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IEEE Signal Processing Society Santa Clara Valley Chapter


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Tuesday, Dec 15, 2015

Quantization Noise

This meeting is hosted/sponsored by IEEE SPS Chapter and co-sponsored by IEEE CIS, CSS, ITS, RAS, SSCS Chapters


Speaker :

   Prof. Bernard Widrow

   Department of Electrical Engineering, Stanford University

 

Location:

   AMD Commons C-6/7/8, 991 Stewart Dr., Sunnyvale, CA (map or Google Maps)

 

Schedule:

   6:30pm: Networking/Light Dinner

   7:00pm: Announcements

   7:05pm: Presentation

   8:15pm: Adjourn

 

Abstract:

The effect of uniform quantization can often be modeled by an additive noise that is uniformly distributed, uncorrelated with the signal being quantized, and uncorrelated over time, being additive white noise having zero mean and mean square of 1/12 q-square, where q is the quantum step size. This simple model is statistical and is based on Nyquist sampling theory applied to the probablity density distribution of the signal being quantized. Linear Nyquist theory is applied to precisely describe uniform quantization, which indeed is nonlinear. The simple model applies almost everywhere. This talk surveys the theory behind the simple model and discusses the conditions for its validity.

The simple model applies to uniform quantization. However, the theory can be extended to apply to non uniform quantization. This leads to a simple model for floating point quantization. Conditions for the validity of the floating point model will be presented.


Biography:

Bernard Widrow received the S.B., S.M., and Sc.D. degrees in Electrical Engineering from the Massachusetts Institute of Technology in 1951, 1953, and 1956, respectively. He joined the MIT faculty and taught there from 1956 to 1959. In 1959, he joined the faculty of Stanford University, where he is currently Professor of Electrical Engineering, Emeritus.


He began research on adaptive filters, learning processes, and artificial neural models in 1957. Together with M.E. Hoff, Jr., his first doctoral student at Stanford, he invented the LMS algorithm in the autumn of 1959. Today, this is the most widely used learning algorithm, used in every MODEM in the world. He has continued working on adaptive signal processing, adaptive controls, and neural networks since that time.


Dr. Widrow is a Life Fellow of the IEEE and a Fellow of AAAS. He received the IEEE Centennial Medal in 1984, the IEEE Alexander Graham Bell Medal in 1986, the IEEE Signal Processing Society Medal in 1986, the IEEE Neural Networks Pioneer Medal in 1991, the IEEE Millennium Medal in 2000, and the Benjamin Franklin Medal for Engineering from the Franklin Institute of Philadelphia in 2001. He was inducted into the National Academy of Engineering in 1995 and into the Silicon Valley Engineering Council Hall of Fame in 1999.


Dr. Widrow is a past president and member of the Governing Board of the International Neural Network Society. He is associate editor of several journals and is the author of over 125 technical papers and 21 patents. He is co-author of Adaptive Signal Processing and Adaptive Inverse Control, both Prentice-Hall books. A new book, Quantization Noise, was published by Cambridge University Press in June 2008.



Talk Slides


Courtesy of Al Friedrich (recording) and Anand Giduthuri (video editing)







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