Explores measurement and behavior of high-frequency circuits and components. Equivalent circuit models for lumped elements. Measurement of standing waves, power, and frequency. Use of vector network analyzers and spectrum analyzers. …
Digital CMOS circuit design and analysis: combinational circuits, sequential circuits, and memory. Second order circuit issues. Global design issues: clocking and interconnect. Use of Cadence CAD tools. Semester long team …
This course explores the intricacies of AI hardware, including the current landscape and anticipating the necessary developments in response to AI's rapid growth and widespread integration across all computing tiers. …
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Focuses on techniques for designing and analyzing dependable computer-based systems. Topics include basic dependability concepts and attributes, fault models and effects, combinatorial and state-space modeling, hardware redundancy, error detecting and …
Integration of computer organization concepts such as data flow, instruction interpretation, memory systems, interfacing, and microprogramming with practical and systematic digital design methods such as behavioral versus structural descriptions, divide-and-conquer, …
Interactions between robots and humans are influenced by form, function and expectations. Quantitative techniques evaluate performance of specific tasks and functions. Qualitative techniques are used to evaluate the interaction and …
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 …
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 …
Special Topics in Distance Learning
Optoelectronics merges optics and microelectronics. Optoelectronic devices and circuits have become core technologies for several key technical areas such as telecommunications, information processing, optical storage, and sensors. This course will …
Design and analysis of analog integrated circuits. Topics include feedback amplifier analysis and design including stability, compensation, and offset-correction; layout and floor-planning issues associated with mixed-signal IC design; selected applications …
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 …
A first graduate course in principles of communications engineering. Topics include a brief review of random process theory, principles of optimum receiver design for discrete and continuous messages, matched filters …
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 comprehensive treatment of information theory and its application to channel coding and source coding. Topics include the nature of information and its mathematical description for discrete and continuous sources; …
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 …