A semesterly recurring series of 15 tutoring sessions designed to support students in their exam preparations. These sessions will be held in the IEEE office throughout December, leading up to final exams. The event provides a comfortable and supportive environment for students to review course material with the help of knowledgeable tutors. This event usually brings in a total of 250-300 students, and grows in popularity every <a href="http://year.800" target="_blank" title="year.800">year.800 King Edward Avenue, Ottawa, Ontario, Canada

DRIVING AI-NATIVE RAN INNOVATION WITH THE SIONNA RESEARCH KITDr. Sebastian Cammerer, Senior Research Scientist, NVIDIARegistration Link: (mailto:<a href="http://[email protected]?subject=From%20Propagation%20Models%20to%20Physics-Based%20Digital%20Twins%20of%20Emerging%20Wireless%20Communication%20Systems%20-%20Saint%20Maurice%20Sect%20Chap,%20COM19)When:" target="_blank" title="[email protected]?subject=From%20Propagation%20Models%20to%20Physics-Based%20Digital%20Twins%20of%20Emerging%20Wireless%20Communication%20Systems%20-%20Saint%20Maurice%20Sect%20Chap,%20COM19)When:">[email protected]?subject=From%20Propagation%20Models%20to%20Physics-Based%20Digital%20Twins%20of%20Emerging%20Wireless%20Communication%20Systems%20-%20Saint%20Maurice%20Sect%20Chap,%20COM19)When: December 2nd at 10H30 AM ESTVia zoom: <a href="https://uqtr.zoom.us/j/81521084215?pwd=bchQDndZg7DTlpVuaeag6bhGwaOvn9.1Meeting" target="_blank" title="https://uqtr.zoom.us/j/81521084215?pwd=bchQDndZg7DTlpVuaeag6bhGwaOvn9.1Meeting">https://uqtr.zoom.us/j/81521084215?pwd=bchQDndZg7DTlpVuaeag6bhGwaOvn9.1Meeting ID: 815 2108 4215Password: 018477ABSTRACTAI will become a cornerstone of future wireless communication systems, enabling radio access networks (RANs) that dynamically adapt to specific radio frequency (RF) environments and enhance their performance even after deployment. Novel paradigms such as end-to-end learning for pilotless transmissions and semantic communications add to the transformative potential of AI. Integrating neural network components into the signal processing pipeline of wireless transceivers poses research challenges, particularly in meeting the stringent, often sub-millisecond, inference latency required by RANs. As such, the full potential of AI-native RANs depends on three main factors: (a) the development of robust software tools, (b) the deployment of specialized hardware platforms for real-time AI acceleration, and (c) the design of fundamentally new transceiver <a href="http://algorithms.In" target="_blank" title="algorithms.In">algorithms.In this talk, we outline a path toward prototyping an AI-native RAN using the Sionna Research Kit—an open-source platform designed for development, training, and deployment of AI-native wireless communication systems. We present a 5G NR-compliant real-time neural receiver connected to commercial user equipment, demonstrating how research ideas can be rapidly transformed into over-the-air prototypes using open-source tools. To foster collaboration and accelerate progress in the field, all experiments and results will be made openly available, lowering the barrier to entry and enabling researchers worldwide to translate their ideas into real-world wireless communication <a href="http://systems.BIOGRAPHYDr" target="_blank" title="systems.BIOGRAPHYDr">systems.BIOGRAPHYDr. Sebastian Cammerer is a Senior Research Scientist at NVIDIA, working at the intersection of wireless communications and machine learning. He is one of the core developers and maintainers of the Sionna open-source link-level simulator. Before joining NVIDIA, he received his PhD in Electrical Engineering and Information Technology from the University of Stuttgart, Germany. His main research interests are machine learning for wireless communications and channel coding. His work has been recognized with several awards, including the VDE ITG Dissertationspreis 2022, the IEEE SPS Young Author Best Paper Award 2019, and third prize in the Nokia Bell Labs Prize 2019.Speaker(s): Dr. Cammerer, Virtual: https://events.vtools.ieee.org/m/517965

IEEE North Saskatchewan Section Meeting57 Campus Dr, Saskatoon, Saskatchewan, Canada, S7N 5A9, Virtual: https://events.vtools.ieee.org/m/469127

This talk will present emerging applications of Artificial Intelligence (AI) in the nuclear energy sector, focusing on how Large Language Models (LLMs) and vision systems can enhance nuclear safety, data interpretation, and operational efficiency. Grounded in recent work involving CANDU systems and Canadian CSA standards, the presentation will explore how AI can be responsibly applied in highly regulated <a href="http://environments.In" target="_blank" title="environments.In">environments.In addition to technical advances, the talk will discuss how AI systems can be effectively regulated within the nuclear context, examining current governing standards and their applicability to intelligent technologies. It will also highlight ongoing developments in AI regulation, emphasizing how evolving frameworks can support innovation while maintaining the industry’s core commitment to nuclear <a href="http://safety.Co-sponsored" target="_blank" title="safety.Co-sponsored">safety.Co-sponsored by: IEEE Canadian Atlantic SectionSpeaker(s): Dr. Issam Hammad, Virtual: https://events.vtools.ieee.org/m/514100