Electrical Engineering (EEEN)

One or more advanced topics. May be repeated when topic changes. (Credit may not be obtained in both EEEN 5303 and CSEN 5303 courses if the topic is the same.)

Introduces the design principles of modern computers. The topics include RISC and CISC architectures, interconnection networks, multiprocessors and multicomputer systems, dataflow and systolic arrays, future outlook for architectures and the basics of parallel algorithms. (Credit may not be obtained in both EEEN 5304 and CSEN 5304.)

Designed for project option students and requires completion of research project. Prerequisite: departmental approval. May be repeated for a maximum of 6 semester hours.

Designed for thesis option students. The course requires completion of thesis research. Prerequisite: departmental approval. May be repeated for maximum of 6 semester hours.

Register operations, arithmetic operations, control of operations, memory systems, methods of input and output. Examples of commercial systems, system design of a general purpose computer.

Actuators and transducers, static and dynamic accuracy of systems, describing functions, compensation, design of typical control systems.

Mathematical analysis of engineering, dynamic systems. Modeling, simulation, transfer functions, state variables, stability of linear systems.

Signal and system norms, Lp functions, adaptive parameter identification and control, stability, Model Reference Adaptive Control (MRAC), multi objective evolutionary/genetic algorithms, adaptive backstepping, and robust adaptive control laws.

Principles of electronic system design using Application-Specific Integrated Circuits (ASIC) approach: digital hardware modeling techniques using an HDL, logic simulation, logic synthesis, standard cells, gate arrays, sea of gates, bit serial hardware design methods and analog methods.

Digital processing of signals, z-transform, digital filters, discrete and fast Fourier transforms, power spectrum, autocorrelation, cepstrum analysis.

Principles of design and fabrication of microelectronic circuits via Very Large Scale Integrated circuitry (VLSI), structured design methods for VLSI systems, use of computer-aided design tools, design projects of small to medium scale integrated circuits.

Introduces the computer vision systems. Topics include edge detection, spatial-domain processing, frequency-domain processing, color processing, texture analysis, shape analysis and making movies from a deck of frames.

Classical and modern control analysis and design methods and techniques. Topics include discrete control system analysis, sampled data systems, discrete equivalents of continuous systems, design using transform techniques, design using state-space methods and the real-time control of dynamic systems using digital computers and micro-controllers.

Embedded system architecture and programming. Role of microprocessors, FPGAs, and PLCs; input/output; analog and digital interfacing; sensor networks and peripherals in hardware integration. (Credit may not be obtained for this course and for MHEN 5373).

Fundamentals of digital signal processing, waveform coding, speech spectrum, voice coders, linear predictive coding, speech recognition, adaptive noise cancellation and multirate signal processing.

Advanced concepts of circuit design for digital Very Large Scale Integrated Circuitry (VLSI) components in state-of-the-art Complementary Metal Oxide Semiconductor (CMOS) technologies. Emphasis is on the design and optimization of high-speed (high performance devices), high density (heterogeneous systems on a chip) and low-power (portable applications) integrated circuits. Prerequisite: EEEN 5333 and EEEN 5330 (or approval of instructor).

This course introduces fundamental concepts and technologies in the area of wireless communication systems such as wireless applications, modulation techniques, wireless channel models, digital communication over wireless channels, multiple access techniques, and wireless standards.

Includes a review of network architectures, perceptron, linear networks, back-propagation and radial basis networks. A real-time laboratory experience in seeing the application of neural networks. Prerequisite: graduate standing in Computer Science. Prerequisite: graduate standing in Electrical Engineering. (Credit may not be obtained in both EEEN 5350 and CSEN 5350.)

Individual or group research on advanced problems conducted under the supervision of a faculty member. Maximum credit 8 semester hours.

Courses offered under this Special Topics denomination concentrate on themes not present in the current EECS curriculum, or can also be offered to strengthen and provide further depth of study in important areas of electrical engineering. Topics vary to reflect new developments and interests on emerging areas of electrical engineering. May be repeated when topic changes.

Students undertaking dissertation research towards fulfilling doctoral dissertation proposal and dissertation requirements are required to register for this course.

Artificial intelligence, Biomimicry, Fuzzy Logic, Neural Networks, Deep Learning, General Algorithms, and Expert Systems for Intelligent Control Systems and Optimization. Prerequisite: Graduate Standing

Nonlinear systems and stability, linearization, phase plane analysis, describing functions, Lyapunov stability, feedback linearization, singular perturbations, model reduction, chaos control and sliding mode control. Prerequisite: Graduate Standing

Signal and system norms, Structured and unstructured uncertainty, Robustness (stability and performance) analysis in time and frequency domains, Convex Optimization (Linear Matrix Inequalities (LMI), Semi-definite programming), Linear Fractional Transformations (LFT), H2 and H∞ and μ controller designs, Youla parametrization, Linear parameter varying (PV) systems. Prerequisite: A Linear Systems Analysis Course.

Antenna Parameters, Terminology, Antenna Arrays, Spatial Filtering, Adaptive Antenna Arrays, Mutual Coupling & Correlation, Narrowband Array Systems (Beam Steering, Grating Lobes, Amplitude Weights), Adaptive Arrays (Spatial Covariance Matrix, Multi-beam Arrays, Scanning Arrays, Switched Beam Beamformers, Fully Adaptive Beamformers, Fourier Method, Capon's Minimum Variance, The MUSIC Algorithm, ESPRIT, Maximum Likelihood Techniques, Spatial Smoothing.