Compression of optical readout biomolecular sensory data
United States Patent 6580831

Abstract:
The present invention provides a system and method for compression 
of image data while preserving the usable information and 
eliminating or reducing associated noise in which the image data 
includes a signal and noise. The image data is transformed using 
a multiscale transform technique (such as the Pyramidal Median 
Transform) such that the image data is represented as a plurality 
of transform coefficients each having a corresponding weight. From 
the respective weights, those transform coefficients associated with 
noise are determined and extracted from the original plurality of 
transform coefficients. The remaining transform coefficients are 
subsequently quantized and coded.

Publication Date:  06/17/2003
Filing Date:       01/14/2002
Asignee:           Infineon Technologies AG (Munich, DE)

Other References include:
J.-L. Starck, F. Murtagh, and M. Louys, "Astronomical Image 
Compression Using the Pyramidal Median Transform", Astronomical 
Data Analysis Software and Systems IV, ASP Conference Series, 
vol. 77, pp. 1-4, 1995.;, vol. 77, pp. 1-4, 1995. 

From the Description:

The PMT was developed for application in compression of astronomical 
images, i.e. huge images with sparse round or disk-like features 
(such as stars etc.), some image distortions, and noise, as further 
described in "Image processing and data analysis: The Multiscale Approach", 
by J. L. Starck, F. Murtagh, and A. Bijaoui, Astronomical Data Analysis 
Software and Systems IV, ASP Conference Series, Vol. 77, pages 1-4, 1995, 
which is hereby incorporated by reference. The invention advantageously 
exploits characteristics of ORBS data and properties of the Pyramidal 
Median Transform (PMT) to permit discrimination between signal and noise.

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Method for reducing data storage and transmission requirements for seismic 
data
United States Patent 5745392

Abstract:
A method for compressing seismic data to reduce data storage and transmission 
requirements applies wavelet transforms to digitized trace sequential data 
obtained from plural arrays of multiple acoustic sensors. The wavelet 
transforms are applied in at least three dimensions, and, in the case of 
underwater exploration, four dimensions. The transformed data is ordered 
and quantized to increase the number of zero data values, and the quantized 
data is compressed using rim-length encoding and entropy coding. The entropy 
coded data is stored for later retrieval or transmitted to a remote location. 
The retrieved or received data is decompressed, dequantized and inverse 
wavelet transformed to construct a representation of the original data. 
The compression can be selected to be in excess of 100:1 to significantly 
reduce the data storage and transmission requirements without significant 
degradation of the reconstructed data.

Inventors: 
Ergas, Raymond A. (Laguna Beach, CA)	
Donoho, Paul L. (Houston, TX)	 
Villasenor, John (Santa Monica, CA)

Publication Date: 04/28/1998
Filing Date: 10/05/1995

Assignee: Chevron U.S.A. Inc. (La Habra, CA)

From Other References:

Starck, J.-L., and Murtagh, F., Multiresolution Image Analysis using 
Wavelets--Recent Results, Bulletin of the American Astronomical Society, 
283, pp. 349-360 (1994).

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Game theoretic prioritization scheme

Abstract:
A method for providing unequal allocation of rights among agents while 
operating according to fair principles, comprising assigning a hierarchal 
rank to each agent; providing a synthetic economic value to a first set 
of agents at the a high level of the hierarchy; allocating portions of 
the synthetic economic value by the first set of agents to a second set 
of agents at respectively different hierarchal rank than the first set 
of agents; and conducting an auction amongst agents using the synthetic 
economic value as the currency.

Application number: 11/005,460
Filing date: Dec 6, 2004
Inventor: Steven M. Hoffberg

References include:
F. Murtagh, J.L. Starck and M.W. Berry, "Overcoming the curse of 
dimensionality in clustering by means of the wavelet transform".