Genetic Algorithms Evolve Optimized Transforms for Signal Processing Applications

Author(s):  
Frank Moore ◽  
Brendan Babb ◽  
Steven Becke ◽  
Heather Koyuk ◽  
Earl Lamson ◽  
...  
Author(s):  
Kim F. Man ◽  
Kit S. Tang ◽  
Sam Kwong ◽  
Wolfgang A. Halang

Author(s):  
Noha Shaaban ◽  
Fukuzo Masuda ◽  
Hidetsugu Morota

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.


1994 ◽  
Vol 02 (03) ◽  
pp. 251-266 ◽  
Author(s):  
PETER GERSTOFT

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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