Data processing: artificial intelligence – Neural network – Learning task
Reexamination Certificate
2005-04-15
2008-09-09
Vincent, David (Department: 2129)
Data processing: artificial intelligence
Neural network
Learning task
Reexamination Certificate
active
07424463
ABSTRACT:
A denoising mechanism uses chosen signal classes and selected analysis dictionaries. The chosen signal class includes a collection of signals. The analysis dictionaries describe signals. The embedding threshold value is initially determined for a training set of signals in the chosen signal class. The update signal is initialized with a signal corrupted by noise. The estimate calculated by: computing coefficients for the updated signal using the analysis dictionaries; computing an embedding index for each of the path(s); extracting a coefficient subset from coefficients for the path(s) whose embedding index exceeds an embedding threshold; adding a coefficient subset to a coefficient collection; generating a partial estimate using the coefficient collection; creating an attenuated partial estimate by attenuating the partial estimate by an attenuation factor; updating the updated signal by subtracting the attenuated partial estimate from the updated signal; and adding the attenuated partial estimate to the estimate.
REFERENCES:
patent: 5781144 (1998-07-01), Hwa
patent: 2004/0071363 (2004-04-01), Kouri et al.
Berenstein Carlos A.
Napoletani Domenico
Sauer Timothy
Struppa Daniele C.
Walnut David
Bharadwaj Kalpana
George Mason Intellectual Properties, Inc.
Grossman David
University of Maryland
Vincent David
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