Abstract: Multimodal sensing and fusion are integral parts of autonomous systems to enable object detection and localization. Past work has largely focused on fusion of information from homogeneous sensors and is typically based on the assumption of statistical independence between sensor observations. In most practical situations, however, these are not realistic assumptions. This talk will present a brief introduction to the theory of copulas and discuss how this theory can be employed to process and fuse dependent multimodal information. In addition, some recent work on copula guided neural networks will be described. Some illustrative examples will be presented. 

Bio: Pramod K. Varshney was born in Allahabad, India, in 1952. He received the B.S. degree in electrical engineering and computer science (with highest honors), and the M.S. and Ph.D. degrees in electrical engineering from the University of Illinois at Urbana-Champaign in 1972, 1974, and 1976 respectively. Since 1976 he has been with Syracuse University, Syracuse, NY where he is currently a Distinguished Professor of Electrical Engineering and Computer Science. His current research interests are in distributed sensor networks and data fusion, detection and estimation theory, wireless communications, machine learning, AI and radar. 

Dr. Varshney was the recipient of the 1981 ASEE Dow Outstanding Young Faculty Award. He was elected to the grade of Fellow of the IEEE in 1997 for his contributions in the area of distributed detection and data fusion. In 2000, he received the Third Millennium Medal from the IEEE and Chancellor’s Citation for exceptional academic achievement at Syracuse University. He is the recipient of the IEEE 2012 Judith A. Resnik Award. He received an honorary Doctor of Engineering degree from Drexel University in 2014, ECE Distinguished Alumni Award from UIUC in 2015, the Yaakov Bar-Shalom Award for Lifetime Excellence in Information Fusion, ISIF in 2018, the Claude Shannon-Harry Nyquist Technical Achievement Award from the IEEE Signal Processing Society, the Pioneer Award from the IEEE Aerospace and Electronic Society in 2021, and Syracuse University Chancellor’s Lifetime Achievement Award in 2023. 

Abstract: Integrating sensing functionality into communication devices is emerging as a key feature of the 6G Radio access network. Dual-function radar communication (DFRC) systems implement both sensing and communication using the same hardware thus saving in power, cost and spectral efficiency. In this talk, we focus on some of the signal processing aspects of designing and implementing DFRC systems and discuss how the convergence of sensing and communication can be utilized to efficiently exploit congested resources and to communicatee intelligence via sensing.  In particular, we begin by introducing several approaches to reduce hardware cost by exploiting sub-Nyquist principles and sparse arrays to sense and communicate jointly at low sampling and bit rates. We then introduce new hardware designs that allow continuous monitoring using event-based sampling and high dynamic range. We next consider several different approaches to waveform design and receive signal processing considering both radar detection mode and target localization including spectrum sharing, joint precoder design, and index modulation techniques. Our approaches allow design flexibility in trading off radar and communication performance, while preserving the radar ambiguity function. We end by discussing future trends in DFRC systems including model-based AI for communication and radar under uncertain channels, near-field communication and radar, and hybrid RIS/DMA to create configurable radiation patterns for scalable and low power sensing and communication. Throughout the talk we will consider both the theory and hardware prototypes and show several demos of real-time DFRC systems, low bit and low power ADCs, and cognitive joint radio and radar systems.

Bio: Yonina Eldar is a Professor in the Department of Mathematics and Computer Science, Weizmann Institute of Science, Rehovot, Israel where she heads the center for Biomedical Engineering and Signal Processing and holds the Dorothy and Patrick Gorman Professorial Chair. She is also a Visiting Professor at MIT, a Visiting Scientist at the Broad Institute, and an Adjunct Professor at Duke University and was a Visiting Professor at Stanford.  She is a member of the Israel Academy of Sciences and Humanities, an IEEE Fellow and a EURASIP Fellow. She received the B.Sc. degree in physics and the B.Sc. degree in electrical engineering from Tel-Aviv University, and the Ph.D. degree in electrical engineering and computer science from MIT, in 2002. She has received many awards for excellence in research and teaching, including the IEEE Signal Processing Society Technical Achievement Award (2013), the IEEE/AESS Fred Nathanson Memorial Radar Award (2014) and the IEEE Kiyo Tomiyasu Award (2016). She was a Horev Fellow of the Leaders in Science and Technology program at the Technion and an Alon Fellow. She received the Michael Bruno Memorial Award from the Rothschild Foundation, the Weizmann Prize for Exact Sciences, the Wolf Foundation Krill Prize for Excellence in Scientific Research, the Henry Taub Prize for Excellence in Research (twice), the Hershel Rich Innovation Award (three times), and the Award for Women with Distinguished Contributions. She received several best paper awards and best demo awards together with her research students and colleagues, was selected as one of the 50 most influential women in Israel, and was a member of the Israel Committee for Higher Education. She is the Editor in Chief of Foundations and Trends in Signal Processing, a member of several IEEE Technical Committees and Award Committees, and heads the Committee for Promoting Gender Fairness in Higher Education Institutions in Israel.

Abstract: In human-autonomous system interactions trust is an important aspect to be monitored. If trust levels are too low, humans will not rely on the assistive technology, making them underused and potentially increasing the risk of human errors. On the other side of the coin, if users over-trust a system, they can rely too much on automation, putting themselves at risk of missing certain threats or potentially propagating errors made by the system. As such, monitoring trust is crucial to ensure the right trade-off is met.

Trust has been measured over the years using different modalities, including questionnaires or other quality-of-service indices. The IMPACTS model, for example, shows the importance of seven elements to ensure humans remain trustworthy towards machines: intention (I), measurability (M), performance (P), adaptability (A), communication (C), transparency (T), and security (S). To date, monitoring trust has usually assumed that it is the user that needs to trust the system. In the era of deep learning based autonomous systems, however, trust should be a bidirectional entity, where two measures of trust are needed: one from the human towards the machine, and another for the machine towards the human commands. Indeed, deep learning based autonomous systems are known to be vulnerable to e.g., (i) adversarial attacks, (2) deepfakes, and more generally, (3) data from out-of-domain distributions. In this talk, I will highlight some of the signal processing tools we have developed over the years to help overcome these issues, as well as our most recent efforts at creating a bi-directional model of trust for human-autonomous system interactions.

Bio: Tiago H. Falk is a Full Professor at the Institut national de la recherche scientifique, Centre on Energy, Materials, and Telecommunications, University of Quebec, where he directs the Multisensory/multimodal Signal Analysis and Enhancement (MuSAE) Lab focused on building next-generation human-machine interfaces. He is also co-director of the INRS-UQO Mixed Research Unit on Cybersecurity, where research is being conducted to make human-machine interfaces more secure and reliable by tackling emerging vulnerabilities to artificial intelligence algorithms. 

Dr. Falk is Co-Chair of the Technical Committee (TC) on Brain-Machine Interface Systems of the IEEE Systems, Man and Cybernetics Society (SMCS), serves as EiC of the SMCS eNewsletter, and was a member-at-large of the IEEE SMCS Board of Governors from 2021 to 2023. He has served as an elected member of the IEEE Signal Processing Society TC on Audio and Acoustics Signal Processing (2019-2021; 2021-2023) and the Speech and Language Processing TC (2012-2014). He is an Associate Editor for the IEEE Transactions on Human-Machine Systems and served as Area Editor for the IEEE SPS Magazine from 2018-2020. He is a Steering Committee member for the IEEE Brain Technical Community 2.0, the SPS Autonomous Systems Initiative, the SPS Data Science Initiative, and the IEEE Future Directions Telepresence Initiative.