See invitation for full details. Register yourself AND your guest as instructed in your invitation (EVent took place on 27 October. Extending event for late payments) See invitation, Pittsburgh, Pennsylvania, United States

MOVE is offering a 3-part, Virtual, Operations Class. This class is required for everyone interested in deploying on MOVE-1 or MOVE-2 truck or has a general interest in the program. It is required for new MOVE members and recommended as a refresher for those that have previously taken it. We discuss what to expect when deploying, what equipment is on the MOVE trucks and several safety procedures when working around the trucks. This is a virtual class that is broken up into 3 required 2-hour sessions. Now that MOVE has two trucks, differences between the trucks will be discussed. If you took this class prior to August 2022, there will be new material due to the 2nd truck differences. This is a 3-part virtual class. - Part 1: 30 Oct 2023 from 8:00-10:00 PM ET >> https://events.vtools.ieee.org/m/376696 - Part 2: 06 Nov 2023 from 8:00-10:00 PM ET >> https://events.vtools.ieee.org/m/376698 - Part 3: 13 Nov 2023 from 8:00-10:00 PM ET >> https://events.vtools.ieee.org/m/376700 There will be a test after the third session - material will be provided for the test. Please use the above links to register for all three training sessions. Co-sponsored by: IEEE-USA MOVE Program Agenda: - Part 1: 30 Oct 2023 from 8:00-10:00 PM ET - Part 2: 06 Nov 2023 from 8:00-10:00 PM ET - Part 3: 13 Nov 2023 from 8:00-10:00 PM ET Virtual: https://events.vtools.ieee.org/m/376698

Autonomous multi-agent UAV and UGV systems are promising for critical applications such as disaster response, logistics and transportation, supplementing cellular networks, agricultural sensing, environmental monitoring, and security and military operations. Achieving the desired multi-agent collaborative behaviors requires joint perception and information sharing over wireless links. In this talk we look at recent progress on graph neural network (GNN) approaches for learning perception-action-control (PAC) loops. This includes learning what information to share among agents that supports a team objective. Going forward, many issues arise, including how to combine learning and model-based control, achieving scalability and resilient group behavior, and melding mobility and networking. The combination of learning, modeling, and cognitive networking is a promising path to collaborative intelligent systems. Speaker(s): Brian M. Sadler, Agenda: 6:30 PM Welcome and introduction 6:35 PM Presentation 7:20 PM Q&A 7:30 PM Conclude Virtual: https://events.vtools.ieee.org/m/380463