BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/Chicago BEGIN:DAYLIGHT DTSTART:20260308T030000 TZOFFSETFROM:-0600 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:CDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20251102T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0600 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:CST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260211T190702Z UID:D24B04CC-F171-400D-AA87-0F2D0D9124AD DTSTART;TZID=America/Chicago:20251106T130000 DTEND;TZID=America/Chicago:20251106T143000 DESCRIPTION:[]\n\nSpeaker: Arya Menon\, Assistant Professor\, Texas A&M Uni versity\n\nTopic: Multi-mode (radar-radiometer) Sensing for Security Appli cations: Improved Characterization of Hidden Dielectric Layers\n\nAbstract :\n\nRising threats of terrorism and contraband smuggling have intensified interest in high-frequency security sensors. This talk presents a non-inv asive approach to extract the complex permittivity and thickness of concea led or embedded dielectrics using multi-mode (active–passive) microwave sensing. Two K-band (18– 26 GHz) sensors are discussed—an active radar and a passive radiometer. Performance challenges arising from RF front-en d imperfections and background clutter are addressed through calibration t echniques that (i) correct RF distortion in frequency-modulated continuous -wave radars\, (ii) compensate mismatch and temperature-dependent insertio n loss in radiometers\, and (iii) mitigate background noise in indoor cond itions. Electromagnetic models are developed to predict radar and radiomet ric responses of multilayer dielectric targets and experimentally verified using dielectric stacks backed by a simplified human phantom. These model s enable material characterization with about 5% accuracy. Although demons trated for security sensing\, the techniques extend to remote sensing\, su bsurface fire detection\, and food safety applications.\n\nBio:\nArya Meno n is an Assistant Professor in the Department of Electrical and Computer E ngineering at Texas A&M University (TAMU). She earned her Ph.D. in Electri cal and Computer Engineering from Oregon State University (2021). She comp leted her postdoctoral fellowship at TAMU in 2023. Dr. Menon’s research explores bio-inspired intelligence in radar systems\, with focus areas in hardware–software co-design for cognitive radars\, development of sharab le wireless datasets\, and radar- based sensor fusion. Her broader interes ts include radiometer design\, sensor calibration\, and dielectric charact erization. She has received several prestigious recognitions\, including t he 2025 IEEE MTT-S Outstanding YP Lecturer Award\, selection as a 2022 DAR PA Riser for her work on “Bioinspired Hardware–Software Co-Design for Intelligent High-Frequency Active Sensors\,” the 2020 ARFTG Roger Pollar d Student Fellowship (Silver)\, and the 2019 IEEE MTT-S Graduate Fellowshi p. As an educator\, she was honored with the 2018 Provost’s Award for Ou tstanding Teaching in STEM at the University of South Florida for developi ng innovative electromagnetics lab experiments.\n\nDate & Time: November 6 \, 2025\, 1:00pm-2:30pm CST\nCost: Free\n\nEvent Co-Sponsored by UTD IEEE AP-S/MTTS-Student Chapters and MTT-S Dallas\n\nRoom: ECSS 2.306\, The Univ ersity of Texas at Dallas\,\, 800 W Campbell Rd\, Richardson\, Texas\, Uni ted States\, 75080\, Virtual: https://events.vtools.ieee.org/m/508664 LOCATION:Room: ECSS 2.306\, The University of Texas at Dallas\,\, 800 W Cam pbell Rd\, Richardson\, Texas\, United States\, 75080\, Virtual: https://e vents.vtools.ieee.org/m/508664 ORGANIZER:zeeesh@gmail.com SEQUENCE:31 SUMMARY:MTT-S Dallas: Multi-mode (radar-radiometer) Sensing for Security Ap plications: Improved Characterization of Hidden Dielectric Layers URL;VALUE=URI:https://events.vtools.ieee.org/m/508664 X-ALT-DESC:Description: <br /><p><strong><img src="https://mtt.org/app/uplo ads/2025/10/Arya-Menon-.png" alt="" width="348" height="250"></strong></p> \n<p><strong>Speaker:&nbsp\;</strong>Arya Menon<span style="font-family: - apple-system\, BlinkMacSystemFont\, 'Segoe UI'\, Roboto\, Oxygen\, Ubuntu\ , Cantarell\, 'Open Sans'\, 'Helvetica Neue'\, sans-serif\;">\,&nbsp\;</sp an>Assistant <span style="font-family: -apple-system\, BlinkMacSystemFont\ , 'Segoe UI'\, Roboto\, Oxygen\, Ubuntu\, Cantarell\, 'Open Sans'\, 'Helve tica Neue'\, sans-serif\;">Professor<em>\, T<span style="font-size: 11.0pt \; font-family: 'Calibri'\,sans-serif\; mso-fareast-font-family: Calibri\; mso-fareast-theme-font: minor-latin\; mso-ansi-language: EN-US\; mso-fare ast-language: EN-US\; mso-bidi-language: AR-SA\;">exas A&amp\;M University </span></em></span></p>\n<p><strong>Topic:&nbsp\;</strong>Multi-mode (rada r-radiometer) Sensing for&nbsp\;Security Applications: Improved Characteri zation <span class="s1">of Hidden Dielectric Layers</span></p>\n<p><strong style="font-family: -apple-system\, BlinkMacSystemFont\, 'Segoe UI'\, Rob oto\, Oxygen\, Ubuntu\, Cantarell\, 'Open Sans'\, 'Helvetica Neue'\, sans- serif\;">Abstract:&nbsp\;</strong></p>\n<p>Rising threats of terrorism and contraband smuggling have intensified interest in high-frequency security sensors. This talk presents a non-invasive approach to extract the comple x permittivity and thickness of concealed or embedded dielectrics using mu lti-mode (active&ndash\;passive) microwave sensing. Two K-band (18&ndash\; 26 GHz) sensors are discussed&mdash\;an active radar and a passive radiom eter. Performance challenges arising from RF front-end imperfections and b ackground clutter are addressed through calibration techniques that (i) co rrect RF distortion in frequency-modulated continuous-wave radars\, (ii) c ompensate mismatch and temperature-dependent insertion loss in radiometers \, and (iii) mitigate background noise in indoor conditions. Electromagnet ic models are developed to predict radar and radiometric responses of mult ilayer dielectric targets and experimentally verified using dielectric sta cks backed by a simplified human phantom. These models enable material cha racterization with about 5% accuracy. Although demonstrated for security s ensing\, the techniques extend to remote sensing\, subsurface fire detecti on\, and food safety applications.</p>\n<div>\n<p><strong>Bio:&nbsp\;<br>< /strong><span style="mso-bidi-font-family: Calibri\;">Arya Menon is an Ass istant Professor in the Department of Electrical and Computer Engineering at Texas A&amp\;M University (TAMU). She earned her Ph.D. in Electrical an d Computer Engineering from Oregon State University (2021). She completed her postdoctoral fellowship at TAMU in 2023. Dr. Menon&rsquo\;s research e xplores bio-inspired intelligence in radar systems\, with focus areas in h ardware&ndash\;software co-design for cognitive radars\, development of sh arable wireless datasets\, and radar- based sensor fusion. Her broader int erests include radiometer design\, sensor calibration\, and dielectric cha racterization. She has received several prestigious recognitions\, includi ng the 2025 IEEE MTT-S Outstanding YP Lecturer Award\, selection as a 2022 DARPA Riser for her work on &ldquo\;Bioinspired Hardware&ndash\;Software Co-Design for Intelligent High-Frequency Active Sensors\,&rdquo\; the 2020 ARFTG Roger Pollard Student Fellowship (Silver)\, and the 2019 IEEE MTT-S Graduate Fellowship. As an educator\, she was honored with the 2018 Provo st&rsquo\;s Award for Outstanding Teaching in STEM at the University of So uth Florida for developing innovative electromagnetics lab experiments.</s pan></p>\n</div>\n<p><strong>Date &amp\; Time:</strong> November 6\, 2025\ , 1:00pm-2:30pm CST<br><strong>Cost: </strong>Free</p>\n<p><strong>Event C o-Sponsored by UTD IEEE AP-S/MTTS-Student Chapters and MTT-S Dallas</stron g></p> END:VEVENT END:VCALENDAR