Information Technology (IT)

School of Information Technology and Engineering

Graduate courses listed under the Departments of Computer Science; Electrical and Computer Engineering; Civil, Environmental, and Infrastructure Engineering; Information and Software Engineering; Systems Engineering and Operations Research; and Applied and Engineering Statistics are appropriately considered as courses forming an inherent part of this program.

101 Introduction to Information Technology (3:3:0). Introduces students to the fundamental concepts in information technology that provide the technical underpinning for state-of-the-art applications. Aperspective on the range of information technology is presented. Historical development and social implications of efforts in information technology form an integral part of the course.

103 Introduction to Computing (3:1:2). Prerequisite: Knowledge of high school algebra. May not be taken for credit after receiving a grade of C or better in any CS course numbered 112 or higher. An introduction, using both lecture and laboratory practice, to the nature and uses of computers. Widely used applications including word processing, spreadsheets, databases, and presentation software are studied. Laboratory projects are required in these areas. Additional lectures address computer systems organization, computer communications and networking, legal and ethical considerations (including privacy, intellectual property, and appropriate uses of technology), the effective presentation of information, computer security, artificial intelligence and the future of computing and the Internet.

108 Programming Fundamentals (3:2:1). Prerequisite: IT 103. Introduction to programming fundamentals for non-technical majors. Software development process is presented. Students learn to write programs in a high level language. IT&E majors cannot receive credit for IT 108 after receiving a C or better in CS 112. IT 108 does not fulfill any IT&E major requirements. IT minor students may take both IT 108 and CS 112 for credit.

212 How Computers Work (3:3:0). Designed for students pursuing the IT minor. A look inside today's personal computers. Covers, in a nontechnical manner, what makes computers "tick" from transistor basics up to accessing the Internet. Describes all the essential components within a PC and how they interact. Also addresses the latest aspects of computer technology (e.g., DVD) and how they affect computer use and operation. Presentations of actual hardware (VLSI integrated circuits, modems, etc.) are included so that students can visually appreciate the complexity of the circuitry involved.

213 Multimedia and Computer Graphics (3:2:1). Prerequisites: IT 103, IT 108. Designed for students pursuing the IT minor. Introduces tools to configure graphical user interfaces (GUIs), multimedia authoring systems, graphical and
multimedia components, and data types and provide experience with a visual programming languageand other commercial tools. This course is offered every semester.

214 Database Fundamentals (3:3:0). Designed for students pursuing the IT minor. Study of relational database systems and their applications. The creation and manipulation of tables and formulation of queries. The use of forms and reports for end-users, with visual element enhancements. Data modeling and the formation of relations. Examination of recent trends in database management, including web applications.

221 Introduction to Information Security Technologies (3:3:0). Prerequisite: IT 108. Overview of information security technologies as applied to operating systems, database management systems, and computer networks. Symmetric and asymmetric cryptography, application of cryptography in internet security protocols. Access control models and mechanisms. Role-based access control. Intrusion detection and response. Secure electronic commerce.

222 Introduction to Information Security Policy and Management (3:3:0). Prerequisite: IT 108. Security policies, mandatory and discretionary access control, Chinese walls, separation of duties and least privilege, security objectives, architectures, models and mechanisms. Privacy policy and technologies. Social implications of biometric identification. Intellectual property protection in cyberspace.

250/STAT 250 Introductory Statistics I (3:3:0). Prerequisite: High school algebra. Elementary introduction to statistics. Topics include descriptive statistics, probability, estimation and hypothesis testing for means and proportions, correlation, and regression. Students use statistical software for assignments. f,s,sum

350 Introduction to Entrepreneurship (3:3:0). This course introduces the student to the concept of entrepreneurship and the skills, concepts, and information that entrepreneurs use. More specifically students will learn about entrepreneurs and that they are neither super human nor particularly gifted. The course also examines why and how entrepreneurs start companies and how this is different from the way large companies expand their operations. Finally, the course is designed to help the student build the skills for starting a company. To this end it provides an introduction through readings, lectures, and exercises to all of the concepts and methods needed to do so. After completing this course the student should have the skills needed to develop and write a good first draft of a business plan.

362/STAT 362 Introduction to Computer Statistical Packages (3:3:0). Prerequisite: IT 250/STAT 250 or equivalent. Use of computer packages in the statistical analysis of data. Topics include data entry, checking, and manipulation, as well as the use of computer statistical packages for regression and analysis of variance.

500 Quantitative Foundations for Information Systems Analysis (3:3:0). Prerequisite: MATH 108 or an equivalent one-semester undergraduate introductory calculus course covering both differential and integral calculus. Provides a common background in basic quantitative areas focused on decision making and information processing. Topics include a review of basic calculus, matrix algebra, problems in optimization, and the calculus of probabilities.y (IT)

557 Introduction to Network Science (3:3:0). Prerequisites: Bachelor's degree in math, science, or engineering; Math 114 and 351. This course is the first of a sequence of two intended to provide a broad treatment of the principles and technologies of modern telecommunications, combined with computing, that create computer networks. Emphasis is on providing sufficient breadth and depth to allow a technical professional to function as an effective entry-level network engineer. This course includes modules in telecommunications principles, telcommunications carrier systems, data communications, local area networks, and wide area network protocols.

657 Advanced Network Science (3:3:0). Prerequisite: IT 557 or permission of instructor. This course is the second of a sequence of two intended to provide a broad treatment of the principles and technologies of modern telecommunications, combined with computing, that create computer networks. Emphasis is on providing sufficient breadth and depth to allow a technical professional to function as an effective entry-level network engineer. This course includes modules in wireless telecommunications, network security, network management, and advanced network protocols.

746/CSI 776 Calculus of Random Signals (3:3:0). Prerequisite: STAT 652 or ECE 630 or 632. Introduction to modern theory of stochastic calculus such as stochastic integrals, martingales, counting processes, diffusion processes, and Ito-type processes in general. Presents applications of the methods to engineering and biology. Focus is on developing the necessary concepts rather than mathematical proofs. Suited for graduate students in information technology, electrical engineering, mathematics, operations research, and statistics. a,f

750/CS 750 Theory and Applications of Data Mining (3:3:0). Prerequisite: CS 681, 687, or 688, or permission of the instructor. Concepts and techniques in data mining and their multidisciplinary applications. Topics include databases, data cleaning and transformation, concept description, association and correlation rules, data classification and predictive modeling, performance analysis and scalability, data mining in advanced database systems including text, audio and images, and emerging themes and future challenges. Term project and topical review required.

776/CSI 778 Real Analysis and Statistics (3:3:0). Prerequisites: STAT 652 or ECE 620, 621, and 630. Advanced calculus and linear algebra needed for doctoral work in statistics and related fields. Topology, vector spaces, matrices, continuity, differentiation, sequences and series of real numbers and real-valued functions, Riemann and Riemann-Stieltjes integrals, and multidimensional calculus. Applications in probability and statistics including response surface methodology are presented. af

796, 797 Directed Reading and Research (1-3:0:0). Reading and research on a specific topic in information technology under the direction of a faculty member. May be repeated as needed.

803, 804 Doctoral Tutorial in Information Technology (3:3:0). Individualized intensive study of particular aspects of information technology. May be repeated as needed.

809 Scaling Technologies for E-Business (3:3:0). Prerequisites: at least one operating systems and one networking course, and admission to an IT&E doctoral program.
This course discusses, from a quantitative point of view, the characteristics of the most important technologies used to support the implementation of e-business sites. The discussion includes topics such as hardware and software architectures of e-business sites, authentication, and payment services, understanding customer behavior, workload characterization, scalability analysis, and performance prediction. A term paper and a project are required.

811 Principles of Machine Learning and Inference (3:3:0). Prerequisite: CS 580, 681, or permission of instructor. Presentation of unifying principles that underlie diverse methods, paradigms, and approaches to machine learning and inference. Reviews the most known learning and inference systems, discusses their strengths and limitations, and suggests the most appropriate areas of their application. Students get a hands-on experience by experimenting with the state-of-the-art learning and inference systems and work on projects tailored to their research interests.

812 Advanced Topics in Natural Language Processing (3:3:0). Prerequisite: CS 680. Advanced treatment of topics in syntax, semantics, and generation of linguistic output. Implementation and applications are also discussed.

814/CSI 801 Foundations of Computational Science (3:3:0). Prerequisite: CS 735 or equivalent. Investigation methods for scientific questions in the presence of teraops computation, gigabyte memory, and gigabit transmission. Mapping of mathematical models to parallel algorithm and architectures, associated data structures, languages, operating systems, networks, and global change demonstrate important scientific accomplishments enabled by computation. Working in teams with scientists and information technologists, students learn the mathematical models, abstract algorithms, and concrete algorithms for these cases, and conduct experiments and simulations with them.

815 Parallel Computation (3:3:0). Prerequisite: CS 635 or IT 816 or CSI 801. Topics illustrating some of the contemporary thinking on architectures, application, development environments, algorithms, operating system related issues, language requirements, and performance for parallel computation.

816 Parallel Architectures, Algorithms, and Applications (3:3:0). Prerequisites: CS 583 and computer architecture course. Familiarization for students in area of parallel architectures, algorithms, and parallel computers. Various algorithms and their applicability to certain architectures are discussed. Comparisons of these parallel algorithms with certain tools are studied, and applications to artificial intelligence, image processing, and database machines are explored.

817 Neural Networks (3:3:0). Prerequisite: CS 688 or permission of instructor. Study of adaptive and competitive principles using distributed and parallel computation. Topics include background from statistics, control, adaptive signal processing, and neurosciences. Basic models, such as those suggested by Grossberg, Hopfield, and Kohonen, are discussed in terms of their analytical characteristics and applications. Neural networks are assessed as universal approximators. Connections to the fuzzy approach are established through the Radial Basis Function approach.
Applications to perception, knowledge-based systems, and robotics are presented.

818 Topics in High Performance Computer Systems (Scalable E-business Site Technologies and Models) (3:3:0). Study of technologies and architectures used to support ebusiness sites: multi-tier software and hardware architectures, security protocols, and payment protocols. Performance metrics for e-business. Performance impacts of e-business technologies. Performance models of ecommerce technologies. Workload characterization for ebusiness.

819 Computational Models for Probabilistic Inference (3:3:0). Prerequisite: SYST 664 or 652. Graphical models for encoding conditional independence assumptions in a multivariate discrete probability distribution. Includes computational methods for updating probabilities when evidence is observed on some variables in the model. Algorithms for finding the most probable instantiation of the network. Applications in expert systems and decision analysis.

821 Software Engineering Seminar (3:3:0). Prerequisite: SWE 621. Study of the application of software engineering principles, design methods, and support tools through real-life problems extracted from faculty/industry projects. May be repeated with a change in topic.

822 Software Maintenance and Reuse (3:3:0). Prerequisites: CS/SWE 621 (or equivalent), data structures, principles of modern programming, discrete mathematics, or permission of instructor. Perfective maintenance, reuse of software components and patterns, evolving software systems, principles of object-oriented analysis and development. Issues regarding technologies supporting perfective software maintenance and reuse are presented.

823 Software for Critical Systems (3:3:0). Prerequisites: SWE 620 and STAT 554. Study of software for systems in which failure can be catastrophic. Techniques to construct and analyze software for critical applications and examination of inherent limitations of such techniques are presented, as well as interaction between techniques used during development and behavior of software during operation. Topics include tolerance of software faults, design redundancy, data redundancy, software safety, formal methods, statistical testing, design for analyzability, and design for testability.

824 Program Analysis for Software Testing (3:3:0). Prerequisite: CS 540 or CS/SWE 637, or permission of instructor. Different methods for analyzing software, primarily for the purpose of testing. Analysis techniques, specific algorithms, tools, and applications. Goals are to explore the current research issues, learn how to build software analysis tools, and understand how these techniques can be applied to software development activities. The primary focus is on applications for testing software, including automatic test data generation, object-oriented testing, and testing client-server applications. Analysis techniques for other software-related activities such as maintenance, reuse, object-oriented development, metrics, and optimization are also considered.

830/ECE 734 Detection and Estimation Theory (3:3:0). Prerequisites: ECE 528, or permission of instructor. Introduction to detection and estimation theory with communication applications. Topics include M-hypotheses, Bayes, minimax, Neyman-Pearson criterion, detection of signals in AWGN and ACGN, Bayes estimation, ML estimation of signal parameters in AWGN and ACGN, estimation of Gaussian waveforms in Gaussian noise, linear MSE estimation, and Kalman and Wiener filters.

832/ECE 735 Speech and Image Coding (3:3:0). Prerequisites: ECE 632 and 635. A study of waveform coding concepts and algorithms and their applications to the analysis and design of data compression systems. Specific schemes involving speech and image coding are discussed. Topics include statistical properties of speech and image signals, rate distortion theory, predictive and adaptive coding techniques, optimum quantization, and bit assignment algorithms.

833/ECE 739 Satellite Communication (3:3:0). Prerequisite: ECE631. Introduction to the theory and applications of modern satellite communications. Topics include satellite channel characterization, channel impairment and transmission degradation, link calculations, modulation, coding, multiple access, broadcasting, random access schemes, demand assignment, synchronization, satellite switching and onboard processing, integrated service digital satellite networks, and satellite transponder, ground stations, packet switching, and optical satellite communications.

834/ECE 742 Telecommunications Networks (3:3:0). Prerequisites: ECE 528 and 642, or permission of instructor. Open Systems Interconnection Reference Model, analysis and modeling of layered network architectures including transport and higher layers, performance evaluation of System Network Architecture, DEC Network Architecture, and other telecommunication architectures. Protocols and standards for local, metropolitan, and wide area networks are also discussed. Topics include high-speed packet switching, broadband multimedia protocols, and congestion control in broadband integrated networks.

835 Computational Vision (3:3:0). Prerequisites: CS 682 and 686, or permission of instructor. Study of recent advances in development of machine vision algorithms and knowledge-based vision systems. Topics include scalespace; Gabor and wavelet processing; distributed and hierarchical processing using neural networks; motion analysis; active, functional, and selective perception; object and target recognition; expert systems; data fusion; and machine learning. Emphasis is on system integration in terms of perception, control, action, and adaptation. Applications to robotics, intelligent highways, inspection, forensic, and data compression are presented.

836/ECE 836 Special Topics in Detection and Estimation Theory (3:3:0). Prerequisite: ECE 734. Advanced topics in detection, estimation, and signal processing in areas of current research interest. Topics may include spectral estimation, speech recognition, array processing, SAR, underwater acoustics, or higher order spectra.

837/ECE 754 Optimum Array Processing I (3:3:0). Prerequisite: ECE 734. Optimum antenna array processing for communications, radar, and sonar systems. Classical synthesis of linear and planar arrays. Characterization of space-time processes. Spatial AR and ARMA models. Optimum waveform estimation. MVDR and MMSE estimators. LCMV beamformers. Generalized sidelobe cancelers. Robust algorithms. Diagonal loading.

838/ECE 638 Signal Processing Algorithms and Architectures (3:3:0). Prerequisite: ECE 635 or permission of instructor. Study of recent advances in the development of fast-signal processing algorithms and parallel architectures. Topics include fast transforms, multirate processing of digital signals, adaptive filtering, high-resolution spectral analysis, parallel computational arrays, and mapping of signal processing algorithms into array processors.logy (IT)

840/CS 685/ECE 750/SYST 672 Intelligent Systems for Robots (3:3:0). Prerequisite: SYST 611, ECE 650, CS 580, SYST 555, or equivalent. Review recent developments in the area of intelligent autonomous systems. The applications of artificial intelligence, control theory, operations research, decision sciences, computer vision, and machine learning to robotics are studied as well as correspondences between various fields. Topics include analysis and design of methods, algorithms and architecture for planning, navigation, sensory data understanding, visual inspection, spatial reasoning, motion control, learning, self-organization, and adaption to the environment.

841/ECE 722 Kalman Filtering with Applications (3:3:0). Prerequisite: ECE 521 and 528 or equivalent, or permission of instructor. Detailed treatment of Kalman Filtering Theory and its applications, including some aspects of stochastic control theory. Topics include state-space models with random inputs, optimum state estimation, filtering, prediction and smoothing of random signals with noisy measurements, all within the framework of Kalman filtering. Additional topics are nonlinear filtering problems, computational methods, and various applications such as Global Positioning System, tracking, system control, and others. Stochastic control problems include linear-quadratic-Gaussian problem and minimum-variance control.

842 Models of Probabilistic Reasoning (3:3:0). Prerequisite: STAT 544 and OR 681. Survey of alternative views about how incomplete, inconclusive, and possibly unreliable evidence might be evaluated and combined. Among the views discussed are the Bayesian, Baconian, Shafer-Dempster, and Fuzzy systems for probabilistic reasoning.

843/ECE 720 Multivariable and Robust Control (3:3:0). Prerequisite: ECE 620 or permission of instructor. Eigenstructure assignment for multivariable systems, the Smith-McMillan form, internal stability, modeling system uncertainty, performance specifications and principal gains, parametrization of controllers, loop shaping and loop transfer recovery, and the Hmethodology.

844/ECE 749 Pattern Recognition (3:3:0). Prerequisite: ECE 549 and 620; CS 580 and 688; or equivalents. Study of the fundamentals of statistical pattern recognition, functional and density approximation, and adaptive systems. Topics include the Bayesian approach, non-parametric statistics and neural networks, adaptive fuzzy systems and control, Bayesian nets and Hidden Markov Models (HMM), andevolutionary computation and genetic algorithms. Applications to clustering and recognition, time-series prediction and model-based identification, forensics, and knowledge-mining are presented.

845/ECE 780 High-Frequency Electronics (3:3:0). Prerequisite: ECE 520. Study of devices and circuits used in high-speed communication systems. Topics include microwave bipolar transistors, GaAs MOSFETs, and high-speed integrated circuits; and the design of linear and power amplifiers using S-parameter techniques and computer simulation.

846/ECE 721 Nonlinear Systems (3:3:0). Prerequisite: ECE 521. Nonlinear dynamical systems. Motivating examples. Analysis techniques include basic fixed point theory, implicit function theorem, dependence of trajectories on
initial data and parameters. Course also covers computational simulation techniques, stability theory, including Lyapunov's direct method, nonlinear control systems: input-output stability, and absolute stability, strong positive real transfer functions. Feedback linearization of nonlinear systems, nonlinear canonical forms; nonlinear decoupling; sliding control; and applications to adaptive control, neural networks, and robotics are also included.

847/ECE 847 Topics in Photonics (3:3:0). Prerequisite: ECE 565 or permission of instructor. In-depth discussion of specific topics in photonics. Topics include optical storage (disks, holographic, 3D), digital optical computing, integrated optics, photonic switching networks, and optoelectronic devices. May be repeated when covering different topics.

848/ECE 743 Digital Video Communications (3:3:0). Prerequisites: ECE 535 and 642. Coding, transport, and modeling of digital video signals; digital coding of waveforms with emphasis on compression techniques for video signals, transform coding including DCT and rate distortion theory for images, subband/wavelet coding of images, treatment of video signals for different television formats, colorimetry and motion estimation/compensation, general characterization of video traffic, modeling of variable bit rate video codecs, transport protocols for video and multimedia, network-delay compensation for video over ATM, VBR video flow control, and discussion of applications ranging from HDTV/TV over ATM, digital HDTV for terrestrial broadcast, to videoconferencing/desktop multimedia over LAN/WAN.

850 Seminar: Topics in Systems Integration Engineering (3:3:0). Prerequisite: SYST 720 or equivalent. Analysis of the Systems Integration life cycle and the tools, techniques, and methods that contribute to the design, development, application, and evaluation of approaches to systems integration. Reviews the current technological advances that support systems integration methods, including functional and nonfunctional SI requirements, risk assessment and risk management, internal protest avoidance mechanisms, and protest management. May be repeated when the topic is different.

851 Seminar: Topics in Software Requirements (3:3:0). Prerequisite: SWE 620 or 624, or CS 624. Emphasis on the latest research ideas in the requirements engineering domain. Discusses the current state-of-the-art and state-of-the-practice in requirements engineering. Focuses on the most critical problems and discusses how their resolutions might further the requirements research knowledge base and enhance the quality and productivity of real software and system developments in industry. May be repeated when the topic is different.

852 Graphical Real-Time Simulation (3:3:0). Prerequisite: CS 652 or IT 875. Current research in advanced computer graphics and its applications in realistic real-time simulations. Topics include physically based modeling, real-time simulation, distributed interactive simulation (DIS), network virtual environments (NVE), and virtual reality(VR).

858 Logic Models in Artificial Intelligence (3:3:0). Prerequisite: CS 580. Examination of the relevance of logic theory to artificial intelligence. Familiarizes students with a variety of formal logics that are used in artificial intelligence, as well as ongoing research in new logics. Topics include first-order predicate calculus, resolution and nonresolution theorem proving, nonmonotonic logic, assumption-based reasoning, the relationship between symbolic and quantitative theories of uncertainty, temporal logics, and their application to planning and metareasoning.

860 Software Analysis and Design of Real-Time Systems (3:3:0). Prerequisite: SWE 623. Background for students who want to conduct research in the software engineering of real-time systems. Students gain an understanding of key real-time software system analysis, design concepts and methods, and how they are used in the development of large-scale, real-time software systems. Students also gain an understanding of the potential impact of emerging technologies in this field. Term project in the design and analysis of a complex real-time software system is undertaken.

861 Distributed Database Management Systems (3:3:0). Prerequisite: INFS 614 or equivalent. Topics in distributed database management including transaction management, concurrency control, deadlocks, replicated database management, query processing reliability, and surveys of commercial systems and research prototypes.

862 Formal Models for Computer Security (3:3:0). Prerequisite: INFS 762. Study of formal mathematical models for computer security. Mathematical properties of these models are identified and analyzed. Models are compared with respect to formal and pragmatic criteria and include lattice-based models, noninterference models, models based on propagation of access rights, multilevel data models, integrity models, and miscellaneous models such as the n-tree model for group authorization.

863 Empirical Methods in Information Technology (3:3:0). Prerequisite: STAT 654. Examination of alternative paradigms of scientific research and their applicability to research in information technology. Topics include fundamental elements of scientific investigation, basic principles of experimental design and statistical induction, philosophy of science and its relation to the information technology sciences, and case studies of information technology research.

864 Scientific Databases (3:3:0). Prerequisite: INFS 614. Study of database support for scientific data management. Requirements and properties of scientific databases; data models for statistical and scientific databases; semantic and object-oriented modeling of application domains; statistical database query languages and query optimization; advanced logic query languages; and case studies such as the human genome project and the earth orbiting satellite are covered.

865 Networks and Distributed Systems Security (3:3:0). Prerequisite: INFS 612 or equivalent. A detailed study of network and distributed systems security. Review of basic cryptography and threats and vulnerabilities in distributed systems. Security services and confidentiality, authentication, integrity, access control, nonrepudiation, and their integration in network protocols are covered. Topics also include key management, cryptographic protocols and their analysis; access control, delegation, and revocation in distributed systems; and security architectures, multilevel systems, and security management and monitoring.

867 Intelligent Databases (3:3:0). Prerequisite: INFS 760 or permission of instructor. Study of models and techniques that empower database systems with intelligent and cooperative behavior, with emphasis on subjects such as knowledge-rich databases, logic databases, epistemological queries, intentional answering, and knowledge discovery. Topics include user interfaces cooperative query interfaces, interactive query constructors, graphical interfaces, and browsers; uncertainty representing, manipulating, and retrieving uncertain, imprecise, or incomplete information; and formulating and interpreting vague or incomplete queries.

870 Organizational Informatics (3:0:0). Prerequisite: doctoral status or permission of instructor. An examination of the effects of informatics on national and international policy; setting of international policy on informatics; ethical and social change in governments and organization; shaping of national policy in informatics; industry growth; and research methods from various scientific discipline.

874 Analysis of Complex Surveys (3:3:0). Prerequisites: STAT 656, 665, and 674 or permission of instructor. Presentation of current theory and methods of statistical analysis of data from complex surveys of finite populations. Includes contingency table analysis and regression analysis; modeling structured populations by multilevel models; and loglinear, logistic, and regression models for stratified and multistage cluster samples. Case studies are used to illustrate the methodology. ir

875/CSI 803 Scientific and Statistical Visualization (3:3:0). Prerequisite: STAT 554 or CS 651. Presentation of visualization methods used to provide new insights and intuition concerning measurements of natural phenomena and scientific and mathematical models. Case study examples from a variety of disciplines to illustrate what can be done are presented. Topics include human perception and cognition, an introduction to the graphics laboratory, elements of graphing data, representation of space-time and vector variables, representation of 3D and higher dimensional data, dynamic graphical methods, and virtual reality. Students are required to work on a visualization project. Emphasizes software tools on the Silicon Graphics workstation, but other workstations and software may be used for the project.

876/CSI 876 Measure and Linear Spaces (3:3:0). Prerequisite: IT 776/CSI 778. Measure theory and integration, convergence theorems, and the theory of linear spaces and functional analysis, including normed linear spaces, inner product spaces, Banach and Hilbert spaces, Sobelev spaces, and reproducing kernels. Topics include wavelets, applications to stochastic processes, and nonparametric functional inference. as

877/CSI 877 Geometric Methods in Statistics (3:3:0). Prerequisite: STAT 751 or permission of instructor. Develops the foundations of geometric methods for statistics. Topics include n-dimension Euclidian geometry, projective geometry, differential geometry including curves, surfaces, and n-dimensional differentiable manifolds, and computational geometry including computation of convex hulls, and tessellations of 2-, 3-, and n-dimensional spaces. Examples include applications to statistics and scientific visualization. af

879 Topics in Stochastic System Simulation (3:3:0). Prerequisite: OR 635 or permission of instructor. Special topics and recent developments in the Monte Carlo simulation methodology for discrete-event stochastic systems. Contents vary and possible topics include statistical analysis of simulation output data, random number and random ariate generation, variance reduction techniques, sensitivity analysis and optimization of simulation models, distributed and parallel simulation, object-oriented simulation, and specialized applications. May be repeated for credit when topics are distinctly different.

880 Queuing Modeling of Computer-Communication Networks (3:3:0). Prerequisite: OR 645, 647; ECE 542; or equivalents. Study of analytical modeling of computer and communication networks and performance evaluations. Topics include Markovian systems, open networks, closed networks, approximations, decomposition, simulation, sensitivity analysis, and optimal operation of systems. Local area networks, manufacturing systems, and other applications are presented.

882 Advanced Topics in Combinatorial Optimizations (3:3:0). Prerequisites: OR 641 and 642. Study of problems using the most recent developments. Topics include cutting plane procedures based on polyhedral combinatorics, column-generation procedures for large complex problems, heuristic approaches (genetic algorithms, simulated annealing, tabu search), the study of special structures, reformulation techniques and bounding approaches. Topics stress the most recent developments in the field. May be repeated for credit when topics are distinctly different.

884 Advanced Topics in Nonlinear Programming (3:3:0). Prerequisite: OR 644. Study of theory and algorithms for solving nonlinear optimization problems. Contents vary, and possible topics include large-scale and parallel-unconstrained optimization, theoretical issues in constrained optimization, duality theory, Lagrangian and sequential quadratic programming methods. May be repeated for credit when topics are distinctly different.

885/ECE 752 Spectral Estimation (3:3:0). Prerequisite: ECE 528 or STAT 652 or permission of instructor. In-depth study of spectral analysis and its application to statistical signal processing. Topics include classical Fourier analysis of deterministic signals and Wiener theory of spectral analysis for random processes; spectral estimation using the Periodogram and the window approaches; maximum entropy spectral estimation and its relation to autoregression modeling; signal subspace approaches for frequency estimation; and the wavelet transform and its relation to the short-time Fourier transform.

886/ECE 751 Information Theory (3:3:0). Prerequisite: ECE 630 or STAT 644 or equivalent or permission of instructor. Indepth study of information theory and its application to communication theory. Topics include measures of information such as entropy, mutual information, and relative entropy; the asymptotic equipartition property; noiseless source coding; Universal Source Coding and the Lemple-Ziv algorithm; channel capacity and the channel coding theorem; the Gaussian channel; and rate distortion theory and quantization.

888/ECE 753 Distributed Estimation and Multisensor Tracking and Fusion (3:3:0). Prerequisite: ECE 734 or SYST 611. Centralized and distributed estimation theory,
hierarchical estimation, tracking and data association, multisensor multitarget tracking and fusion, distributed tracking in distributed sensor networks, track-to-track association and fusion, and Bayesian networks for fusion.

890 Special Topics in Urban Transportation (3:3:0). Prerequisite: CEIE 660, 560 or equivalent; or permission of instructor. Special topics and recent developments in Urban Transportation. Possible subjects include traffic safety analysis, simulation in transportation, intelligent transportation systems, and advanced public transportation systems. Congestion management, travel demand management, geographic information systems in transportation, innovative refinancing and public -private partnerships in transportation, information technology in transportation. May be repeated for credit when topics are distinctly different.

891 Special Topics in Applications of Information Technology to Urban Systems Engineering (3:3:0). Prerequisites: CEIE 670 or permission of the instructor. Special topics and recent developments in the area of Information Technology as applied to civil engineering. Possible topics include inventive engineering, design engineering, network computing, building and using intelligent agents in engineering, proactive design, etc. May be repeated for credit when topics are distinctly different.

892 Special Topics in Environmental and Water Resource Systems Engineering (3:3:0). Prerequisite: CEIE 601. Special topics and recent developments in environmental and water resources systems engineering analysis and design. Possible topics include studies in waste minimization; pollution prevention; hazardous waste management; wastewater management; air pollution control; solid waste management; environmental decision making; sustainability; water resource and environmental economics; wetlands management, design and construction; groundwater contamination modeling; stochastic hydrology; river basin planning and management and water quality modeling. May be repeated for credit when topics are distinctly different.

894 Design and Inventive Engineering (3:3:0). Prerequisite: SYST 573, CEIE 670, or OR 681 or permission of instructor. Topics include evolution of engineering, design engineering, inventive engineering, general design methodology, conceptual versus detailed design, axiomatic design theory, inferential design theory, engineering method in design, design paradigms, case-based design, proactive design, design evaluation, virtual design studio, Internet and browsers in design, creative problem solving, problem solving methods, and computer tools to support design creativity.

910 Advanced Topics in Artificial Intelligence (3:3:0). Prerequisite: Graduate course in artificial intelligence. Special topics in artificial intelligence not occurring in the regular computer science sequence. Requires substantial student participation. Subject matter may include continuation of existing 600- or 700-level courses in artificial intelligence and/or other topics. May be repeated for credit when subject matter differs.

915 Advanced Topics in Parallel Computation (3:3:0). Prerequisite: IT 815. Discussion of current research topics in parallel computation. Topics vary according to student and faculty interest. Possible topics include formal models of concurrency, specification and design of parallel programming languages, logic programming in a parallel environment, and parallel distributed processing (neural networks).

922 Concurrent Object-Oriented Systems (3:3:0). Prerequisite: IT 822. Comparative study of existing concurrent object-oriented approaches to problem analysis and software construction. Introduces current research issues in concurrent object-oriented systems, concurrency models, and concurrent object-oriented programming languages and development tools.

932/ECE 737 Spread Spectrum Communications (3:3:0).Prerequisite: ECE 731. Fundamentals of spread spectrum communications. Major topics include pseudonoise spreadspectrum systems, acquisition, synchronization, timehopping, frequency hopping, and multiple access communication.

937/ECE 755 Optimum Array Processing II (3:3:0). Prerequisite: IT 837. Adaptive beamformers. SMI and
RLS estimators. Spatial smoothing and FB averaging. QRdecomposition. LMS algorithm. Optimum detection. Optimum parameter estimation. UML and CML estimation. Cramer-Rao bounds. IQML. Weighted subspace fitting. Subspace algorithms: MUSIC, ESPRIT. Root-versions. Beamspace algorithms. Sensitivity, robustness, and calibration.

940 Advanced Topics in Control and Robotics (3:3:0). Prerequisites: ECE 620, 621, 624, and 650. Advanced and newly developed topics in control and robotics. Content varies depending on current faculty interests and student demand. Topics such as knowledge-based control, intelligent control, hierarchical and distributed control, robust control, and reasoning under uncertainty are included.

941 System Identification and Adaptive Control (3:3:0). Prerequisite: ECE 621 or permission of instructor. Advanced treatment of identification and adaptive control. Topics include identification algorithms, their convergence and accuracy, and computational aspects; model reference and self-tuning adaptive control, transients, stability and robustness; and intelligent schemes to improve robustness. Students are also required to study the literature and to complete a computer project.

944 The Process of Discovery and Its Enhancement in Engineering Applications (3:3:0). Prerequisite: IT 842 or permission of instructor. Study of ingredients of imaginative reasoning as it concerns the efficient discovery of new ideas and valid evidential test of them. Topics include different interpretations of Peirce's theory of abductive reasoning, other forms of reasoning, Hintikka's analysis of the process of inquiry, and current attempts to design systems that provide assistance in discovery-related or investigative activities.

945/ECE 945 Advanced Topics in Microelectronics (3:3:0). Prerequisite: IT 845. Current topics of advanced research in microelectronics. Topics include very high speed integrated circuits, monolithic microwave integrated circuits, optoelectronic integrated circuits, novel device structures, and advances in semiconductor device technology. May be repeated with a change in topic.

950 Design and Management Aspects of Information Systems (3:3:0). Prerequisite: INFS 790 or equivalent. Impact of organizations and management of information systems (IS) and vice versa. Topics include problems of introducing IS; the effect on organizational economic and political framework; participative design and new techniques
for specification, analysis, design, and implementation of IS; rapid prototyping and expert systems; possible conflicts; methods in life-cycle management; and economic analysis.

958 Basic and Applied Decision Support Systems Technology (3:3:0). Prerequisite: SYST 642. Analysis of tools, techniques, and methods that contribute to the design, development, application, and evaluation of interactive computer-based decision support systems. State of the art and state of the expectation of basic and applied decision support systems technologies like requirements definition, software engineering, analytical methods assessment, and structured evaluation are analyzed.

972/CSI 972 Mathematical Statistics I (3:3:0). Prerequisite: STAT 652 or equivalent. Focus on the theory of estimation. Principles of estimation are explored, including the method of moments, least squares, maximum likelihood, and maximum entropy methods. Methods of minimum variance unbiased estimation are covered in detail. Topics include sufficiency and completeness of statistics, Fisher information, Cramer-Rao bounds, Bhattacharyya bounds, asymptotic consistency and distributions, statistical decision theory, minimax and Bayesian decision rules, and applications to engineering and scientific problems.af

973/CSI 973 Mathematical Statistics II (3:3:0). Prerequisite: IT 972. Continuation of IT 972. Concentration on the theory of hypothesis testing. Topics include characterizing the decision process, simple versus simple hypothesis tests, Neyman-Pearson Lemma, uniformly most powerful tests, unbiasedness of tests, invariance of tests, randomized tests, and sequential tests. Applications of the testing principles are made to situations in the normal distribution family and to other families of distributions. as

976/CSI 976 Statistical Inference for Stochastic Processes (3:3:0). Prerequisite: IT 746/CSI 776. Modern theory of parameter estimation and hypothesis testing for stochastic processes, counting processes with random intensities, and solutions to stochastic differential equations driven by martingales. Applications to engineering, biology, and economics are considered. as

978/CSI 978 Statistical Analysis of Signals (3:3:0). Prerequisites: STAT 544 and 658 or equivalent. Advanced course in the analysis of discrete- and continuous-time signals using methods of stochastic differential equations and time series. Familiarity with the methods of harmonic analysis and times series modeling is presumed. Topics include state-space modeling and eigen-value processing, nonlinear modeling of signals, non-Gaussian stochastic process structure, detection and estimation of vector-valued signals, robust signal detection, with applications to array processing and target tracking.

979/CSI 979 Topics in Statistical Aspects of Information Technology (3:3:0). Prerequisite: STAT 652 or equivalent. Study of statistical science and the body of methods and techniques that convert raw data into information. Contents vary. Such topics as high-interaction statistical graphics, stochastic methods for parallel computing, cryptography and covert communications, order-restricted inference, treatments of imprecision, and the foundations of inference are covered. May be repeated when topics are distinctly different.

980 Advanced Topics in Applied Probability (3:3:0). Prerequisite: OR 645, 647, or permission of instructor depending on the topic(s) for the semester. Special topics and recent developments in the field of applied probability. Contents vary and possible topics include computational probability, stochastic point processes, advanced queuing theory, traffic and transportation models, percolation, processes of random aggregation and coagulation, and Markov decision processes. May be repeated for credit when topics are distinctly different.

981 Advanced Topics in Optimization (3:3:0). Prerequisite: IT 741, 750, 881, 882, or 884. Special topics and recent developments in optimization theory and computation. Contents vary and may include topics in linear, nonlinear, combinatorial, network, global, or stochastic optimization. Prepares students to perform research in optimization, and requires active student participation. May be repeated for credit when topics are distinctly different.

983 Advanced Topics in Network Optimization (3:3:0). Prerequisite: OR 643. Recent developments in solving optimization problems on networks. Prepares doctoral students to perform advanced research on network-related problems. Topics include linear, discrete, nonlinear, and stochastic problems. Several aspects of these problems are also studied, including computational complexity, exact algorithms, heuristics, solvable special cases, and computer implementation issues.

990 Dissertation Topic Presentation (1:0:0). Prerequisite: Completion of all course requirements for Ph.D. in IT or permission of instructor. Opportunity for Ph.D. students to present their research proposal for critique to interested faculty and students. Covers the presentation of the research topic for the Ph.D. in Information Technology, and is required of all Ph.D. students. The student will complete a dissertation research proposal. May be repeated with a change in topic, although degree credit is given once.

991 Engineer Project Presentation (1:0:0). Prerequisite: Completion of all course requirements for the engineer degree in information technology, or permission of instructor. Opportunity for engineer degree students to present their project proposal for critique to interested faculty and students. Covers the presentation of the project topic for the engineer degree in information technology, and is required of all engineer degree students. The student will complete a project proposal. May be repeated with a change in topic, although degree credit is only given once.

996 Engineer Project Proposal (1-6:0:0). Work on a project proposal that forms the basis for the dissertation for the engineer degree. May be repeated. No more than 12 credit hours of IT 996 and 997 may be applied to engineer degree requirements.

997 Engineer Project Dissertation (1-6:0:0). Prerequisite: Admission to candidacy. Formal record of commitment to engineer project dissertation under the direction of an advisory committee in information technology. May be repeated as needed.

998 Doctoral Dissertation Proposal (1-12:0:0). Work on a research proposal that forms the basis for a doctoral dissertation. May be repeated. No more than 24 credit hours of IT 998 and 999 may be applied to doctoral degree requirements.

999 Doctoral Dissertation (1-12). Prerequisite: Admission to candidacy. Formal record of commitment to doctoral dissertation research under the direction of a faculty member in information technology. May be repeated as needed.