Application of Discrete-Time Statistical Signal Processing: Part 1


The Long Island (LI) Chapter of IEEE Signal Processing Society (SPS) in collaboration with the Educational Activities Committee of IEEE LI and Renewable Energy and Sustainability Center of Farmingdale State College Presents the following Lecture:

This is an introductory lecture, with no math. It mostly concerns applications of detecting, identifying and interpreting, a signal embedded in a noisy background in speech, image, SONAR, and RADAR processing with Weiner and Kalman filters. Both filters are optimum in minimizing the least squares error in their output signal.

Developments in statistical 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 characteristic to its autocorrelation. An optimum filter, that minimizes mean square error, in extracting a signal from noise, is named for him. The next big advance in filtering occurred when Rudolf Kalman published a description of his filter in 1960. This filter updates continuously with a recursive solution that offers a low computational burden, and yields both the signal and systems state. A Kalman filter was in the Apollo navigation computer used by Neil Armstrong to go to the moon, and is in many modern applications, particularly autonomous navigation.

  Date and Time




  • Date: 09 Mar 2017
  • Time: 06:00 PM to 08:00 PM
  • All times are (GMT-05:00) US/Eastern
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  • SUNY College at Farmingdale
  • Farmingdale, New York
  • United States
  • Building: Lupton Hall
  • Room Number: 130 A

  • Dr. Donaldson, Chair, SPS, IEEE LI Section (

    Prof. Issapour, Chair, IEEE LI Section (

  • Co-sponsored by Dr. Donaldson
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  • Starts 01 January 2017 12:00 AM
  • Ends 07 March 2017 12:00 PM
  • All times are (GMT-05:00) US/Eastern
  • No Admission Charge


Lecturer: Mr. Alan Lipsky


Application of Discrete-Time Statistical Signal Processing: Part 1


Alan Lipsky specializes in feedback controls. His recent design experience includes: a 500-watt off-line power supply for an ultrasonic generator, a 6 KW degaussing power amplifier, and a data acquisition system that extrapolates. He has: a patent for a sonar signal processing equipment. His degrees consist of a Master’s degree from MIT, and a bachelor’s from RPI where he was elected to beta-kappa-Nu and Tau-Beta-Pi.


6:00pm - 6:30pm Meet and Greet (Networking/PIZZA)

6:30 pm - 6:45pm Introduction

6:45pm - 7:45pm Mr. Lipsky's Lecture

7:45pm - 8:00pm Questions and Answers

FREE PIZZA and WATER will be served.

Free for all Registration Participants (Members/Non-Members/Students)