Matrix analysis, state variables, state space techniques for continuous time systems, matrix fraction descriptions. Controllability, observability, realization theory. Feedback and observers. Stability analysis.

Introductory course in machine learning designed for senior level undergraduates and graduate students. The class will briefly cover topics in regression, classification, mixture models, trees, neural networks, deep learning, and ensemble methods. The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work.

This course introduces the theoretical foundations of embedded systems, with a focus on applications in the domains of medical devices and other low-power systems. The topics covered will include modeling, scheduling, analysis and verification of systems with discrete, continuous, and hybrid dynamics. Course is intended for graduate students and senior undergraduates.

This course provides an overview of the core design principles used in the holistic design of low power embedded computing systems. The topics for the course will include wireless and ambient energy harvesting, alternative energy storage technologies, low power radio design, efficient sensor data processing, and low power AI accelerators. The course will include a survey of papers from recent top-tier conferences and journals. Students will have the opportunity to design a low power embedded system for IoT, mobile health, or other HCI application and empirically evaluate its performance.

Students will work low power portable Edge devices for data acquisition and DL-based processing with an eye towards computer vision and healthcare analytics. Hands-on experiments with NVIDIA Jetson Nano/ Google Coral/ Intel Neural Compute Stick. This course will not provide any existing infrastructure and is ideally suited for students with strong motivation to master Deep-Learning on Edge with limited guidance.

Introduction to antennas and radiowave propagation for microwave frequency applications. Antenna topics include basic antenna parameters, antennas in communication and radar systems, wire antennas. Propagation topics include direct transmission between a transmitter and a receiver, reflection and refraction, and propagation properties in ionosphere.

This course introduces concepts, techniques, and algorithms from artificial intelligence and machine learning, such as classification, regression, support vector machines, decision trees, and neural networks, as well as deep neural networks. The application of these techniques to solve practical problems is a focus of this course. Project assignments are used to reinforce concepts learned in lectures.

Not available at this time

Analysis and design of passive microwave devices, including resonators, filters, and ferrite devices, in various transmission-line media. Noise and noise effects in detectors, mixers, and modulators. Introduction to FET amplifier design. Prerequisite: E&C-ENG 584.

Theory and applications of modern optoelectronic components such as waveguides and optical fibers, photodetectors, light emitting diodes, and semiconductor lasers. Emphasis on the physics and operating characteristics of optoelectronic semiconductor devices. Prerequisite: E&C-ENG 344.