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Selected topics of contemporary interest on microwave systems: advanced radars, active and passive microwave remote sensing instruments, large scale antennas, and microwave integrated circuits. Prerequisites: E&C-Eng 584 and graduate standing.

This class is an 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 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.

This course will provide a working knowledge of the key parameters of modern phased array antenna systems. Phased arrays are a key component in radars, wireless and satellite communications, remote sensing and imaging systems, and in scientific instruments such as radio telescopes. Students will learn to critically evaluate the performance of phased array antenna systems with emphasis on factors that are important for high-performance radar and communication systems such as scanning, sidelobe levels, gain, bandwidth, sensitivity, linearity, etc.

Basic fundamentals related to nanoscale phenomena will be first introduced, followed by seminar-like lectures covering some general nanotechnology/nanoscience areas and latest development.

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.

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Theory and techniques used in the design of modern microwave and millimeter wave active circuits. Emphasis on amplifier and oscillator circuits using devices such as FETs, HEMTs, HBTs and optoelectronic devices. Modern reference material used as much as possible.

To explore concepts related to the design, analysis, and construction of microwave systems. This course will discuss the fundamental tradeoffs governing system design: the hardware components and technologies that comprise working systems, the models used for characterizing the transmission and reception of signals, the physics of wave propagation and interaction, and estimation theory which seeks to separate signals from sources of error and guide algorithms for extracting information from received signals.