Electrical Engineering (EE)

EE 2100. Circuits I Lab.

This course accompanies EE2300 Circuits I. It includes the lab component, focusing on practical circuit analysis and measurement of DC and voltage. Students will explore resistive, capacitive, and inductive circuits through basic techniques to understand circuit behaviors and effects. It covers practical skills, experiment design, analysis, and computer-aided implementation. The course ensures a foundational understanding of electrical system principles and analytic skills for electronic systems. Prerequisite: MATH 2471 with a grade "C" or better. Corequisite: EE 2300 with a grade "C" or better.

EE 2120. Digital Logic Lab.

This course is an introduction to fundamental computer technologies, including Boolean logic design, logic circuits and devices, and basic computer hardware are studied. Laboratories provide hands-on experience with electricity, combinational and sequential digital circuits, and computer hardware Corequisite: CS 1428 and EE 2320 with a grade of "C" or better.

EE 2300. Circuits I.

This course provides an introduction to the profession of Electrical Engineering and its specialties. Fundamental DC and sinusoidal steady-state circuit analysis techniques include Ohm's law, power, Kirchoff's laws, and Thevenin and Norton equivalent circuits. Prerequisites: MATH 2471 with a grade of "C" or better. Corequisites: EE 2100 with a grade "C" or better.

EE 2320. Digital Logic.

This course is an introduction to fundamental computer technologies, including Boolean logic design, logic circuits and devices, and basic computer hardware are studied. Laboratories provide hands-on experience with electricity, combinational and sequential digital circuits, and computer hardware. Corequisites: CS 1428 and EE 2120 with a grade of "C" or better.

EE 2400. Circuits I.

This course provides an introduction to the profession of Electrical Engineering and its specialties. Fundamental DC and sinusoidal steady-state circuit analysis techniques include Ohm's law, power, Kirchoff's laws, and Thevenin and Norton equivalent circuits. Prerequisites: MATH 2471 with a grade of "C" or better.

EE 2420. Digital Logic.

An introduction to fundamental computer technologies, including Boolean logic design, logic circuits and devices, and basic computer hardware are studied. Laboratories provide hands-on experience with electricity, combinational and sequential digital circuits, and computer hardware. Corequisite: CS 1428 with a grade of "C" or better.

EE 3100. Instrumentation Laboratory.

This course provides the student with proficiency in the use of lab instrumentation and circuit simulation. Prerequisite: EE 2300 and MATH 3323 both with grades of "C" or better. Corequisite: EE 3300 with grade of "C" or better.

EE 3120. Microprocessors Lab.

The course is an introduction to microprocessors, their programming, and interfaces to peripherals. Prerequisite: EE 2320 and EE 2120 with grades of "C" or better. Corequisite: EE 3320 with a grade of "C" or better.

EE 3150. Microelectronics Laboratory.

This laboratory course provides training in the analysis and characterization of analog circuits. Prerequisite: EE 3300 and EE 3100 with grades of "C" or better. Corequisite: EE 3350 with a grade of "C" or better.

EE 3300. Circuits II.

This course discusses transient analysis, application of Laplace transforms, Bode plots, and network principles. Prerequisites: EE 2300 and EE 2100 and MATH 3323 with grades of "C" or better. Corequisites: EE 3100 with grade of "C" or better.

EE 3320. Microprocessors.

This course is an introduction to microprocessors, principles of operation, assembly language programming, timing analysis, and I/O interfacing. Prerequisites: EE 2320 and EE 2120 with a grade of "C" or better. Corequisites: EE 3120 with a grade of "C" or better.

EE 3326. Numerical and Scientific Data Analysis Using Python.

This course introduces Python programming for engineers. Topics include basics of Python programming, introduction to numerical Python (NumPy), scientific programming using Python (SciPy), data visualization using Matplotlib, data processing using Pandas and introduction to Object Oriented Programming using Python. Prerequisite: CS 1342 or CS 1428 with a grade of "C" or better.

EE 3340. Electromagnetics.

This course covers wave propagation, Maxwell’s equations, transmission lines, wave guides, and antennas. Prerequisite: [EE 3300 or EE 3400] and MATH 2393 and PHYS 2326 and PHYS 2335 all with grades of "C" or better.

EE 3350. Microelectronics.

This course focuses on analysis and design of active device equivalent circuits with emphasis on transistors, switching circuits, operational amplifiers, integrated circuits, feedback, and frequency response. Prerequisites: EE 3300 or EE 3400 with a grade of "C" or better. Corequisite: EE 3150 with a grade of "C" or better.

EE 3355. Solid State Devices.

This course covers semiconductor materials, principles of carrier motion, operating principles and circuit models for diodes, bipolar transistors and field-effect transistors, and an introduction to integrated circuits. Prerequisite: [EE 3300 or EE3400] and PHYS 2326 both with grades of "C" or better.

EE 3370. Signals and Systems.

This course covers frequency domain representation of signals and systems and frequency domain concepts for circuit analysis and design. Other topics include transfer function and frequency response, Laplace and z-transforms, Fourier series, Fourier transform, and sampling. Prerequisite: EE 3300 or EE3400 with a grade of "C" or better.

EE 3400. Circuits II.

This course includes a brief review of EE 2400, transient analysis, application of Laplace transforms, Bode plots, and network principles. Materials learning in EE 2400 is extended and applied here. Prerequisites: EE 2400 and MATH 3323 both with grades of "C" or better.

EE 3420. Microprocessors.

Introduction to microprocessors, principles of operation, assembly language programming, timing analysis, and I/O interfacing. Prerequisites: EE 2420 with a grade of "C" or better.

EE 4152. Introduction to VLSI Design Lab.

This course is an introduction to CAD tools for VLSI design and verification. Prerequisite: EE 3350 and [CS 2420 or EE 2320] both with grades of "C" or better. Corequisite: EE 4252 with grade of "C" or better.

EE 4155. Analog and Mixed-Signal Lab.

This course explores operational amplifier design applications, feedback, offset, stability, compensation, random signals and noise, discrete time circuitry analog-to-digital converters, and digital-to-analog converters. Prerequisite: EE 3370 and [EE 4350 or [EE 3350 and EE 3150]] with grades of "C" or better. Corequisite: EE 4255 with a grade of "C" or better.

EE 4180. Electric Machines Lab.

This course is the lab component of EE 4380 Electric Machines and consists of the hands-on exploration and analysis of various electric machines and their controllers. Prerequisite: EE 3340 with a grade of "C" or better. Corequisite: EE 4380 with a grade of "C" or better.

EE 4192. Microelectronics Manufacturing I Laboratory.

This course introduces students to the various process modules related to semiconductor fabrication, including oxidation, diffusion, thin film deposition, lithography and etching. Prerequisite: [CHEM 1341 or CHEM 1335] with a grade of "C" or better. Corequisite: EE 4392 with a grade of "C" or better.

EE 4252. Introduction to VLSI Design.

This course addresses the analysis and design of CMOS integrated circuits, and includes an introduction to CAD tools for VLSI design. Prerequisite: EE 3350 and [CS 2420 or EE 2320] both with grades of "C" or better. Corequisite: EE 4152 with grade of "C" or better.

EE 4255. Analog and Mixed-Signal Design.

This course focuses on operational amplifier design applications, feedback, offset, stability, and compensation. Introduction to random signals and noise, discrete time circuitry analog-to-digital converters, and digital-to-analog converters. Prerequisite: EE 3370 and [EE 4350 or [EE 3350 and EE 3150]] with grades of C or better. Corequisite: EE 4155 with a grade of "C" or better.

EE 4290. Electrical Engineering Design I.

Senior Design is the culmination of the Texas State University engineering experience. This course is a team-based design of a system or component using design processes as practiced in industry. The team will design, implement and verify their project, while documenting the project definition, design decisions, and implementation details. The outcomes will include oral presentations, written reports, and project demonstrations. Prerequisite: EE 3320 and EE 3120 and EE 3350 and EE 3370 and IE 3320 with grades of "C" or better. Corequisite: EE 4252 or EE 4356 or EE 4360 or EE 4370 with a grade of "C" or better.

EE 4291. Electrical Engineering Design II.

Senior Design is the culmination of the Texas State University engineering experience. This course is a team-based design of a system or component using design processes as practiced in industry. The team will design, implement and verify their project, while documenting the project definition, design decisions, and implementation details. The outcomes will include oral presentations, written reports, and project demonstrations. Prerequisite: EE 4290 with a grade of "C" or better. Corequisite: EE 4252 or EE 4370 with a grade of "C" or better.

EE 4321. Digital Systems Design Using HDL.

This course will cover the design of digital systems using HDL including implementation of custom microprocessor and peripheral architectures. Prerequisite: [EE 3320 or EE 3420] with a grade of "C" or better.

EE 4323. Digital Image Processing.

This course provides the necessary fundamental techniques to analyze and process digital images. It covers principles, concepts, and techniques of digital image processing and computer vision. Prerequisite: EE 3370 and [EE 3320 or EE 3420] with grades of "C" or better.

EE 4331. Introduction to Machine Learning for Engineering Applications.

This course covers an introduction to machine learning focused on deep learning techniques using engineering applications with Python. Topics include model characteristics, neural network theory, classifiers for network and signal processing applications, regression and convolutional modeling for object-detection, time-series and forecasting machine learning models for Smart City concepts. Prerequisite: CS 1428 or CS 1342 with a grade of "C" or better.

EE 4332. Introduction to Computer-Aided Engineering Simulation on HPC Systems.

This course covers the introductory development of simulations for engineering applications that are solved using High-Performance Computing environments. Topics include programming techniques for multicore processors, processor and memory architecture, computation for dense and sparse linear algebra applications, computational temperature analysis, fluid dynamics, stencil, stochastic algorithms, and other applications. Prerequisite: CS 1428 with a grade of "C" or better.

EE 4350. Electronics II.

Analysis and design of integrated circuits, feedback, and frequency response. Prerequisites: EE 3350 with a grade of "C" or better.

EE 4351. Fundamentals of Electroceramics.

Introduction to binary and ternary phase diagrams, non-centro-symmetric crystal structures and symmetry groups, nonlinear dielectrics (including ferroelectricity, piezoelectricity, pyroelectricity), nonlinear magnetics, oxide wideband gap semiconductors, detectors and sensors, brief introduction to MEMS, radhard electronics, and spintronics technology. Research oriented labs related to materials processing, characterization, fabrication, and testing. Prerequisite: ENGR 2300 with a grade of "C" or better and a minimum 2.25 Overall GPA. Corequisite: EE 3355 with a grade of "C" or better.

EE 4352. Introduction to VLSI Design.

Analysis of design of CMOS integrated circuits. Introduction to CAD tools for VLSI design. Prerequisites: EE 3350 and [CS 2420 or EE 2420] both with grades of "C" or better.

EE 4353. Fundamentals of Advanced Semiconductor Technology.

Key concepts of advanced semiconductor technology including Moore’s law, MOSFETs and CMOS, CMOS scaling, high-K gate dielectrics, new channel materials replacing silicon, three dimensional device structures, compound semiconductor MESFET, HEMT, LED, Lasers and solar cells. Prerequisite: EE 3355 with a grade of "C" or better.

EE 4354. Flexible Electronics.

This course will cover the materials systems, processes, device physics and applications of flexible electronics. The materials range from amorphous and nanocrystalline silicon, organic and polymeric semiconductors to solution cast films of carbon nanotubes. Real device discussions include high speed transistors, photovoltaics, flexible flat-panel displays, medical image sensors, etc. Prerequisites: EE 3350 and EE 3355 and EE 4350 all with grades of "C" or better or instructor approval.

EE 4355. Analog and Mixed Signal Design.

Operational amplifier design applications, feedback, offset, stability, and compensation. Introduction to random signals and noise, discrete time circuitry analog-to-digital converters, and digital-to-analog converters. Prerequisites: EE 3370 and EE 4350 both with grades of "C" or better.

EE 4356. Power Electronics.

This course provides an introduction to power electronics and the use of such circuits for the control and conversion of electric power. Topics include semiconductor power devices and characteristics, DC-DC and multilevel converters, power inverters, and AC voltage controllers. Prerequisite: [EE 4350 or [EE 3350 and EE 3150]] with a grade of "C" or better.

EE 4357. Introduction to Power Systems.

This course introduces the analysis of various elements of power systems, including power generation, transformer action, transmission line modeling, symmetrical components, power factor correction, real and quadrature power calculations, load flow analysis, and economic considerations in operating systems. Prerequisite: [EE 3300 or EE 3400 or ENGR 3373] with a grade of "C" or better.

EE 4359. Advanced Electronic Materials and Devices.

This course introduces students to modern fabrication techniques, properties, and applications of conventional and emerging electronic materials. Topics include thin film deposition techniques and modern fabrication concepts, heterointerfaces, and structural, electronic, thermal, magnetic, and optical properties of electronic materials. The course includes discussions about practical devices, including solar cells, light-emitting devices, display devices, and emerging flexible electronic devices. Prerequisite: EE 3350 with a grade "C" or better.

EE 4360. Linear Control Systems.

This course provides an introduction to linear continuous-time and discrete-time automatic control systems. Topics include time and frequency domain modeling and analysis, state variable analysis, feedback, transient and steady state response, stability, and sensitivity. Prerequisite: EE 3370 and MATH 3376 both with grades of "C" or better.

EE 4370. Communication Systems.

This course covers transmission of signals through linear systems, analog and digital modulation, filtering, and noise. Prerequisites: EE 3370 and IE 3320 both with grades of "C" or better.

EE 4372. Communication Networks.

This course covers data communication concepts, protocols, algorithms, 7-layer OSI model, physical media, LAN architecture and components, Ethernet, TCP/IP, and related standards. Prerequisite: EE 3320 or EE 3420 with a grade of "C" or better. Corequisite: EE 3370 with a grade of "C" or better.

EE 4374. Introduction to Wireless Communication.

This course covers the principles, practice, and system overview of mobile systems. Topics include modulation, demodulation, coding, encoding, and multiple access techniques. Prerequisites: EE 4370 with a grade of "C" or better.

EE 4375. Building a Smart Grid Architecture.

In this course, students will learn the current 20th-century power grid structure and the key elements required to transform it to a 21st-century Smart Grid. Topics include two-way power/data flow to monitor, control, manage and integrate traditional bulk generation and bulk/renewable/distributed generation. Prerequisite: EE 3370 with a grade of "C" or better.

EE 4376. Introduction to Telecommunications.

This course covers the fundamentals of telecommunications, telephone networks, switching and transmission systems, circuit and packet switching, cell processing, and queuing theory and applications. Co-requisite: EE 4370 with a grade of "C" or better.

EE 4377. Introduction to Digital Signal Processing.

This course covers discrete systems, convolution, spectral analysis, and FIR and IIR filter design. Prerequisites: EE 3370 with a grade of "C" or better.

EE 4378. Data Compression and Error Control Coding.

Introduction to information theory, information content of messages, entropy and source coding, data compression, channel capacity data translation codes, and fundamentals of error correcting codes. Corequisite: EE 4370 with a grade of "C" or better.

EE 4380. Electric Machines.

This course teaches the principles and analysis of electromechanical systems. Students will develop analytical techniques for predicting device and system interaction characteristics, strengthen understanding of the phenomena and interactions in electromechanics, and learn to design major classes of electric machines. Prerequisite: EE 3340 with a grade of "C" or better. Corequisite: EE 4180 and EE 4360 with a grade of "C" or better.

EE 4381. Sustainable Energy & Storage.

This course examines the consumption and production of energy and the principles and technologies behind renewable energy sources. It also introduces the basics of energy storage systems such as batteries, gravitational, and hybrid. Prerequisite: [EE 3300 or EE 3400] and PHYS 2326 and CHEM 1335 all with a grade of "C" or better.

EE 4382. Advanced Power Systems.

This course is an advanced treatment of various elements of power systems, including symmetrical and unsymmetrical faults, symmetrical components, system protection, transient stability, transient operation of transmission lines, and supervisory control and data acquisition (SCADA). Prerequisite: EE 4357 with a grade of "C" or better.

EE 4390. Electrical Engineering Design I.

This course is a team-based design of a system or component, which will include oral presentations and written reports. (WI) Prerequisites: EE 3420 and EE 3350 and EE 3370 and IE 3320 all with grades of "C" or better. Corequisites: EE 4352 or EE 4356 or EE 4360 or EE 4370 any with a grade of "C" or better.

EE 4391. Electrical Engineering Design II.

Advanced team-based design of a system or component, which will include oral presentations and written reports. (WI) Prerequisites: EE 4390 with a grade of "C" or better. Corequisite: EE 4352 or EE 4370 either with a grade of "C" or better.

EE 4392. Microelectronics Manufacturing I.

This course provides an overview of integrated circuit fabrication. This includes crystal growth, wafer preparation, epitaxial growth, oxidation, diffusion, ion-implantation, thin film deposition, lithography, etching, device and circuit formation, packaging, and testing. The laboratory component involves production and testing of a functional semiconductor device. Prerequisite: [CHEM 1341 or CHEM 1335] with a grade of "C" or better.

EE 5320. Advanced Computer Architecture and Arithmetic.

This course teaches design and analysis of high-performance computer systems, focusing on quantitative analysis of the latest processors and compilers. Current processor architectures are surveyed for system design. Topics include instruction sets, parallelizing architectures, pipelining, I/O, memory and cache organization, parallel/vector processing, fast arithmetic units design, and implementation using HDL.

EE 5321. Computer-Aided Engineering Simulations on HPC Systems.

This course covers development of simulations for engineering applications that are solved using High Performance Computing (HPC) environments. Topics include programming techniques for multicore processors, processor and memory architecture, computation for dense and spare linear algebra applications, computational temperature analysis, fluid dynamics, stencil and stochastic algorithms, and other applications. Prerequisite: EE 5320 with a grade of "C" or better.

EE 5323. Digital Image Processing.

This course provides the necessary fundamental techniques to analyze and process digital images. It covers principles, concepts, and techniques of digital image processing and computer vision. Restricted to students enrolled in the MS Engineering program.

EE 5330. Embedded and Real-Time Computing.

This course teaches development of embedded computing systems with strong resource constraints. Key concepts include managing constrained memory and processing speed limitations, and programming for soft and hard real-time constraints. Students will learn use of a Real-Time Operating System (RTOS).

EE 5331. Machine Learning for Engineering Applications.

This course covers an introduction to machine learning focused on deep learning techniques using engineering applications with Python. Topics include model characteristics, neural network theory, classifiers for network and signal processing applications, regression and convolutional modeling for object-detection, time-series and forecasting machine learning models for Smart City concepts. Prerequisite: ENGR 5310 with a grade of "C" or better.

EE 5350. Advanced Electronic Circuit Design.

This course includes low and high power RF amplifier design techniques, oscillators, FM demodulators, limiters, and mixer design. Additional topics include circuit design to minimize intermodulation and other forms of distortion, and RD and high-speed analog circuits with emphasis on digital-friendly applications.

EE 5353. Fundamentals of Advanced Semiconductor Technology.

In this course students will learn key concepts and trends of advanced semiconductor device technology. Topics include Moore’s law, MOSFET, CMOS and scaling, high-K gate dielectrics, new channel materials replacing silicon, three dimensional and compound semiconductor device structures. In addition students will be involved in laboratories and seminar presentations. Prerequisite: Instructor approval.

EE 5354. Flexible Electronics.

This course will cover the materials systems, processes, device physics and applications of flexible electronics. The materials range from amorphous and nanocrystalline silicon, organic and polymeric semiconductors to solution cast films of carbon nanotubes. Real device discussions include high speed transistors, photovoltaics, flexible flat-panel displays, medical image sensors, etc. Prerequisite: Instructor approval.

EE 5355. Electronic Materials and Devices.

This course covers theoretical concepts applicable to the understanding of unique properties exhibited by electronic materials, especially by dielectrics, oxide semiconductors, ferroelectrics, pyroelectrics, piezoelectrics, magnetic, and multifunctional and multiferroic materials. The various microelectronic devices and modern novel technologies based on these materials are emphasized.

EE 5357. Power Systems for Engineering.

This course introduces the analysis of various elements of power systems, including power generation, transformer action, transmission line modeling, symmetrical components, power factor correction, real and quadrature power calculations, load flow analysis, and economic considerations in operating systems.

EE 5360. Thin Film Technology.

This course covers the theoretical and practical aspects of thin film technology in modern devices. The design and fabrication of thin film heterostructures is discussed. Growth and nucleation of epitaxial thin films with diverse properties and devices with combined properties will be emphasized.

EE 5361. Nanofabrication Technology for Semiconductor Device Processing.

This course provides an overview of nanofabrication techniques for conventional and emerging micro- and nano-electronic devices. Topics include semiconductor crystal growth, wafer preparation, epitaxial growth, oxidation, control of dopant profiles for the formation of shallow junctions, ion-implantation, thin film deposition, photolithography, metallization etching, device and circuit formation, and testing.

EE 5372. Advanced Networking.

This course develops important theoretical and application topics related to advanced networking. Theoretical topics are introduced using mathematical treatments, including queuing theory and some random processes. The course includes applications of these topics to communications networks, and focuses on architectures, applications and technologies which affect modern computer and data networks.

EE 5374. Advanced Wireless Communication.

This course teaches principles and practices in designing and analyzing cellular and other wireless communication systems. Topics include RF propagation modeling, fast and slow fading, modulation, demodulation, coding, and multiple access techniques.

EE 5375. Smart Grid: an Application Development Platform.

This course introduces students to the development of real applications for the smart grid and models its performance with simulations and stochastic models. Topics include energy informatics, smart metering, home energy management, demand response, load disaggregation and APIs/OpenData. The mathematical tools used include: Optimization/Control, Machine Learning and Stochastic Processes.

EE 5377. Statistical Signal Processing.

This course develops the theory and applications of random processes using mathematical treatments, including elementary discrete and continuous time linear systems theory, elementary probability, and transform theory. Topics include applications of random processes to information and communication theory, estimation and detection, control, signal processing, and stochastic systems theory. Prerequisite: ENGR 5310 with a grade of "C" or better or instructor approval.

EE 5380. Advanced Electric Machines.

This course teaches the principles and analysis of electromechanical systems. Students will develop analytical techniques for predicting device and system interaction characteristics as well as learn to design major classes of electric machines.

EE 5381. Advanced Sustainable Energy & Storage.

This course examines the consumption and production of energy and the principles and technologies behind renewable energy sources. It also introduces the basics of energy storage systems such as batteries, gravitational, and hybrid. Current research in the field is examined.

EE 5382. Advanced Power Systems Analysis.

This course is an advanced treatment of various elements of power systems, including case studies, analysis of relevant peer-reviewed literature, symmetrical and unsymmetrical faults, symmetrical components, system protection, transient stability, transient operation of transmission lines, and supervisory control and data acquisition (SCADA).

EE 5398A. Antenna Theory, Design and Applications.

This course covers the basic theory, design and applications of antennas. The topics include antenna radiation, fundamental parameters of antennas, linear wire antennas, loop antennas, antenna arrays, long-periodic antennas, horn antennas, microstrip antennas and modern nano-antennas.

EE 5398B. Electronic Materials and Beyond for Sustainable Energy.

This course covers the basic science and technology for sustainable energy from the view of materials, where electronic materials are highly emphasized. The topics include solar cells, thermoelectrics, batteries, supercapacitors, artificial photosynthesis, fuel cells, biomass and nuclear energy.

EE 7199. Dissertation.

This course includes original research and writing in electrical engineering, to be accomplished under direct supervision of the PhD research advisor. While conducting dissertation research and writing, students must be continuously enrolled each long semester.

EE 7299. Dissertation.

This course includes original research and writing in electrical engineering, to be accomplished under direct supervision of the dissertation advisor. While conducting dissertation research and writing, students must be continuously enrolled each long semester.

EE 7300. Research Methods and Technical Writing in Electrical and Computer Engineering.

This course prepares students for advanced research by examining how to plan, conduct, and report on empirical investigations. This course covers techniques applicable to each of the steps of a research project, including formulating research questions, theory building, data analysis, building evidence, assessing validity, and publishing. Students practice a variety of research methodologies and explore the capacities and limitations of specific approaches. Other topics covered are principles of good writing, format of a scientific manuscript, issues in publication and peer review, utilizing different online databases, attributing credit for prior work, respecting intellectual property rights, and maintaining ethics in research.

EE 7301. Advanced Digital System Design.

This course covers digital systems design using hardware description languages and their associated tooling to capture, integrate, verify, simulate, and synthesize digital hardware. The course examines modern hardware design flows using high-level synthesis and register-transfer-level (RTL) synthesis. It covers the role of hardware description languages in the verification, simulation, and integration process of hardware modules in large digital systems. The course projects offer an integrated experience in advanced digital systems design combining hardware description languages, hardware design methodologies, and hardware design practice on a programmable target, such as an FPGA or ASIC.

EE 7302. Hardware Acceleration for Machine Learning.

This course is a comprehensive exploration of AI system design, from the intricacies of deep learning architecture to hardware considerations. The course covers a range of computing platforms in CPUs, TPUs, and GPUs, emphasizes FPGA fundamentals and programming, and delves into memory structures crucial for AI. It also provides strategies for optimizing power consumption in system-level and RT-level design.

EE 7303. Physical Electronics.

This course addresses the advanced concepts of semiconductor device physics and operations. The course helps students to gain a foundation in the area of physical electronics as a basis of continued course work and research in nano- and micro- electronics devices and systems. Topics will include quantum mechanics, the statistics of particles, transport in crystalline semiconductors, and optoelectronic devices.

EE 7304. Modern Semiconductor Devices.

This course reviews and deepens understanding of advanced topics in semiconductor devices. Topics of this course include semiconductor physics, metal-semiconductor contacts, the physical principles and operation of P-N junctions, MOS capacitors, MOS field-effect transistors, scaling and short-channel effects of modern and future MOSFETs, and optoelectronic devices such as photodetectors, solar cells, light emitters and display devices.

EE 7305. Energy Storage and Sustainability.

This course provides a basic understanding of the various mechanisms and related technologies that are currently employed for energy storage and sustainability. Topics covered range from basic concepts to the most important methods for energy storage. General principles involved in various electro-chemical technologies are introduced, followed by a presentation of the most important battery systems. Most important storage methods and major areas of application are discussed, in addition to policies and actions needed to transition to 100% clean, renewable energy and storage for businesses, nonprofit and government organizations. State-of-the-art and challenging research topics are discussed.

EE 7306. Artificial Intelligence in Smart Grids.

This course introduces artificial intelligence (AI) and machine learning (ML) techniques, algorithms, and tools for smart grid applications. Topics covered range from fundamentals of machine learning and artificial intelligence to state-of-the-art research on using AI and ML algorithms and tools to design, operate, and manage smart grids. Related applications are covered, including but not limited to energy forecasting, smart meter data analytics, and nonintrusive load monitoring.

EE 7307. Mobile and Microgrid Design and Operations.

This course covers advanced modeling, control, resilience and security technologies useful for grid modernization from the angle of microgrid design, analysis and operation. Topics include smart inverters, microgrid architectures, distributed energy resources modeling, microgrid hierarchical control, microgrid stability, fault management, resilient microgrids through programmable networks, reliable networked microgrids, and cyber security.

EE 7308. High and Medium Voltage Power Transmission.

This course covers electric power transmission and distribution systems. With increased amounts of distributed generation (photovoltaics, small-scale wind), distributed storage, and controllable loads, it has become more and more important for researchers and power industry professionals to understand power distribution and transmission systems. This course provides an introduction to distribution grids, including their components, typical topologies, and operational strategies. Then, it covers power flows in distribution grids and distribution transformers. Additionally, the course covers the fundamentals of electric loads, including electric load modeling, analysis, and control methodologies.

EE 7331. AI and Machine Learning for Engineers.

This course provides an in-depth exploration of fundamental machine learning concepts and techniques, including supervised, unsupervised, and semi-supervised learning, alongside practical applications of these concepts in real-world problems. It further delves into the intricate world of deep learning, investigating critical topics such as deep neural networks, convolutional neural networks, and recurrent neural networks, as well as recent advancements in the field. Additionally, the course explores essential reinforcement learning concepts, including Markov Decision Processes, Q-learning, and policy gradients, preparing students for advanced AI research and development for engineering applications.

EE 7354. Advanced Flexible Electronics.

This course covers the materials systems, processes, device physics and applications of flexible electronics. Study of materials will include amorphous and nanocrystalline silicon, organic and polymeric semiconductors, and solution cast films of carbon nanotubes, graphene and other 2D materials. Contemporary research and advancement in the areas of high-speed transistors, switches, photovoltaics, and communication devices will be covered.

EE 7359. Research in Electrical Engineering.

This research course is for doctoral students in electrical engineering who have not yet passed their candidacy exam, typically under supervision of the PhD Research Advisor.

EE 7372. Wireless and Mobile Networks.

This course provides an in-depth presentation of concepts, fundamentals, and technologies of modern wireless and mobile networks. Topics covered include wireless signal propagation characteristics and modeling, coding and modulation, link-layer techniques for reliable communications, and multiple access schemes and protocols. Wireless local area networks, wireless personal area networks, sensor networks, and wide area networks (5G/6G networks) are also covered, as are low-power wide area networks energy-aware and energy-harvesting schemes and protocols. Additional topics include mobile ad-hoc networks and variants, such as unmanned aerial networks and vehicle ad hoc networks, Internet of Things, and AI- and ML-based mobile and wireless networks.

EE 7374. Smart Data Networks.

This course covers fundamentals for the design of smart data networks, which represent the evolution of data networks by taking advantage of artificial intelligence and machine-learning technologies to push the boundaries of traditional networks and the internet. All layers of the protocol stack are re-examined with a focus on how AI and ML-based technologies can advance the design of new protocols and algorithms. Hence, the course covers AI- and ML-enhanced medium access control protocols, routing, congestion control, transport protocols, multimedia streaming, network applications, traffic characterization, and advanced networking topics, such as IoT and digital twins.

EE 7399. Dissertation.

This course includes original research and writing in electrical engineering, to be accomplished under direct supervision of the dissertation advisor. While conducting dissertation research and writing, students must be continuously enrolled each long semester.

EE 7599. Dissertation.

This course includes original research and writing in electrical engineering, to be accomplished under direct supervision of the dissertation advisor. While conducting dissertation research and writing, students must be continuously enrolled each long semester.

EE 7699. Dissertation.

This course includes original research and writing in electrical engineering, to be accomplished under direct supervision of the dissertation advisor. While conducting dissertation research and writing, students must be continuously enrolled each long semester.

EE 7999. Dissertation.

This course includes original research and writing in electrical engineering, to be accomplished under direct supervision of the dissertation advisor. While conducting dissertation research and writing, students must be continuously enrolled each long semester.