BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:US/Eastern BEGIN:DAYLIGHT DTSTART:20190310T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20191103T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20190820T025840Z UID:1F7A4055-2F53-41EF-9567-FB9E782B5D16 DTSTART;TZID=US/Eastern:20190404T180000 DTEND;TZID=US/Eastern:20190404T203000 DESCRIPTION:Lecture & Social Networking Opportunity before & after lecture at the Nashua Public Library (NH)\n\nLecture Abstract:\n\nArray-based sens ing systems\, such as ultrasonic\, radar and optical (LIDAR) are becoming increasingly important in a variety of applications\, including robotics\, autonomous driving\, medical imaging\, and virtual reality\, among others . This has led to continuous improvements in sensing hardware\, but also t o increasing demand for theory and methods to inform the system design and improve the processing. In this talk we will discuss how recent advances in formulating and solving inverse problems\, such as compressed sensing\, blind deconvolution\, and sparse signal modeling can be applied to signif icantly reduce the cost and improve the capabilities of array-based and mu ltichannel sensing systems. We show that these systems share a common math ematical framework\, which allows us to describe both the acquisition hard ware and the scene being acquired. Under this framework we can exploit pri or knowledge on the scene\, the system\, and a variety of errors that migh t occur\, allowing for significant improvements in the reconstruction accu racy. Furthermore\, we can consider the design of the system itself in the context of the inverse problem\, leading to designs that are more efficie nt\, more accurate\, or less expensive\, depending on the application. In the talk we will explore applications of this model to LIDAR and depth sen sing\, radar and distributed radar\, and ultrasonic sensing. In the contex t of these applications\, we will describe how different models can lead t o improved specifications in ultrasonic systems\, robustness to position a nd timing errors in distributed array systems\, and cost reduction and new capabilities in LIDAR systems.\n\nLecture Topic Description:\n\nArray-bas ed sensing systems\, such as ultrasonic\, radar and optical (LIDAR) are be coming increasingly important in a variety of applications\, including rob otics\, autonomous driving\, medical imaging\, and virtual reality\, among others. This has led to continuous improvements in sensing hardware\, but also to increasing demand for theory and methods to inform the system des ign and improve the processing. In this talk we will discuss how recent ad vances in formulating and solving inverse problems\, such as compressed se nsing\, blind deconvolution\, and sparse signal modeling can be applied to significantly reduce the cost and improve the capabilities of array-based and multichannel sensing systems. We show that these systems share a comm on mathematical framework\, which allows us to describe both the acquisiti on hardware and the scene being acquired. Under this framework we can expl oit prior knowledge on the scene\, the system\, and a variety of errors th at might occur\, allowing for significant improvements in the reconstructi on accuracy. Furthermore\, we can consider the design of the system itself in the context of the inverse problem\, leading to designs that are more efficient\, more accurate\, or less expensive\, depending on the applicati on. In the talk we will explore applications of this model to LIDAR and de pth sensing\, radar and distributed radar\, and ultrasonic sensing. In the context of these applications\, we will describe how different models can lead to improved specifications in ultrasonic systems\, robustness to pos ition and timing errors in distributed array systems\, and cost reduction and new capabilities in LIDAR systems.\n\nLecturer: Dr. Petros T. Boufouno s\n\nPetros T. Boufounos is Senior Principal Research Scientist and the Co mputational Sensing Team Leader at Mitsubishi Electric Research Laboratori es (MERL)\, and a visiting scholar at the Rice University Electrical and C omputer Engineering department. Dr. Boufounos completed his undergraduate and graduate studies at MIT. He received the S.B. degree in Economics in 2 000\, the S.B. and M.Eng. degrees in Electrical Engineering and Computer S cience (EECS) in 2002\, and the Sc.D. degree in EECS in 2006. Between Sept ember 2006 and December 2008\, he was a postdoctoral associate with the Di gital Signal Processing Group at Rice University. Dr. Boufounos joined MER L in January 2009\, where he has been heading the Computational Sensing Te am since 2016. Dr. Boufounos' immediate research focus includes signal acq uisition and processing\, inverse problems\, frame theory\, quantization a nd data representations\, as well as their interaction with machine learni ng\, robotics and dynamical system theory. Dr. Boufounos has served as an Area Editor and a Senior Area Editor at IEEE Signal Processing Letters\, h as been part of the SigPort editorial board\, and is currently a member of the IEEE Signal Processing Society Theory and Methods technical committee and a Signal Processing Society Distinguished Lecturer for 2019-2020.\n\n Organizers: NH ComSig Chair: Mimi Tam\, Co-Chair: Mary Brzezenski\n\nSpeak er(s): Dr Petros T. Boufounos\, \n\nAgenda: \nComSig Lecture: Topic: An In verse Problem Framework for Array Processing Systems\n\nHeld in Nashua Pub lic Library (NH) – April 4th 2019 6:00 – 8:30 pm EST\n\nFee: FREE - L ight Refreshments Served – Social Networking before & after Lecture\n\nR oom: Large Meeting Room\, 2 Court St\, Nashua\, New Hampshire\, United Sta tes\, 03060 LOCATION:Room: Large Meeting Room\, 2 Court St\, Nashua\, New Hampshire\, U nited States\, 03060 ORGANIZER:mimi.tam@ieee.org SEQUENCE:6 SUMMARY:ComSig Lecture: An Inverse Problem Framework for Array Processing S ystems URL;VALUE=URI:https://events.vtools.ieee.org/m/196273 X-ALT-DESC:Description: <br /><p>Lecture &amp\; Social Networking Opportuni ty before &amp\; after lecture at the Nashua Public Library (NH)</p>\n<p>L ecture Abstract:</p>\n<p><span style="font-weight: 400\;">Array-based sens ing systems\, such as ultrasonic\, radar and optical (LIDAR) are becoming increasingly important in a variety of applications\, including robotics\, autonomous driving\, medical imaging\, and virtual reality\, among others . This has led to continuous improvements in sensing hardware\, but also t o increasing demand for theory and methods to inform the system design and improve the processing. In this talk we will discuss how recent advances in formulating and solving inverse problems\, such as compressed sensing\, blind deconvolution\, and sparse signal modeling can be applied to signif icantly reduce the cost and improve the capabilities of array-based and mu ltichannel sensing systems. We show that these systems share a common math ematical framework\, which allows us to describe both the acquisition hard ware and the scene being acquired. Under this framework we can exploit pri or knowledge on the scene\, the system\, and a variety of errors that migh t occur\, allowing for significant improvements in the reconstruction accu racy. Furthermore\, we can consider the design of the system itself in the context of the inverse problem\, leading to designs that are more efficie nt\, more accurate\, or less expensive\, depending on the application. In the talk we will explore applications of this model&nbsp\; to LIDAR and de pth sensing\, radar and distributed radar\, and ultrasonic sensing. In the context of these applications\, we will describe how different models can lead to improved specifications in ultrasonic systems\, robustness to pos ition and timing errors in distributed array systems\, and cost reduction and new capabilities in LIDAR systems.</span></p>\n<p>&nbsp\;</p>\n<p>&nbs p\;</p>\n<p>&nbsp\;</p>\n<p>&nbsp\;</p>\n<p>&nbsp\;</p>\n<p>&nbsp\;</p>\n< p>&nbsp\;</p>\n<p>&nbsp\;</p>\n<p><strong>Lecture Topic Description:</stro ng></p>\n<p><span style="font-weight: 400\;">Array-based sensing systems\, such as ultrasonic\, radar and optical (LIDAR) are becoming increasingly important in a variety of applications\, including robotics\, autonomous d riving\, medical imaging\, and virtual reality\, among others. This has le d to continuous improvements in sensing hardware\, but also to increasing demand for theory and methods to inform the system design and improve the processing. In this talk we will discuss how recent advances in formulatin g and solving inverse problems\, such as compressed sensing\, blind deconv olution\, and sparse signal modeling can be applied to significantly reduc e the cost and improve the capabilities of array-based and multichannel se nsing systems. We show that these systems share a common mathematical fram ework\, which allows us to describe both the acquisition hardware and the scene being acquired. Under this framework we can exploit prior knowledge on the scene\, the system\, and a variety of errors that might occur\, all owing for significant improvements in the reconstruction accuracy. Further more\, we can consider the design of the system itself in the context of t he inverse problem\, leading to designs that are more efficient\, more acc urate\, or less expensive\, depending on the application. In the talk we w ill explore applications of this model&nbsp\; to LIDAR and depth sensing\, radar and distributed radar\, and ultrasonic sensing. In the context of t hese applications\, we will describe how different models can lead to impr oved specifications in ultrasonic systems\, robustness to position and tim ing errors in distributed array systems\, and cost reduction and new capab ilities in LIDAR systems.</span></p>\n<p><strong>Lecturer</strong><span st yle="font-weight: 400\;">: Dr.&nbsp\;</span><span style="font-weight: 400\ ;">Petros T. Boufounos </span></p>\n<p><span style="font-weight: 400\;">Pe tros T. Boufounos is Senior Principal Research Scientist and the Computati onal Sensing Team Leader at Mitsubishi Electric Research Laboratories (MER L)\, and a visiting scholar at the Rice University Electrical and Computer Engineering department. Dr. Boufounos completed his undergraduate and gra duate studies at MIT. He received the S.B. degree in Economics in 2000\, t he S.B. and M.Eng. degrees in Electrical Engineering and Computer Science (EECS) in 2002\, and the Sc.D. degree in EECS in 2006. Between September 2 006 and December 2008\, he was a postdoctoral associate with the Digital S ignal Processing Group at Rice University. Dr. Boufounos joined MERL in Ja nuary 2009\, where he has been heading the Computational Sensing Team sinc e 2016. Dr. Boufounos' immediate research focus includes signal acquisitio n and processing\, inverse problems\, frame theory\, quantization and data representations\, as well as their interaction with machine learning\, ro botics and dynamical system theory. Dr. Boufounos has served as an Area Ed itor and a Senior Area Editor at IEEE Signal Processing Letters\, has been part of the SigPort editorial board\, and is currently a member of the IE EE Signal Processing Society Theory and Methods technical committee and a Signal Processing Society Distinguished Lecturer for 2019-2020.</span></p> \n<p><strong><em>Organizers: NH ComSig Chair:</em></strong><em><span style ="font-weight: 400\;"> Mimi Tam\, </span></em><strong><em>Co-Chair</em></s trong><em><span style="font-weight: 400\;">: Mary Brzezenski</span></em></ p><br /><br />Agenda: <br /><p>ComSig Lecture: <strong>Topic</strong><span style="font-weight: 400\;">: </span><span style="font-weight: 400\;">An I nverse Problem Framework for Array Processing Systems</span></p>\n<p><stro ng>Held in Nashua Public Library (NH) &ndash\; April 4</strong><strong>th< /strong><strong> 2019 6:00 &ndash\; 8:30 pm EST</strong></p>\n<p><strong>< span style="font-weight: 400\;">Fee: FREE</span> <span style="font-weight: 400\;"> - &nbsp\;Light Refreshments Served &ndash\; Social Networking bef ore &amp\; after Lecture</span></strong></p> END:VEVENT END:VCALENDAR