BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:US/Eastern BEGIN:DAYLIGHT DTSTART:20170312T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20161106T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20170925T014920Z UID:4C4695FD-E658-11E6-A7C6-0050568D7F66 DTSTART;TZID=US/Eastern:20170309T180000 DTEND;TZID=US/Eastern:20170309T200000 DESCRIPTION:This is an introductory lecture\, with no math. It mostly conce rns applications of detecting\, identifying and interpreting\, a signal em bedded in a noisy background in speech\, image\, SONAR\, and RADAR process ing with Weiner and Kalman filters. Both filters are optimum in minimizing the least squares error in their output signal.\n\nDevelopments in statis tical signal processing can be traced back to the early 1800’s when both Gauss and Legendre used the method of least squares to extract a comet’ s orbit from noisy measurements. In the 1940’s Norbert Wiener published “Extrapolation. Interpolation and smoothing of stationary time series. ” He related a random signal’s power density versus frequency characte ristic to its autocorrelation. An optimum filter\, that minimizes mean squ are error\, in extracting a signal from noise\, is named for him. The next big advance in filtering occurred when Rudolf Kalman published a descript ion of his filter in 1960. This filter updates continuously with a recursi ve solution that offers a low computational burden\, and yields both the s ignal and systems state. A Kalman filter was in the Apollo navigation comp uter used by Neil Armstrong to go to the moon\, and is in many modern appl ications\, particularly autonomous navigation.\n\nCo-sponsored by: Dr. Don aldson\n\nSpeaker(s): Lecturer: Mr. Alan Lipsky\, \n\nAgenda: \n6:00pm - 6 :30pm Meet and Greet (Networking/PIZZA)\n\n6:30 pm - 6:45pm Introduction\n \n6:45pm - 7:45pm Mr. Lipsky's Lecture\n\n7:45pm - 8:00pm Questions and An swers\n\nRoom: 130 A\, Bldg: Lupton Hall\, SUNY College at Farmingdale\, Farmingdale\, New York\, United States LOCATION:Room: 130 A\, Bldg: Lupton Hall\, SUNY College at Farmingdale\, F armingdale\, New York\, United States ORGANIZER:Signal@ieee.li SEQUENCE:7 SUMMARY:Application of Discrete-Time Statistical Signal Processing: Part 1 URL;VALUE=URI:https://events.vtools.ieee.org/m/43477 X-ALT-DESC:Description: <br /><p>This is an introductory lecture\, with no math. It mostly concerns applications of detecting\, identifying and inter preting\, a signal embedded in a noisy background in speech\, image\, SONA R\, and RADAR processing with Weiner and Kalman filters. Both filters are optimum in minimizing the least squares error in their output signal.</p>\ n<p>Developments in statistical signal processing can be traced back to th e early 1800&rsquo\;s when both Gauss and Legendre used the method of leas t squares to extract a comet&rsquo\;s orbit from noisy measurements. In th e 1940&rsquo\;s Norbert Wiener published &ldquo\;Extrapolation. Interpolat ion and smoothing of stationary time series.&rdquo\; He related a random s ignal&rsquo\;s power density versus frequency characteristic to its autoco rrelation. An optimum filter\, that minimizes mean square error\, in extra cting a signal from noise\, is named for him. The next big advance in filt ering occurred when Rudolf Kalman published a description of his filter in 1960. This filter updates continuously with a recursive solution that off ers a low computational burden\, and yields both the signal and systems st ate. A Kalman filter was in the Apollo navigation computer used by Neil Ar mstrong to go to the moon\, and is in many modern applications\, particula rly autonomous navigation.</p><br /><br />Agenda: <br /><p>6:00pm - 6:30pm Meet and Greet (Networking/PIZZA)</p>\n<p>6:30 pm - 6:45pm Introduction</ p>\n<p>6:45pm - 7:45pm Mr. Lipsky's&nbsp\;Lecture</p>\n<p>7:45pm - 8:00pm Questions and Answers</p> END:VEVENT END:VCALENDAR