Genetic Algorithms Evolve Optimized Transforms for Signal Processing Applications

2005 ◽  
Author(s):  
Frank Moore ◽  
Brendan Babb ◽  
Steven Becke ◽  
Heather Koyuk ◽  
Earl Lamson ◽  
...  
Keyword(s):  
Signal Processing ◽  
Genetic Algorithms

Genetic Algorithms for Control and Signal Processing

1997 ◽  
Author(s):  
Kim F. Man ◽  
Kit S. Tang ◽  
Sam Kwong ◽  
Wolfgang A. Halang
Keyword(s):  
Signal Processing ◽  
Genetic Algorithms

A New Waveform Signal Processing Method Based on Adaptive Clustering-Genetic Algorithms

2006 ◽  
Author(s):  
Noha Shaaban ◽  
Fukuzo Masuda ◽  
Hidetsugu Morota
Keyword(s):  
Signal Processing ◽  
Genetic Algorithms ◽  
Pulse Height ◽  
Digital Signal ◽  
Processing Method ◽  
Pulse Height Spectrum ◽  
Semiconductor Detectors ◽  
Shape Variation ◽  
Signal Shape ◽  
Signal Processing Method

We present a fast digital signal processing method for numerical analysis of individual pulses from CdZnTe compound semiconductor detectors. Using Maxi-Mini Distance Algorithm and Genetic Algorithms based discrimination technique. A parametric approach has been used for classifying the discriminated waveforms into a set of clusters each has a similar signal shape with a corresponding pulse height spectrum. A corrected total pulse height spectrum was obtained by applying a normalization factor for the full energy peak for each cluster with a highly improvements in the energy spectrum characteristics. This method applied successfully for both simulated and real measured data, it can be applied to any detector suffers from signal shape variation.

GLOBAL INVERSION BY GENETIC ALGORITHMS FOR BOTH SOURCE POSITION AND ENVIRONMENTAL PARAMETERS

1994 ◽  
Vol 02 (03) ◽  
pp. 251-266 ◽  
Author(s):  
PETER GERSTOFT
Keyword(s):  
Signal Processing ◽  
Genetic Algorithms ◽  
Parameter Space ◽  
Environmental Parameters ◽  
Environmental Parameter ◽  
Model Parameters ◽  
A Posteriori ◽  
Source Position ◽  
Data Set ◽  
Acoustic Models

The data set from the Workshop on Acoustic Models in Signal Processing (May 1993) is inverted in order to find both the environmental parameters and the source position, Genetic algorithms are used for the optimization. When using genetic algorithms the responses from many environmental parameter sets are computed in order to estimate the solution. All these samples of the parameter space are used to estimate the a posteriori probabilities of the model parameters. Thus the uniqueness and uncertainty of the model parameters are assessed.

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