BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Mexico/General BEGIN:DAYLIGHT DTSTART:20210404T030000 TZOFFSETFROM:-0600 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=4 TZNAME:CDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20211031T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0600 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:CST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20210613T032544Z UID:3879C6B5-77F6-4193-ADD7-2BC406EDAC22 DTSTART;TZID=Mexico/General:20210608T100000 DTEND;TZID=Mexico/General:20210608T113000 DESCRIPTION:Abstract:\nTraditional signal processing is based on the idea t hat an analogue waveform should be converted in digital form by recording its amplitude information at specific time instants. Nearly all data acqui sition\, processing and communication methods have progressed by relying o n this fundamental sampling paradigm.\n\nInterestingly\, we know that the brain operates differently and represents signals using networks of spikin g neurons where the timing of the spikes encodes the signal’s informatio n. This form of processing by spikes is more efficient and is inspiring a new generation of event-based audio-visual sensing and processing architec tures.\n\nIn the first part of this talk\, we investigate time encoding as an alternative method to classical sampling\, and address the problem of reconstructing classes of sparse non-bandlimited signals from time-based s amples. We consider a sampling mechanism based on first filtering the inpu t\,\nbefore obtaining the timing information using a time encoding machine . Leveraging specific properties of these filters\, we derive sufficient c onditions and propose novel algorithms for perfect reconstruction of class es of sparse signals.\n\nIn the second part of the talk we consider physic al fields induced by a finite number of instantaneous diffusion sources\, which we sample using a mobile sensor\, along unknown trajectories compose d of multiple linear segments. We address the problem of estimating the so urces\, as well as the trajectory of the mobile sensor and validate our ap proach on real thermal data.\n\nWe finally conclude by highlighting furthe r avenues for research in the emerging area of event-based sensing and sam pling along trajectories with mobile sensors.\n\nSpeaker(s): Ph.D. Pier Lu igi Dragotti\, \n\nGuadalajara\, Jalisco\, Mexico\, Virtual: https://event s.vtools.ieee.org/m/265257 LOCATION:Guadalajara\, Jalisco\, Mexico\, Virtual: https://events.vtools.ie ee.org/m/265257 ORGANIZER:r.calderonr@ieee.org SEQUENCE:7 SUMMARY:New Sparse Sampling Methods: Time-based sampling and sampling along trajectories\, by Dr. Pier Luigi Dragotti URL;VALUE=URI:https://events.vtools.ieee.org/m/265257 X-ALT-DESC:Description: <br /><p><strong>Abstract:</strong><br />Traditiona l signal processing is based on the idea that an analogue waveform should be&nbsp\;converted in digital form by recording its amplitude information at specific time instants.&nbsp\;Nearly all data acquisition\, processing and communication methods have progressed by&nbsp\;relying on this fundame ntal sampling paradigm.<br /><br />Interestingly\, we know that the brain operates differently and represents signals using&nbsp\;networks of spikin g neurons where the timing of the spikes encodes the signal&rsquo\;s&nbsp\ ;information. This form of processing by spikes is more efficient and is i nspiring a new&nbsp\;generation of event-based audio-visual sensing and pr ocessing architectures.<br /><br />In the first part of this talk\, we inv estigate time encoding as an alternative method to classical sampling\,&nb sp\;and address the problem of reconstructing classes of sparse non-bandli mited signals&nbsp\;from time-based samples. We consider a sampling mechan ism based on first filtering the input\,<br />before obtaining the timing information using a time encoding machine. Leveraging specific properties of these filters\, we derive sufficient conditions and propose novel algor ithms for perfect reconstruction of classes of sparse signals.<br /><br /> In the second part of the talk we consider physical fields induced by a fi nite number of instantaneous diffusion sources\,&nbsp\;which we sample usi ng a mobile sensor\, along unknown trajectories composed of multiple linea r segments.&nbsp\;We address the problem of estimating the sources\, as we ll as the trajectory of the mobile sensor and validate our approach on rea l thermal data.</p>\n<p>We finally conclude by highlighting further avenue s for research in the emerging area of event-based sensing and sampling al ong trajectories with mobile sensors.</p> END:VEVENT END:VCALENDAR