Event description

The IEEE Signal Processing Society (SPS) Greece Chapter invites you to the online lecture of Dr. Petros Boufounos, Senior Principal Research Scientist at the Mitsubishi Electric Research Laboratories (MERL) and IEEE SPS Distinguished Lecturer for 2019-2020.

The lecture will be held on Thursday, 5 November 2020, at 16:00 pm and will be delivered through the zoom platform at the following link:
https://upatras-gr.zoom.us/j/91963403866?pwd=UmkydnNDN2sreEJkVVVSc2dPYlhrZz09
Meeting ID: 919 6340 3866
Passcode: 156347

The title and abstract of the talk and a short bio of Dr. P. Boufounos are given below.

Title: The Computational Sensing Revolution in Array Processing

Abstract: Recent advances in inverse problems, including sparse signal recovery and non-convex optimization have shifted the design paradigm for sensing systems. Computational methods have become an integral part of the design toolbox, enabling the use of algorithms to address some of the hardware challenges in designing such systems. One of the most promising applications of this paradigm shift has been in array imaging systems, such as ultrasonic, radar and optical (LIDAR). The impact is also timely, as array processing is becoming increasingly important in a variety of applications, including robotics, autonomous driving, medical imaging, and virtual reality, among others. This has led to continuous improvements in sensing hardware, but also to increasing demand for theory and methods to inform the system design and improve the processing. This talk will present a general inverse problem framework for array processing systems, which allows us to describe both the acquisition hardware and the scene being acquired. Under this framework we can exploit prior knowledge on the scene, the system, and the nature of a variety of errors that might occur, allowing for significant improvements in the reconstruction accuracy. Furthermore, we can consider the design of the system itself in the context of the inverse problem, leading to designs that are more efficient, more accurate, or less expensive, depending on the application. We will explore applications of this model to LIDAR and depth sensing, radar and distributed radar, and ultrasonic sensing. In the context of these applications, we will describe how different models can lead to improved specifications in radar and ultrasonic systems, robustness to position and timing errors in distributed array systems, and cost reduction and new capabilities in LIDAR systems.

Bio: Petros T. Boufounos is Senior Principal Research Scientist and the Computational Sensing Team Leader at Mitsubishi Electric Research Laboratories (MERL). Dr. Boufounos completed his undergraduate and graduate studies at MIT. He received the S.B. degree in Economics in 2000, the S.B. and M.Eng. degrees in Electrical Engineering and Computer Science (EECS) in 2002, and the Sc.D. degree in EECS in 2006. Between September 2006 and December 2008, he was a postdoctoral associate with the Digital Signal Processing Group at Rice University. Dr. Boufounos joined MERL in January 2009, where he has been heading the Computational Sensing Team since 2016.
Dr. Boufounos’ immediate research focus includes signal acquisition and processing, computational sensing, inverse problems, frame theory, quantization, and data representations. He is also interested in how signal acquisition interacts with other fields that use sensing extensively, such as machine learning, robotics, and dynamical system theory. Dr. Boufounos has served as an Area Editor and a Senior Area Editor for the IEEE Signal Processing Letters. He has been a part of the SigPort editorial board and is currently a member of the IEEE Signal Processing Society Theory and Methods Technical Committee and an Associate Editor at IEEE Transactions on Computational Imaging. He was also named IEEE SPS Distinguished Lecturer for 2019-2020.