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Engineering Management

Course Descriptions


Systems Engineering - SY**

ENEE 660: Systems Engineering Principles [3]

This course provides the foundational framework to understand the system engineering (SE) process, selection of specialized SE tools and the execution of SE under differing design or acquisition philosophies. The course addresses: (1) SE principles (2) SE processes and methodologies (3) integration of technical disciplines and (4) SE management. Prerequisite: B.S. degree in EE or related field.

ENEE 661: System Architecture and Design [3]

This course covers the development of a system architecture and hardware/software system design within the overall systems engineering(SE) process. Major topics include development of an operational concept, functional decomposition, requirements allocation and partitioning, interface definition, inclusion of integrity, reliability, and maintainability within the design concept, validation and verification, technical performance budgeting, quality function deployment techniques, and statistical and linear models. Prerequisite: B.S. degree in EE or related field. ENEE 660 (SE Principles) may be taken concurrently.

ENEE 662: Modeling, Simulation, and Analysis [3]

This course addresses simulation architectures. Topics addressed include cost and risk analysis;experimental design; simulation control and interfaces; requirements and architecture definition; simulation design and implementation; verification, validation, and accreditation; estimating, planning and controlling simulation efforts. Prerequisites: B.S. degree in EE or related field and a working knowledge of C/C++ or a similar programming language. ENEE 660 and ENEE 661 or consent of instructor.

ENEE 663: System Implementation, Integration, and Test [3]

This course covers the translation of design specifications into product elements, integration of these elments into a system, and the verification that the resulting system performs as intended in it operational environment. Prerequisites: ENEE 660 and ENEE 661 or consent of instructor.

** A student who completes the above four Systems Engineering courses plus ENEE 670 - Systems Engineering Project will earn the Graduate Certificate in Systems Engineering, as described at Systems Engineering - Graduate Certificate.

ENEE 670: Systems Engineering Project [3]

This course is either an individual or group project on an approved topic in the Systems Engineering (SE) Certificate, the SE track within the Master’s in Electrical Engineering or the SE track within the Masters in Computer Science, non-thesis option. The project serves as an integrating mechanism to allow the student to demonstrate a working level understanding by applying the knowledge gained in the coursework leading up to this capstone course. The "project" requires a sequence of tasks required to achieve an end result that requires the use of multiple EE domains, disciplines and tools. Underlying the need for the result of the project are the essential lessons learned from case studies of major programs. The project will be tailored to allow completion and submission as partial satisfaction for a Certificate in Systems Engineering.

ENEE 664: Advanced Systems Architecture [3]

This course emphasizes the many partitioning alternatives that can be employed when developing physical systems architectures, including hierarchical partitioning, federated partitioning, scalable architectures, high availability architectures, and collaborative systems.  The course also deals with methods for architecting successful systems, such as achieving data integrity, managing system workflow, and constructing representation models.

Prerequisites: It is assumed that the student is familiar with the systems engineering process and the various development methodologies. ENEE 660: Systems Engineering Principles and ENEE 661: Systems Architecture and Design

ENEE 672: Decision and Risk Analysis [3]

This course provides an overview of decision and risk analysis techniques. It focuses on how to make rational decisions in the presence of uncertainty and conflicting objectives. It covers modeling uncertainty, the principles of rational decision-making, representing and solving decision problems using influence diagrams and decision trees, sensitivity analysis, Bayesian decision analysis, deductive and inductive reasoning, objective and subjective probabilities, probability distributions, regression analysis, defining and calculating the value of information, modeling risk attitudes and utility functions. Concepts will be illustrated through case studies and practiced by students through homework.

CYBR 620:  Introduction to Cybersecurity [3]

This course surveys the topic of Cybersecurity, examining aspects of information, people and technology, the evolution of information security to Cybersecurity, and its technical, legal, and impact perspectives.   Students will learn how to apply the Cybersecurity concepts of protection and response to government, critical infrastructure, commercial, and individual situations; analyzing the threats and risks of those environments in light of information processing objectives and threats; and applying an appropriate strategy to build and operate appropriate defenses to mitigate potential impacts upon processing systems.

CMPE 684: Wireless Sensor Networks [3]

A wide range of applications such as disaster management, military and security have fueled the interest in sensor networks during the past few years. Sensors are typically capable of wireless communication and are significantly constrained in the amount of available resources such as energy, storage and computation. Such constraints make the design and operation of sensor networks considerably different from contemporary wireless networks, and necessitate the development of resource conscious protocols and management techniques. This course provides a broad coverage of challenges and latest research results related to the design and management of wireless sensor networks. Covered topics include network architectures, node discovery and localization, deployment strategies, node coverage, routing protocols, medium access arbitration, fault-tolerance, and network security.

CMPE 685: Principles of Communications Networks [3]

This course provides an overview of network communications terms, concepts, architectures, protocols, and technologies. Upon completion of the course, students will be able to construct, and assess the completeness of architectures for simple LAN and WAN communications networks. Topics include wire/fiber and wireless WANs and LANs, the OSI and TCP/IP models, propagation media, analog and digital data and signals, error detection, error correction, data link layer protocols, multiple access techniques, medium access control, circuit and packet switching, X.25, TCP/IP, ATM, Ethernet, switches, routers, routing techniques, congestion control, queuing theory, quality of service (QoS) metrics, network architectures, and network security.

CMSC 626:  Principles of Computer Security [3]

This course will provide an introduction to computer security with a specific focus on the computing aspects. Topics covered include: basics of computer security, including an overview of threat, attack and adversary models; social engineering; essentials of cryptography; traditional computing security models; malicious software; secure programming; operating system security in practice; trusted operating system design; public policy issues, including legal, privacy and ethical issues; network and database security overview.

ENMG 664:  Quality Engineering & Management [3]

This course provides an overview of the basic principles and tools of quality and their applications from an engineering perspective. The primary quality schools of thought or methodologies, including Total Quality Management, Six Sigma and Lean Six Sigma, and quality approaches from key figures in the development and application of quality as a business practice, including W. Edwards Deming and Joseph M. Juran will be analyzed. Some of the key mathematical tools used in quality systems will be discussed, including Pareto charts, measurement systems analysis, design of experiments, response surface methodology, and statistical process control. Students will apply these techniques to solve engineering problems using the R software. Reading assignments, homework, exams, and the project will emphasize quality approaches, techniques, and problem solving.