ComSig Lecture: An Inverse Problem Framework for Array Processing Systems

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Lecture & Social Networking Opportunity before & after lecture at the Nashua Public Library (NH)

Lecture Abstract:

Array-based sensing systems, such as ultrasonic, radar and optical (LIDAR) are 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. In this talk we will discuss how recent advances in formulating and solving inverse problems, such as compressed sensing, blind deconvolution, and sparse signal modeling can be applied to significantly reduce the cost and improve the capabilities of array-based and multichannel sensing systems. We show that these systems share a common mathematical framework, 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 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. In the talk 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 ultrasonic systems, robustness to position and timing errors in distributed array systems, and cost reduction and new capabilities in LIDAR systems.

 

 

 

 

 

 

 

 

Lecture Topic Description:

Array-based sensing systems, such as ultrasonic, radar and optical (LIDAR) are 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. In this talk we will discuss how recent advances in formulating and solving inverse problems, such as compressed sensing, blind deconvolution, and sparse signal modeling can be applied to significantly reduce the cost and improve the capabilities of array-based and multichannel sensing systems. We show that these systems share a common mathematical framework, 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 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. In the talk 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 ultrasonic systems, robustness to position and timing errors in distributed array systems, and cost reduction and new capabilities in LIDAR systems.

Lecturer: Dr. Petros T. Boufounos

Petros T. Boufounos is Senior Principal Research Scientist and the Computational Sensing Team Leader at Mitsubishi Electric Research Laboratories (MERL), and a visiting scholar at the Rice University Electrical and Computer Engineering department. 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, inverse problems, frame theory, quantization and data representations, as well as their interaction with machine learning, robotics and dynamical system theory. Dr. Boufounos has served as an Area Editor and a Senior Area Editor at IEEE Signal Processing Letters, has been part of the SigPort editorial board, and is currently a member of the IEEE Signal Processing Society Theory and Methods technical committee and a Signal Processing Society Distinguished Lecturer for 2019-2020.

Organizers: NH ComSig Chair: Mimi Tam, Co-Chair: Mary Brzezenski

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ComSig Lecture: Topic: An Inverse Problem Framework for Array Processing Systems

Held in Nashua Public Library (NH) – April 4th 2019 6:00 – 8:30 pm EST

Fee: FREE -  Light Refreshments Served – Social Networking before & after Lecture

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