Systems Engineering Courses (SYST)

Related Catalog Entry: School of Information Technology and Engineering / Systems Engineering

Related Mason Website: Systems Engineering (http://ite.gmu.edu/~syst/)

Systems Engineering

201 Systems Modeling I (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, electrical, computer, biological, economic, urban, and social systems. Linear and nonlinear systems and linearization. A discrete time system formulation is used to study the properties and behavior of such systems. f

202 Systems Modeling II (3:3:0).Prerequisite: SYST 201; corequisites: MATH 203 and 214. 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. Simulation. s

203 Systems Modeling Laboratory (2:0:6). Corequisite: SYST 202. Modeling and simulation of dynamic systems on personal computers. Introduction to computer modeling using spreadsheets with graphics and databases. Use of built-in functions and construction of macros. Graphical presentation of results. s

301 Systems Methodology and Design I (3:3:0).Prerequisite: 60 credits. 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 Methodology and Design II (4:3:1).Prerequisites: MATH 203, MATH 213, and STAT 344. Analysis methods of system engineering design and management. Decision analysis, economic models and evaluation, optimization in design and operations, probability and statistical methods, queueing 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

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). Corequisite: SYST 417 or OR 441, 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. 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 360. 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).Prerequisites: SYST 202 and SYST 203. 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 (4:4:0).Prerequisites: CS 211 and 60 credits. 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). integration in organizations to support human decision making. Evaluation of DDS. The course emphasizes that a DDS is the end-product of the design process, and it is this process that is key to successful integration of a DDS 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, inferencing, system components, and system design. Introduction to knowledge engineering tools and programming of case study examples using an expert system shell. f

455 Intelligent Systems Engineering (3:3:0).Prerequisite: SYST 451. Survey of methods and techniques relevant to developing "intelligent systems." Principles and interrelationships between basic methods in the field including symbolic (e.g., state-space search) and subsymbolic reasoning (e.g., neural networks), probabilistic and approximate reasoning (e.g., Bayesian networks, fuzzy logic), and intelligent control. Emphasis is on engineering analysis system principles and engineering applications. s

470 Human Factors Engineering (3:3:0).Prerequisites: SYST 301, STAT 344, PSYC 100. 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

471 Systems Engineering Management (3:3:0).Prerequisite: 75 credits. 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. 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. f,s

490 Senior Design Project I (3:2:1). Corequisites: SYST 301 and 471. 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 credits, SYST 302; must be arranged with an instructor and approved by the department faculty advisor 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. f,s,sum

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 (1-3:0:0).Prerequisites: 60 credits; 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 six 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 six credits if the topics are substantially different.

500 Quantitative Foundations for Systems Engineering (3:3:0).Prerequisites: Math 213 and 214. Provides the quantitative foundations necessary for core courses in the systems engineering master's program and the certificate program in C3I. Topics include vectors and matrices; differential and difference equations; linear systems; Fourier, Laplace and Z-transforms and probability theory. Engineering applications of the topics are emphasized. Students receive graduate credit for this course that, when used on a plan of study, extends the minimum credit 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

511 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 architecting principles. f,s

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 and Enterprise Integration (3:3:0).Prerequisites: SYST 511 and 512. Approaches to enable information integration and management, data and tool integration and management, and environment and framework integration in the systems engineering of large scale and information technology systems. The role of integrated product development teams, and standards and commercial off-the-shelf products in enterprise integration at the level of product and organizational processes. Architecture driven system characteristics including: open systems, layered protocols, common network and user services. The role of open architectures, application migration, common operating environments and client server computing in total systems engineering and enterprise integration. Information architectures for enhancement of an organization's tactical and operational capabilities at personal, application, functional, mission, and enterprise levels. Transition management of legacy systems and continued discussion of reengineering at the level of organization, process, and product. f

520 System Design and Integration (3:3:0).Prerequisite: Graduate standing. Life cycle of systems is addressed; generation and analysis of life cycle requirements; development 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. Software tools are introduced and used for portions of the systems engineering cycle. s

521 Network Analysis (3:3:0). Prerequisits: MATH 213 or equivalent; OR 441 or 441/541. 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, s,sum

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

542 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 graduate standing. 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 subsymbolic 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

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. Study of the basics of systems engineering management. This includes 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. f

572 Introduction to Systems Integration Engineering (3:3:0).Prerequisite: SYST 301 or SYST 510 or SYST 520. 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. (Offered concurrently with SYST 472. Students may not receive credit for both SYST 472 and SYST 572.) f

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. Provides a broad, yet rigorous, introduction to methodologies for systems engineering. Emphasis is on systems modeling and performance. These methodologies address system performance issues and assist in evaluating alternative system designs. Resource allocation for planning and control is introduced. f

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

664/STAT 664 Bayesian Inference and Decision Analysis (3:3:0).Prerequisite: STAT 544 or STAT 554 or equivalent. Fundamentals of Bayesian decision theory, and its application in statistical inference and decision analysis. Random variables. Prior distributions and Bayes theorem. Proper scoring rules. Precise measurement. Conjugate priors. Approximate posterior distributions. Multiattribute utility theory. Influence diagrams and Bayesian networks. Measuring utilities and probability distributions. s

671/OR 671 Judgment and Choice Processing and Decision Making (3:3:0). Prerequisite: STAT 610. 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 554, STAT 610, 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)--Part I (3:3:0).Prerequisite: ECE 528 or OR 542 or equivalent. Fundamentals of C3I are developed from a descriptive, theoretical, and quantitative perspective. Topics include C2 process; quantitative models for combat, sensing, data fusion; individual and team decision making; organizational theory; tools for modeling C2 systems; and evaluations of C2 systems. F

681/ECE 671/OR 684 Principles of Command, Control, Communications, and Intelligence (C3I)--Part II (3:3:0).Prerequisite: SYST 680/ECE 670/OR 684. Technology required for C2 systems is developed. Technology areas include sensors, communications, and computer-based systems. The C3I required for mission areas such as strategic, theater, and tactical are developed and analyzed. Electronic warfare and counter-C3I is discussed. s

683 Modeling, Simulation, and Gaming (3:3:0).Prerequisites: MATH 213 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 Bayesiannetworks, 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. Topics include estimation for kinematic models, track initiation, bearing-only tracking, tracking of maneuvering targets with adaptive filtering, MM and IMM algorithms. Tracking of single target in clutter, nearest neighbor algorithm, tracking and data association.

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.

761 Advanced Topics in Command, Control, Communications, and Intelligence Systems Engineering (3:3:0).Prerequisites: SYST 680, SYST 681, and SYST 683. Advanced topics in the C3I. Representative areas include advanced modeling and analysis techniques, case studies of C3 architectures, and applications of detection and estimation techniques in ASW.

777/OR 777 The Modeling of Nonlinear Dynamic Systems (3:3:0).Prerequisites: OR 541 or ECE 521 and OR 682/STAT 682 or equivalent. Introduction to the use of nonlinear ordinary differential, and difference and integral equations in modeling dynamic phenomena in engineering, the natural sciences, and the social sciences. Emphasis is on the art of constructing and solving very large-scale complex dynamic models. Examples are drawn from operations research, environmental engineering, mathematical biology, economics, transportation, and other fields.

798 Research Project (3:0:0).Prerequisite: 9 graduate credits. Research project chosen and completed under the guidance of a graduate faculty member, resulting in an acceptable technical report. f,s,sum

799 Master's Thesis (1-6:0:0).Prerequisites: 9 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-faculty-member committee, and an oral defense. f,s,sum