BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20240310T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20231105T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20231220T210106Z UID:5D051CEF-7CCD-4FAA-A282-6F50B483FAC3 DTSTART;TZID=America/New_York:20231213T180000 DTEND;TZID=America/New_York:20231213T193000 DESCRIPTION:Next-generation RF arrays will have the ability to generate dat a at tremendous rates. In this talk we will discuss how this data deluge c an be managed using dimensionality reduction at the array. We will start b y giving an overview of past approaches of dimensionality reduction\, incl uding classical beamforming and compressed sensing\, and the trade-offs in herent in adaptivity and performance.\n\nWe then discuss how array snapsho ts of broadband signals can be (provably) embedded in a low dimensional su bspace without loss of array gain. One consequence of this model is a new approach to broadband beamforming\, which is both computationally efficien t and outperforms classical methods while being highly flexible in regards to array geometry and signal bandwidth. A second consequence is a new tec hnique for dimensionality reduction that is built directly into the analog -to-digital conversion and can dramatically reduce the hardware requiremen ts for broadband beamforming. Finally\, we will discuss how these dimensio nality reduction techniques can be adapted to changing environmental condi tions.\n\nDr. Romberg is also presenting another talk at 1pm [Minimax Prob lems in Reinforcement Learning](https://events.vtools.ieee.org/m/384372)\n \nSpeaker(s): Justin Romberg\, \n\nRoom: CST 4-201\, Bldg: Center of Scien ce & Technology\, Syracuse University\, 111 College Pl\, Syracuse\, New Yo rk\, United States\, 13210\, Virtual: https://events.vtools.ieee.org/m/384 377 LOCATION:Room: CST 4-201\, Bldg: Center of Science & Technology\, Syracuse University\, 111 College Pl\, Syracuse\, New York\, United States\, 13210\ , Virtual: https://events.vtools.ieee.org/m/384377 ORGANIZER:stone@ieee.org SEQUENCE:12 SUMMARY:Dimensionality Reduction for Sensor Arrays URL;VALUE=URI:https://events.vtools.ieee.org/m/384377 X-ALT-DESC:Description: <br /><p style="font-weight: 400\;">Next-generation RF arrays will have the ability to generate data at tremendous rates.&nbs p\; In this talk we will discuss how this data deluge can be managed using dimensionality reduction at the array.&nbsp\; We will start by giving an overview of past approaches of dimensionality reduction\, including classi cal beamforming and compressed sensing\, and the trade-offs inherent in ad aptivity and performance.&nbsp\;</p>\n<p style="font-weight: 400\;">&nbsp\ ;</p>\n<p style="font-weight: 400\;">We then discuss how array snapshots o f broadband signals can be (provably) embedded in a low dimensional subspa ce without loss of array gain.&nbsp\; One consequence of this model is a n ew approach to broadband beamforming\, which is both computationally effic ient and outperforms classical methods while being highly flexible in rega rds to array geometry and signal bandwidth.&nbsp\; A second consequence is a new technique for dimensionality reduction that is built directly into the analog-to-digital conversion and can dramatically reduce the hardware requirements for broadband beamforming.&nbsp\; Finally\, we will discuss h ow these dimensionality reduction techniques can be adapted to changing en vironmental conditions.</p>\n<p style="font-weight: 400\;">Dr. Romberg is also presenting another talk at 1pm <a href="https://events.vtools.ieee.or g/m/384372" target="_blank" rel="noopener">Minimax Problems in Reinforceme nt Learning</a></p> END:VEVENT END:VCALENDAR