Master of Science (M.S.) Major in Electrical Engineering (Non-Thesis Option)

EE 2100. Circuits I Lab.

This course examines the principles of circuit analysis through practical measurement and experimentation of direct current and voltage. Students analyze the behavior of resistive, capacitive, and inductive circuits using standard measurement techniques and instrumentation. Inquiry focuses on the application of fundamental theorems to diverse circuit configurations and the design of experimental procedures. Students examine the DC and AC analysis of electrical systems to evaluate theoretical models against physical data. Analysis includes the use of computer-aided tools for circuit implementation and the verification of system performance. Students investigate the impact of component tolerances and measurement errors on overall analytic accuracy. This exploration provides a framework for evaluating the electric system design. 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 examines the principles of digital logic design through the implementation and analysis of discrete components and logic devices. Students analyze combinational logic circuits, including encoders, decoders, and multiplexers, to evaluate Boolean algebraic expressions and minimization techniques. Inquiry focuses on the design and verification of sequential logic systems such as flip-flops, counters, and registers. Students examine the behavior of finite state machines and their application in synchronous and asynchronous digital systems. Analysis includes the use of hardware description languages to simulate and synthesize digital architectures on integrated circuits. Students investigate the impact of propagation delays, timing constraints, and power consumption on system reliability. Discussions evaluate the transition from theoretical logic gates to physical hardware realization within modern electronic frameworks. Systematic evaluation of these elements provides a foundation for the architectural design of complex processing units. Corequisite: CS 1428 and EE 2320 with grades of "C" or better.

EE 2300. Circuits I.

This course examines the fundamental concepts of electric circuit analysis, including Kirchhoff’s laws, Ohm’s law, and basic network theorems. Students analyze resistive circuits using nodal and mesh analysis techniques to evaluate current and voltage distributions within complex networks. Inquiry focuses on the behavior of energy storage elements, specifically capacitors and inductors, within first-order RL and RC circuits. Analysis includes the study of power calculations, maximum power transfer, and the response of circuits to constant and time-varying sources. Students investigate the characteristics of steady-state direct current systems and the transient behavior of switched networks. Evaluation of these circuit properties provides a technical foundation for the modeling and design of modern electrical systems. 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 examines the fundamental principles of digital systems through the study of Boolean algebra, logic gates, and number representation. Students analyze combinational logic design techniques, including minimization using Karnaugh maps and the implementation of arithmetic circuits. Inquiry focuses on the design and operation of sequential logic elements such as latches, flip-flops, and counters. Students examine the architecture of finite state machines and their role in controlling complex digital processes. Analysis includes the investigation of synchronous and asynchronous timing, propagation delays, and memory organization within digital architectures. These concepts lay the groundwork for understanding the hierarchical layers within modern computing hardware and support the design of robust electronic systems. Corequisites: CS 1428 and EE 2120 with a grade of "C" or better.

EE 3100. Instrumentation Laboratory.

This course examines the principles of modern measurement systems and the characterization of electronic instrumentation. Students analyze the performance of various sensors and transducers used to convert physical phenomena into electrical signals. Inquiry focuses on signal conditioning techniques, including amplification, and filtering to ensure data integrity. Students examine the impact of noise, resolution, and sensitivity on measurement accuracy within complex data acquisition systems. Analysis includes the statistical evaluation of experimental data and the application of error analysis to determine system reliability. Students investigate the integration of computer-aided tools for real-time monitoring and control of physical processes. Evaluation of these instrumentation architectures identifies the relationship between hardware precision and analytical results. Systematic inquiry into measurement theory provides a framework for the development of sophisticated diagnostic and monitoring tools. Prerequisite: EE 2300 and MATH 3323 with grades of "C" or better. Corequisite: EE 3300 with grade of "C" or better.

EE 3120. Microprocessors Lab.

This course examines the architecture and programming of microprocessors and microcontrollers within embedded systems. Students analyze instruction sets, addressing modes, and the execution of assembly and high-level language programs. Inquiry focuses on the interfacing of processing units with external hardware, including sensors, actuators, and communication modules. Students examine the behavior of interrupts, timers, and input/output ports during real-time system operation. Analysis includes the implementation of memory mapping and the evaluation of bus architectures for efficient data transfer. Students investigate the impact of clock speed, power consumption, and peripheral integration on system performance. Evaluation of these hardware-software interactions identifies the relationship between low-level code and physical device control. Systematic inquiry into embedded logic provides a framework for the development of integrated processing platforms. 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 course examines the characterization and application of semiconductor devices including diodes, bipolar junction transistors, and field-effect transistors. Students analyze the operation of single-stage and multi-stage amplifiers to evaluate voltage gain, input/output impedance, and frequency response. Inquiry focuses on the design of biasing networks and the implementation of small-signal models for signal processing applications. Students examine the behavior of operational amplifiers and their internal integrated circuit architectures within various feedback configurations. Analysis includes the impact of non-ideal characteristics, such as saturation, parasitic capacitance, and thermal effects, on overall circuit performance. Students investigate the integration of discrete components into complex analog systems to verify theoretical models against physical data. Evaluation of these microelectronic structures identifies the fundamental relationship between device physics and functional circuit design. Systematic inquiry into electronic hardware supports the development of precision signal conditioning and processing units. 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 examines advanced alternating current circuit analysis, including phasors, impedance, and sinusoidal steady-state response. Students analyze complex power, power factor correction, and the operation of three-phase systems within power distribution networks. Inquiry focuses on the application of Laplace transforms to evaluate the transient and steady-state responses of higher-order linear circuits. Students examine the frequency response of electrical networks, incorporating the design of passive filters and resonant systems. Analysis includes the study of magnetically coupled circuits, transformers, and two-port network parameters for comprehensive system modeling. Students investigate the relationship between pole-zero locations and circuit stability. Evaluation of frequency-domain techniques provides a framework for analyzing signal transmission and power conversion. Systematic inquiry into network theory supports the development of robust electrical architectures. 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 examines the architecture and operation of microprocessors, focusing on computing hardware and digital logic. Students analyze assembly language programming and instruction set execution to evaluate interactions between hardware and software. Topics include timing analysis, input/output interfacing, memory organization, bus structures, and interrupt-driven communication. Students investigate embedded system components, including peripheral integration and real-time processing considerations. Emphasis is placed on analyzing hardware-software interfaces and evaluating system performance using concepts such as caching and pipelining. 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 examines Python programming through the application of numerical and scientific computing libraries tailored for engineering analysis. Students analyze fundamental syntax and data structures to develop efficient computational algorithms. Inquiry focuses on the implementation of NumPy for multidimensional array processing and linear algebra operations. Students examine scientific computing methodologies using SciPy to solve complex mathematical problems involving integration, optimization, and signal processing. Analysis includes data manipulation techniques using Pandas for structured dataset management and statistical evaluation. Students investigate data visualization frameworks such as Matplotlib to interpret and communicate engineering results through graphical representation. Discussions evaluate the principles of object-oriented programming to create modular and scalable software architectures for technical applications. Systematic inquiry into these computational tools provides a framework for automating data-driven engineering tasks. Prerequisite: CS 1342 or CS 1428 with a grade of "C" or better.

EE 3340. Electromagnetics.

This course examines the fundamental principles of electromagnetic field theory and its application to high-frequency systems. Students analyze Maxwell’s equations in both differential and integral forms to evaluate the behavior of electric and magnetic fields in diverse media. Inquiry focuses on wave propagation characteristics, including reflection, refraction, and polarization within unbounded and bounded regions. Students examine the theory of transmission lines through the study of Smith charts, impedance matching, and transient analysis. Analysis includes the operation of wave guides and the fundamental parameters of antenna systems for wireless communication. Students investigate the interaction between electromagnetic waves and material boundaries to determine energy distribution and power flow. Evaluation of these electrodynamic concepts identifies the physical constraints of signal transmission. Systematic inquiry into field theory provides a framework for the design of microwave and radio frequency components. Prerequisite: [EE 3300 or EE 3400] and MATH 2393 and PHYS 2326 and PHYS 2335 with grades of "C" or better.

EE 3350. Microelectronics.

This course examines the analysis and design of active device equivalent circuits with a focus on semiconductor technologies. Students analyze the operation of bipolar junction and field-effect transistors within various circuit topologies to evaluate small-signal and large-signal behaviors. Inquiry focuses on the implementation of operational amplifiers and their integrated circuit architectures for signal processing applications. Students examine the principles of feedback and its impact on gain stability, bandwidth, and input/output impedance. Analysis includes the frequency response of multi-stage amplifiers and the design of switching circuits for digital and power electronic functions. Students investigate the relationship between physical device parameters and functional system performance within integrated environments. Evaluation of these microelectronic structures identifies the trade-offs between speed, power consumption, and linearity. Systematic inquiry into active device modeling provides a framework for the development of modern analog and mixed-signal electronic systems. 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 examines the physical principles and operational characteristics of semiconductor materials and electronic devices. Students analyze the mechanics of carrier motion, including drift, diffusion, and recombination processes within crystalline structures. Inquiry focuses on the development of physical and mathematical models for p-n junction diodes and their application in rectification and signal modulation. Students examine the internal physics and terminal behaviors of bipolar junction transistors and field-effect transistors to evaluate their roles as switches and amplifiers. Analysis includes the investigation of metal-oxide-semiconductor structures and their integration into complex microelectronic circuits. Students investigate the impact of doping concentrations, temperature variations, and geometric scaling on device performance and reliability. Discussions evaluate the transition from discrete-component physics to the architectural constraints of integrated-circuit fabrication. Systematic inquiry into solid-state theory provides a framework for the development of modern microelectronic technologies. Prerequisite: [EE 3300 or EE3400] and PHYS 2326 with grades of "C" or better.

EE 3370. Signals and Systems.

This course examines the mathematical representation of signals and systems in both time and frequency domains. Students analyze the properties of linear time-invariant systems using convolution and differential equations to evaluate system stability. Inquiry focuses on the application of Fourier series and Fourier transforms to characterize periodic and aperiodic signals. Students examine the role of Laplace transforms in circuit analysis and the derivation of transfer functions for system design. Analysis includes the study of z-transforms and sampling theory to evaluate the transition to discrete-time processing. Students investigate frequency response characteristics and filter design to determine signal modification through various network architectures. Evaluation of these transformation techniques provides a framework for analyzing communication and control systems. Systematic inquiry into signal processing identifies the fundamental limits of information transmission and system performance. Prerequisite: EE 3300 or EE3400 with a grade of "C" or better.

EE 4152. Introduction to VLSI Design Lab.

This course examines the implementation of Very Large Scale Integration (VLSI) systems through the application of computer-aided design (CAD) tools and verification methodologies. Students analyze the physical design flow of Complementary Metal-Oxide-Semiconductor (CMOS) integrated circuits, including schematic capture, layout generation, and parasitic extraction. Inquiry focuses on the verification of circuit functionality through design rule checks, layout-versus-schematic comparisons, and timing simulations. Students examine the impact of physical constraints such as area, power, and delay on the performance of complex digital architectures. Analysis includes the optimization of cell-based designs and the synthesis of hardware descriptions into physical realizations. Students investigate the relationship between semiconductor fabrication limits and design rule specifications. Discussions evaluate the transition from high-level logic representations to silicon-level physical layouts. Systematic evaluation of these design processes provides a framework for the development of high-performance microelectronic systems. Prerequisite: EE 3350 and [CS 2420 or EE 2320] with grades of "C" or better. Corequisite: EE 4252 with grade of "C" or better.

EE 4155. Analog and Mixed-Signal Lab.

This course examines the design and verification of analog and mixed-signal circuits through the implementation of integrated system architectures. Students analyze the performance of operational amplifiers, comparators, and voltage references within complex feedback networks. Inquiry focuses on the characterization of data converters, specifically analog-to-digital and digital-to-analog interfaces, to evaluate resolution and sampling rates. Students examine the impact of noise, supply variations, and parasitic elements on signal integrity and dynamic range. Analysis includes the implementation of layout techniques and the use of simulation tools to verify circuit functionality against physical specifications. Students investigate the relationship between discrete-time and continuous-time signals within hybrid electronic environments. Evaluation of these mixed-signal structures identifies the fundamental limits of precision and speed in modern electronic systems. Systematic inquiry into integrated design processes provides a framework for the development of high-performance communication and sensor platforms. 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 examines the principles of electromechanical energy conversion through the study of magnetic circuits and rotating machines. Students analyze the operational characteristics of transformers, direct current machines, and induction motors to evaluate performance parameters such as torque, speed, and efficiency. Inquiry focuses on the implementation of equivalent circuit models and the synchronization of three-phase systems within electrical networks. Students examine the behavior of synchronous machines under varying load conditions and excitation levels to determine stability. Analysis includes the study of magnetic saturation, winding configurations, and power flow within industrial drive systems. Students investigate the impact of control strategies on motor dynamics and the conversion of electrical energy into mechanical work. Evaluation of these electromagnetic devices identifies the functional limits of energy distribution and mechanical actuation. Systematic inquiry into machine theory provides a framework for the development of sustainable power and propulsion systems. Prerequisite: EE 3340 with a grade of "C" or better. Corequisite: EE 4380 with a grade of "C" or better.

EE 4192. Microelectronics Manufacturing Laboratory.

This course examines the principles and methodologies of semiconductor fabrication through the study of unit processes and metrology. Students analyze metrology data, including film thickness and sheet resistance, to evaluate the consistency of thermal oxidation and diffusion. Inquiry focuses on the impact of photolithography parameters such as exposure duration and development time on critical dimension control and pattern resolution. Students examine etch selectivity and anisotropy by investigating the results of wet and dry etching modules on multilayer semiconductor structures. Analysis includes the design of fabrication process flows for microelectronic devices such as metal-oxide-semiconductor capacitors and junction diodes. Students investigate the impact of cleanroom protocols and environmental contamination on device yield and functional reliability through statistical methods. Evaluation of experimental results supports the formulation of optimized process recipes to address non-uniformity and structural defects. Systematic inquiry into manufacturing cycles identifies the relationship between process parameters and device performance. 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 Very Large Scale Design (VLSD).

This course examines the analysis and design of Complementary Metal-Oxide-Semiconductor (CMOS) integrated circuits through the study of transistor-level architectures and physical layout. Students analyze the electrical characteristics of MOSFET devices, including switching behavior, power dissipation, and propagation delay. Inquiry focuses on the implementation of static and dynamic logic families and the design of complex arithmetic units such as adders and multipliers. Students examine the hierarchical design flow, from schematic capture to physical verification and timing analysis. Analysis includes the investigation of semiconductor fabrication constraints, design rule specifications, and the optimization of area-power-delay tradeoffs. Students investigate the architecture of memory arrays and the integration of digital systems within Very Large Scale Integration (VLSI) frameworks. Evaluation of these microelectronic structures identifies the relationship between circuit topology and system performance. Systematic inquiry into silicon-level design provides a framework for the development of high-density electronic processors. Prerequisite: EE 3350 and [CS 2420 or EE 2320] with grades of "C" or better. Corequisite: EE 4152 with grade of "C" or better.

EE 4255. Analog and Mixed-Signal Design.

This course examines the design and application of operational amplifiers, focusing on feedback topologies, offset voltage, and frequency stability. Students analyze compensation techniques to evaluate system performance and maintain closed-loop stability across varying operating conditions. Inquiry focuses on the characterization of random signals and the impact of noise on precision analog architectures. Students examine the operation of discrete-time circuitry and the fundamental principles of sampled-data systems. Analysis includes the architecture of data converters, specifically the conversion of signals between continuous and discrete domains using analog-to-digital and digital-to-analog interfaces. Students investigate the relationship between sampling rates, quantization error, and dynamic range within mixed-signal frameworks. Evaluation of these hybrid electronic structures identifies the fundamental limits of signal processing accuracy. Systematic inquiry into integrated design processes provides a framework for the development of high-performance communication and sensor platforms. 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.

This course examines the integration of engineering principles through the collaborative design of electrical systems or components. Students analyze industry-standard design processes to facilitate the conceptualization, implementation, and verification of technical projects. Inquiry focuses on the systematic documentation of project definitions, design trade-offs, and implementation specifications. Students examine the relationship between theoretical constraints and physical realization within a team-based environment. Analysis includes the evaluation of project performance against predefined criteria and the synthesis of complex data for technical communication. Students investigate methodologies for effective project management, risk assessment, and resource allocation. Discussions evaluate the impact of design decisions on system reliability and functional outcomes. Professional inquiry into these collaborative processes provides a framework for the execution of complex engineering tasks. 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.

This course examines the advanced phases of the engineering design cycle, focusing on the rigorous implementation and verification of complex electrical systems. Students analyze experimental results and performance metrics to validate project specifications and functional reliability. Inquiry focuses on the iterative refinement of design prototypes through systematic testing and troubleshooting. Students examine the impact of physical constraints, component tolerances, and environmental factors on final system performance. Analysis includes the comprehensive documentation of design decisions, implementation details, and verification procedures within a professional engineering framework. Students investigate the relationship between theoretical modeling and realized hardware or software performance. Discussions evaluate the transition from initial conceptualization to a fully functional engineering solution. Systematic inquiry into these validation processes provides a framework for the successful execution of industry-standard technical projects. 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 examines the design and implementation of digital systems using hardware description languages (HDL) to realize complex logic architectures. Students analyze register-transfer level (RTL) modeling techniques to evaluate the performance and efficiency of digital circuits. Inquiry focuses on the development of custom microprocessor cores and the integration of peripheral control units within programmable logic environments. Students examine the synthesis process and the mapping of HDL code to physical hardware resources such as field-programmable gate arrays. Analysis includes timing verification, functional simulation, and the optimization of area-power-delay characteristics for high-speed digital systems. Students investigate the design of finite state machines and memory hierarchies to support advanced computational tasks. Evaluation of these architectural frameworks identifies the transition from high-level behavioral descriptions to hardware-specific implementations. Systematic inquiry into digital design methodologies provides a framework for the development of modern integrated processing platforms. Prerequisite: [EE 3320 or EE 3420] with a grade of "C" or better.

EE 4323. Digital Image Processing.

This course examines the fundamental principles and methodologies of digital image processing and computer vision. Students analyze digital image representation, sampling, and quantization to evaluate data integrity within visual systems. Inquiry focuses on the application of spatial and frequency domain filtering techniques for image enhancement and restoration. Students examine the behavior of image segmentation algorithms and feature extraction processes for object recognition. Analysis includes the study of image compression standards, morphological operations, and color image processing within diverse technical frameworks. Students investigate the impact of noise and distortion on automated visual analysis and system reliability. Evaluation of these computational techniques identifies the relationship between pixel-level manipulation and high-level scene interpretation. Systematic inquiry into digital processing supports the development of robust visual perception systems. 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 examines the fundamental principles of machine learning with a focus on deep learning architectures for engineering applications. Students analyze model characteristics and neural network theory to evaluate system performance and learning efficiency. Inquiry focuses on the implementation of classifiers for signal processing and network optimization tasks. Students examine regression models and convolutional neural networks for object detection and visual data analysis. Analysis includes the study of time-series forecasting and the integration of predictive modeling within smart city frameworks. Students investigate the relationship between dataset quality, hyperparameter tuning, and algorithm reliability. Discussions evaluate the transition from theoretical mathematical models to functional implementations in technical environments. Systematic inquiry into these computational tools provides a framework for the development of intelligent engineering systems. Prerequisite: CS 1428 or CS 1342 with a grade of "C" or better.

EE 4332. Introduction to Computer-Aided Engineering (CAE) Simulation on High-Performance Computing (HPC) Syst.

This course examines the development of engineering simulations optimized for high-performance computing environments. Students analyze programming techniques for multicore processors and the architectural constraints of memory systems. Inquiry focuses on the implementation of algorithms for dense and sparse linear algebra applications within parallel frameworks. Students examine computational models for thermal analysis, fluid dynamics, and stencil operations to evaluate system behavior. Analysis includes the application of stochastic algorithms and other numerical methods to complex engineering problems. Students investigate the relationship between hardware architecture and computational efficiency in large-scale simulation. Evaluation of these high-performance strategies identifies the fundamental limits of speed and scalability in technical computing. Systematic inquiry into parallel architectures provides a framework for the design of robust simulation tools for various engineering disciplines. Prerequisite: CS 1428 with a grade of "C" or better.

EE 4351. Fundamentals of Electroceramics.

This course examines the principles of electroceramic materials, focusing on the relationship between crystal structure and macroscopic electrical properties. Students analyze binary and ternary phase diagrams to evaluate material stability and phase transitions within complex oxide systems. Inquiry focuses on the symmetry groups and non-centro-symmetric crystal structures that govern nonlinear dielectric behaviors, including ferroelectricity, piezoelectricity, and pyroelectricity. Students examine the application of nonlinear magnetics and wideband gap semiconductors in the design of advanced detectors and sensors. Analysis includes the investigation of radiation-hardened electronics, spintronics technology, and the integration of micro-electro-mechanical systems (MEMS). Students investigate methodologies for materials processing, characterization, and fabrication to determine the physical constraints of device performance. Discussions evaluate the impact of structural defects and grain boundaries on the functional reliability of ceramic components. Systematic inquiry into electroceramic theory provides a framework for the development of high-performance electronic and magnetic platforms. 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 4353. Fundamentals of Advanced Semiconductor Technology.

This course examines the fundamental principles of advanced semiconductor technology and the evolution of integrated circuit scaling beyond Moore’s Law. Students analyze the physical limits of MOSFET and CMOS architectures and the impact of geometric scaling on device performance. Inquiry focuses on the implementation of high-K gate dielectrics and the integration of new channel materials to replace traditional silicon substrates. Students examine three-dimensional device structures, such as FinFETs and gate-all-around architectures, to evaluate electrostatic control and leakage current. Analysis includes the operation of compound semiconductor devices, specifically MESFETs and high-electron-mobility transistors (HEMTs), for high-frequency applications. Students investigate the physics of optoelectronic components, including light-emitting diodes, lasers, and solar cells, within various material systems. Evaluation of these emerging technologies identifies the relationship between material properties and functional device characteristics. Systematic inquiry into advanced fabrication techniques provides a framework for the development of next-generation microelectronic and photonic systems. Prerequisite: EE 3355 with a grade of "C" or better.

EE 4354. Flexible Electronics.

This course examines the materials systems, fabrication processes, and device physics of flexible electronic architectures. Students analyze the properties of semiconductor materials, including amorphous silicon, nanocrystalline silicon, and organic or polymeric substrates. Inquiry focuses on the application of solution-cast films, such as carbon nanotubes, and their role in achieving mechanical flexibility. Students examine the operational principles of high-speed transistors, thin-film photovoltaics, and flexible flat-panel displays. Analysis includes the study of medical image sensors and other integrated flexible systems to evaluate performance under mechanical strain. Students investigate the relationship between material deposition techniques and the functional reliability of conformable electronic devices. Evaluation of these emerging technologies identifies the technical challenges of integrating non-rigid components into modern electronic platforms. Systematic inquiry into flexible electronics provides a framework for the development of next-generation wearable and portable technologies. Prerequisite: EE 3350 and EE 3355 and EE 4350 with grades of "C" or better.

EE 4356. Power Electronics.

This course examines the principles of power electronics and the application of semiconductor circuits for the efficient control and conversion of electrical energy. Students analyze the operational characteristics and switching behaviors of power semiconductor devices, including diodes, thyristors, and transistors. Inquiry focuses on the design and implementation of DC-DC converters and multilevel converter architectures to evaluate voltage regulation and power density. Students examine the theory of power inverters and AC voltage controllers to determine the conversion of energy between direct and alternating current systems. Analysis includes the investigation of pulse-width modulation and harmonic distortion to evaluate signal quality and efficiency. Students investigate the relationship between thermal management, electromagnetic interference, and the reliability of high-power electronic systems. Evaluation of these conversion topologies identifies the fundamental limits of energy efficiency in industrial and renewable applications. Systematic inquiry into power electronic theory provides a framework for the development of modern energy systems. Prerequisite: [EE 4350 or [EE 3350 and EE 3150]] with a grade of "C" or better.

EE 4357. Introduction to Power Systems.

This course examines the fundamental principles of electrical power systems and the analysis of grid-level energy distribution. Students analyze the operational characteristics of power generation units and the mechanics of transformer action within interconnected networks. Inquiry focuses on transmission line modeling and the application of symmetrical components to evaluate system behavior under various loading conditions. Students examine real and quadrature power calculations alongside methodologies for power factor correction to optimize energy efficiency. Analysis includes the implementation of load flow algorithms to determine voltage profiles and power distribution across complex bus architectures. Students investigate economic considerations in system operations, including dispatch strategies and the management of generation resources. Evaluation of these power system elements identifies the technical constraints of maintaining grid stability and reliability. Systematic inquiry into power engineering provides a framework for the development of sustainable energy infrastructures. 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 examines the principles of modern fabrication techniques and the properties of conventional and emerging electronic materials. Students analyze thin film deposition methodologies and advanced manufacturing concepts to evaluate material growth and structural integrity. Inquiry focuses on the role of heterointerfaces and the characterization of electronic, thermal, magnetic, and optical properties within solid-state systems. Students examine the operational physics of practical devices, including photovoltaic cells, light-emitting diodes, and high-resolution display technologies. Analysis includes the investigation of emerging flexible electronic platforms and the challenges of integrating non-rigid materials into functional architectures. Students investigate the relationship between material morphology and the resulting performance of optoelectronic and microelectronic components. Discussions evaluate the transition from fundamental material science to the realization of high-performance technical devices. Systematic inquiry into electronic materials provides a framework for the development of next-generation solid-state and flexible technologies. Prerequisite: EE 3350 with a grade "C" or better.

EE 4360. Linear Control Systems.

This course examines the principles of linear continuous-time and discrete-time automatic control systems through the application of mathematical modeling and feedback theory. Students analyze system dynamics in both time and frequency domains to evaluate the performance of closed-loop architectures. Inquiry focuses on the implementation of transfer function representations and state variable analysis for system characterization. Students examine transient and steady-state responses to determine the impact of damping, natural frequency, and error constants on precision. Analysis includes the study of stability criteria, such as the Routh-Hurwitz and Nyquist methods, alongside sensitivity analysis to evaluate robustness against parameter variations. Students investigate the design of compensators and controllers to achieve desired system behaviors. Evaluation of these control strategies identifies the fundamental limits of regulation and tracking in technical environments. Systematic inquiry into linear systems provides a framework for the development of automated and autonomous technologies. Prerequisite: EE 3370 and MATH 3376 with grades of "C" or better.

EE 4370. Communication Systems.

This course examines the transmission of signals through linear systems and the fundamental principles of communication theory. Students analyze analog and digital modulation techniques, including amplitude, frequency, and phase-shift keying, to evaluate bandwidth efficiency and signal integrity. Inquiry focuses on the characterization of noise and its impact on information recovery within stochastic environments. Students examine the design of optimal filters and synchronization methods to determine the limits of data transmission over diverse channels. Analysis includes the investigation of signal-to-noise ratios and bit error rates to evaluate system performance. Students investigate the relationship between sampling theory, quantization, and the reconstruction of continuous-time signals. Evaluation of these modulation frameworks identifies the technical trade-offs between power, bandwidth, and complexity. Systematic inquiry into communication architectures provides a framework for the development of modern wireless and wired networks. Prerequisites: EE 3370 and IE 3320 with grades of "C" or better.

EE 4372. Communication Networks.

This course examines the architecture and functional principles of modern data communication systems through the study of networked environments. Students analyze the 7-layer OSI model to evaluate how protocols and algorithms facilitate seamless information exchange across diverse platforms. Inquiry focuses on the physical media and local area network (LAN) components that define the hardware constraints of signal transmission. Students examine the implementation of Ethernet and TCP/IP suites as the foundational standards for global connectivity and reliable data routing. Analysis includes the investigation of network topologies and the performance metrics of various communication standards. Students investigate the relationship between layered architectures and the security, scalability, and efficiency of interconnected systems. Evaluation of these networking frameworks identifies the technical challenges of managing high-speed data traffic. Systematic inquiry into communication protocols provides a framework for the development of robust and interoperable information networks. 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 examines the fundamental principles and operational architectures of modern wireless communication systems. Students analyze the mechanics of signal modulation and demodulation to evaluate spectral efficiency and information integrity across mobile channels. Inquiry focuses on the implementation of source and channel coding techniques to ensure robust data transmission in the presence of interference and noise. Students examine multiple access methodologies, including frequency, time, and code division protocols, to determine how network resources are shared among multiple users. Analysis includes the investigation of radio frequency propagation models and the physical constraints of cellular environments. Students investigate the relationship between link budgets, fading phenomena, and system capacity. Evaluation of these mobile technologies identifies the technical trade-offs between coverage, power, and data rates. Systematic inquiry into wireless theory provides a framework for the development of next-generation telecommunication networks. Prerequisites: EE 4370 with a grade of "C" or better.

EE 4375. Building a Smart Grid Architecture.

This course examines the transition from traditional centralized power grid architectures to decentralized smart grid frameworks. Students analyze the structural differences between 20th-century unidirectional power systems and 21st-century multidirectional energy networks. Inquiry focuses on the implementation of two-way power and data flows to facilitate real-time monitoring, control, and management of electrical resources. Students examine the integration of traditional bulk generation with renewable and distributed energy resources to evaluate grid stability and reliability. Analysis includes the investigation of communication protocols and sensor technologies required for automated energy distribution. Students investigate the relationship between information infrastructure and the optimization of power consumption across diverse load profiles. Evaluation of these modernization strategies identifies the technical challenges of managing complex energy portfolios. Systematic inquiry into smart grid architectures provides a framework for the development of sustainable and resilient power infrastructures. Prerequisite: EE 3370 with a grade of "C" or better.

EE 4376. Introduction to Telecommunications.

This course examines the fundamental principles of telecommunications and the architectural evolution of global communication networks. Students analyze the operation of telephone networks and the mechanics of switching and transmission systems. Inquiry focuses on the differentiation between circuit-switched and packet-switched paradigms to evaluate data throughput, latency, and resource allocation. Students examine the processes of cell processing and the application of queuing theory to determine system capacity and congestion management within high-traffic environments. Analysis includes the investigation of signal propagation through diverse transmission media and the performance metrics of modern telecommunication links. Students investigate the relationship between network topology and the reliability of information exchange across distributed systems. Evaluation of these switching frameworks identifies the technical trade-offs between fixed-bandwidth and demand-based connectivity strategies. Systematic inquiry into telecommunication theory provides a framework for the development of modern voice and data infrastructures. Co-requisite: EE 4370 with a grade of "C" or better.

EE 4377. Introduction to Digital Signal Processing.

This course examines the fundamental principles of discrete-time signals and systems within the context of digital signal processing. Students analyze discrete systems, convolution, and spectral analysis to evaluate signal behavior in the digital domain. Inquiry focuses on the design and implementation of Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) filters. Students examine the Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT) algorithms to determine frequency components and computational efficiency. Analysis includes the study of z-transforms, sampling theory, and the impact of quantization effects on system performance. Students investigate the relationship between windowing functions and spectral leakage in signal characterization. Evaluation of these processing techniques identifies the trade-offs between computational complexity, phase linearity, and filter response accuracy. Systematic inquiry into digital signal theory provides a framework for the development of modern audio, video, and communication processing platforms. Prerequisites: EE 3370 with a grade of "C" or better.

EE 4378. Data Compression and Error Control Coding.

This course examines the principles of information theory and the mathematical foundations of data representation and transmission. Students analyze the information content of messages and the concept of entropy to evaluate the limits of source coding and data compression. Inquiry focuses on the determination of channel capacity and the implementation of data translation codes to optimize communication efficiency. Students examine the fundamentals of error-correcting codes, including linear block and convolutional structures, to ensure signal integrity across noisy channels. Analysis includes the study of Huffman coding and Lempel-Ziv algorithms to determine optimal compression ratios. Students investigate the relationship between redundancy, bandwidth, and the probability of error in digital systems. Evaluation of these coding strategies identifies the technical trade-offs between data density and error recovery capabilities. Systematic inquiry into information theory provides a framework for the development of secure and efficient data storage and transmission platforms. Corequisite: EE 4370 with a grade of "C" or better.

EE 4380. Electric Machines.

This course examines the principles and analysis of electromechanical systems and the fundamental conversion of energy between electrical and mechanical domains. Students analyze electromagnetic field interactions and the mathematical laws governing motor and generator operations. Inquiry focuses on the development of analytical models to predict device performance and system interaction characteristics under varying load conditions. Students examine the operational physics of major classes of electric machines, including direct current, synchronous, and induction motors. Analysis includes the study of equivalent circuit models, magnetic flux distribution, and torque production within rotating machinery. Students investigate the relationship between power flow, efficiency, and the thermal constraints of magnetic materials. Evaluation of these electromechanical frameworks identifies the technical trade-offs between speed regulation, starting torque, and power density. Systematic inquiry into machine design provides a framework for the development of modern industrial drives and energy conversion systems. Prerequisite: EE 3340 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. Students analyze the operational physics of solar, wind, and hydroelectric systems to evaluate power generation efficiency. Inquiry focuses on the integration of intermittent energy resources into the electrical grid and the impact on system stability. Students examine the mechanics of various energy storage technologies, including electrochemical batteries, gravitational systems, and hybrid configurations. Analysis includes the study of energy conversion processes, thermal constraints, and the lifecycle assessment of sustainable power architectures. Students investigate the relationship between energy demand profiles and the scalability of distributed generation. Evaluation of these renewable frameworks identifies the technical trade-offs between cost, environmental impact, and grid reliability. Systematic inquiry into energy systems provides a framework for the development of resilient and sustainable power infrastructures. Prerequisite: [EE 3300 or EE 3400] and PHYS 2326 and CHEM 1335 with grades of "C" or better.

EE 4382. Advanced Power Systems.

This course examines the advanced principles of power system analysis and the operational dynamics of interconnected electrical networks. Students analyze symmetrical and unsymmetrical faults to evaluate system response and protection requirements under abnormal conditions. Inquiry focuses on the application of symmetrical components to characterize unbalanced circuits and sequence networks. Students examine the functional principles of system protection, including relay coordination and circuit breaker operation. Analysis includes the investigation of transient stability and the power-angle relationship to determine grid resilience against sudden disturbances. Students investigate the transient behavior of transmission lines and the mechanics of surge propagation. Evaluation of supervisory control and data acquisition (SCADA) frameworks identifies the technical infrastructure required for real-time monitoring and automated system management. Systematic inquiry into power system dynamics provides a framework for the development of secure and stable energy infrastructures. Prerequisite: EE 4357 with a grade of "C" or better.

EE 4392. Microelectronics Manufacturing.

This course examines the principles of integrated circuit fabrication and the sequential processes of semiconductor manufacturing. Students analyze crystal growth and wafer preparation techniques to evaluate substrate quality and lattice integrity. Inquiry focuses on epitaxial growth, thermal oxidation, and diffusion processes as methods for altering material properties. Students examine the physics of ion implantation and thin-film deposition to determine dopant profiles and interconnect reliability. Analysis includes the study of photolithography and etching methodologies required for precise feature patterning at the microscale. Students investigate device and circuit formation alongside the technical constraints of packaging and electrical testing. Evaluation of these manufacturing stages identifies the relationship between process control and functional yield. Systematic inquiry into microelectronic fabrication provides a framework for the development of modern solid-state technologies. Prerequisite: CHEM 1341 or CHEM 1335 with a grade of "C" or better.

EE 5320. Advanced Computer Architecture and Arithmetic.

This course examines the design and analysis of high-performance computer systems through the quantitative evaluation of modern processor and compiler technologies. Students analyze current processor architectures to facilitate effective system design and performance optimization. Inquiry focuses on instruction set architectures, parallelizing structures, and advanced pipelining techniques. Students examine the organization of I/O subsystems, memory hierarchies, and multi-level cache structures. Analysis includes parallel and vector processing paradigms alongside the design of high-speed arithmetic units. Students investigate the implementation of these complex architectural components using hardware description languages (HDL). Evaluation of these computational frameworks identifies the fundamental limits of processing speed and system efficiency. Systematic inquiry into computer organization provides a framework for the development of advanced processing platforms.

EE 5321. Computer-Aided Engineering (CAE) Simulations on High-Performance Computing (HPC) Systems.

This course examines the development of engineering simulations optimized for high-performance computing (HPC) environments. Students analyze programming techniques for multicore processors and the architectural constraints of processor and memory systems. Inquiry focuses on the implementation of algorithms for dense and sparse linear algebra within parallel frameworks. Students examine computational models for thermal analysis, fluid dynamics, and stencil operations to evaluate system behavior. Analysis includes the application of stochastic algorithms and other numerical methods to complex engineering problems. Students investigate the relationship between hardware architecture and computational efficiency in large-scale simulation. Evaluation of these high-performance strategies identifies the fundamental limits of speed and scalability in technical computing. Systematic inquiry into parallel architectures provides a framework for the design of robust simulation tools for various engineering disciplines. Prerequisite: EE 5320 with a grade of "C" or better.

EE 5323. Digital Image Processing.

This course examines the fundamental principles and methodologies of digital image processing and computer vision. Students analyze digital image representation, sampling, and quantization to evaluate data integrity within visual systems. Inquiry focuses on the application of spatial and frequency domain filtering techniques for image enhancement and restoration. Students examine the behavior of image segmentation algorithms and feature extraction processes for object recognition. Analysis includes the study of image compression standards, morphological operations, and color image processing within diverse technical frameworks. Students investigate the impact of noise and distortion on automated visual analysis and system reliability. Evaluation of these computational techniques identifies the relationship between pixel-level manipulation and high-level scene interpretation. Systematic inquiry into digital processing supports the development of robust visual perception systems.

EE 5330. Embedded and Real-Time Computing.

This course examines the development of embedded computing systems operating under significant hardware and resource constraints. Students analyze strategies for managing limited memory and processing cycles to evaluate system performance and reliability. Inquiry focuses on the design and implementation of software for both soft and hard real-time environments. Students examine the functional principles of Real-Time Operating Systems (RTOS) to determine task scheduling and resource allocation methodologies. Analysis includes the study of interrupt latency, priority inversion, and concurrency control within deterministic systems. Students investigate the relationship between low-level hardware interfaces and high-level software abstractions. Evaluation of these embedded frameworks identifies the technical trade-offs between responsiveness and power consumption in specialized computing applications. Systematic inquiry into real-time theory provides a framework for the development of robust and predictable autonomous systems.

EE 5331. Machine Learning for Engineering Applications.

This course examines the principles of machine learning and deep learning architectures through the lens of engineering applications. Students analyze neural network theory and model characteristics to evaluate the performance of diverse computational structures. Inquiry focuses on the implementation of classifiers for network traffic and signal processing to determine signal integrity and classification accuracy. Students examine regression models and convolutional neural networks for object detection and feature extraction in complex visual data. Analysis includes the study of time-series analysis and forecasting models. Students investigate the relationship between data preprocessing, model optimization, and the reliability of predictive systems in real-world environments. Evaluation of these learning algorithms identifies the trade-offs between computational complexity and inference speed. Systematic inquiry into machine learning provides a framework for the development of intelligent and adaptive engineering solutions. Prerequisite: ENGR 5310 with a grade of "C" or better.

EE 5350. Advanced Electronic Circuit Design.

This course examines the principles of high-performance analog and radio frequency (RF) integrated circuit design through the analysis of active and passive components. Students analyze low and high-power RF amplifier topologies to evaluate gain, stability, and power-added efficiency. Inquiry focuses on the operational physics of oscillators, FM demodulators, limiters, and mixers within modern communication systems. Students examine circuit methodologies to minimize intermodulation and other nonlinear forms of distortion to optimize signal fidelity. Analysis includes the investigation of high-speed analog circuits with an emphasis on digital-friendly architectures for integrated system-on-chip applications. Students investigate the relationship between matching network design and noise figure characterization in RF front-ends. Evaluation of these circuit frameworks identifies the technical trade-offs between power consumption, linearity, and operating frequency. Systematic inquiry into advanced electronic design provides a framework for the development of high-speed telecommunication and data acquisition hardware.

EE 5353. Fundamentals of Advanced Semiconductor Technology.

This course examines the fundamental concepts and evolutionary trends of advanced semiconductor device technology within the context of global scaling limits. Students analyze the progression of Moore’s Law and the physical constraints of MOSFET and CMOS scaling. Inquiry focuses on the implementation of high-K gate dielectrics and the characterization of new channel materials intended to replace traditional silicon substrates. Students examine the transition from planar to three-dimensional architectures, such as FinFET and gate-all-around structures. Analysis includes the study of compound semiconductor devices and their performance in high-frequency and high-power applications. Students investigate the relationship between material properties and carrier transport in nanometer-scale transistors. Evaluation of these emerging technologies identifies the technical challenges of maintaining performance gains in post-silicon electronics. Systematic inquiry into semiconductor physics provides a framework for the development of next-generation integrated circuits.

EE 5354. Flexible Electronics.

This course examines the materials systems, fabrication processes, and device physics of flexible electronic architectures. Students analyze the properties of semiconductor materials, including amorphous silicon, nanocrystalline silicon, and organic or polymeric substrates. Inquiry focuses on the application of solution-cast films, such as carbon nanotubes, and their role in achieving mechanical flexibility. Students examine the operational principles of high-speed transistors, thin-film photovoltaics, and flexible flat-panel displays. Analysis includes the study of medical image sensors and other integrated flexible systems to evaluate performance under mechanical strain. Students investigate the relationship between material deposition techniques and the functional reliability of conformable electronic devices. Evaluation of these emerging technologies identifies the technical challenges of integrating non-rigid components into modern electronic platforms. Systematic inquiry into flexible electronics provides a framework for the development of next-generation wearable and portable technologies.

EE 5355. Electronic Materials and Devices.

This course examines the theoretical concepts and functional properties of advanced electronic materials and their applications in modern microelectronic systems. Students analyze the physics of dielectrics and oxide semiconductors to evaluate their role in charge storage and carrier transport. Inquiry focuses on the unique characteristics of ferroelectric, pyroelectric, and piezoelectric materials to determine their response to electric fields, thermal gradients, and mechanical stimuli. Students examine the operational principles of magnetic, multifunctional, and multiferroic materials within integrated device architectures. Analysis includes the investigation of modern novel technologies based on these materials, including high-density sensors, actuators, and non-volatile memory components. Students investigate the relationship between material morphology and the resulting electrical, magnetic, and optical functionalities. Evaluation of these complex material systems identifies the technical challenges of integrating smart materials into high-performance electronic platforms. Systematic inquiry into material science provides a framework for the development of next-generation multifunctional devices.

EE 5357. Power Systems for Engineering.

This course examines the analysis of electrical power system components and the operational principles of interconnected energy networks. Students analyze the mechanics of power generation and the functional role of transformer action within high-voltage distribution systems. Inquiry focuses on transmission line modeling and the application of symmetrical components to evaluate system stability under unbalanced conditions. Students examine real and quadrature power calculations alongside methodologies for power factor correction to optimize energy efficiency. Analysis includes the implementation of load flow algorithms to determine voltage regulation and power distribution across complex grid architectures. Students investigate the economic considerations of system operations, including optimal resource dispatch and the management of generation assets. Evaluation of these power system elements identifies the technical constraints of maintaining grid reliability and stability. Systematic inquiry into power engineering provides a framework for the development of modern and sustainable energy infrastructures.

EE 5360. Thin Film Technology.

This course examines the theoretical and practical aspects of thin film technology and its application in modern microelectronic and optoelectronic devices. Students analyze the design and fabrication of thin film heterostructures to evaluate material interface properties and device performance. Inquiry focuses on the physical mechanisms of growth and nucleation in epitaxial thin films to determine structural and electronic characteristics. Students examine the deposition of films with diverse properties and the realization of devices that integrate multifunctional characteristics. Analysis includes the study of physical and chemical vapor deposition techniques alongside the characterization of film morphology and crystallinity. Students investigate the relationship between process parameters and the resulting functional reliability of thin film systems. Evaluation of these fabrication methodologies identifies the technical challenges of developing high-performance thin film architectures. Systematic inquiry into thin film science provides a framework for the development of advanced solid-state and nanotechnology-based devices.

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 examines the theoretical and application-based principles of advanced networking through the study of modern computer and data architectures. Students analyze communication networks using mathematical treatments, including queuing theory and random processes, to evaluate system performance and resource allocation. Inquiry focuses on the design and implementation of network architectures and the technologies that define high-speed data exchange. Students examine the protocols and algorithms governing packet switching, traffic management, and quality of service within heterogeneous environments. Analysis includes the investigation of network delay models, throughput optimization, and the impact of stochastic variables on link reliability. Students investigate the relationship between physical layer constraints and upper-layer application requirements in distributed systems. Evaluation of these networking frameworks identifies the technical trade-offs between latency, scalability, and security in modern infrastructures. Systematic inquiry into advanced networking provides a framework for the development of robust and efficient information systems.

EE 5374. Advanced Wireless Communication.

This course examines the principles and analytical frameworks required for the design and evaluation of cellular and advanced wireless communication systems. Students analyze radio frequency (RF) propagation modeling and the mathematical characterization of fast and slow fading environments. Inquiry focuses on the implementation of modulation and demodulation techniques to optimize spectral efficiency and signal integrity. Students examine the role of channel coding in ensuring reliable data transmission across stochastic wireless channels. Analysis includes the study of multiple access techniques, such as TDMA, FDMA, and CDMA, alongside modern orthogonal frequency-division multiplexing paradigms. Students investigate the relationship between signal-to-noise ratios and system capacity in multi-user environments. Evaluation of these wireless frameworks identifies the technical trade-offs between coverage, mobility, and data throughput. Systematic inquiry into communication theory provides a framework for the development of next-generation mobile networks and satellite systems.

EE 5375. Smart Grid: An Application Development Platform.

This course examines the development of functional applications for the smart grid and the mathematical modeling of system performance through stochastic simulations. Students analyze energy informatics and smart metering infrastructures to evaluate data-driven energy management strategies. Inquiry focuses on the implementation of home energy management systems and demand response protocols to optimize grid stability. Students examine the mechanics of load disaggregation and the integration of APIs and OpenData into application platforms. Analysis includes the application of optimization and control theory alongside machine learning algorithms to address dynamic energy demands. Students investigate the relationship between stochastic processes and the reliability of distributed energy resources. Evaluation of these application platforms identifies the technical trade-offs between computational overhead and grid responsiveness. Systematic inquiry into smart grid architectures provides a framework for the development of intelligent and resilient power distribution systems.

EE 5377. Statistical Signal Processing.

This course examines the theoretical and applied principles of random processes within the context of linear systems and transform theory. Students analyze discrete and continuous-time signals using probability theory to evaluate the behavior of stochastic systems. Inquiry focuses on the application of random processes to estimation and detection theory for signal characterization. Students examine the mathematical frameworks of information and communication theory to determine channel capacity and data integrity. Analysis includes the study of optimal filtering and control strategies under conditions of uncertainty and noise. Students investigate the relationship between spectral density and the performance of signal processing algorithms. Evaluation of these statistical models identifies the technical trade-offs between computational complexity and estimation accuracy. Systematic inquiry into stochastic systems provides a framework for the development of advanced communications and automated control platforms. Prerequisite: ENGR 5310 with a grade of "C" or better.

EE 5380. Advanced Electric Machines.

This course examines the advanced principles of electromechanical energy conversion and the mathematical analysis of rotating electrical machinery. Students analyze electromagnetic field interactions and the fundamental laws governing motor and generator dynamics. Inquiry focuses on the development of high-fidelity analytical models to predict transient and steady-state device performance. Students examine the design and operational physics of major classes of electric machines, including synchronous, induction, and permanent magnet architectures. Analysis includes the study of magnetic circuit modeling, winding configurations, and the impact of non-linear material properties on torque production. Students investigate the relationship between energy efficiency, thermal constraints, and power density in complex electromechanical systems. Evaluation of these design frameworks identifies the technical trade-offs between speed-torque characteristics and system control requirements. Systematic inquiry into advanced machine theory provides a framework for the development of modern electric propulsion and industrial automation systems.

EE 5381. Advanced Sustainable Energy & Storage.

This course examines the principles of sustainable energy conversion and the technical frameworks of advanced storage technologies. Students analyze the operational physics of solar, wind, and geothermal systems to evaluate power generation capacity and efficiency. Inquiry focuses on the integration of variable renewable resources into modern electrical grids and the impact on system stability. Students examine the mechanics of electrochemical batteries, gravitational storage, and hybrid energy systems to determine discharge characteristics. Analysis includes the study of energy density, lifecycle assessment, and the thermodynamic limits of conversion processes. Students investigate the relationship between energy demand profiles and the scalability of distributed generation infrastructures. Evaluation of these sustainable frameworks identifies the technical trade-offs between environmental impact, cost, and grid reliability. Systematic inquiry into energy storage and production provides a framework for the development of resilient and carbon-neutral power systems.

EE 5382. Advanced Power Systems Analysis.

This course examines the principles of power system analysis and the operational dynamics of interconnected electrical networks through the study of technical case studies and peer-reviewed literature. Students analyze symmetrical and unsymmetrical faults to evaluate system response and protection requirements using symmetrical component theory. Inquiry focuses on the implementation of protection schemes and the integration of Supervisory Control and Data Acquisition (SCADA) frameworks for real-time monitoring. Students examine transient stability and the power-angle relationship to determine grid resilience against sudden disturbances. Analysis includes the investigation of transient operations on transmission lines and the mechanics of surge propagation. Students investigate the relationship between fault analysis, relay coordination, and the reliability of high-voltage infrastructures. Evaluation of these power system elements identifies the technical trade-offs between protection sensitivity and grid stability. Systematic inquiry into power dynamics provides a framework for the development of secure and resilient energy systems.

EE 7199. Dissertation.

This course examines the principles of original research and advanced technical writing in electrical and computer engineering. Students analyze academic literature and current technologies to identify gaps in existing knowledge. Topics include formulation of research questions, development of experimental or theoretical frameworks, and design of prototypes, simulations, or analytical models under faculty supervision. Students collect and evaluate data to support original findings and assess research validity. The course also addresses ethical considerations and professional standards in scholarly research. Emphasis is placed on producing a dissertation that demonstrates technical rigor and contributes to the field.

EE 7299. Dissertation.

This course examines the principles of original research and advanced technical writing within the specialized domains of electrical and computer engineering. Students analyze academic literature and current technologies to identify gaps in engineering knowledge. Inquiry focuses on formulating hypotheses and developing experimental or theoretical frameworks to address complex technical problems. Students examine the design and implementation of prototypes, simulation models, or mathematical proofs under the guidance of a doctoral research advisor. Analysis includes the collection and evaluation of data to validate findings. Students also examine the ethical and societal implications of research while adhering to professional standards. The course culminates in the production of a dissertation demonstrating technical rigor and contribution to the field.

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

This course examines the principles of empirical investigation and the methodologies required for advanced research within the electrical and computer engineering domain. Students analyze the systematic processes of formulating research questions and building theoretical frameworks to address complex engineering problems. Inquiry focuses on the techniques of data analysis and evidence construction to evaluate the validity of experimental findings. Students examine the structure of scientific manuscripts and the protocols of the peer-review process for academic publishing. Analysis includes the utilization of digital databases and the ethical attribution of credit for prior work. Students investigate the legal constraints of intellectual property rights and the ethical standards governing professional research conduct. Evaluation of diverse research methodologies identifies the capacities and limitations of specific qualitative and quantitative approaches. Systematic inquiry into technical communication provides a framework for the dissemination of original engineering contributions.

EE 7301. Advanced Digital System Design.

This course examines the principles of digital systems design using hardware description languages and automated design toolchains to evaluate the integration and verification of complex hardware. Students analyze modern design flows, including high-level synthesis (HLS) and register-transfer-level (RTL) synthesis, to determine system performance and resource utilization. Inquiry focuses on the functional role of hardware description languages in simulating and validating modular hardware components in large-scale digital systems. Students examine advanced design methodologies through the implementation of digital architectures on programmable targets, such as Field-Programmable Gate Arrays (FPGA) or Application-Specific Integrated Circuits (ASIC). Analysis includes the study of timing closure, power optimization, and hardware-software co-design within high-performance computing frameworks. Students investigate the relationship between architectural abstractions and physical hardware synthesis. Evaluation of these design frameworks identifies the technical trade-offs between logic density and operational speed. Systematic inquiry into digital systems provides a framework for the development of robust and scalable hardware.

EE 7302. Hardware Acceleration for Machine Learning.

This course examines the design principles of artificial intelligence systems and the architectural frameworks required for deep learning acceleration. Students analyze computing platforms, including CPUs, GPUs, and TPUs, to evaluate performance in neural network training and inference. Inquiry focuses on FPGA architectures and specialized programming models for hardware synthesis. Students examine memory organization and dataflow patterns to optimize processing for large-scale AI models. Analysis includes power consumption strategies at the system and register-transfer level to support energy-efficient acceleration. Students also investigate relationships between algorithmic complexity and hardware constraints. The course emphasizes evaluation of trade-offs between computational throughput and power-area efficiency in machine learning hardware.

EE 7303. Physical Electronics.

This course examines the advanced principles of semiconductor device physics and the operational dynamics of micro- and nano-electronic systems. Students analyze the fundamental laws of quantum mechanics to evaluate the behavior of carriers in confined and periodic potentials. Inquiry focuses on the application of Fermi-Dirac statistics and the density of states to determine carrier concentrations and distribution within energy bands. Students examine charge transport mechanisms in crystalline semiconductors, including drift, diffusion, and scattering processes, to evaluate mobility and conductivity. Analysis includes the study of electronic transitions and the operational physics of optoelectronic devices, such as photodetectors, light-emitting diodes, and semiconductor lasers. Students investigate the relationship between material properties and the functional reliability of solid-state architectures. Evaluation of these physical electronics frameworks identifies the technical constraints of maintaining performance at reduced scales. Systematic inquiry into device physics provides a framework for the development of next-generation electronic and photonic technologies.

EE 7304. Modern Semiconductor Devices.

This course examines the advanced physics and operational dynamics of modern semiconductor architectures within the context of nanoscale integration. Students analyze metal-semiconductor contacts and the physical principles of p-n junction operation to evaluate charge carrier behavior and rectifying characteristics. Inquiry focuses on the electrostatics of MOS capacitors and the functional characteristics of metal-oxide-semiconductor field-effect transistors (MOSFETs). Students examine the impact of scaling and short-channel effects on the performance of contemporary and emerging transistor technologies. Analysis includes the study of optoelectronic devices, including photodetectors, solar cells, and light-emitting structures, to determine their quantum efficiency and spectral response. Students investigate the relationship between material parameters and the functional reliability of solid-state display devices. Evaluation of these semiconductor frameworks identifies the technical challenges of maintaining device control at reduced dimensions. Systematic inquiry into device physics provides a framework for the development of high-performance integrated circuits and photonic systems.

EE 7305. Energy Storage and Sustainability.

This course examines the principles of energy storage mechanisms and the technical frameworks relevant to sustainability. Students analyze electrochemical processes governing storage technologies to evaluate energy density and cycle life across battery chemistries. Inquiry focuses on the operational physics of advanced storage systems and their integration into renewable energy grids. Students examine technical, economic, and policy considerations associated with energy infrastructure transitions across sectors. Analysis includes decentralized power management and socio-economic factors influencing energy system adoption. Students investigate research trends in hybrid storage and thermodynamic limits of emerging technologies. The course emphasizes evaluation of trade-offs between cost, environmental impact, and grid reliability in energy storage systems.

EE 7306. Artificial Intelligence in Smart Grids.

This course examines the integration of artificial intelligence (AI) and machine learning (ML) architectures within modern smart grid infrastructures. Students analyze machine learning algorithms and computational tools to evaluate their application in power system design, operation, and reliability. Inquiry focuses on neural networks and statistical models for energy forecasting and demand response optimization. Students examine smart meter data analytics to assess consumption patterns and distributed energy resource efficiency. Analysis includes nonintrusive load monitoring techniques to identify appliance-level signals. Students investigate the relationship between stochastic data inputs and real-time grid control processes. The course emphasizes evaluation of trade-offs between algorithmic complexity and latency in critical power systems.

EE 7307. Mobile and Microgrid Design and Operations.

This course examines the advanced principles of modeling, control, and security within the context of microgrid design and grid modernization. Students analyze microgrid architectures and the characterization of distributed energy resources to evaluate system performance and integration. Inquiry focuses on the implementation of smart inverters and hierarchical control strategies to determine voltage and frequency stability in both islanded and grid-tied modes. Students examine the mechanics of fault management and the development of resilient microgrids through programmable networks and software-defined technologies. Analysis includes the investigation of reliable networked microgrids and the application of cyber security frameworks to protect critical energy infrastructure. Students investigate the relationship between energy management systems and the dynamic constraints of distributed generation. Evaluation of these microgrid frameworks identifies the technical trade-offs between system autonomy and interconnected reliability. Systematic inquiry into modern grid operations provides a framework for the development of secure and adaptive energy distribution systems.

EE 7308. High and Medium Voltage Power Transmission.

This course examines the principles of electric power transmission and distribution systems within the context of grid modernization and distributed energy integration. Students analyze the structural components and typical topologies of distribution grids to evaluate operational strategies for high and medium voltage networks. Inquiry focuses on the integration of distributed generation, including photovoltaics and wind, and the role of storage and controllable loads in system stability. Students examine the mathematical frameworks of power flow analysis and the functional characteristics of distribution transformers. Analysis includes the study of electric load modeling and the development of control methodologies to address variable demand profiles. Students investigate the relationship between transmission efficiency and the deployment of smart grid technologies. Evaluation of these power systems identifies the technical trade-offs between grid reliability and the increasing penetration of intermittent energy resources. Systematic inquiry into distribution provides a framework for the development of resilient electrical infrastructures.

EE 7331. AI and Machine Learning for Engineers.

This course examines the principles of artificial intelligence and machine learning architectures through the analysis of supervised, unsupervised, and semi-supervised learning paradigms. Students analyze the functional characteristics of deep neural networks, convolutional neural networks, and recurrent neural networks to evaluate their efficacy in engineering applications. Inquiry focuses on reinforcement learning frameworks, including Markov Decision Processes, Q-learning, and policy gradients, for dynamic decision-making problems. Students examine the mathematical foundations of model optimization and generalization to assess predictive reliability. Analysis includes recent advancements in deep learning and their integration into engineering platforms. The course emphasizes trade-offs between computational overhead and inference accuracy in AI systems.

EE 7354. Advanced Flexible Electronics.

This course examines the materials systems, fabrication processes, and device physics of advanced flexible electronic architectures within the context of contemporary research. Students analyze the structural and electronic properties of semiconductor materials, including amorphous silicon, nanocrystalline silicon, and organic polymers. Inquiry focuses on the implementation of solution-cast films from carbon nanotubes, graphene, and other two-dimensional materials to determine mechanical and electrical performance. Students examine the operational principles of high-speed transistors, switches, and photovoltaics designed for non-rigid substrates. Analysis includes the study of integration strategies for flexible communication devices and the impact of mechanical strain on carrier transport. Students investigate the relationship between material deposition techniques and the functional reliability of conformable electronics. Evaluation of these emerging technologies identifies the technical challenges of maintaining high-frequency performance in flexible systems. Systematic inquiry into flexible electronics provides a framework for the development of next-generation wearable and portable hardware.

EE 7359. Research in Electrical Engineering.

This course examines the principles of foundational research and experimental design for doctoral students within the electrical engineering discipline. Students analyze specialized technical literature and current engineering methodologies to define significant research problems. Inquiry focuses on the development of preliminary theoretical models and the implementation of initial simulation or laboratory experiments. Students examine the integration of advanced mathematical tools and physical principles to evaluate the feasibility of novel engineering solutions. Analysis includes the systematic collection of data and the critical assessment of preliminary findings under the guidance of a doctoral research advisor. Students investigate the relationship between established engineering standards and emerging technological trends to formulate a comprehensive research direction. Evaluation of these investigative techniques identifies the technical requirements for advanced doctoral-level inquiry and future dissertation development. Systematic inquiry into electrical engineering research provides a framework for the advancement of innovative hardware and software systems.

EE 7372. Wireless and Mobile Networks.

This course examines the principles and architectural frameworks of modern wireless and mobile networking technologies. Students analyze wireless signal propagation characteristics and stochastic modeling to evaluate communication link reliability across diverse environments. Inquiry focuses on the implementation of advanced coding, modulation, and link-layer techniques to optimize data transmission. Students examine the mechanics of multiple access schemes and protocols across local, personal, and wide-area networks, including 5G and 6G paradigms. Analysis includes the study of sensor networks and low-power wide-area networks (LPWAN) with an emphasis on energy-aware and energy-harvesting protocols. Students investigate the dynamics of mobile ad-hoc networks and specialized variants such as unmanned aerial networks and vehicular ad-hoc networks. Evaluation of these networking frameworks identifies the integration of Internet of Things architectures and machine learning algorithms to enhance network autonomy. Systematic inquiry into mobile communication theory provides a framework for the development of next-generation intelligent wireless infrastructures.

EE 7374. Smart Data Networks.

This course examines the design principles of smart data networks and the integration of artificial intelligence (AI) within traditional network architectures. Students analyze the evolution of the protocol stack to evaluate how autonomous algorithms optimize performance beyond conventional boundaries. Inquiry focuses on the implementation of AI-enhanced medium access control (MAC) protocols and intelligent routing strategies. Students examine the role of machine learning in congestion control and transport protocols to ensure efficient multimedia streaming. Analysis includes the study of traffic characterization and the development of predictive models for network management. Students investigate the relationship between advanced networking paradigms, such as the Internet of Things (IoT) and digital twins, and system-wide scalability. Evaluation of these intelligent frameworks identifies the technical trade-offs between computational complexity and real-time responsiveness. Systematic inquiry into smart networking provides a framework for the development of adaptive and self-healing internet infrastructures.

EE 7399. Dissertation.

This course examines the principles of original research and advanced technical communication within the specialized domains of electrical and computer engineering. Students analyze contemporary engineering challenges and existing scholarly literature to define a significant contribution to the field. Inquiry focuses on the development of rigorous theoretical frameworks and the implementation of sophisticated experimental methodologies to address complex technical problems. Students examine the design and validation of novel prototypes, simulation architectures, or mathematical proofs under the guidance of a dissertation advisor. Analysis includes the systematic evaluation of data and the critical assessment of research outcomes to ensure technical accuracy and scholarly integrity. Students investigate the broader impacts and ethical considerations of their technological innovations. Evaluation of these research processes supports the synthesis of a formal dissertation that demonstrates mastery of specialized engineering concepts. Systematic inquiry into doctoral-level research provides a framework for the advancement of state-of-the-art engineering solutions.

EE 7599. Dissertation.

This course examines the principles of original research and advanced technical communication within the specialized domains of electrical and computer engineering. Students analyze contemporary engineering challenges and existing scholarly literature to define a significant contribution to the field. Inquiry focuses on the development of rigorous theoretical frameworks and the implementation of sophisticated experimental methodologies to address complex technical problems. Students examine the design and validation of novel prototypes, simulation architectures, or mathematical proofs under the guidance of a dissertation advisor. Analysis includes the systematic evaluation of data and the critical assessment of research outcomes to ensure technical accuracy and scholarly integrity. Students investigate the broader impacts and ethical considerations of their technological innovations. Evaluation of these research processes supports the synthesis of a formal dissertation that demonstrates mastery of specialized engineering concepts. Systematic inquiry into doctoral-level research provides a framework for the advancement of state-of-the-art engineering solutions.

EE 7699. Dissertation.

This course examines the principles of original research and advanced technical communication within the specialized domains of electrical and computer engineering. Students analyze contemporary engineering challenges and existing scholarly literature to define a significant contribution to the field. Inquiry focuses on the development of rigorous theoretical frameworks and the implementation of sophisticated experimental methodologies to address complex technical problems. Students examine the design and validation of novel prototypes, simulation architectures, or mathematical proofs under the guidance of a dissertation advisor. Analysis includes the systematic evaluation of data and the critical assessment of research outcomes to ensure technical accuracy and scholarly integrity. Students investigate the broader impacts and ethical considerations of their technological innovations. Evaluation of these research processes supports the synthesis of a formal dissertation that demonstrates mastery of specialized engineering concepts. Systematic inquiry into doctoral-level research provides a framework for the advancement of state-of-the-art engineering solutions.

EE 7999. Dissertation.

This course examines the principles of original research and advanced technical communication within the specialized domains of electrical and computer engineering. Students analyze contemporary engineering challenges and existing scholarly literature to define a significant contribution to the field. Inquiry focuses on the development of rigorous theoretical frameworks and the implementation of sophisticated experimental methodologies to address complex technical problems. Students examine the design and validation of novel prototypes, simulation architectures, or mathematical proofs under the guidance of a dissertation advisor. Analysis includes the systematic evaluation of data and the critical assessment of research outcomes to ensure technical accuracy and scholarly integrity. Students investigate the broader impacts and ethical considerations of their technological innovations. Evaluation of these research processes supports the synthesis of a formal dissertation that demonstrates mastery of specialized engineering concepts. Systematic inquiry into doctoral-level research provides a framework for the advancement of state-of-the-art engineering solutions.