Application of Discrete-Time Statistical Signal Processing: Part 2


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:

The concept of probability density and distribution functions are introduced and illustrated with the normal and uniform density functions. The normal density is shown to be a function of its mean and variance only. The notion of a random variable is explained and illustrated.  The concept of computing the sample mean is illustrated with a few simple examples 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 the equations for linear regression that minimize the mean square error between 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 mean, referred to as expectation, and the sample mean is demonstrated. Computation 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, wherever possible discussions are illustrated with discrete-time rather than a continuous time independent variable.

  Date and Time




  • SUNY College at Farmingdale
  • Farmingdale, New York
  • United States
  • Building: Lupton Hall
  • Room Number: 130 A

Staticmap?size=250x200&sensor=false&zoom=14&markers=40.7525499%2c 73
  • Dr. Donaldson, Chair, SPS, IEEE LI Section (

    Prof. Issapour, Chair, IEEE LI Section (

  • Co-sponsored by Dr. Donaldson
  • Survey: Fill out the survey
  • Starts 01 January 2017 12:00 AM
  • Ends 23 March 2017 08:00 PM
  • All times are US/Eastern
  • No Admission Charge
  • Register


Mr. Alan Lipsky


Application of Discrete-Time Statistical Signal Processing: Part 2


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)