[] 100-300 GHz Wireless: transistors, ICs, systems By Professor Mark Rodwell, UCSB IEEE-MTT Distinguished Microwave Lecturer We describe the opportunities, and the research challenges, presented in the development of 100-300GHz wireless communications and imaging systems. In such links, short wavelengths permit massive spatial multiplexing both for network nodes and point-point links, permitting aggregate transmission capacities approaching 1Tb/s. 100-300GHz radar imaging systems can provide thousands of image pixels and sub-degree angular resolution from small apertures, supporting foul-weather driving and aviation. Challenges include the mm-wave IC designs, the physical design of the front-end modules, the complexity of the back-end digital beamformer required for spatial multiplexing, and, for imaging, the development of system architectures requiring far fewer RF channels than the number of image pixels. We will describe transistor development, IC design, and system design, and describe our efforts to develop 140GHz massive MIMO wireless hubs, and 210GHz and 280GHz MIMO backhaul links. This is a virtual on line lecture. Please register on IEEE-Vtools. Professor Mark Rodwell holds the Doluca Family Endowed Chair in Electrical and Computer Engineering at UCSB and directs the SRC/DARPA Center for Converged TeraHertz Communications and Sensing. His research group develops nm and THz transistors, and high-frequency integrated circuits and systems. Prof. Rodwell received the 2010 IEEE Sarnoff Award, the 2012 Marconi Prize Paper Award, the 1997 IEEE Microwave Prize, the 2009 IEEE IPRM Conference Award, and the 1998 European Microwave Conference Microwave Prize. More information https://mtt.org/profile/mark-rodwell/ Speaker(s): Mark Rodwell, , Virtual: https://events.vtools.ieee.org/m/468178

AI for Control: Reinforcement Learning Reinforcement learning, a subset of artificial intelligence, is a computational approach that models decision-making by exploring the cause-and-effect relationships between actions and rewards. It provides a framework for solving optimization problems where an agent interacts with its environment and refines its policies over time. Closely related to both optimal and adaptive control, reinforcement learning has significant applications in control systems. This study explores the fundamental principles of reinforcement learning and its integration into control applications. Speaker(s): Chang-hee Won, PhD Agenda: WEBINAR: 6:30 - 9:00 P.M. The Zoom Webinar link and password will be forwarded to all registered participants after Noon on the day of the meeting. Check your spam folder if you don't see the email. PDH certificates are available and an evaluation form will be emailed to you after the meeting. PDH certificate are sent by IEEE USA 3-4 weeks after the meeting. Virtual: https://events.vtools.ieee.org/m/467824