Under faculty supervision, students plan a project of at least one semester's duration, conduct the analysis or design and test, and report on the results. If this work is to …
Under faculty supervision, students plan a project of at least one semester's duration, conduct the analysis or design and test, and report on the results. If this work is to …
Reviews the electromagnetic principles of optics; Maxwell's equations; reflection and transmission of electromagnetic fields at dielectric interfaces; Gaussian beams; interference and diffraction; laser theory with illustrations chosen from atomic, gas …
Design and analysis of passive microwave circuits. Topics include transmission lines, electromagnetic field theory, waveguides, microwave network analysis and signal flow graphs, impedance matching and tuning, resonators, power dividers and …
This course will teach students the required skills, concepts, and algorithms to develop mobile robots that act autonomously in complex environments. The main emphasis is on mobile robot locomotion and …
Quantum electronics, the study of light and matter interaction, has become the cornerstone in many areas of optical science and technology. This course reviews the principles of lasers then introduces …
A first-level graduate course covering a topic not normally covered in the graduate course offerings. The topic will usually reflect new developments in the electrical and computer engineering field. Offering …
This one-hour weekly seminar course features presentations given by ECE faculty members, to introduce various research areas, topics, and advances in Electrical and Computer Engineering. This course is required for …
This course introduces students to the key concepts in convex optimization theory with the goal of enabling them to formulate and solve various convex optimization problems arising in engineering, data …
Topics include probability spaces; random variables and vectors; and random sequences and processes; especially specification and classification. Includes detailed discussion of second-order stationary processes and Markov processes; inequalities, convergence, laws …
Covers foundations of estimation theory and machine learning in a probabilistic modeling framework. Topics include frequentist and Bayesian estimation, analysis of estimators, linear regression, linear classification, graphical models, Markov models, …
A first graduate course in digital signal processing. Topics include discrete-time signals and systems, application of z-transforms, the discrete-time Fourier transform, sampling, digital filter design, the discrete Fourier transform, the …
This course focuses on an in-depth study of advanced topics and interests in image data analysis. Students will learn practical image techniques and gain mathematical fundamentals in machine learning needed …
This course aims to provide an instruction to basic principles and tools for the analysis and design of control systems. It is intended for general graduate students in engineering and …
Detailed study of graduate course material on an independent basis under the guidance of a faculty member.
A guided teaching experience for Ph.D. students, with selected teaching assignments and directed performance evaluation, under the supervision of a faculty member, as a part of Ph.D. training designed for …
An in-depth treatment of digital communications techniques and performance. Topics include performance of uncoded systems such as Mary, PSK, FSK, and multi-level signaling; orthogonal and bi-orthogonal codes; block and convolutional …
For master's students.
Formal record of student commitment to master's thesis research under the guidance of a faculty advisor. May be repeated as necessary.
For doctoral students.