IEEE Methodist College of Engineering & Technology Student Branch

Data communications between Earth and devices on spacecraft, such as satellites, have traditionally been carried out through dedicated links. Shared links using Internet Protocol-based communication offers a number of advantages over dedicated links. The movement of devices on spacecrafts however gives rise to mobility management issues. This talk will discuss various mobility management solutions for extending the Internet connection to devices on the spacecraft. The talk will provide an overview of the network layer based solution being developed by the Internet Engineering Task Force and compare it with the transport layer-based solution that has been developed at the University of Oklahoma in conjunction with the National Aeronautics and Space Administration. Network in motion is an extension of the host mobility protocols for managing the mobility of networks which are in motion, such as those in airplanes and trains. The application of networks in motion will be illustrated for terrestrial and space environments. Speaker(s): Mohammed Atiquzzaman, Ph.D, Virtual: https://events.vtools.ieee.org/m/328500

The Montreal Chapter of the IEEE Signal Processing Society, in collaboration with STARaCom, cordially invites you to attend the following talks, as part of its series of Short Hybrid-Seminars on Selected Topics in Signal Processing. Now that restrictions imposed by Covid-19 have been relaxed, this event will take a hybrid form, with participants having the option to attend in person or virtually. Co-sponsored by: STARaCom Speaker(s): Hamed Hojatian, Minh Dat Nguyen Bldg: McConnell Engineering, 3480 University St., ECE Department, Room 603, Montreal, Quebec, Canada, H3A 0E9, Virtual: https://events.vtools.ieee.org/m/328482

Abstract: Due to the explosive growth of new users and new applications, it is expected that the wireless spectrum will need to be used in a dynamic fashion starting in the near future. This can be achieved by using the concept of cognitive radio, giving users access to the unused spectrum under dynamic spectrum access. It is generally accepted that conventional methods of cognitive radio will fall short of being able to handle the enormous demand for spectral resources, and therefore it is expected that techniques from artificial intelligence or machine learning will help provide dynamic control for spectrum sharing. The process of spectrum sharing begins with sensing the spectrum. Recently, a number of techniques for spectrum sensing employing machine learning have been introduced. In this talk, we employ a machine learning approach known as generative adversarial networks towards this purpose. This particular approach is known to be very successful for anomaly detection in image processing. We alter performance criteria used in this set of networks from image processing applications to wireless and employ such networks for spectrum sensing, both in conventional and cooperative spectrum sensing. Initial results show the efficacy of this approach. Speaker(s): Dr. Ender, Virtual: https://events.vtools.ieee.org/m/329642