BEGIN:VCALENDAR VERSION:2.0 X-WR-CALNAME:EventsCalendar PRODID:-//hacksw/handcal//NONSGML v1.0//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York LAST-MODIFIED:20240422T053451Z TZURL:https://www.tzurl.org/zoneinfo-outlook/America/New_York X-LIC-LOCATION:America/New_York BEGIN:DAYLIGHT TZNAME:EDT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 DTSTART:19700308T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU END:DAYLIGHT BEGIN:STANDARD TZNAME:EST TZOFFSETFROM:-0400 TZOFFSETTO:-0500 DTSTART:19701101T020000 RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT CATEGORIES:College of Engineering DESCRIPTION:Topic: Computational Bayesian Inference for Localization in Act ive Sonar and Distributed Acoustic Sensing Systems  Abstract: Spatiotempo ral inference regarding objects from sensed acoustic and seismic fields is challenging due to the nature of the environment and the objects’ effec t on propagating wavefields. Nevertheless, there are often diverse streams of useful and readily available prior information on both the media and t he object’s composition and state of motion that should be brought to be ar on such localization problems. This dissertation proposal seeks to adva nce the breadth of the Bayesian framework, focusing on computationally eff icient methods for inference in active underwater acoustic systems and rem ote passive seismic sensing. Two inverse problems are of interest: localiz ation of a mobile submerged object using high-frequency active sonar with a small-aperture array in refractive ocean waveguides, and terrestrial sei smic spatial inference using a fiber–optic distributed acoustic sensing (DAS) system. In the active sonar case, the complex dependencies of acoust ic propagation in refractive, multipath environments and a rich scattering body motivate the development of methods capable of resolving closely spa ced arrivals. Computational Bayesian methods are proposed to allow resourc es to be allocated judiciously while preserving fidelity to prior informat ion and acoustic observations. Joint posterior density functions of eigenr ays’ wavevectors characterize the scattered field and are associated wit h the angle and Doppler spread arrivals in an uncertain refractive wavegui de. A lower–dimensional subspace representation of sound speed uncertain ty must be exploited, with preliminary results showing promise [Barros, Ge ndron JASA-EL 2025]. For DAS, challenges arise from its strain measurement principle and channel directivity, limiting the direct application of tra ditional array processing techniques. Uncertainty in seismic propagation s peeds and propagation characteristics of surface waves further complicate inference. The proposed research aims to improve DAS localization performa nce beyond conventional and naive “proximity” detection through the de velopment of a hierarchical Bayesian approach.  Advisor: Dr. Paul J. Gend ron, Associate Professor, Department of Electrical & Computer Engineering,   Committee members:Dr. David A. Brown, Professor, Depart ment of Electrical & Computer Engineering, ;Dr. Dayalan P. Kasilingam, Professor & Chairperson, Department of Electrical & Computer E ngineering, ;Dr. Zoi-Heleni Michalopoulou, Professor, Mathe matical Sciences, New Jersey Institute of Technology;Dr. Tod Luginbuhl, Se nior Research Scientist, Naval Undersea Warfare Center Division Newport  NOTE: All ECE Graduate Students are ENCOURAGED to attend. All interested p arties are invited to attend. Open to the public.  *For further informati on, Zoom Link: https://umassd.zoom.us/j/99573148168Meeting ID: 9957314816 8Passcode: 761573\nEvent page: /events/cms/elee-oral -comprehensive-exam-for-doctoral-candidacy-by-abner-c-barros---ece.php X-ALT-DESC;FMTTYPE=text/html:

Topic: Computa tional Bayesian Inference for Localization in Active Sonar and Distributed Acoustic Sensing Systems 

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Abstract:

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Spa tiotemporal inference regarding objects from sensed acoustic and seismic f ields is challenging due to the nature of the environment and the objects ’ effect on propagating wavefields. Nevertheless\, there are often diver se streams of useful and readily available prior information on both the m edia and the object’s composition and state of motion that should be bro ught to bear on such localization problems. This dissertation proposal see ks to advance the breadth of the Bayesian framework\, focusing on computat ionally efficient methods for inference in active underwater acoustic syst ems and remote passive seismic sensing. Two inverse problems are of intere st: localization of a mobile submerged object using high-frequency active sonar with a small-aperture array in refractive ocean waveguides\, and ter restrial seismic spatial inference using a fiber–optic distributed acous tic sensing (DAS) system. In the active sonar case\, the complex dependenc ies of acoustic propagation in refractive\, multipath environments and a r ich scattering body motivate the development of methods capable of resolvi ng closely spaced arrivals. Computational Bayesian methods are proposed to allow resources to be allocated judiciously while preserving fidelity to prior information and acoustic observations. Joint posterior density funct ions of eigenrays’ wavevectors characterize the scattered field and are associated with the angle and Doppler spread arrivals in an uncertain refr active waveguide. A lower–dimensional subspace representation of sound s peed uncertainty must be exploited\, with preliminary results showing prom ise [Barros\, Gendron JASA-EL 2025]. For DAS\, challenges arise from its s train measurement principle and channel directivity\, limiting the direct application of traditional array processing techniques. Uncertainty in sei smic propagation speeds and propagation characteristics of surface waves f urther complicate inference. The proposed research aims to improve DAS loc alization performance beyond conventional and naive “proximity” detect ion through the development of a hierarchical Bayesian approach. 

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Advisor: Dr. Paul J. Gendron\, Associate Professor\, Dep artment of Electrical & Computer Engineering\,  

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< strong>Committee members:
Dr. David A. Brown\, Professor\, D epartment of Electrical & Computer Engineering\, \;
Dr . Dayalan P. Kasilingam\, Professor & Chairperson\, Department of Electric al & Computer Engineering\, \;
Dr. Zoi-Heleni Michalop oulou\, Professor\, Mathematical Sciences\, New Jersey Institute of Techno logy\;
Dr. Tod Luginbuhl\, Senior Research Scientist\, Naval Undersea Warfare Center Division Newport 

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NOTE: All ECE Graduate Students are ENCOURAGED to attend. All interested parties are invited to attend. O pen to the public. 

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*For further information\,

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Z oom Link:
Meeting ID: 9957314 8168
Passcode: 761573

Event page: /events/cms/el ee-oral-comprehensive-exam-for-doctoral-candidacy-by-abner-c-barros---ece. php

DTSTAMP:20250606T230408 DTSTART;TZID=America/New_York:20250606T130000 DTEND;TZID=America/New_York:20250606T150000 LOCATION:Lester W. Cory Conference Room, Science & Engineering Building (SENG), Room 213A\n285 Old Westport Road, North Dartmouth, MA 02747 SUMMARY;LANGUAGE=en-us:ELEE Oral Comprehensive Exam for Doctoral Candidacy by Abner C. Barros - ECE UID:56e14abeecdba021f9b73533538f3338@www.umassd.edu END:VEVENT END:VCALENDAR