Want to be a part of our 100 years celebration!? See your sponsorship opportunities below and select your sponsorship level by clicking the 'Register' button. PLATINUM - Three job listings on IEEE Nebraska's website job listing page for one year. - Company logo on 100 year anniversary event page. - Centerpiece logo displayed on banquet table. - Company logo on IEEE Nebraska Section sponsor web page. - Table of six for you to invite your friends, family, employees, etc. GOLD - Three job listings on IEEE Nebraska's website job listing page for one year. - Company logo on 100 year anniversary event page. - Centerpiece logo displayed on banquet table. - Company logo on IEEE Nebraska Section sponsor web page. - Company logo in event program SILVER - Two job listings on IEEE Nebraska's website job listing page for one year. - Company logo on 100 year anniversary event page. - Centerpiece logo displayed on banquet table. - Company logo in event program BRONZE - Job listing on IEEE Nebraska's website job listing page for one year. - Company logo on 100 year anniversary event page. - Company logo in event program Virtual: https://events.vtools.ieee.org/m/501221

The Trident Chapter is pleased to host AP-S Distinguished Lecturer Professor Ahman Hoorfar from Villanova University. Prof. Hoorfar will give a seminar, "Real-Time and Sparse-Reconstructed Radar Imaging Through Stratified Media" at 12:00 pm ET on Monday, October 13. The seminar will be in a hybrid format, with the in-person talk in room EECS 1500 on the University of Michigan North Campus in Ann Arbor, MI, and a simulcast via Zoom. Prof. Hoorfar will be available before and after the talk for questions and 1-on-1 meetings. To arrange a meeting, please contact the hosts. SEM Trident Chapter (AP03/ED15/MTT17/PHO36) (https://r4.ieee.org/sem/chapter-iv-trident/) Co-host: SEM Chapter 6 (GRS29) Co-sponsor: University of Michigan Radiation Laboratory ((https://radlab.engin.umich.edu/)) Speaker(s): Prof. Ahmad Hoorfar Agenda: Welcome: 12:00 pm Presentation Q&A Closing IEEE AP-S Distinguished Lecture Real-Time and Sparse-Reconstructed Radar Imaging Through Stratified Media Prof. Ahmad Hoorfar, Ph.D. Professor, Department of Electrical and Computer Engineering Director, Antenna Research Laboratory Villanova University Villanova, PA 19085, USA email: [email protected] Abstract: The problem of imaging of objects within or through multilayered dielectric media appears in many areas, including those in ground-penetrating radar (GPR) imaging, through-the-wall radar imaging (TWRI), intra-wall and subsurface imaging, and medical imaging. In most practical situations the imaging of targets should be done in real-time, requiring the development of fast data acquisition schemes as well as highly efficient microwave imaging techniques that can fully account for wave propagation through various dielectric layers or walls. In this lecture, an overview of various image reconstruction techniques for objects in stratified media will be given for both SAR-based and multiple-input multiple-output (MIMO) based systems, and for both real-time imaging and sparsity-based imaging scenarios. For the former, details of fast polarimetric and tomographic based imaging algorithms for 2D and 3D scenarios will be given, and imaging results for various realistic scenarios using both numerical simulations and laboratory experiments will be presented. Such fast-imaging techniques, however, do not address the problem posed by long data acquisition time associated with most microwave-imaging scenarios. To address this problem, one can resort to the use of Compressive Sensing (CS) to significantly reduce the number of antennas and/or collected frequency points. In our implementation of CS, the stratified media effects are accurately and efficiently accounted for in the sparse-image reconstruction. In particular, the use of total variation minimization (TVM) and its advantages over the l1-norm minimization, which is often used in the standard radar implementation of CS, will be detailed. Results for DT-based and TVM-based radar imaging in various GPR, subsurface inverse profiling, and TWRI scenarios will be given in the presentation. Bio: Prof Ahmad Hoorfar is a professor and graduate chair of the Department of Electrical and Computer Engineering, and the director of the Antenna Research Laboratory at Villanova University. He received his B.S. in electronics engineering from the University of Tehran and his M.S. and Ph.D. degrees in electrical engineering from the University of Colorado at Boulder. His research contributions over the last thirty-five years cover areas in electromagnetic field theory, numerical modeling and novel designs of multifunction printed and low-profile antennas, metamaterial media and surfaces, inverse scattering, microwave sensing and imaging, and stochastic optimization methods. He has been a pioneer in the development and application of evolutionary and global optimization algorithms in electromagnetics, development of electromagnetic-based techniques for through-the-wall radar imaging (TWRI) and ground penetrating radar (GPR), compressive sensing applied to GPR and TWRI, and the use of the mathematical concept of space-filling curves in design of electrically small antennas, RFID tags, artificial magnetic conductors, and metasurfaces. Dr. Hoorfar is a life Fellow member of IEEE, a member of International Union of Radio Science (URSI) Commission B, and a member of the Franklin Institute Committee on Science and the Arts. He was the recipient of Villanova University’s Outstanding Faculty Research Scholar Award in 2007, and the recipient of the Philadelphia section ‘IEEE chapter of the year award' for his leadership in chairing the AP/MTT joint chapter in 1995. He was the general chair of the 12th and 13th Benjamin Franklin Symposia in Microwave and Antenna Technology held in 1994 and 1995, and co-organizer of the 22nd Antenna Measurement Technique Association (AMTA) Symposium in 2000, as well as a member of the organizing committee of the 2003 and 2018 IEEE International Microwave Symposia in Philadelphia. He has served on the review board of various IEEE and other technical publications and has also been on the technical program committees of numerous international symposia and conferences, including IEEE AP-S, IEEE MTT, IEEE Aerospace, IEEE Radio and Wireless, IEEE Radar Conference, International Union of Radio Science (URSI), and Progress in Electromagnetic Research symposia. Dr. Hoorfar spent his sabbatical leaves in 2002 and 2009 at the NASA Jet Propulsion Laboratory (JPL) in Pasadena, California, where he contributed to the development of a general optimization code for design of feed horns for NASA’s deep space communication network. Room: 1500 EECS, Bldg: EECS Building, 1301 Beal Ave, University of Michigan North Campus, Ann Arbor, Florida, United States, 48109, Virtual: https://events.vtools.ieee.org/m/500721

The problem of imaging of objects within or through multilayered dielectric media appears in many areas, including those in ground-penetrating radar (GPR) imaging, through-the-wall radar imaging (TWRI), intra-wall and subsurface imaging, and medical imaging. In most practical situations the imaging of targets should be done in real-time, requiring the development of fast data acquisition schemes as well as highly efficient microwave imaging techniques that can fully account for wave propagation through various dielectric layers or walls. In this lecture, an overview of various image reconstruction techniques for objects in stratified media will be given for both SAR-based and multiple-input multiple-output (MIMO) based systems, and for both real-time imaging and sparsity-based imaging scenarios. For the former, details of fast polarimetric and tomographic based imaging algorithms for 2D and 3D scenarios will be given, and imaging results for various realistic scenarios using both numerical simulations and laboratory experiments will be presented. Such fast-imaging techniques, however, do not address the problem posed by long data acquisition time associated with most microwave-imaging scenarios. To address this problem, one can resort to the use of Compressive Sensing (CS) to significantly reduce the number of antennas and/or collected frequency points. In our implementation of CS, the stratified media effects are accurately and efficiently accounted for in the sparse-image reconstruction. In particular, the use of total variation minimization (TVM) and its advantages over the l1-norm minimization, which is often used in the standard radar implementation of CS, will be detailed. Results for DT-based and TVM-based radar imaging in various GPR, subsurface inverse profiling, and TWRI scenarios will be given in the presentation. Co-sponsored by: Univ of Michigan, Radlab Speaker(s): Ahamd Hoorfar, Room: 1500, Bldg: EECS Bldg, 1301 Beal Ave, Ann Arbor, Michigan, United States, 48109-2122