Selected topics in applied engineering mathematics. Topics include advanced linear algebra, signal theory, and optimization methods.

Computer-automated design of digital circuits. Functional specification; structural and behavioral modeling using hardware description languages; simulation for design verification and timing analysis; circuit synthesis for FPGA implementation; testing and fault diagnosis.

Basics of embedded System on a Chip (SoC) design implemented in a Field Programmable Gate Array (FPGA) including memory systems, I/O design (memory mapping, SPI, I2C), Xilinx Microblaze, video, testing, and how to design for test. Based on the Microblaze processor, the course will cover how to design an SoC and extend it via wrapping interface modules for a bus-based communication architecture. Drivers and programming the SoC will also be covered.

Advanced topics in computer architecture, including instruction set design, instruction pipelines, super scaler and very-long instruction word processors, cache and virtual memory systems, multiprocessor systems, large data storage systems and computer networks.

Hardware and software characteristics of real-time concurrent and distributed reactive control systems; design methodologies; performance analysis; case studies and development projects.

This course offers in-depth exploration of the fundamental and state-of-art methods, techniques, and knowledge for certifying customer requirements of any given digital system through a proven validation process. You will also acquire an understanding of determining whether a digital system is built correctly through formal verification. Verification and validation are essential for computer engineers of any system from the design phase to manufacturing phase to establish a product’s quality and fit for purpose prior to deployment. You will learn the significance of verification and validation through a combination of lectures and hands-on assignments designed to reinforce core values of the engineering process. Students may not earn credit in both ELC 4317 and 5317.

Programming massively parallel systems, such as graphics processing units (GPU). Topics covered include CUDA fundamentals, thread mapping and resource allocation, scheduling and latency, memory types, performance considerations, numerical issues, vector and matrix operations, convolution, sparsity, and Convolutional Neural Networks (CNN).

Investigation of the materials used in electrical and optical devices, including metals, insulators, and semiconductors. Topics include crystal structure, quantum theory, band structure, and thermal, electrical, optical, dielectric, magnetic, and superconducting properties of solids.

Topics will include an introduction to semiconductor materials (optical and electronic properties), p-n junctions, transistors, bipolar junction transistors, field effect transistors, light-emitting diodes, lasers, and photodetectors.

Introduction to the principles of operation of lasers, including interaction of light and matter, spontaneous and stimulated emission, optical gain and absorption, population inversion, optical resonators, laser rate equations, waveguides, Gaussian beams and wave propagation. Additional topics include characteristics and applications of specific lasers, including gas lasers and semiconductor lasers.

Intermediate to advanced knowledge of optoelectronics and photonics with emphasis on optical and electronic materials and devices including optical fibers, semiconductor light-emitting diodes and lasers, photodetectors, integrated photonic devices and circuits.

An in-depth study of electromagnetic fields and waves and their applications in modern wireless communication and sensor systems. Topics include Maxwell's equation for complex media, scalar and vector potentials, non-ideal transmission lines, cylindrical waveguides, general properties of guided waves, and antennas.

Fundamentals of microwave sensor design and applications. Emphasis on understanding the basic principles, fundamental electrical and magnetic properties of materials, and the sensor configurations of RF/microwave instruments used in industrial and biomedical application.

Design and analysis of solid-state electronic circuits at RF and microwave frequencies. Emphasis on operational characteristics and design procedures for two- and three-terminal semiconductor devices and the associated passive components and circuit fabrication techniques used for generating, amplifying, and processing signals in this frequency range.

The design of linear amplifiers and oscillators at microwave frequencies, including an emphasis on design procedures for optimum gain, stability, and noise performance of amplifiers and the negative resistance method for oscillators.

Electromagnetics of radar, signal processing of radar, radar imaging, Doppler processing, and radar antenna arrays. Analysis and design principles, simulation, and measurement.

Introduction to the processing and analysis of images in higher dimensions, including images and video. Characterization of higher dimensional signals. Multidimensional Fourier analysis, FFT's, systems and convolution. Reconstruction of images from projections. Tomography, Abel transforms, Radon transforms. Synthesis and restoration of signals using projection methods. Alternating projections onto convex sets.

Applications of signal theory and digital signal processing concepts toward biomedical signals. Topics include filters, signal modeling, adaptive methods, spectral analysis and statistical signal processing methods.

Foundational treatment of probability, random variables and stochastic processes used in the analysis of random signals and noise in many areas of engineering. Topics include the modeling and properties of probability, scalar and vector random variables, the central limit theorem, stochastic processes, stationarity, ergodicity, the Karhunen-Loeve expansion, power spectral densities, response of linear systems to random signals, and Markov chains.

Overview of medical imaging systems including physics, modelling, and image reconstruction techniques. Systems covered include x-ray and ion CT, MRI, ultrasound, and PET/SPECT.

Unified introduction to the theory, implementation, and applications of statistical and adaptive signal processing methods. Key topics focus on spectral estimation, signal modeling, adaptive filtering, and signal detection.

See BME 5357 for course information.

Foundational knowledge of computational intelligence and its application to engineering problems. Discriminant analysis, artificial neural networks, perception training and inversion, fuzzy logic, fuzzy inference engines, evolutionary computation, particle swarms, intelligent agents, and swarm intelligence.

Analysis of linear systems, including system modeling, state-variable representations, discrete-time systems, linear algebra, linear dynamic equations, stability, observability, controllability, state-feedback and state-estimators, realization, and pole placement.

Optimal control problems, static optimization, optimal control of discrete-time systems, the variational approach to optimal control, linear quadratic regulator problems, the maximum principle, extensions of LQR problem, time-optimal control problems, dynamic programming.

Introduction to intelligent control and optimization using a control-engineering approach. Topics include decision-making techniques, neural network architectures for modeling and control, system identification, fuzzy systems, evolutionary algorithms, and swarm intelligence.

Includes deep learning principles, artificial neural networks, layered architecture, feature extraction and representation learning, nonlinear transformation, backpropagation and gradient descent, probabilistic inference, regularization and generalization, and transfer learning. It introduces convolutional neural networks, recurrent neural networks, attention mechanisms, generative adversarial networks, and deep reinforcement learning, all within the context of artificial intelligence applications.

Topics include: information models, entropy measures, data compression, coding theory, error correcting codes, the Kraft inequality, optimal codes, Shannon coding theorem, Burg’s theorem, evolutionary informatics, Kolmogorov complexity, algorithmic information theory, and Chaitin's number.

Introduction to distributed power generation, power conversion topologies and their control, power factor correction circuits, harmonic concepts and power quality, modeling and control of grid-connected loads and filters, interconnection standards and control issues, and control systems for rotating machines.

Designed for students in the process of selection of thesis or project topic. Students will gain experience in literature and/or laboratory research methods and formulation of a project appropriate for their area.

See EGR 5396 for course information.

See EGR 5397 for course information.

Students completing a master's program with a thesis must complete six hours of ELC 5V99.

For research credit prior to admission to candidacy for an advanced degree. Credit will be given for the amount of work done. May be repeated for credit through 45 hours.

Supervised research for developing a dissertation prospectus that will be the subject of the preliminary exam that will admit students to candidacy. A student may repeat this course for credit with a maximum of ten total hours. Registration for this course is sufficient for achieving full-time status.

Required of all doctoral candidates. No fewer than 24 semester hours will be accepted for a dissertation. Students may not enroll for dissertation hours until they have been officially accepted into candidacy for the doctoral degree. Doctoral candidates must register for at least one semester hour of dissertation every semester (summer semester excluded).