Professor Yasamin Mostofi, Dept. of Electrical & Computer Engineering, University of California, Santa Barbara.
Communication signals such as WiFi are ubiquitous these days. This has inspired researchers to investigate using them beyond communication, e.g., for sensing and learning about the environment. In this talk, I will present an overview of our recent work on new mathematical tools, design principles, and applications for RF sensing, in order to motivate future directions in this area. I will start by considering body motions, from minute natural fidgets to abrupt motions. I will show how seemingly-unrelated decades-old tools from queuing theory and FM radio design can play a key role in analyzing the received RF signals and extracting meaningful information about both the individuals as well as their collective behaviors. I will then apply the proposed principles to achieve crowd analytics with WiFi, and further demonstrate their impact in the healthcare domain.
Next, the talk will focus on scene understanding. Extracting information from still objects is a considerably more challenging task than sensing in the presence of body motions. I will then set forth that the scattered RF signals off of objects carry rich information about the edges of the objects. Based on this observation, I then propose a completely different way of thinking about RF imaging and scene understanding, via edge tracing. More specifically, I will show how the Geometrical Theory of Diffraction (GTD) and the corresponding Keller cones can be exploited to image edges of the objects. I will then demonstrate the applicability of this approach by showing how WiFi can image and read the English alphabets through walls. Finally, in the third part of the talk, I will address one major issue in applying deep learning to RF sensing problems: lack of large enough RF data to achieve generalizable results. Along this line, I will then show how online vision datasets can be translated to synthetic RF data, in order to generate massive RF data for training deep learning pipelines for RF sensing applications.
Yasamin Mostofi received the B.S. degree in electrical engineering from Sharif University of Technology, and the M.S. and Ph.D. degrees from Stanford University. She is currently a professor in the Department of Electrical and Computer Engineering at the University of California Santa Barbara. Yasamin is the recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE), the Antonio Ruberti Prize from the IEEE Control Systems Society (research contribution award for 40 and under), the National Science Foundation (NSF) CAREER award, and the IEEE Outstanding Engineer Award of Region 6 (more than 10 Western U.S. states), among other awards. She is a fellow of IEEE. She was a semi-plenary speaker at the 2018 IEEE Conference on Decision and Control (CDC) and a keynote speaker at the 2018 Mediterranean Conference on Control and Automation (MED). Yasamin's research is multi-disciplinary, in the two areas of wireless systems and robotics. Current high-level research thrusts include 1) RF sensing for several different applications such as through-wall imaging, occupancy analytics, smart health, and smart spaces; and 2) communication-aware robotics, UAV-assisted connectivity, and joint robotic path planning and communication. Her research has appeared in several reputable news venues such as BBC, Huffington Post, Daily Mail, Engadget, TechCrunch, NSF Science360, ACM News, and IEEE Spectrum, among others. Yasamin has served in many different professional capacities over the years. Recent samples include serving on the Board of Governors of IEEE CSS, serving as a senior editor for IEEE TCNS, and serving as a program co-chair for ACM MobiCom 2022, among others.
Joerg Widmer, Research Professor & Director, IMDEA Networks, Madrid, Spain.
The high bandwidth available at millimeter-wave frequencies allows for very high data rates, and at the same time enables highly accurate localization and environment sensing. This keynote highlights the practical design aspects of localization and sensing systems, and in particular the challenges of joint communication and sensing. We discuss how to achieve decimeter-level location accuracy with simple commercial millimeter-wave off-the-shelf communication devices and how sub-6 GHz information can help to further improve the reliability of the system. We also discuss how to use communication hardware to perform zero-cost monitoring of human movement and activities in indoor spaces (rather than using dedicated radars). To this end, access points can be retrofitted to perform radar-like extraction of the tiny micro-Doppler effects caused by the human motion. Such systems can then be used to enable fine-grained sensing applications such as simultaneous activity recognition and person identification of multiple human subjects. The keynote will specifically focus on the practical implementation aspects, testbed design and experimental results with such systems.
Joerg Widmer is Research Professor and Research Director of IMDEA Networks in Madrid, Spain. Before, he held positions at DOCOMO Euro-Labs in Munich, Germany and EPFL, Switzerland. He was a visiting researcher at the International Computer Science Institute in Berkeley, USA, University College London, UK, and TU Darmstadt, Germany. His research focuses on wireless networks, ranging from extremely high frequency millimeter-wave communication and MAC layer design to mobile network architectures. Joerg Widmer authored more than 200 conference and journal papers and three IETF RFCs, and holds 14 patents. He was awarded an ERC consolidator grant, the Friedrich Wilhelm Bessel Research Award of the Alexander von Humboldt Foundation, a Mercator Fellowship of the German Research Foundation, a Spanish Ramon y Cajal grant, as well as nine best paper awards. He is an IEEE Fellow and Distinguished Member of the ACM.
Mani Srivastava, Professor of Electrical & Computer Engineering, and Professor of Computer Science, UCLA.
The previously discrete technologies of IoT and AI have now entered a tight virtuous embrace. IoT allows sensing and actuation in our physical, social, and urban spaces with unimaginable ubiquity. AI allows sophisticated inferences and decisions to be made algorithmically using deep neural networks, even from unstructured and high-dimensional data, with uncanny performance. Together they seek to perform sophisticated perception-cognition-communication-action loops in diverse applications. However, designers of learning-enabled IoT systems face the challenge of extremely resource-constrained edge platforms operating in uncertain environments while assuring performance and trustworthiness. Moreover, in many applications, the systems go beyond taking actions based on rich inferences about the world state to perform long-term reasoning about complex events and obey the underlying physics, rules, and constraints. Based on our experience in designing such systems in applications including mHealth, ocean animal health, agriculture robotics, and military, this talk explores meeting these challenges through a combination of (i) neurosymbolic architectures that allow the incorporation of physics awareness and human knowledge while enhancing user trust, (ii) automatic platform-aware architecture search and code generation, and (iii) techniques to efficiently adapt to the deployment environment.
Mani Srivastava is Distinguished Professor and Vice Chair at UCLA’s ECE Department with a joint appointment in the CS Department. His research is broadly in human-cyber-physical and IoT systems that are learning-enabled, resource-constrained, and trustworthy. It spans problems across the entire spectrum of applications, architectures, algorithms, and technologies in the context of systems and applications for mHealth, sustainable buildings, smart environments, etc. He is a Fellow of the ACM and the IEEE.