Events for Tuesday, November 23, 2021

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3GPP Standards for 5G New Radio: from Release 15 and beyond

Montreal, Quebec, Canada

Event to introduce the 3GPP standardization process and discuss the existing and future specifications for 5G New Radio. The fifth and latest generation of cellular mobile communication protocols (5G) is meant to address use cases well beyond the next decade. The first set of technical specifications for 5G, also referred to as “New Radio” or NR in 3GPP, were completed as part of 3GPP Release 15. The standardization work for 5G NR continues and new features are continuously added to address more advanced use cases and verticals. This presentation will provide an overview of the standardization process in 3GPP and an overview of the technical features for Release 16 and Release 17 of the specifications. The presentation will conclude with an outlook of future wireless evolution. Speaker(s): Benoît Pelletier, Montreal, Quebec, Canada

Third Richard Marceau Energy Symposium

Toronto, Ontario, Canada, Virtual: https://events.vtools.ieee.org/m/289246

IEEE Toronto Section will be the host for the Third Richard Marceau Energy Symposium. Extending the success of prior events, the Third Richard Marceau Energy Symposium is a collaboration by the Bowman Centre for Sustainable Energy, the Canadian Academy of Engineering, and the Canadian Society of Senior Engineers. Agenda: - Welcome by IEEE Toronto Section Chair– Dr. Ali Nabavi - The Continuing Legacy of Dr. Richard Marceau - Canadian Academy of Engineering: Dr. Oskar Sigvaldason - Sigvaldason is the Project Manager for Trottier Energy Futures Project - - CANADA: Evaluation of Three Energy System Chains - Bowman Centre for Sustainable Energy: Marshall Kern - Marshall Kern is the President of the Bowman Centre for Sustainable Energy - - Super-Grid to Strengthen North American Electrical Energy Security - Canadian Society of Senior Engineers: Guy Van Uytven - Guy Van Uytven is the President of the CSSE Please register using the link provided below. Co-sponsored by: Ali Nabavi Speaker(s): ., Agenda: Agenda: - Welcome by IEEE Toronto Section Chair– Dr. Ali Nabavi - The Continuing Legacy of Dr. Richard Marceau - Canadian Academy of Engineering: Dr. Oskar Sigvaldason - Sigvaldason is the Project Manager for Trottier Energy Futures Project - - CANADA: Evaluation of Three Energy System Chains - Bowman Centre for Sustainable Energy: Marshall Kern - Marshall Kern is the President of the Bowman Centre for Sustainable Energy - - Super-Grid to Strengthen North American Electrical Energy Security - Canadian Society of Senior Engineers: Guy Van Uytven - Guy Van Uytven is the President of the CSSE Toronto, Ontario, Canada, Virtual: https://events.vtools.ieee.org/m/289246

Reinforcement Learning Game Tree / Markoff Chains

Virtual: https://events.vtools.ieee.org/m/287737

Prerequisites: You do not need to have attended the earlier talks. If you know zero math and zero machine learning, then this talk is for you. Jeff will do his best to explain fairly hard mathematics to you. If you know a bunch of math and/or a bunch machine learning, then these talks are for you. Jeff tries to spin the ideas in new ways. Longer Abstract: At the risk of being non-standard, Jeff will tell you the way he thinks about this topic. Both "Game Trees" and "Markoff Chains" represent the graph of states through which your agent will traverse a path while completing the task. Suppose we could learn for each such state a value measuring "how good" this state is for the agent. Then competing the task in an optimal way would be easy. If our current state is one within which our agent gets to choose the next action, then she will choose the action that maximizes the value of our next state. On the other hand, if our adversary gets to choose, he will choose the action that minimizes this value. Finally, if our current state is one within which the universe flips a coin, then each edge leaving this state will be labeled with the probability of taking it. Knowing that that is how the game is played, we can compute how good each state is. The states in which the task is complete is worth whatever reward the agent receives in the said state. These values somehow trickle backwards until we learn the value of the start state. The computational challenge is that there are way more states then we can ever look at. Speaker(s): Prof. Jeff Edmonds, Virtual: https://events.vtools.ieee.org/m/287737