Systems Engineering (SYST)

Systems Engineering

101 Understanding Systems Engineering (3:3:0). This course introduces students to the profession of systems engineering and the curriculum for a B.S. in Systems Engineering at George Mason University. The students will be introduced to large and small systems and asked to under stand these systems through the provision of some hands-on experiences. Key concepts will include the understanding of the requirements for a system and the translation of system-level requirements to component-level requirements. Several different kinds of example systems will be presented and discussed, specifically, what objectives of the system are, the system's major components, how the system works, and what some of the major design issues are. Each student will give a similar presentation on a system of the student's choice. Students working in groups will design, develop and test a system, and give an oral presentation to the class on the system they developed. The students will be responsible for writing several short papers on the curriculum and the presentations that they have heard. s

201 Discrete Dynamic Systems Modeling (3:3:0). Prerequisite: MATH 114. An introduction to the modeling of dynamic systems with examples from many fields in engineering, science, and social sciences: mechanical, computer, biological, economic, urban, and social systems. Linear and nonlinear systems and linearization of such systems. A discrete time system formulation is used to study the properties and behavior of such systems. f

202 Continuous Dynamic Systems Modeling (3:3:0). Prerequisite: SYST 201; corequisites: MATH 203, 214, and PHYS 260. A continuation of SYST 201. Systems with many variables. Vector-matrix representation and state variables. Continuous time systems. Block diagrams and signal flow graphs. Systems behavior. Discretization and computational methods. s

203 Systems Modeling Laboratory (1:0:3). Corequisite: SYST 202. Introduction to computer modeling using an engineering modeling environment such as MATLAB. Solution to systems of linear equations, numerical integration and differentiation, interpolation and curve fitting, solution of ordinary differential equations. Simulation and numerical solution of continuous dynamic systems. Discretization of continuous time systems. Use of built-in functions and construction of macros. Graphical presentation of results. s

301 Systems Design (3:3:0). Prerequisite: Junior standing; corequisite: SYST 201. Systems engineering design and integration process, the development of functional, physical, and operational architectures. Emphasis is on requirements engineering, functional modeling for design, and formulation and analysis of physical design alternatives. Methods and software tools for systems engineering design are introduced. f

302 Systems Methods (3:3:0). Corequisites: CS 112, STAT 344, and SYST 202. Prerequisites: MATH 114. Analysis methods of system engineering design and management. Decision analysis, economic models and evaluation, optimization in design and operations, probability and statistical methods, queuing theory and analysis, management control techniques, reliability and maintainability analysis, and economic and life-cycle cost analysis. Laboratory exercise with different software programs is included. s

335/OR 335 Discrete Systems Modeling and Simulation (3:3:0). Corequisites: CS 112, STAT 344, SYST 202. An introduction to the basic concepts of modeling complex discrete systems by computer simulation. Topics include Monte-Carlo methods, discrete-event modeling, a specialized simulation language, and the statistics of input and output analysis. s

371 Systems Engineering Management (3:3:0). Prerequisite: SYST 301; corequisite: SYST 302. Study of the basics of systems engineering management. This includes engineering economics, planning, organizing, staffing, monitoring, and controlling the process of designing, developing, and producing a system that will meet a stated need in an effective and efficient manner. Management tools, processes, and procedures will be discussed, including various engineering documentation templates, managerial processes, and dealing with personnel issues. f

410 Modern Telecommunications (3:3:0). Prerequisites: A grade of B or better in CS 211, STAT 344, and SYST 202. A comprehensive overview of telecommunications, including current status and future directions. Topics include a review of the evolution of telecommunications; voice and data services; basics of signals and noise; digital transmission; network architecture and protocols; local area, metropolitan, and wide area networks and narrow band ISDN; asynchronous transfer mode and broadband ISDN; satellite systems, optical communications, cellular radio, personal communication systems, and multimedia services. Examples of real life networks are provided to illustrate the basic concepts and gain further insight.

417 Optimization Methods in Systems Engineering (3:3:0). Prerequisite: SYST 202. An introduction to optimization for systems engineers and others wishing to gain, through a single course, a foundation in linear programming, nonlinear programming, integer programming, dynamic programming, discrete time optimal control, continuous time optimal control, and artificial intelligence techniques for solving optimization problems. Examples drawn primarily from systems engineering, including telecommunications, water resources, transportation, capital budgeting and project management. Emphasis on the geometric motivation and interpretation of key theoretical results and on efficient numerical algorithms. f

419 Engineering of Large-Scale Systems (3:3:0). Prerequisite: SYST 417 or OR 441, and MATH 213 or permission of instructor. Formulation and solution of large-scale static and dynamic models of complex systems. Techniques of relaxation and decomposition. Exploitation of special structure. Parallelism. Test and evaluation. Applications to manufacturing, transportation, water resources, and defense.s

420 Network Analysis (3:3:0). Prerequisite: SYST 417 or OR 441 and MATH 213. Network nomenclature. Elementary graph theory. Linear and nonlinear network models: multicommodity flow, mathematical games and equilibria on networks, network design and control; dynamic network models; applications to transportation, telecommunications, data communications, and water resource systems. f

421/ECE 421 Classical Systems and Control Theory (3:3:0). Prerequisite: A grade of C or better in ECE 220. Introduction to the analysis and synthesis of feedback systems. Functional description of linear and nonlinear systems. Block diagrams and signal flow graphs. State-space representation of dynamical systems. Frequency response methods: Root Locus, Nyquist, and other stability criteria. Application to mechanical and electromechanical control systems. f,s,sum

422 Data Communication and Networks (3:3:0). Prerequisite: A grade of B or better in SYST 410. Introduction to the concepts and design issues in data communication systems. Emphasis on impact of communications technology on information systems. s

430 Integration of Hardware and Software (3:3:0). Prerequisite: A grade of B or better in SYST 422. Introduction to hardware and software components of computer systems. Study of hardware and software interchangeability. Understanding and analysis of factors that impact the effectiveness and efficiency of hardware and software integration. Topics include engineering fundamentals for computer design, hardware and software components, tradeoff between hardware and software, analysis of data representations and addressing, impact of the operation design and flow control design on the performance of computer systems, global control, operating system, memory management, input/output characteristics, bus systems, and efficiency analysis. Macro-engineering of computer systems. Study of practical examples in the area of hardware and software design and development in the information technology industry. s

442 Decision Support Systems Design (3:3:0). Prerequisite: SYST 301. Studies the design of computerized systems to support individual or organizational decisions. The course teaches a systems engineering approach to decision support system (DSS) development. A DSS is the end product of a development process, and it is this process that is key to successfully integrating a DSS into an organization. Evaluation of DSS. The course emphasizes that a DSS is the end-product of the design process, and it is this process that is key to successful integration of a DSS into an organization. A systems engineering approach to DSS design is taken, in which the implications of the research on human information processing for development of a DSS is considered. f

451 Knowledge-Based Systems Design and Engineering (3:3:0). Prerequisites: CS 211 and 60 credits. Introduction to the design of expert systems. Fundamentals of expert systems development, including knowledge acquisition and representation, inference, system components, and system design. Introduction to knowledge engineering tools and programming of case study examples using an expert system shell. f

460 Introduction to Air Traffic Control (3:3:0). Prerequisites: STAT 344 and SYST 335. This course is intended as an introduction to Air Traffic Control (ATC) for those who plan professions in the air transportation industry. It is a necessary introduction for students who will later specialize and take more in-depth courses. The course will survey the entire field, presenting the history of ATC and how it came to be as it is, the technology on which the system is based, the procedures used by controllers to meet the safety and efficiency goals of the system, the organizational structure of the FAA, challenges facing the system and means under investigation to meet these challenges. Some field work will be required to acquire and analyze airport operational data. A brief introduction to airport design will be discussed.

465/ECON 496/Math 493 Pricing in Optimization and Game Theory (3:3:0). Prerequisites: Math 203 or 216, and OR 441, or permission of instructor. Allocation of limited resources among competing activities to maximize the outcome or minimization of expenses required to produce a given assortment of goods and services are two typical problems faced by any economic institution. Mathematical modeling of such problems and finding efficient mathematical tools for solving them are two main goals of modern optimization theory. Pricing limited resources, goods, and services is the key instrument for theoretical analysis of complex economical systems. Pricing theory can also give rise to numerical methods for finding optimal solutions and economic equilibrium. Fundamental tools in pricing theory are the classical Lagrangian and Lagrange multipliers for constrained optimization. In this course we will cover the basic ideas and methods of linear programming and matrix games. Particular emphasis will be given to pricing for both theoretical analysis and numerical methods.

470 Human Factors Engineering (3:3:0). Prerequisites: SYST 301, STAT 344. Human information processing, inferential analysis, biases and heuristics in human information processing, support systems to aid in human information processing, human-system interaction, and software systems engineering considerations. f

472 Introduction to Systems Integration (3:3:0). Prerequisite: SYST 301. Examination and application of systems integration methodology and methods as a part of systems engineering and as a companion to systems architecting: system integration engineering. Approaches to systems assessment, as a basis for effective systems integration, are considered and applied. The format for the conduct of the course includes a balance of seminars and lectures with competitive small-team system integration tasks that include regular peer reviews and collaboration.

473 Decision and Risk Analysis (3:3:0). Prerequisite: STAT 344. Study analytic techniques for rational decision making that address uncertainty, conflicting objectives, and risk attitudes. The course covers modeling uncertainty; rational decision-making principles; representing decision problems with value trees, decision trees, and influence diagrams; solving value hierarchies, decision trees and influence diagrams; defining and calculating the value of information; incorporating risk attitudes into the analysis; and conducting sensitivity analyses.

480/ECON 440 Economic Systems Design I: Principles and Experiments (3:3:0). Corequisite: SYST 465. Prerequisite: OR 441. This course introduces students to the design principles used in developing systems used to allocate resources. Students will be required to participate in experiment demonstrations of different allocation mechanisms. In addition, students will be exposed to experimental methods in economics and market design.

481/ECON 441 Economic Systems Design II: Case Studies and Analysis (3:3:0). Students design specific allocation mechanisms for specific problems. Students will be required to design and develop a mechanism to a specific allocation problem. Students must develop both an analytical model and a working engineering model of their mechanism.

489 Senior Seminar (3:3:0). Corequisite: SYST 490. This course is designed to introduce the students to several important topics in systems engineering, provide additional experience to the students in writing and giving presentations, and obtain feedback on the curriculum for the B.S. in Systems Engineering. Several lectures will be devoted to ethics in systems engineering. Writing and making presentations for systems engineering will also be covered early in the semester. Students will attend technical lectures and write a paper on material covered in the lectures. Students will also be required to write a long paper on new technology. The instructor and guest lecturers will present material that is not part of the required course load to expand the horizons of the students. Examples of "hot" topics are "knowledge-based" design, enterprise-wide reengineering, electronic commerce, and optimization by "natural analogy" (simulated annealing, neural networks, genetic algorithms). In addition, students will work in teams to critique and redesign the curriculum in Systems Engineering. Each group will deliver a written product and provide at least one briefing to the class. The best critique and redesign will be presented to the faculty.

490 Senior Design Project I (3:2:1). Prerequisites: SYST 335, 371; corequisites: SYST 417, 470. The first part of a capstone course in the systems engineering program. Students apply the knowledge they have gained in systems engineering methods to a group project. During the first semester of the senior design course, students perform concept definition and requirements analysis. A plan for carrying out the project is developed, culminating in a proposal presented to faculty at the end of the semester.

491 Industrial Project (1-3:0:3-9). Prerequisite: 75 credit hours, SYST 302; must be arranged with an instructor and approved by the department faculty chair before registering. Semester-long work experience in systems engineering in an industrial or governmental organization. The work is supervised jointly by a systems engineer from the sponsoring organization and a faculty member of the department. The project and the arrangements for supervision must be approved by the student's faculty advisor. Periodic reports, a written final report, and a presentation are required.

495 Senior Design Project II (3:1:2). Prerequisite: SYST 490. The second part of the capstone course in the systems engineering program. The design project plans formulated in SYST 490 are reviewed and modified. Additional instruction on documentation and project management is given. The design project is completed, and a formal report is prepared, presented, and evaluated. s

498 Independent Study in Systems Engineering (13:0:0). Prerequisites: 60 credits and GPA of at least 3.000; must be arranged with an instructor and approved by the department chair before registering. Directed self-study of special topics of current interest in systems engineering. May be repeated for a maximum of 6 credits if the topics are substantially different. f,s,sum

499 Special Topics in Systems Engineering (3:3:0). Prerequisites: 60 credits; specific prerequisites vary with nature of topic. Topics of special interest to undergraduates. May be repeated for a maximum of 6 credits if the topics are substantially different.

500/CSI 600 Quantitative Foundations for Systems Engineering (3:3:0). Prerequisite: Math 213, 214. This course provides the quantitative foundations necessary for core courses in the Systems Engineering and Operations Research master's program and the certificate program in C³I. Topics include vectors and matrices, differential and difference equations; linear systems; Fourier, Laplace and Ztransforms, and probability theory. Engineering applications of the topics will be emphasized. Students will receive graduate credit for this course which will, when used on a plan of study, extend the minimum credit hour requirements for the degree. f

510 Systems Definition and Cost Modeling (3:3:0). Prerequisite: Graduate standing. Comprehensive examination of the methods and processes for the identification and representation of system requirements. Investigation of the systems acquisition life cycle with emphasis on requirements definition, including functional problem analysis. Examination of the systems engineering definition phase including requirements, problem analysis, definition, and functional economics. Specification of functional and nonfunctional requirements, and associated requirements prototyping. Functional economic analysis, including the use of prevailing cost estimation models and planning and control of common operating environments. Lecture and group project including creation of requirements and use of cost estimation model. f

512 Systems Engineering for Design and Development (3:3:0). Prerequisite: SYST 510 or equivalent. Intensive study of the design and development portion of the systems engineering life cycle for information technology and software intensive systems. Analysis and design processes for information system engineering. Entity-relationship models, object-oriented modeling and analysis, structured analysis and design. Life cycle models for the development of systems. Technical direction and systems management of organizational processes. Systems engineering and information technology standards. s

513 Total Systems Engineering, Reengineering and Enterprise Integration (3:3:0). Prerequisites: SYST 510 or SYST 520. Principles of strategic quality, including TQM. Quality standards including ISO9000 and 14000. Organizational leadership, cultures, and process maturity, reengineering. Quality, organization learning and reengineering approaches to enable information integration and management and environment and framework integration in the systems engineering of knowledge intensive systems. Emphasis is placed on the role of integrated product and process design teams, standard and commercial off-the-shelf products in enterprise integration. Architecture driven system characteristics are studied, as is transition management of legacy systems.

520 System Design and Integration (3:3:0). Prerequisite: Graduate standing. System design and integration methods are studied and practiced, including both structured analysis and object-oriented based techniques. The course includes the development process of functional, physical, and operational architectures for the allocation and derivation of component-level requirements for the purpose of specification production; examination of interfaces and development of interface architectures. Life cycle of systems is addressed; generation and analysis of life cycle requirements. Software tools are introduced and used for portions of the systems engineering cycle. Students are expected to develop a system design for a system of their choice using both the structured analysis and object-oriented techniques presented in class, and they will make presentations on these designs. s

521/OR 643 Network Analysis (3:3:0). Prerequisites: MATH 213 and MATH 203 or equivalent; OR 441 or OR 541. Network nomenclature. Elementary graph theory. Linear and nonlinear network models: multi-commodity flow, mathematical games and equilibria on networks, network design and control. Dynamic network models. Applications to transportation, telecommunications, data communications, and water resource systems. f

530 System Management and Evaluation (3:3:0). Prerequisite: Graduate standing. Provides the necessary techniques for evaluating the cost and operational effectiveness of system designs and systems management strategies. Performance measurement, work breakdown structures, cost estimating, and quality management are discussed. Configuration management, standards, and case studies of systems from different application areas are discussed. f,s

542/EEP 602 Decision Support Systems Engineering (3:3:0). Prerequisite: SYST 301 or graduate standing. Studies the design of computerized systems to support individual or organizational decisions. The course teaches a systems engineering approach to decision support system (DSS) development. A DSS is the end product of a development process, and it is this process that is key to successfully integrating a DSS into an organization. Any DSS is built on a theory (usually implicit) of what makes for successful decision support in the given context. Empirical evaluation of the specific DSS and the underlying theory should be carried on throughout the development process. The course examines some prevailing theories of decision support, considers the issues involved in obtaining empirical validation for a theory, and discusses what, if any, empirical support exists for the theories considered. Students design a decision support system for a semester project. f

555 Introduction to Intelligent Systems Engineering (3:3:0). Prerequisite: SYST 451 or SYST 520. Introductory course to Intelligent Systems Engineering for students planning to study systems engineering. This course covers the principles and interrelationships among basic methods in the field, including symbolic and sub-symbolic reasoning, imprecise and approximate reasoning (e.g., fuzzy logic), and neural networks, and emphasizes engineering analysis and system design and implementation. Basic intelligent system principles as well as various engineering applications are covered. This course includes hands-on experience and the design of an experimental intelligent system with state-of-the-art tools. s

560 Introduction to Air Traffic Control (3:3:0). Prerequisite: Graduate standing. This course is intended as an introduction to Air Traffic Control (ATC) for those who plan professions in the aviation industry. It is a necessary introduction for students who will later specialize and take more in-depth courses. The course will survey the entire field, presenting the history of ATC and how it came to be as it is, the technology on which the system is based, the procedures used by controllers to meet the safety and efficiency goals of the system, the organizational structure of the FAA, challenges facing the system and means under investigation to meet these challenges. This course will involve some field work for data collection and analysis. A class project requiring a system simulation will be required.f

563 Research Methods in Systems Engineering and Information Technology (3:3:0). Prerequisite: STAT 344 and 354 or equivalent. Provides the foundation for one of the most important activities in systems engineering: information gathering to support drawing conclusions and making decisions about design options and process improvements. The course begins by developing an understanding of the scientific process, the use of empirical evidence to support and refute scientific hypotheses, and the use of scientific information in decision-making. The course covers different sources of scientific evidence: designed experiments, quasi-experiments, field studies, surveys, and case studies. The process of formulating testable hypotheses is discussed. Methods of measurement are discussed, including approaches to measuring soft, hard-to-quantify factors. Presentation of results is discussed. Students do a project involving empirical research. f

571 Systems Engineering Management (3:3:0). Prerequisite: SYST 471 or SYST 530. This course is a study of more advanced topics in systems engineering management. This is a seminar style course, and students are expected to read a number of selections from the current literature as well as make presentations and produce papers on engineering management topics. A number of issues in systems engineering management, such as multiproject management, quality programs, and the impacts of process change on the organization will be examined. The course focuses strongly on the practical impacts of various system engineering management techniques and practices on projects, organizations, and personnel. f

572 Introduction to Systems Integration Engineering (3:3:0). Prerequisite SYST 301 or 510 or SYST 520. Lifecycles in systems engineering. Large systems comprised of heterogeneous components. The human, organizational, and technological basis for integration. Societal and cultural basis for systems integration. Conceptual frameworks for systems integration. Structure, function, and purpose of the systems integration industry. Risk management and systems integration. User requirements and functional specifications in systems integration. The bid and proposal process for systems integration. Systems integration and the federal government. Integrated process and product development, and systems integration. Systems integration architectures. Systems management and cost estimation in systems integration. Systems integration reengineering. Quality management for systems integration. Increasing returns to scale, network effects, and path dependency issues in systems integration. Systems integration ecology and evolutionary systems integration.

573 Decision and Risk Analysis (3:3:0). Prerequisite: STAT 344 or equivalent. Study of analytic techniques for rational decision making that address uncertainty, conflicting objectives, and risk attitudes. This course covers modeling uncertainty; rational decision-making principles; representing decision problems with value trees, decision trees, and influence diagrams; solving value hierarchies, decision trees, and influence diagrams; defining and calculating the value of information; incorporating risk attitudes into the analysis; and conducting sensitivity analysis. (Offered concurrently with SYST 473. Students may not receive credit for both SYST 473 and SYST 573.) f,s

595/ECE 595 Discrete Event Systems (3:3:0). Prerequisite: SYST 500 or equivalent. Introduction to the modeling and analysis of discrete event dynamical systems. Elements of discrete mathematics including sets and multisets, lattices, relations, and graph theory. Systems and models. Untimed and timed models of discrete event systems. Condition/event nets; place/transition nets and their properties. Concurrent and asynchronous processes. Colored Petri nets and the modeling of systems. Simulation and performance analysis. Applications from several domains: Command and control, air traffic control, flexible manufacturing systems, robotics, decision making organizations, and decision support systems. s

611 System Methodology and Modeling (3:3:0). Prerequisite: SYST 500 or equivalent. This course provides a broad, yet rigorous, introduction to methodologies for Systems Engineering. Emphasis is on systems modeling and performance. Topics include system model and behavior analysis, linear and nonlinear systems, discretization and linearization, optimization, dynamic programming and optimal control. These methodologies address system performance issues and assist in the evaluation of alternative system designs. Resource allocation for planning and control is also introduced. f

621 Systems Architecture for Large-Scale Systems (3:3:0). Prerequisite: SYST 510 or equivalent. Introduction to system architecture for the technical description of large-scale systems. An intensive study of the relationships between the different types of architecture representations and the methodologies used to obtain them. Systems engineering approaches for transitioning from functional descriptions to structure and architectural descriptions. Analysis of existing architectures and design of new architectures. The role of modeling, prototyping, and simulation in architecture development. Executable models of system architectures and performance evaluation. The role of the systems architect, the systems architecting process, and systems management of architecture and design activities. System interoperability, integration, and interfaces. A case study of a large-scale system conceptual architecture will be used to demonstrate application of systems architecture principles. f,s

659 Topics in Systems Engineering (3:3:0). Prerequisite: Permission of instructor. Topics not covered in the department's regular systems engineering offerings. Course content may vary each semester depending on instructor and the perception of students' needs. Course may be repeated once for credit. f,s

660/OR 660 Air Transportation Systems Modeling (3:3:0). Prerequisite: SYST 460/560 or permission of instructor. The student will be introduced to a wide range of current issues in air transportation. The issues include: public policy toward the industry, industry economics, system capacity, current system modeling capability, human factors considerations, safety analysis and surveillance systems, and new technological developments. The student is expected to develop a broad understanding of the contemporary and future issues. The student's knowledge will be evaluated through class discussions, a take-home midterm exam and a term project to be completed by the end of the semester. s

664/STAT 664 Bayesian Inference and Decision Theory (3:3:0). Prerequisite: STAT 544 or STAT 554 or equivalent. This course introduces students to decision theory and its relationship to Bayesian statistical inference. Students will learn the commonalities and differences between the Bayesian and frequentist approaches to statistical inference, how to approach a statistics problem from the Bayesian perspective, and how to combine data with informed expert judgment in a sound way to derive useful and policy relevant conclusions. Students will learn the necessary theory to develop a firm understanding of when and how to apply Bayesian and frequentist methods, and will also learn practical procedures for inference, hypothesis testing, and developing statistical models for phenomena. Specifically, students will learn the fundamentals of the Bayesian theory of inference, including probability as a representation for degrees of belief, the likelihood principle, the use of Bayes Rule to revise beliefs based on evidence, conjugate prior distributions for common statistical models, and methods for approximating the posterior distribution. Graphical models are introduced for constructing complex probability and decision models from modular components. s

671/OR 671 Judgment and Choice Processing and Decision Making (3:3:0). Prerequisite: STAT 510. Intuitive nature of human judgment and decision making, and some methods currently being used for improving individual and group decision. The nature of judgment emphasizing limitations on human information processing abilities. The use of decision-analytic techniques to improve decision making.f

672/ECE 651/CS 685: Intelligent Systems for Robots (3:3:0). Prerequisite: SYST 611 or ECE 650 or CS 580 or SYST 555. Review of recent developments in the area of intelligent autonomous systems. Study of the applications of artificial intelligence, control theory, operations research, decision science, computer vision, and machine learning to robotics. Correspondences between various fields are also studied. Topics include analysis and design of methods, algorithms and architectures for planning, navigation, sensory data understanding, visual inspection, spatial reasoning, motion control, learning, self-organization, and adaptation to the environment. s

677/OR 677/STAT 677 Statistical Process Control (3:3:0). Prerequisites: STAT 510, STAT 554, or STAT 544 or equivalent. Introduction to the concepts of quality control and reliability. Acceptance sampling, control charts, and economic design of quality control systems are discussed, as are system reliability, fault-free analysis, life testing, repairable systems, and the role of reliability, quality control, and maintainability in life-cycle costing. The role of MIL and ANSI standards in reliability and quality programs is also considered.

680/ECE 670/OR 683 Principles of Command, Control, Communications, and Intelligence (C³I) (3:3:0). Prerequisite: ECE 528 or SYST 611 or equivalent. This course provides a broad introduction to fundamental principles of Command, Control, Communication, and Intelligence (C³I). The principles and techniques are applicable to a wide range of civilian and military situations. Modeling and simulation of combat operations are discussed. The sensing, fusion, and situation assessment processes are studied in detail. Optimal decision-making rules are derived. The concepts of C³ architectures are discussed. Tools to evaluate and design C³ systems such as queuing theory are also developed. f

683 Modeling, Simulation, and Gaming (3:3:0). Prerequisites: MATH 213, SYST 500 or equivalent and graduate standing. Develops methods for designing combat models and games. Existing combat models are critical to the C³I process. Exercises and games are used to demonstrate the value of properly developed C³I modules in a combat simulation.

684 Sensor Data Fusion (3:3:0). Prerequisites: SYST 680 or ECE 670. Examines design issues in multisensor fusion systems. Studies the use of probability, evidence, and possibility theories for object identification. Studies Bayesian networks, blackboard architectures, and spatial and temporal reasoning for situation assessment.

685 Estimation and Tracking: Principles and Techniques (3:3:0). Prerequisite: ECE 528 or OR 542 or STAT 544 or equivalent. Principles and estimation techniques for static and dynamic systems, linear and nonlinear, discrete and continuous time. Estimation for kinematic models, track initiation, bearing-only tracking, tracking maneuvering targets with adaptive filtering, MM (Multiple Model) and interactive MM algorithms. Tracking single target in clutter, nearest neighbor algorithm, tracking and data association, Multiple hypothesis tracking. Tracking performance evaluation.

691/EEP 601 Introduction to Enterprise Engineering: Engineering and Policy (4:3:1). Prerequisite: INFS 614, or equivalent. This course provides an overview of Extended Enterprise Integration. Lectures focus on the SAP architecture and the R/3 standard software solution. Laboratory requires students to complete an end-to-end implementation project with the Great Plains Software midrange ERP solution, Dynamics C/S +. For modeling, students must demonstrate complete proficiency in the Architecture of Information Systems (ARIS) methodology, and the supporting ARIS Toolset.

692/EEP 602 Decision Support for Enterprise Integration (3:3:0). Prerequisite: SYST 542 and SYST 691. Lectures focus on the use of "business intelligence" to enhance competitive advantage; developing an information driven set of controls to improve profitability; and emphasizing the creation of a balanced business with aligned corporate direction and strategic intent. Solutions provided within ERP systems are examined.

693/EEP 603 Supply Chain Integration and Management (Business-to-Business Electronic Commerce) (3:3:0). Prerequisite: SYST 691. Lectures focus on two issues: Supply chain integration from an information technology perspective, and supply chain management from a decision support perspective. The motivation for the course is the merging of enterprise computing with operations research, primarily through customer/supply chain management systems. Topics include ERP/web integration, advanced planning, and customer relationship management.

694/EEP 604 E-Commerce Architectures (Business-to-Consumer Electronic Commerce) (3:3:0). Prerequisite: SYST 691. Introduction to the network and system architectures that support high volume business to consumer web sites and portals. Course provides insight into the structure of the modern web enabled storefront. Critical business and technology issues include Storage Area Networks (SANs), server clustering, load balancing techniques at the server and network level, fault tolerance, and recovery of database and application servers.

695/EEP 605 Economics of Electronic Commerce (3:3:0). Prerequisite: SYST 691. Focuses on gaining competitive advantage through electronic commerce implementation; the identification and growing of new market opportunities, as well as the electronic enabling of existing business relationships; business-to-consumer relationships, as well as the economics of strategic procurement, ERP hosting, customer relationship management, catalog hosting, portal operations, and supplier management.

696/EEP 606 Customer Relationship Management (3:3:0). Prerequisite: SYST 691. Focuses on the "front office" and its integration with the "back office." The modern world of e-commerce extends intra-enterprise integration as implemented in Enterprise Resource Planning (ERP) systems to include external constituents, such as customers, partners, and suppliers. This course is focused on modern system support for the demand chain and the value creation process that results from integrating the front office systems (e.g., CRM) with the back office systems (ERP).

697/EEP 607 Critical Information Technology Infrastructures (3:3:0). Prerequisite: SYST 694. Design and implementation of high-speed network and application services in support of modern Enterprise Resource Planning (ERP) systems. Critical technologies include high-speed data communication, switched vs. routed data flow, workflow engines, business rule and web application servers, and load balancing technologies. A large-scale web-enabled ERP system architecture will be examined in detail.

698 Independent Study and Research (3:3:0). Prerequisites: Graduate standing, completion of at least two core courses, permission of instructor. Study of a selected area in systems engineering or C³I under the supervision of a faculty member. A written report is required. f,s,sum

760 Special Topics in Command, Control, Communications, and Intelligence Systems Engineering (3:3:0). Prerequisite: SYST 680. Special topics in the C³I area, with different content in different terms. Representative areas include quantitative evaluation of C³ systems, applications of artificial intelligence in C³ systems, and military communications systems.

781/INFS 781/STAT 781: Data Mining and Knowledge Discovery (3:3:0). Prerequisites: SYST/STAT644 or CS 650 or INFS 623 or equivalent. This course is concerned with methods and systems for deriving user-oriented knowledge from large databases and other information sources, and applying this knowledge to support decision making. Information sources can be in numerical, textual, visual, or multimedia forms. The course covers theoretical and practical aspects of current methods and selected systems for data mining, knowledge discovery, and knowledge management, including those for text mining, multimedia mining, and web mining.

798 Research Project (3:0:0). Prerequisite: 21 graduate credits. Research project chosen and completed under the guidance of a graduate faculty member, resulting in an acceptable technical report.

799 Master's Thesis (1-6:0:0). Prerequisites: 21 graduate credits and permission of instructor. Research project chosen and completed under the guidance of a graduate faculty member, which results in a technical report acceptable to a three-member faculty committee, and an oral defense.