MIT Department of Electrical Engineering & Computer Science
Signal Processing and Communication with Soliton Signals
Andrew C. Singer
Massachusetts Institute of Technology
Thursday, April 11, 1996
4:15 PM (4:00 refreshments)
Grier Room, 34-401B
EECS Special Seminar
Abstract
Traditional signal processing algorithms rely heavily on models that
are inherently linear. Such models are attractive both for their
mathematical tractability and their applicability to the rich class of
signals that can be represented with Fourier methods. Nonlinear
systems that support soliton solutions share many of the properties
that make linear systems attractive from an engineering standpoint.
Although nonlinear, these systems are solvable through inverse
scattering, a technique analogous to the Fourier transform for linear
systems. Solitons are eigenfunctions of these systems which satisfy a
nonlinear form of superposition and display rich signal dynamics as
they interact. By using solitons for signal synthesis, the
corresponding nonlinear systems become specialized signal processors
which are naturally suited to a number of complex signal processing
tasks. Analog circuits can generate soliton signals and can be used
as natural multiplexers and demultiplexers in a number of potential
soliton communication applications. These circuit models play an
important role in investigating the effects of noise on soliton
behavior. Finally, the soliton signal dynamics also provide a
mechanism for decreasing signal energy while enhancing detection and
estimation performance.
URL of this page:
http://www-eecs.mit.edu/AY95-96/events/25.html
Created: Mar 7, 1996
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Modified: Jun 25, 1997
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