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Course Criteria
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3.00 Credits
Risk analysis of products and systems will be explored using product functionality as the starting point. Traditional probabilistic risk assessment techniques will be covered along with recent approaches that use historical data to produce automatic risk assessments. Prerequisite: Graduate standing.
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3.00 Credits
This course will discuss issues related to distributed systems architecting, modeling, analysis and representation, with specific focus on discrete-part manufacturing domain. Distributed modeling techniques and other model decomposition methods using simulation modeling and scalability issues will also be addressed.
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3.00 Credits
This course covers the use of models to represent systems and the underlying system elements, components, etc. Topics also include SysML, executable systems architectures, model repositories, integration of models and information, and use of MBSE in distributed systems. Prerequisite: Sys Eng 433.
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3.00 Credits
An introduction to cluster analysis and clustering algorithms rooted in computational intelligence, computer science and statistics. Clustering in sequential data, massive data and high dimensional data. Students will be evaluated by individual or group research projects and research presentations. Prerequisite: At least one graduate course in statistics, data mining, algorithms, computational intelligence, or neural networks, consistent with student's degree program. (Co-listed with Comp Eng 439, Elec Eng 439, Comp Sci 449 and Stat 439)
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3.00 Credits
Introduction to ad hoc and sensor networks, IEEE standards, heterogeneity, quality of service, wireless channel issues, energy awareness, power and topology control, routing, scheduling, rate adaptation, self-organization, admission and flow control, energy harvesting, security and trust levels, hardware and applications. Prerequisite: Comp Eng 348 or Comp Eng 349 or equivalent. (Co-listed with Comp Eng 443 and Elec Eng 443)
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3.00 Credits
This course presents reliability and fault tolerance for network-centric systems, including models, metrics, and analysis techniques. This course also concentrates on security, including technical tools and methods for audit and assessment as well as management and policy issues. Prerequisite: Sys Eng/Comp Eng 419 or Comp Eng 349. (Co-listed with Comp Eng 449)
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3.00 Credits
Review of Neurocontrol and Optimization, Introduction to Approximate Dynamic Programming (ADP), Reinforcement Learning (RL), Combined Concepts of ADP and RL - Heuristic Dynamic Programming (HDP), Dual Heuristic Programming (DHP), Global Dual Heuristic Programming (GDHP), and Case Studies. Prerequisite: Elec Eng 368 Neural Networks or equivalent (Computational Intelligence Comp Eng 301) (Co-listed with Comp Eng, Elec Eng, Mech Eng and Aero Eng 458)
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3.00 Credits
This course uses customized case studies based on team projects from prior courses. Topics covered include physical and functional analysis, analysis and traceability of requirements and specifications, verification and validation, optimization, simulation, and trade studies. Prerequisite: Sys Eng 368.
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3.00 Credits
The objective of the course is to provide the basic tools and concepts of architecting complex engineering systems. Systems thinking, ambuguity in system architecting, search as an architecting process, SysML and DoDAF Architecting Framework, System of Systems and Network-Centric Architectures. Prerequisite: Sys Eng 468.
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3.00 Credits
Basic tools and concepts of architecting complex software intensive systems are introduced. The following topics are covered under four main sections; namely Architecting Process, Architecting Heuristics, Architecting Patterns and Frameworks, and Architecture Assessment. Prerequisite: Graduate Standing.
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