This course introduces computational problem solving. Basic problem-solving techniques and algorithm development through the process of top-down stepwise refinement and functional decomposition are introduced throughout the course. Classical numerical problems encountered in science and engineering are used to demonstrate the development of algorithms and their implementations. May not be taken for credit by Computer Science, Software Engineering, or Computer Engineering majors. This course is designed for Electrical Engineering and Micro-Electronic Engineering majors and students interested in the Electrical Engineering minor.

This course is one of the capstone options in the MS in Computing Security program. It offers students the opportunity to investigate a selected topic and make an original contribution which extends knowledge within the computing security domain. Students must submit an acceptable proposal to a thesis committee (chair, reader, and observer) before they may be registered by the department for the MS Thesis. Students must defend their work in an open thesis defense and complete a written report of their work before a pass/fail grade is awarded. As part of their original work, students are expected to write and submit an article for publication in a peer reviewed journal or conference.

This course covers the first principles and fundamentals of clean and renewable energy systems and sources. Various quantum-mechanical and electromagnetic devices and systems will be analyzed, designed and examined using software and CAD tools. Topics include: geothermal, hydro, nuclear, solar, wind, and other energy sources. Societal, ethical, economical, and environmental aspects of nanotechnology-enabled clean energy and power are also discussed.

Linear Systems provides the foundations of continuous and discrete signal and system analysis and modeling. Topics include a description of continuous linear systems via differential equations, a description of discrete systems via difference equations, input-output relationship of continuous and discrete linear systems, the continuous time convolution integral, the discrete time convolution sum, application of convolution principles to system response calculations, exponential and trigonometric forms of Fourier series and their properties, Fourier transforms including energy spectrum and energy spectral density. Sampling of continuous time signals and the sampling theorem, the Laplace, Z and DTFT. The solution of differential equations and circuit analysis problems using Laplace transforms, transfer functions of physical systems, block diagram algebra and transfer function realization is also covered. A comprehensive study of the z transform and its inverse, which includes system transfer function concepts, system frequency response and its interpretation, and the relationship of the z transform to the Fourier and Laplace transform is also covered. Finally, an introduction to the design of digital filters, which includes filter block diagrams for Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) filters is introduced.

This course introduces students to the study of linear continuous-time classical control systems, their behavior, design, and use in augmenting engineering system performance. The course is based on classical control methods using Laplace-transforms, block-diagrams, root-locus, and frequency-domain analysis. Topics include: Laplace-transform review; Bode plot review; system modeling for control; relationships of transfer-function poles and zeros to time-response behaviors; stability analysis; steady-state error, error constants, and error specification; feedback control properties; relationships between stability margins and transient behavior; lead, lag, and PID control; root-locus analysis and design; frequency-response design and Nyquist stability. A laboratory will provide students with hands-on analysis and design-build-test experience, and includes the use of computer-aided design software such as MATLAB.

One semester of paid work experience in electrical engineering.

This course deals with the topics related to electric power transmission and distribution. Topics covered in this course include: Three Phase System – Wye and Delta connections, Transformers – equivalent circuit –performance characteristics, Balanced and Unbalanced System Analysis, Transmission and Distribution Line Design Considerations, Transmission Line Protection, Transmission Line Faults and Fault Analysis.

This course will introduce the details of electric power markets and the techniques to better use the available resources. Topics include the description of steam generation and renewable energy sources. Formulation of the cost associated with the generation and the optimization methods to minimize this cost in the economic dispatch problem. Unit commitment. Optimal power flow formulation and its solution methods. Introduction to smart grid technologies and challenges.

This course deals with the topics related to electric power transmission and distribution. Topics covered in this course include: Three Phase System – Wye and Delta connections, Transformers – equivalent circuit –performance characteristics, Balanced and Unbalanced System Analysis, Transmission and Distribution Line Design Considerations, Transmission Line Protection, Transmission Line Faults and Fault Analysis.

This course will introduce the details of electric power markets and the techniques to better use the available resources. Topics include the description of steam generation and renewable energy sources. Formulation of the cost associated with the generation and the optimization methods to minimize this cost in the economic dispatch problem. Unit commitment. Optimal power flow formulation and its solution methods. Introduction to smart grid technologies and challenges.

This course deals with a complete description of physical systems its analysis and design of controllers to achieve desired performance. The emphasis in the course will be on continuous linear systems. Major topics are: state space representation of physical systems, similarities/differences between input-output representation (transfer function) and state spate representations, conversion of one form to the other, minimal realization, solution of state equations, controllability, observability, design of control systems for desired performance, state feedback, observers and their realizations.

The course covers different optimization techniques, as applied to feedback control systems. The main emphasis will be on the design of optimal controllers for digital control systems. The major topics are: Different performance indices, formulation of optimization problem with equality constraints, Lagrange multipliers, Hamiltonian and solution of discrete optimization problem. Discrete Linear Quadratic Regulators (LQR), optimal and suboptimal feedback gains, Riccati equation and its solution, linear quadratic tracking problem. Dynamic Programming - Bellman's principle of optimality - Optimal controllers for discrete and continuous systems - Systems with magnitude constraints on inputs and states.

An independent engineering project or research problem to demonstrate professional maturity. A formal written thesis and an oral defense are required. The student must obtain the approval of an appropriate faculty member to guide the thesis before registering for the thesis. A thesis may be used to earn a maximum of 6 credits.

This course is used to fulfill the graduate paper requirement under the non-thesis option for the MS degree in electrical engineering. The student must obtain the approval of an appropriate faculty member to supervise the paper before registering for this course.

This course introduces students to the study of linear control system behavior for design and use in augmenting system performance. This is accomplished through classical control methods using Laplace transforms, block diagrams, root locus, and frequency domain analysis. Topics include: Laplace transform review, system modeling for control, fundamentals of time response behavior, stability analysis, steady-state error and design, feedback control properties, PID control, root locus analysis and design, and frequency response design. A laboratory will provide students with hands-on analysis and design-build-test experience.

Leveraging big data to deliver solutions to complex challenges requires an organizational leadership that is responsible for understanding and directing these approaches to achieve their business goals. Rather, organizational leadership is responsible for understanding and directing these approaches to achieve business goals. Toward this end, this course provides students with the knowledge and confidence needed to imbue organizations with innovative, efficient, and sustainable aspects that will carry them into the future through an understanding and application of business analytics and artificial intelligence (AI). Students will gain a theoretical and working knowledge of data science, enabling the identification of the challenges that analytics, machine learning, and artificial intelligence can address. An introduction to the ethical and social implications of analytics and AI in terms of guiding an organization’s strategic assets for the future will also be presented.