EXCOM Meeting Virtual: https://events.vtools.ieee.org/m/399079

EXCOM Meeting Virtual: https://events.vtools.ieee.org/m/399079

Security is an important part of airport operations. LiDAR or light detection and ranging is used to boost airports' security and efficiency. With increased shootings and stabbings at airports, airports have adopted advanced security systems such as video surveillance and access control. LiDAR is also used in airports to enhance security, operational efficiency as well as passenger experience. LiDAR works by way of shooting laser pulses into the environment; the pulses bounce off objects and return to the sensor, allowing the system to draw a highly detailed 3D map of the surroundings. This is an advantage when there are dense areas of people. This technology can track intruders. Co-sponsored by: IEEE Power and Energy Speaker(s): Chad Cooper, Agenda: 4:00 pm PDT IEEE Announcements 4:02 pm PDT Introduction of Speaker 4:04 pm PDT Presentation 4:45 pm PDT Q&A Room: 330, Bldg: 16300, 16300 Christensen Road , Tukwila, Washington, United States, 98188

Security is an important part of airport operations. LiDAR or light detection and ranging is used to boost airports' security and efficiency. With increased shootings and stabbings at airports, airports have adopted advanced security systems such as video surveillance and access control. LiDAR is also used in airports to enhance security, operational efficiency as well as passenger experience. LiDAR works by way of shooting laser pulses into the environment; the pulses bounce off objects and return to the sensor, allowing the system to draw a highly detailed 3D map of the surroundings. This is an advantage when there are dense areas of people. This technology can track intruders. Co-sponsored by: IEEE Power and Energy Speaker(s): Chad Cooper, Agenda: 4:00 pm PDT IEEE Announcements 4:02 pm PDT Introduction of Speaker 4:04 pm PDT Presentation 4:45 pm PDT Q&A Room: 330, Bldg: 16300, 16300 Christensen Road , Tukwila, Washington, United States, 98188

[] The ultimate goal of AI research is to provide the tools, techniques, and methods for building usable systems that make decisions or assist in making decisions that model and "understand" the world, gather relevant knowledge, and act responsibly. In many cases, the decisions and actions are associated with high risks, and certain assurance or robustness is required from the system that uses AI components. What are the ultimate questions that we need to answer in order to build the required runtime robustness for our systems? We'll explore the central role that three types of unknowns play in our quest for robustness. These are the unobserved entities that need our research attention to make any assurance guarantees. First, we'll analyze the unknown observations that we need to deal with at runtime. Next, we'll explore the unknown features – the additional dimensions along which we need to reason and that are needed to rely upon at decision time. Finally, we'll discuss the unknown constraints – the unknown functions that we need to optimize against. Co-sponsored by: Seattle University Student Chapter Speaker(s): Dragos Margineantu, PhD Agenda: 5.30 PM to 6.00 PM Networking and Light Dinner "Pie Day Specials" 6.00 PM to 6.05 Welcome by Seattle Section Chair 6.05 PM to 6.50 PM Techtalk Room: Room #: Sinegal 100, Bldg: Jim and Janet Sinegal Center for   Science and Innovation, Seattle University, 901 12th Ave, Seattle, WA 98122, Seattle, Washington, United States, 98122, Virtual: https://events.vtools.ieee.org/m/410555

IEEE Seattle Section Excom meeting with chapter and society chairs. Agenda: IEEE Seattle Section Excom meeting with chapter and society chairs. - Approval of past minutes. - Treasurer’s report - Secretary’s report - Chapter Reports - Affinity Group Reports - Student Branch Reports - Committee Chair Reports - On-going Business - Follow-up - New Business Items - Action Items; review and updates Room: Room #: Sinegal 100, Bldg: Jim and Janet Sinegal Center for Science and Innovation , Seattle University, 901 12th Ave, Seattle, Washington, United States, 98122, Virtual: https://events.vtools.ieee.org/m/410627

Discover the future of edge AI in our upcoming talk by Dr. Jun Zhang, an IEEE Fellow and Associate Professor at the Hong Kong University of Science and Technology. Delve into the shift from traditional data-oriented communications to task-oriented approaches, optimizing data transmission for specific inference tasks. Learn about the development of effective feature encoders and the introduction of EdgeGPT, an autonomous edge AI system. This presentation will highlight innovations in edge video analytics and mobile robotics, offering insights into achieving high accuracy and low latency in resource-constrained devices. Join us to explore cutting-edge strategies for enhancing edge computing solutions. Abstract Deep learning has achieved remarkable successes in many application domains, such as computer vision, image processing, and natural language processing. However, deploying powerful deep learning models on resource-constrained mobile devices (e.g., wearable or IoT devices) faces great challenges. Recently, edge AI techniques that rely on the emerging mobile edge computing platforms have been proposed, which forward intermediate features to be processed by a powerful edge server. To achieve high-accuracy and low-latency inference, effective feature encoders with low complexity and high compression capability will be needed. This calls for a paradigm shift in wireless communications, from “data-oriented communications”, which maximize data rates, to “task-oriented communications”, where the data transmission is an intermediate step to be optimized for the downstream inference task. This talk will introduce recent progresses on task-oriented communication for edge inference. An effective design principle based on information bottleneck will be firstly introduced, which will then be extended to multi-device cooperative perception based on a distributed information bottleneck framework. Use cases on edge video analytics and edge-assisted localization for mobile robots will be presented, followed by introduction of EdgeGPT, an autonomous edge AI system empowered by large language models. Bio: Jun Zhang received his Ph.D. degree in Electrical and Computer Engineering from the University of Texas at Austin. He is an IEEE Fellow and an IEEE ComSoc Distinguished Lecturer. He is an Associate Professor in the Department of Electronic and Computer Engineering at the Hong Kong University of Science and Technology. His research interests include wireless communications and networking, mobile edge computing and edge AI, and cooperative AI. Dr. Zhang co-authored the book Fundamentals of LTE (Prentice-Hall, 2010). He is a co-recipient of several best paper awards, including the 2021 Best Survey Paper Award of IEEE Communications Society, the 2019 IEEE Communications Society & Information Theory Society Joint Paper Award, and the 2016 Marconi Prize Paper Award in Wireless Communications. He also received the 2016 IEEE ComSoc Asia-Pacific Best Young Researcher Award. He is an Editor of IEEE Transactions on Communications and IEEE Transactions on Machine Learning in Communications and Networking, and was an editor of IEEE Transactions on Wireless Communications (2015-2020). Co-sponsored by: Pradeep Kumar Virtual: https://events.vtools.ieee.org/m/406837

Discover the future of edge AI in our upcoming talk by Dr. Jun Zhang, an IEEE Fellow and Associate Professor at the Hong Kong University of Science and Technology. Delve into the shift from traditional data-oriented communications to task-oriented approaches, optimizing data transmission for specific inference tasks. Learn about the development of effective feature encoders and the introduction of EdgeGPT, an autonomous edge AI system. This presentation will highlight innovations in edge video analytics and mobile robotics, offering insights into achieving high accuracy and low latency in resource-constrained devices. Join us to explore cutting-edge strategies for enhancing edge computing solutions. Abstract Deep learning has achieved remarkable successes in many application domains, such as computer vision, image processing, and natural language processing. However, deploying powerful deep learning models on resource-constrained mobile devices (e.g., wearable or IoT devices) faces great challenges. Recently, edge AI techniques that rely on the emerging mobile edge computing platforms have been proposed, which forward intermediate features to be processed by a powerful edge server. To achieve high-accuracy and low-latency inference, effective feature encoders with low complexity and high compression capability will be needed. This calls for a paradigm shift in wireless communications, from “data-oriented communications”, which maximize data rates, to “task-oriented communications”, where the data transmission is an intermediate step to be optimized for the downstream inference task. This talk will introduce recent progresses on task-oriented communication for edge inference. An effective design principle based on information bottleneck will be firstly introduced, which will then be extended to multi-device cooperative perception based on a distributed information bottleneck framework. Use cases on edge video analytics and edge-assisted localization for mobile robots will be presented, followed by introduction of EdgeGPT, an autonomous edge AI system empowered by large language models. Bio: Jun Zhang received his Ph.D. degree in Electrical and Computer Engineering from the University of Texas at Austin. He is an IEEE Fellow and an IEEE ComSoc Distinguished Lecturer. He is an Associate Professor in the Department of Electronic and Computer Engineering at the Hong Kong University of Science and Technology. His research interests include wireless communications and networking, mobile edge computing and edge AI, and cooperative AI. Dr. Zhang co-authored the book Fundamentals of LTE (Prentice-Hall, 2010). He is a co-recipient of several best paper awards, including the 2021 Best Survey Paper Award of IEEE Communications Society, the 2019 IEEE Communications Society & Information Theory Society Joint Paper Award, and the 2016 Marconi Prize Paper Award in Wireless Communications. He also received the 2016 IEEE ComSoc Asia-Pacific Best Young Researcher Award. He is an Editor of IEEE Transactions on Communications and IEEE Transactions on Machine Learning in Communications and Networking, and was an editor of IEEE Transactions on Wireless Communications (2015-2020). Co-sponsored by: Pradeep Kumar Virtual: https://events.vtools.ieee.org/m/406837

Join us for a networking event to celebrate the Luck of the Irish in downtown Seattle (T.S. McHugh’s Irish Pub & Restaurant) over looking the Space Needle. Find out about new projects in 2024 and beyond. Meet up with your colleagues and find out what technical events are coming your way. Co-sponsored by: IEEE Power and Energy Society Agenda: 12:00 pm PDT Meet and greet 12:05 pm PDT IEEE Reservations 12:10 pm PDT Lunch 1:00 pm PDT End 21 Mercer Street Seattle, WA 98109, Seattle, Washington, United States, 98109

Join us for a networking event to celebrate the Luck of the Irish in downtown Seattle (T.S. McHugh’s Irish Pub & Restaurant) over looking the Space Needle. Find out about new projects in 2024 and beyond. Meet up with your colleagues and find out what technical events are coming your way. Co-sponsored by: IEEE Power and Energy Society Agenda: 12:00 pm PDT Meet and greet 12:05 pm PDT IEEE Reservations 12:10 pm PDT Lunch 1:00 pm PDT End 21 Mercer Street Seattle, WA 98109, Seattle, Washington, United States, 98109