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Fundamental physical limits apply to many key antenna performance parameters (gain, Q-factor, efficiency), particularly when device dimensions are small relative to operating wavelengths – the “electrically small” regime. These limitations cannot be exceeded by linear time-invariant (LTI) devices, and can manifest as critical bottlenecks for communications and sensing technology. In this talk, we survey recent work on moving antenna technology away from the classical LTI design paradigm and toward active, non-linear, and time-varying systems. Emphasis is placed on research incorporating time-varying elements into antennas and matching networks with the goal of exceeding physical bounds on LTI antenna metrics. This includes direct antenna modulation (DAM), which treats time-varying antennas and matching networks as active parts of the modulation process, allowing for efficient, extreme instantaneous radiated signal bandwidths from electrically small apertures. Additionally, the co-design of electrically small antennas with parametric pumping networks for broadband, low-noise receive applications will be presented. Modeling challenges and simulation approaches for time-varying radiators will be discussed and several theoretical concepts in the area of time-varying antennas will also be surveyed, including the extension of effective aperture to time-varying antennas and the effect of cross-frequency coupling on the effective receiver noise temperature. Speaker(s): Kurt Schab, Virtual: https://events.vtools.ieee.org/m/441508

Planning session primarily focused on planning the 2025 Project Echo Milestone Event. Agenda: - Determine Agenda for the Milestone event day on May 17, 2025. Milestone Project Plan: https://www.google.com/url?q=https://docs.google.com/spreadsheets/d/1-VvTlmC8HXimiwnZ20dLfbVDZ8ZyJI5G?rtpof%3Dtrue%26usp%3Ddrive_fs&sa=D&source=calendar&usd=2&usg=AOvVaw02GPafJ9BCDrG7NyVP4mPphttps://docs.google.com/spreadsheets/d/1-VvTlmC8HXimiwnZ20dLfbVDZ8ZyJI5G?rtpof=true&usp=drive_fs Virtual: https://events.vtools.ieee.org/m/439850

Nov 6th 4:30 to 6:00 PM (+30 min to 6:30 for questions): Advanced topic 2 - thermal management Co-sponsored by: IEEE Cornell Student Section Room: 155, Bldg: Olin Hall, 113 Ho Plaza, Ithaca, New York, United States, 14853

The IEEE North Jersey Section's Executive Committee (EXCOM) monthly meeting will be held hybridly. The EXCOM meeting starts at 6:30 pm EST and typically ends at 8:30 pm. The meeting is meant to discuss and coordinate the activities of the Section and its Chapters and Groups, as well as new initiatives. Everyone is welcome to attend this meeting. Please register in advance for this meeting using vTools (Please make a note if you join the meeting remotely) You can change/cancel the registration if your plans change. For more information, please contact our IEEE North Jersey Section Chair Hong Zhao ([email protected]) , or Secretary, Adriaan van Wijngaarden, ([email protected]). Location: New Jersey Institute of Technology To join remotely by the following Zoom link: https://fdu.zoom.us/j/99736508515 Meeting ID: 997 3650 8515 Note: If you are unable to join the meeting, please send your chapter activity report to the section chair at [email protected] Agenda: 06:30 pm - 06:45 pm Networking 06:45 pm - 08:30 pm IEEE North Jersey Section Executive Committee Meeting The meeting agenda typically includes news related to the IEEE and the IEEE North Jersey Section, Secretary and Treasurer reports, Chapter and Affinity Group reports, Committee reports, and reports by various Chairs and Representatives, Committee Chairs, and planning, and new initiatives. Room: ECE 202, Bldg: Electrical and Computer Engineering (ECE) Building, University Heights, Newark, New Jersey, United States, 07102, Virtual: https://events.vtools.ieee.org/m/441739

Abstract: Many constrained sequential decision-making processes, such as safe AV navigation, wireless network control, caching, cloud computing, etc., can be cast as Constrained Markov Decision Processes (CMDP). Reinforcement Learning (RL) algorithms have been used to learn optimal policies for unknown unconstrained MDP. Extending these RL algorithms to unknown CMDP brings the additional challenge of maximizing the reward and satisfying the constraints. In this talk, I will present algorithms that can learn safe policies effectively. In the second part of the talk, I will demonstrate how the theoretical understanding of the constrained MDP can help us to develop algorithms for practical applications. As an application, I show how to learn to obtain optimal beam directions under time-varying interference-constrained channels for a mobile service robot. Optimal beam selection in mmWave is challenging because of its time-varying nature. We propose a primal-dual Gaussian process bandit with adaptive reinitialization to handle non-stationarity and interference constraints. We demonstrate how our approach learns to adapt effectively to time-varying channel conditions. Co-sponsored by: IEEE North Jersey Section Speaker(s): Dr. Arnob Ghosh, Agenda: Nov 6th, Talk: 7:00 PM - 8:00 PM Discussion Q/A: 8:00 PM - 8:15 PM Room: ECE 202, Bldg: Electrical and Computer Engineering, 154 Summit Street, Newark, New Jersey, United States, 07102