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:20171105T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20170925T014823Z UID:13B9A371-E65A-11E6-A7C6-0050568D7F66 DTSTART;TZID=US/Eastern:20170323T180000 DTEND;TZID=US/Eastern:20170323T200000 DESCRIPTION:The concept of probability density and distribution functions a re introduced and illustrated with the normal and uniform density function s. The normal density is shown to be a function of its mean and variance o nly. The notion of a random variable is explained and illustrated. The con cept of computing the sample mean is illustrated with a few simple example s such as the average expected from a large number of casts of a die. The sample means and mean square values are further illustrated by deriving th e equations for linear regression that minimize the mean square error betw een the measured data and a straight line. Auto and Cross correlation time functions are defined along with convolution and the unit sample response . For a stationary random process\, the equivalence between the ensemble m ean\, referred to as expectation\, and the sample mean is demonstrated. Co mputation of expectation using the probability density is generalized and illustrated with computation of the mean\, mean square value\, variance\, and correlation. Because most signal processing is in discrete time\, wher ever possible discussions are illustrated with discrete-time rather than a continuous time independent variable.\n\nCo-sponsored by: Dr. Donaldson\n \nSpeaker(s): Mr. Alan Lipsky\, \n\nAgenda: \n6:00pm - 6:30pm Meet and Gre et (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 Answers\n\nRoom: 13 0 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:10 SUMMARY:Application of Discrete-Time Statistical Signal Processing: Part 2 URL;VALUE=URI:https://events.vtools.ieee.org/m/43478 X-ALT-DESC:Description: <br /><p>The concept of probability density and dis tribution functions are introduced and illustrated with the normal and uni form density functions. The normal density is shown to be a function of it s mean and variance only. The notion of a random variable is explained and illustrated.&nbsp\; The concept of computing the sample mean is illustrat ed with a few simple examples such as the average expected from a large nu mber of casts of a die. The sample means and mean square values are furthe r illustrated by deriving the equations for linear regression that minimiz e the mean square error between the measured data and a straight line. Aut o and Cross correlation time functions are defined along with convolution and the unit sample response.&nbsp\; For a stationary random process\, the equivalence between the ensemble mean\, referred to as expectation\, and the sample mean is demonstrated. Computation of expectation using the prob ability density is generalized and illustrated with computation of the mea n\, mean square value\, variance\, and correlation. Because most signal pr ocessing is in discrete time\, wherever possible discussions are illustrat ed with discrete-time rather than a continuous time independent variable.< /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 Lecture</p>\n<p>7:45pm - 8:00pm Questions and Answers</p> END:VEVENT END:VCALENDAR