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Title: Analog system for computing sparse codes

Abstract

A parallel dynamical system for computing sparse representations of data, i.e., where the data can be fully represented in terms of a small number of non-zero code elements, and for reconstructing compressively sensed images. The system is based on the principles of thresholding and local competition that solves a family of sparse approximation problems corresponding to various sparsity metrics. The system utilizes Locally Competitive Algorithms (LCAs), nodes in a population continually compete with neighboring units using (usually one-way) lateral inhibition to calculate coefficients representing an input in an over complete dictionary.

Inventors:
 [1];  [2];  [2];  [3];  [2]
  1. (El Cerrito, CA)
  2. (Houston, TX)
  3. (San Francisco, CA)
Issue Date:
Research Org.:
William Marsh Rice University (Houston, TX)
Sponsoring Org.:
USDOE
OSTI Identifier:
1013614
Patent Number(s):
7,783,459
Application Number:
12/035,424
Assignee:
William Marsh Rice University (Houston, TX) CHO
DOE Contract Number:  
FC02-01ER25462
Resource Type:
Patent
Country of Publication:
United States
Language:
English

Citation Formats

Rozell, Christopher John, Johnson, Don Herrick, Baraniuk, Richard Gordon, Olshausen, Bruno A., and Ortman, Robert Lowell. Analog system for computing sparse codes. United States: N. p., 2010. Web.
Rozell, Christopher John, Johnson, Don Herrick, Baraniuk, Richard Gordon, Olshausen, Bruno A., & Ortman, Robert Lowell. Analog system for computing sparse codes. United States.
Rozell, Christopher John, Johnson, Don Herrick, Baraniuk, Richard Gordon, Olshausen, Bruno A., and Ortman, Robert Lowell. Tue . "Analog system for computing sparse codes". United States. https://www.osti.gov/servlets/purl/1013614.
@article{osti_1013614,
title = {Analog system for computing sparse codes},
author = {Rozell, Christopher John and Johnson, Don Herrick and Baraniuk, Richard Gordon and Olshausen, Bruno A. and Ortman, Robert Lowell},
abstractNote = {A parallel dynamical system for computing sparse representations of data, i.e., where the data can be fully represented in terms of a small number of non-zero code elements, and for reconstructing compressively sensed images. The system is based on the principles of thresholding and local competition that solves a family of sparse approximation problems corresponding to various sparsity metrics. The system utilizes Locally Competitive Algorithms (LCAs), nodes in a population continually compete with neighboring units using (usually one-way) lateral inhibition to calculate coefficients representing an input in an over complete dictionary.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2010},
month = {8}
}

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