Genetic Algorithm Wavelet Design for Signal Classification

Authors:
Eric Jones;Paul Runkle;Nilanjan Dasgupta;Luise Couchman;Lawrence Carin
Affiliations:
Duke Univ., Durhan, NC;Duke Univ., Durhan, NC;Duke Univ., Durhan, NC;Naval Research Lab., Washington, DC;Duke Univ., Durhan, NC
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2001

Citing 9
Cited 3

Continuously variable duration hidden Markov models for automatic speech recognition

Computer Speech and Language
Genetic programming: on the programming of computers by means of natural selection

Genetic programming: on the programming of computers by means of natural selection
Genetic programming II: automatic discovery of reusable programs

Genetic programming II: automatic discovery of reusable programs
Revised report on the algorithm language ALGOL 60

Communications of the ACM
Rotation and Gray Scale Transform Invariant Texture Identification using Wavelet Decomposition and Hidden Markov Model

IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic design of antennas and electronic circuits

Genetic design of antennas and electronic circuits
Schema theory for genetic programming with one-point crossover and point mutation

Evolutionary Computation
An investigation into the sensitivity of genetic programming to the frequency of leaf selection during subtree crossover

GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Hidden Markov models for multiaspect target classification

IEEE Transactions on Signal Processing

Edge, Shade and Mixed Range Detection by Fuzzy Gaussian Filter for an Autonomous Robot

Journal of Intelligent and Robotic Systems
Targeted filter evolution for improved image reconstruction resolution

Proceedings of the 9th annual conference on Genetic and evolutionary computation
Tunable halfband-pair wavelet filter banks and application to multifocus image fusion

Pattern Recognition

Quantified Score

Hi-index	0.14

Visualization

Abstract

Biorthogonal wavelets are applied to parse multiaspect transient scattering data in the context of signal classification. A language-based genetic algorithm is used to design wavelet filters that enhance classification performance. The biorthogonal wavelets are implemented via the lifting procedure and the optimization is carried out using a classification-based cost function. Example results are presented for target classification using measured scattering data.