ELECTRICAL AND COMPUTER ENGINEERING SEMINAR SERIES
Dr. Jose Martinez-Lorenzo
Director of Sensing, Imaging, Control and Actuation Laboratory
Thursday, May 31, 2018
3:00 p.m. - 4:00 p.m.
“4D-Coded Compressive Systems for High-Capacity Sensing and Imaging”
Millimeter-wave sensing and imaging systems are used ubiquitously for a wide range of applications, such as atmospheric sounding of the earth to forecast the weather, non-destructive-testing to assess the condition of civil infrastructure, deep space observation to explore the composition of the galaxies, security monitoring to detect potential threats in airport checkpoints, and biological imaging of superficial tissues for wound diagnosis and healing. These systems typically operate well when the scene does not change rapidly. Unfortunately, this is not the case in emerging societally-important applications like swarms of drones in rescue missions, smart self-driving cars on roadways, or cyber-physical systems searching for suicide bombers when they are on the move. These new applications require sensing and imaging at high video frame, as well as adapting the sensing process based on the evolution of the scene. With this challenge in mind, one of the key features of the next generation sensing and imaging systems will be the ability to extract the maximum amount of information (measured in bits)–which describes properties of an object located in the imaging domain– from the data collected by the sensing system in a given period of time (seconds); this is the information rate (measured in bits per seconds). This information rate has an upper bound, which is the maximum amount of information that can be transferred by an electromagnetic wave from one region of space into another (the system’s physical capacity). This maximum information rate can only be approached when the mutual information of successive measurements is minimized. One way to achieve this is to merge traditional 1D temporal coding with 3D dynamical coding of the wavefield in space –a novel technique known as 3D spatial wave-field coding or spatial modulation. 1D- temporal codes enable one to reach a 1D-information rate close to the upper bound imposed by the 1D-physical capacity, also known as Shannon capacity. Combining 3D-volumetric wavefield coding and 1-D-temporal codes should enable one to reach an information rate close to that imposed by the 4D-physical capacity. This talk will cover the theoretical principles and fundamental limitations of adaptable compressive sensing and imaging systems using 4D coding. This coding can be implemented by novel Multi-Coded Compressive System using the following physical structures: spatial modulators, vortex-meta-lenses, and compressive reflectors. Preliminary results will be presented in four domains: 1) multi-scale computational modeling; 2) system design and optimization; 3) real-time distributed imaging; and 4) hardware design integration and validation.
Jose Martinez-Lorenzo is an Assistant Professor at Northeastern University, Boston, MA; where he acts as the Director of the Sensing, Imaging, Control and Actuation Laboratory. He holds a joint appointment in the Departments of Electrical and Computer Engineering and Mechanical and Industrial Engineering. Prof. Martinez-Lorenzo’s research focus is on high-capacity sensing and imaging systems, with an emphasis on computational modeling of acoustic, thermoacoustics, and electromagnetic waves through complex media, including analysis and design of electromagnetic and acoustic metamaterials and sensors; physics-based signal processing and machine learning, including compressive sensing and information-based imaging and optimization; microwave, millimeter wave and acoustic mechatronics system design and integration; and high-performance computing in multi-core architectures. This fundamental research is applied to diverse problems, including but not limited to “on-the-move” detection of explosives-related threats, early detection of breast cancer, and environmental sensing using distributed sensor networks. He received a 2017 NSF Early CAREER Award, for the research program entitled 4D mm-Wave Compressive Sensing and Imaging at One Thousand Volumetric Frames per Second. His work on computational modeling, sensors, compressive sensing, and machine learning has received several Best Paper Awards: 2016 and 2014 IEEE EuCAP; 2015 Annual Meeting of the American Burn Association; and 2012 IEEE Homeland Security Technology Conference. He has published over 60 journal papers and 120 conference papers, and his work has been featured by CNN, NBC, New York Times, and Wired Magazine.
Faculty Host: Dr. Jeffrey Nanzer (email@example.com)
Michigan State University is committed to providing equal opportunity for participation in all programs, services and activities. Accommodations for persons with disabilities may be requested by contacting Dr. Cagri Ulusoy (Ulusoy@msu.edu)