Courses
The listing of a course description here does not guarantee a course’s being offered in a particular semester. Please refer to the published schedule of classes on the MyBU Student Portal for confirmation a class is actually being taught and for specific course meeting dates and times.
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ENG MS 925: No Longer Offered
Undergraduate Prerequisites: By petition only.
No longer offered -
ENG MS 951: Independent Study
Undergraduate Prerequisites: By petition only
Graduate students may study, under a faculty member's supervision, subjects not covered in a regularly offered course. Final report and/or written examination normally required. -
ENG MS 952: MS Mentored Project
Students who are pursuing a project to satisfy their practicum requirement for the MS without Thesis and MEng degrees will register for up to 4 credits of this course. The course may be taken more than once up to four credits (ex. two credits in Fall, two credits in Spring). Students will select a suitable project, with a mentor, that can be completed in 4 credits. The Graduate Committee must approve all proposed projects. Each student must write a project report at the end of the course that will be graded P/F by their project mentor. -
ENG MS 954: MS Thesis
Undergraduate Prerequisites: Graduate standing.
Graduate Prerequisites: Restricted to MS students by petition only.
Participation in a research project under the direction of a faculty advisor leading to the preparation of an original MS thesis. For students pursuing an MS thesis to satisfy the practicum requirement for the MS degree. -
ENG MS 991: PhD Dissertation
Undergraduate Prerequisites: Graduate standing.
Graduate Prerequisites: MS 900; restricted to post-prospectus PhD students.
Participation in a research project under the direction of a faculty advisor leading to the preparation and defense of an original PhD dissertation. -
ENG SE 501: Dynamic Systems Theory
Undergraduate Prerequisites: Familiarity with differential equations and matrices at the level of ENG ME 404 or CAS MA 242, or consent of instructor.
Introduction to analytical concepts and examples of dynamic systems and control. Mathematical description and state space formation of dynamic systems; modeling, controllability, and observability. Eigenvector and transform analysis of linear systems including canonical forms. Performance specifications. State feedback: pole placement and the linear quadratic regulator. Introduction to MIMO design and system identification using computer tools and laboratory experiments. Meets with ENGEC501 and ENGME501; students may not receive credit for both. -
ENG SE 523: Deep Learning
Mathematical and machine learning background for deep learning. Feed-forward networks., Backpropagation. Training strategies for deep networks. Convolutional networks. Recurrent neural networks. Deep reinforcement learning. Deep unsupervised learning. Exposure to Tensorflow and other modern programming tools. Other recent topics, time permitting. Same as CAS CS 523 and ENG EC 523. Students may not receive credits for both. -
ENG SE 524: Optimization Theory and Methods
Undergraduate Prerequisites: ENG EK 102 or CAS MA 142.
Introduction to optimization problems and algorithms emphasizing problem formulation, basic methodologies, and underlying mathematical structures. Classical optimization theory as well as recent advances in the field. Topics include modeling issues and formulations, simplex method, duality theory, sensitivity analysis, large-scale optimization, integer programming, interior-point methods, non-linear programming optimality conditions, gradient methods, and conjugate direction methods. Applications are considered; case studies included. Extensive paradigms from production planning and scheduling in manufacturing systems. Other illustrative applications include fleet management, air traffic flow management, optimal routing in communication networks, and optimal portfolio selection. Same as ENG Ec 524, ENG EC 674, ENG SE 674. Students may not receive credit for both. -
ENG SE 543: Sustainable Power Systems: Planning, Operation and Markets
Undergraduate Prerequisites: Graduate/Senior status and consent of instructor.
Breakthroughs in clean energy generation technologies and the advantage of exploiting efficiently the available work in fossil fuels will render electricity the dominant energy form in a sustainable environment future. We review the key characteristics of Electric Power Transmission and Distribution (T&D) networks and the associated planning and operation requirements that ensure supply adequacy, system security and stability. Capital asset investment and operation cost minimization is discussed in a systems engineering context where the assets as well as the dynamic behavior of generators, T&D networks, and loads interact. Recent developments in the formation of competitive wholesale markets at the High Voltage Transmission system level, the associated market participation and clearing rules and the market clearing optimization algorithms are presented and analyzed in terms of their effectiveness in fostering cost reflective price signals and competitive conditions that encourage optimal distributed/not-centralized investment and operating decisions. Finally, we present T&D congestion and supply-demand imbalance related barriers to the widespread adoption of environmentally friendly and economically efficient technological breakthroughs, and propose a systems engineering and real-time retail-market based coordination of centralized as well as decentralized generation, storage and load management resources that is able to achieve desirable synergies and mitigate these barriers. Same as ENG EC 543 and ENG ME 543. Students may not receive credits for both. -
ENG SE 544: Networking the Physical World
Undergraduate Prerequisites: ENG EC 312 or ENG EC 450; ENG EC 441 is desirable, C programming experience required.
Considers the evolution of embedded network sensing systems with the introduction of wireless network connectivity. Key themes are computing optimized for resource constrained (cost, energy, memory and storage space) applications and sensing interfaces to connect to the physical world. Studies current technology for networked embedded network sensors including protocol standards. A laboratory component of the course introduces students to the unique characteristics of distributed sensor motes including programming, reliable communication, sensing modalities, calibration, and application development. Same as ENG EC 544 and ENG ME 544. Students may not receive credit for both. -
ENG SE 545: Cyber-Physical Systems
Undergraduate Prerequisites: ENG EC 311 ; ENG EC 327 ; ENG EC 330; Or equivalent knowledge of Boolean algebra and finite state machines.Experience with programming embedded systems (eg EC535) is recommended but not required.
This course introduces students to the principles underlying the design and analysis of cyber-physical systems - computational systems that interact with the physical world. We will study a wide range of applications of such systems ranging from robotics, through medical devices, to smart manufacturing plants. A strong emphasis will be put on building high-assurance systems with real-time and concurrent behaviors. The student will gain both in-depth knowledge and hands-on experience on the specification, modeling, design, and analysis of representative cyber-physical systems. Meets with ENG EC 545. Students may not receive credit for both. -
ENG SE 674: Optimization Theory and Methods 2
Introduction to optimization problems and algorithms emphasizing problem formulation, basic methodologies, and underlying mathematical structures. Classical optimization theory focusing primarily on linear optimization as well as recent advances in the field. Topics include modeling issues and formulations, linear programming and its duality theory, sensitivity analysis, large-scale optimization, integer programming, introduction to non-linear optimization, interior-point methods, and network optimization problems Applications considered include production planning, resource allocation, network routing, transportation, fleet management, graph problems, and problems from finance and computational biology. Meets with ENG SE 524 but requires more advanced problem sets and exams. Same as ENG EC 524, ENG EC 674, ENG SE 524. Students may not receive credit for both. -
ENG SE 700: Advanced Special Topics
Undergraduate Prerequisites: Graduate standing or consent of instructor.
Advanced study of a specific research topic in systems engineering. Intended primarily for advanced graduate students. On Demand. Var cr. -
ENG SE 701: Optimal and Robust Control
Undergraduate Prerequisites: ENG EC 501 or ENG ME 501 or ENG SE 501.
This course is aimed at an introduction (with rigorous treatment) to the fundamentals of optimal and robust control. It will be divided roughly into two parts. The first will cover aspects of robust control including model reduction, H_2 and H_ infinity control, and feedback control of uncertain systems. The second will delve into optimal control including topics such as the linear quadratic regulator, the calculus of variations, the maximum principle, and the Hamilton-Jacobi-Bellman equation. Same as ENG EC701 and ME 701. Students may not receive credits for both. -
ENG SE 710: Dynamic Programming and Stochastic Control
Undergraduate Prerequisites: CAS MA 381 or ENG EK 500 or ENG ME 308; and ENGEC402, ENGEC501 or ENGME510
Introduction to sequential decision making via dynamic programming. The principle of optimality as a unified approach to optimal control of dynamic systems and Markovian decision problems. Applications from control theory and operations research include linear-quadratic problems, the discrete Kalman Filter, inventory control, network, investment, and resource allocation models. Adaptive control and numerical solutions through successive approximation and policy iteration, suboptimal control, and neural network applications involving functional approximations and learning. Same as ENG EC 710 and ENG ME 710. Students may not receive credits for both. -
ENG SE 714: Advanced Stochastic Modeling and Simulation
Undergraduate Prerequisites: ENG EK 500; or equivalent, knowledge of stochastic processes, or consent of the instructor.
Introduction to Markov chains, point processes, diffusion processes as models of stochastic systems of practical interest. The course focuses on numerical and simulation methods for performance evaluation, optimization, and control of such systems. Same as ENG ME 714. Students may not receive credits for both. -
ENG SE 724: Advanced Optimization Theory and Methods
Undergraduate Prerequisites: ENGEC524 or consent of instructor.
Complements ENGEC524 by introducing advanced optimization techniques. Emphasis on nonlinear optimization and recent developments in the field. Topics include: unconstrained optimization methods such as gradient and incremental gradient, conjugate direction, Newton and quasi-Newton methods; constrained optimization methods such as projection, feasible directions, barrier and interior point methods; duality; and stochastic approximation algorithms. Introduction to modern convex optimization including semi-definite programming, conic programming, and robust optimization. Applications drawn from control, production and capacity planning, resource allocation, communication and sensor networks, and bioinformatics. Same as ENG EC 724 and ENG ME 724. Students may not receive credits for both. -
ENG SE 725: Queueing Systems
Undergraduate Prerequisites: ENG EK 500 or ENG EC 505; or consent of instructor.
Performance modeling using queueing networks, analysis of product form and non-product form networks, numerical methods for performance evaluation, approximate models of queueing systems, optimal design and control of queueing networks. Applications from manufacturing systems, computer systems and communication networks. Same as ENG EC 725 and ENG ME 725. Students may not receive credits for both. -
ENG SE 732: Combinatorial Optimization and Graph Algorithms
Undergraduate Prerequisites: ENG ME 411 or CAS CS 330; or equivalent course on optimization or algorithms.
Design data structures and efficient algorithms for priority queues, minimum spanning trees, searching in graphs, strongly connected components, shortest paths, maximum matching, and maximum network flow. Some discussion of intractable problems and distributed network algorithms. Same as ENG EC 732, ENG ME 732. Students may not receive credits for both. -
ENG SE 733: Discrete Event and Hybrid Systems
Undergraduate Prerequisites: ENG EK 500; or equivalent or consent of instructor.
Review of system theory fundamentals distinguishing between time-driven and event-driven dynamics. Modeling of Discrete Event and Hybrid Systems; Automata, Hybrid Automata, Petri Nets, basic queueing models, and stochastic flow models. Monte Carlo computer simulation: basic structure and output analysis. Analysis, control, and optimization techniques based on Markov Decision Process theory with applications to scheduling, resource allocation, and games of chance. Perturbation Analysis and Rapid Learning methods with applications to communication networks, manufacturing systems, and command-control. Same as ENG EC 733 and ENG ME 733. Students may not receive credits for both.
