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DTSTART;TZID=America/New_York:20231117T083000
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UID:39994-1700209800-1700213400@coe.northeastern.edu
SUMMARY:Mahshid Asri PhD Dissertation Defense
DESCRIPTION:Title:\nDevelopment of Anomaly Detection and Characterization Algorithms Using Wideband Radar Image Processing for Security Applications \nDate:\n11/17/2023 \nTime:\n8:30:00 AM \nLocation: 302 Stearns \nCommittee Members:\nProf. Carey Rappaport (Advisor)\nProf. Charles DiMarzio\nProf. Edwin Marengo \nAbstract:\nDetection and characterization of suspicious body-worn objects is necessary for safe and effective personnel screening. In airports\, developing a precise system that can distinguish threats and explosives from objects like money belt can reduce the pat-down significantly while maintaining effective security. This dissertation proposes two main algorithms which are developed for different millimeter-wave radar systems. The first project is a material characterization algorithm designed for a 30 GHz wideband multi bi-static radar system used for passenger screening in airports. The proposed algorithm can automatically distinguish lossless materials from lossy ones and calculate their thickness and permittivities. Starting from the radar reconstructed image showing a cross-section of the body\, we extract the nominal body contour using Fourier series\, separate body and object responses\, categorize the object as lossy or lossless based on the depression and protrusion of the body contour\, and finally predict possible values for the object’s permittivity and thickness. Our resulting classification is good\, implying fewer nuisance alarms at check points. We have also trained a deep learning model for pixel-wise localization of body worn anomalies. The second project is a metal detection algorithm developed to monitor pedestrians walking along a sidewalk for large\, concealed metallic objects. Finite Difference Frequency Domain and SAR algorithms are used to simulate the images produced by this 6 GHz wideband radar system. A deep learning model has then been used to predict a pixel level mask for the body and anomaly based on the inputted radar image.
URL:https://coe.northeastern.edu/event/mahshid-asri-phd-dissertation-defense/
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LAST-MODIFIED:20230816T195124Z
UID:37876-1700226000-1700229600@coe.northeastern.edu
SUMMARY:FacDev Fridays: How Faculty Can Support Student Mental Health
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URL:https://coe.northeastern.edu/event/facdev-fridays-how-faculty-can-support-student-mental-health/
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