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Title: Biologically-based signal processing system applied to noise removal for signal extraction

Abstract

The method and system described herein use a biologically-based signal processing system for noise removal for signal extraction. A wavelet transform may be used in conjunction with a neural network to imitate a biological system. The neural network may be trained using ideal data derived from physical principles or noiseless signals to determine to remove noise from the signal.

Inventors:
;
Issue Date:
Research Org.:
Univ. of California, Oakland, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1174948
Patent Number(s):
6763339
Application Number:
09/888,965
Assignee:
University Of California, The Regents Of
Patent Classifications (CPCs):
G - PHYSICS G06 - COMPUTING G06N - COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
H - ELECTRICITY H03 - BASIC ELECTRONIC CIRCUITRY H03H - IMPEDANCE NETWORKS, e.g. RESONANT CIRCUITS
DOE Contract Number:  
W-7405-ENG-48
Resource Type:
Patent
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION

Citation Formats

Fu, Chi Yung, and Petrich, Loren I. Biologically-based signal processing system applied to noise removal for signal extraction. United States: N. p., 2004. Web.
Fu, Chi Yung, & Petrich, Loren I. Biologically-based signal processing system applied to noise removal for signal extraction. United States.
Fu, Chi Yung, and Petrich, Loren I. Tue . "Biologically-based signal processing system applied to noise removal for signal extraction". United States. https://www.osti.gov/servlets/purl/1174948.
@article{osti_1174948,
title = {Biologically-based signal processing system applied to noise removal for signal extraction},
author = {Fu, Chi Yung and Petrich, Loren I.},
abstractNote = {The method and system described herein use a biologically-based signal processing system for noise removal for signal extraction. A wavelet transform may be used in conjunction with a neural network to imitate a biological system. The neural network may be trained using ideal data derived from physical principles or noiseless signals to determine to remove noise from the signal.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Jul 13 00:00:00 EDT 2004},
month = {Tue Jul 13 00:00:00 EDT 2004}
}