Master of Engineering inElectrical Engineering

The major topics covered in this course include Signals and Systems representations, classifications, and analysis using; Difference and Differential Equations, Laplace Transform, Z-Transform, Fourier series, Fourier Transform, Fast Fourier Transform (FFT), Discrete-Time Fourier Transform (DTFT) and Discrete Fourier Transform (DFT).

Review of probability theory and random variables; mathematical description of random signals; linear system response; Wiener, Kalman, and other filtering.

Supervision of individual research projects leading to MS or MEng papers. Written and oral reports are required.

This course will cover the foundations on neural networks and deep learning networks. It covers the core concepts of deep neural networks, including the convolutional neural networks for image recognition, recurrent neural networks for sequence generation, and generative adversarial networks for image generation.

This course covers basic as well as advanced concepts to gain a detailed understanding of Natural Language Processing tasks such as language modeling, text to speech generation, natural language understanding, and natural language generation. Students can learn the necessary skills to design a range of applications, including sentiment analysis, translating between languages, and answering questions. The practical implementation of these applications with deep neural networks is also discussed.

This course will teach the foundations of AI and give students a practical understanding of the field. This course gives an overview of the core concepts of AI, including the intelligent agents, knowledge and reasoning, reinforcement learning, planning and acting, belief networks, computational learning, Markov decision process, and more.

This course focuses on the design of computer-based, machine vision systems using appropriate algorithms and best practices. Students will learn image representation and structuring; feature extraction and segmentation; and information extraction, filtering, and analysis.

Theories and practices of solar electric systems, including component selection, performance simulation, grid interconnection, codes, and design documentation.

Comprehensive study of the fundamentals, process characteristics, economics, and practical applications of various additive manufacturing processes.

Explores processes with a focus on the fundamentals of sintering and fusion of metals, ceramics, and polymers.

Explore design methods and tools for additive manufacturing, including opportunistic and restrictive aspects of different additive manufacturing processes and their related industry applications.

This course will explore the development of analytics systems and the application of best practices and established software design principles using the Python programming language and its several toolkits.

This course delves into the utilization of Programmable Logic Devices (PLDs), specifically Field Programmable Gate Arrays (FPGAs), renowned for their application in rapid prototyping and real-time designs.

Provides detailed performance analysis of communications systems first studied in introductory communications courses such as EE 360 or EE 461.

Fundamentals, power transformers, transmission lines, power flow, fault calculations, power system controls.

Electromagnetic field theory fundamentals with application to transmission lines, waveguides, cavities, antennas, radar, and radio propagation.

Parametric modeling, spectral estimation, efficient transforms and convolution algorithms, multirate processing, and selected applications involving non-linear and time-variant filters.

Examine neural networks by exploiting graph theory for offering alternate solutions to classical problems in signal processing and control.

Continuous and discrete-time linear control systems; state variable models; analytical design for deterministic and random inputs; time-varying systems and stability.

Variational methods in control system design; classical calculus of variations, dynamic programming, maximum principle; optimal digital control systems; state estimation.

Steady-state and dynamic model of synchronous machines, excitation systems, unit commitment, control of generation, optimal power flow.

Covers the application of advanced power electronics in power apparatus.

Tools to analyze and design discrete time (digital) control hardware and software systems; advantages of discrete time control, including increased flexibility in control modification and tuning, improved system reliability, easier system integration, and reduced design time.

An overview of renewable energy technologies and energy system analysis.

Technical and theoretical background for utility-scale solar energy conversion technologies to generate electric power.

Traditional and contemporary leadership theory is analyzed to determine effective strategies for leading projects and innovation within an engineering context.

This course conducts investigations of one-dimensional, two-dimensional, and three-dimensional dynamics, kinematics and design integrated into the study of vehicle dynamics.

This course covers ordinary and partial differential equations, linear algebra, numerical methods, special functions, vector calculus, Fourier methods, and complex analysis.

This course provides an overview of the fundamental principles and methods of optimal control, dynamic programming, and extremum-seeking control, with a focus on the application of these tools to a variety of problems in the energy generation, storage, and management domain.

Analysis of robotic systems; end effectors, vision systems, sensors, stability and control, off-line programming, simulation of robotic systems.

Descriptive statistics, hypothesis testing, power, estimation, confidence intervals, regression, one- and two-way ANOVA, chi-square tests, diagnostics.

Analysis of multivariate data; T-squared tests; partial correlation; discrimination; MANOVA; cluster analysis; regression; growth curves; factor analysis; principal components; canonical correlations.