundamental graph algorithms, basic concepts of distributed algorithms, centralized and distributed network topology design, routing and multicasting, capacity assignment, network reliability, network performance, modeling and simulation, wireless mobile networks. Prerequisites: CSE 5311, CSE 4344/5344, or consent of instructor.

CSE5348- MULTIMEDIA SYSTEMS (3 - 0)
Representations and techniques for processing, communicating, and compression of text, audio, graphics, and video in real time. Project integrating these topics.Prerequisite: CSE 3320.

CSE5350- COMPUTER ARCHITECTURE II (3 - 0)
A study of advanced uniprocessor and basic multiprocessor systems. Topics may include memory management systems, pipelined processors, array and vector processors, and introduction to architecture of multiprocessor systems. Prerequisite: CSE 3322 or consent of instructor.

CSE5351- PARALLEL PROCESSING (3 - 0)
Covers the theory and practice of parallel processing. Theoretical topics include: abstract models and algorithms for shared memory computation (PRAM); algorithms for various topologies such as meshes and hypercubes; efficiency and speedup analysis. Problem areas include data structures, numerical methods, graphs, combinatorics. Practical topics include synchronization, routing, scheduling, parallelizing serial computations, programming languages. Includes programming exercises using one or more concurrent programming languages, on one or more parallel computers. Prerequisite: CSE 3320 or consent of instructor.

CSE5353- DISTRIBUTED COMPUTING (3 - 0)
Programming languages, support components, coordination models, and fundamental algorithms for distributed and clustered systems. Prerequisite: CSE 5306.

CSE5355- COMPUTER SYSTEM PERFORMANCE EVALUATION (3 - 0)
Queueing network models and simulation for studying the performance of overall computer systems. Theory and applications of Markov process, Random Walk, Renewal Process, and Birth and Death Process. Topics also include bottleneck identification, capacity planning, hardware selection and upgrade, and performance tuning. Data collection, presentation and interpretation, benchmarking and the proper choice of performance metrics will be emphasized. Prerequisite: CSE 3320.

CSE5360- ARTIFICIAL INTELLIGENCE I (3 - 0)
Introduction to the methods, concepts and applications of artificial intelligence, including knowledge representation, search, theorem proving, planning, natural language processing, and study of AI programming languages. Prerequisite: CSE 2320 and 3315, or consent of instructor.

CSE5361- ARTIFICIAL INTELLIGENCE II (3 - 0)
Continuation of artificial intelligence methods and techniques, including uncertainty reasoning, machine learning, perception, and advanced topics in knowledge representation, search and planning. Emphasis on design and implementation of AI solutions. Prerequisite: CSE 5360 or consent of instructor.

CSE5364- ROBOTICS (2 - 3)
An introduction to robotics and the design and programming of autonomous robot systems. Topics include basic kinematics, dynamics, and control, as well as sensors, knowledge representation, and programming techniques. Coursework includes individual and group projects involving the building and programming of simulated and real robots. Prerequisite: CSE 2320 and CSE 3442.

CSE5365- COMPUTER GRAPHICS (3 - 0)
Input/output devices and programming techniques suitable for the visual representation of data and images. Prerequisite: CSE 1320, analytic geometry and linear algebra, or consent of instructor.

CSE5366- DIGITAL SIGNAL PROCESSING (3 - 0)
Introduction to principles and applications of digital signal processing. Topics include: analysis of signals and systems, Fourier and Z transforms, digital filter design techniques (FIR and IIR), autoregressive (AR) and autoregressive moving average (ARMA) modeling. Applications to science and engineering include: financial predictions and processing of digital music. Laboratory work includes some programming and use of high quality library routines and packages such as Mathematica, Matlab. Prerequisite: CSE 1320 and consent of Graduate Advisor.

CSE5367- PATTERN RECOGNITION (3 - 0)
Principles and various approaches of pattern recognition processes, including Bayesian classification, parametric/non-parametric classifier design, feature extraction for signal representation, and techniques for classification and clustering. Current issues in pattern recognition research will also be examine. Prerequisite: CSE 2320 , MATH 3313.

CSE5368- NEURAL NETWORKS (3 - 0)
Theoretical principles of neurocomputing. Learning algorithms, information capacity, and mapping properties of feedforward and recurrent networks. Different neural network models will be implemented and their practical applications discussed. Prerequisite: CSE 1320 and calculus II, or consent of instuctor.

CSE5370- BIOINFORMATICS (3 - 0)
Basic biology of genome and common laboratory techniques Overview of discrete probability theory, random variables and processes. Issues in genome mapping, sequencing and analysis: sequence alignments and alignment algorithms; genomic databases and information access; structure and features of DNA sequences. Techniques in contemporary biotechnology, including proteomics and gene expression analysis using microarray chips. Prerequisite: CSE 5311 or consent of instructor.

CSE5391- INDIVIDUAL STUDY IN COMPUTER SCIENCE (3 - 0)
Topics dealing with special problems in Computer Science on an individual instruction basis. May be repeated for credit.

CSE5392- TOPICS IN COMPUTER SCIENCE (3 - 0)
May be repeated for credit when the topics vary.

CSE5393- DIRECTED STUDY IN COMPUTER SCIENCE (3 - 0)

CSE5394- MASTER'S PROJECT I (3 - 0)

CSE5395- MASTER'S PROJECT II (3 - 0)

CSE5397- MASTER'S THESIS I (3 - 0)
Preliminary research effort for the master's thesis, including problem definition and literature search, along with identification of resources, milestones, examining committee members, and external publication venue.

CSE5398- MASTER'S THESIS II (3 - 0)
Completion of tasks in support of the thesis defined in Master's Thesis I, including oral defense of the written documents.

CSE5442- EMBEDDED COMPUTER SYSTEMS (3 - 3)
Design of micro computer-based systems; microcomputer programming, component and system architectures, memory interfacing, parallel and serial I/O interfacing, A/D and D/A conversion, and typical applications. Prerequisite: CSE 3322 or consent of instructor.

CSE5698- THESIS (6 - 0)

CSE6197- RESEARCH IN COMPUTER SCIENCE (1 - 0)
Individually supervised research projects.

CSE6297- RESEARCH IN COMPUTER SCIENCE (2 - 0)
Individually supervised research projects.

CSE6306- ADVANCED TOPICS IN OPERATING SYSTEMS (3 - 0)
May be repeated for credit when topics change. Prerequisite: CSE 5306 or consent of instructor.

CSE6314- ADVANCED TOPICS IN THEORETICAL COMPUTER SCIENCE (3 - 0)
May be repeated for credit when topics change. Prerequisite: CSE 5314 or consent of instructor.

CSE6323- FORMAL METHODS IN SOFTWARE ENGINEERING (3 - 0)
Methods for modeling and reasoning that play a fundamental role in computer science. Topics include: advanced mathematical logic, formal proof methods, set theory, and formal specification languages and their applications to software engineering. Prerequisite: CSE 5324 or consent of instructor.

CSE6324- ADVANCED TOPICS IN SOFTWARE ENGINEERING (3 - 0)
May be repeated for credit when topics change. Prerequisite: CSE 5325 (or concurrent enrollment) and consent of instructor.

CSE6331- ADVANCED TOPICS IN DATABASE SYSTEMS (3 - 0)
May be repeated for credit when topics change. Prerequisite: CSE 5332 and consent of instructor.

CSE6332- TECHNIQUES FOR MULTIMEDIA DATABASES (3 - 0)
Overview of data types, formats and compression techniques for audio, video and image data; operating systems techniques for multimedia; video delivery techniques; indexing and retrieval techniques; content-based video modeling; multimedia data on the Web. Prerequisite: CSE 5331 or consent of instrucor.

CSE6344- ADVANCED TOPICS IN COMMUNICATION NETWORKS (3 - 0)
May be repeated for credit when topics change. Prerequisite: CSE 5346 or consent of instructor.

CSE6345- MOBILE COMPUTING SYSTEMS (3 - 0)
Mobility management, Mobile IP, hand-off, routing, multicasting, and reliable communication in wireless networks. Data management, push-pull based data acquisition, issues in wireless mobile systems, resource allocation, QoS issues and multimedia transmission over wireless, WAP and Bluetooth technologies, Third Generation systems. Prerequisite: CSE 5346.

CSE6347- WIRELESS MOBILE NETWORKING AND COMPUTING (3 - 0)
Wireless architectures and networking; multiple access protocols; channel assignment and resource allocation; mobility and location management mobile data access; wireless data networking and multimedia; call admission control and QoS provisioning; performance modeling; mobile IP and wireless Internet. Prerequisite: CSE 5347 or consent of instructor.

CSE6350- ADVANCED TOPICS IN COMPUTER ARCHITECTURE (3 - 0)
May be repeated for credit when topics change. Prerequisite: CSE 5350 and consent of instructor.

CSE6351- TOPICS IN PARALLEL AND DISTRIBUTED COMPUTING (3 - 0)
May be repeated for credit when topics change. Prerequisite: CSE 5350, 5351, or consent of instructor.

CSE6352- FAULT-TOLERANT COMPUTING (3 - 0)
Topics in reliable and fault-tolerant computing. May be repeated for credit when topics change. Prerequisite: CSE 5350 and consent of instructor.

CSE6362- ADVANCED TOPICS IN ARTIFICIAL INTELLIGENCE (3 - 0)
May be repeated for credit when the topic changes. Prerequisite: CSE 5361 and consent of instructor.

CSE6363- MACHINE LEARNING (3 - 0)
A detailed investigation of current machine learning methods, including statistical, connectionist, and symbolic learning. Presents theoretical results for comparing methods and determining what is learnable. Current issues in machine learning research will also be examined. Prerequisite: CSE 5361 and consent of instructor.

CSE6366- DIGITAL IMAGE PROCESSING (3 - 0)
Digitization and coding of images, characterization and representation of digital images in spatial and frequency domains, picture restoration and enhancement, filtering of two-dimensional signals, image reconstruction. Prerequisite: CSE 5366 or consent of instructor.

CSE6367- COMPUTER VISION (3 - 0)
Advanced techniques for interpretation, analysis, and classification of digital images. Topics include methods for segmentation, feature extraction, recognition, stereo vision, 3-D modeling, and analysis of time-varying imagery. Also taught as EE 6358. Prerequisite: CSE 6366 or EE 5356 or EE 5357, and consent of instructor.

CSE6392- SPECIAL TOPICS IN ADVANCED COMPUTER SCIENCE (3 - 0)
May be repeated for credit when the topics vary.

CSE6397- RESEARCH IN COMPUTER SCIENCE (3 - 0)
Individually supervised research projects.

CSE6399- DISSERTATION (3 - 0)
Preparation of dissertation in computer science or computer science and engineering. Graded F, R.

CSE6697- RESEARCH IN COMPUTER SCIENCE (6 - 0)
Individually supervised research projects.

CSE6699- DISSERTATION (6 - 0)
Preparation of dissertation in computer science or computer science and engineering. Graded F, R.

CSE6997- RESEARCH IN COMPUTER SCIENCE (9 - 0)
Individually supervised research projects.

CSE6999- DISSERTATION (9 - 0)
Preparation of dissertation in computer science or computer science and engineering. Graded P, F, R.

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