Genetic algorithm wavelet design for signal classification

Eric Jones; Paul Runkle; Nilanjan Dasgupta; Luise Couchman; Lawrence Carin

doi:10.1109/34.946991

Genetic algorithm wavelet design for signal classification

Eric Jones, Paul Runkle, Nilanjan Dasgupta, Luise Couchman, Lawrence Carin

Research output: Contribution to journal › Article › peer-review

36 Scopus citations

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.

Original language	English (US)
Pages (from-to)	890-895
Number of pages	6
Journal	IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume	23
Issue number	8
DOIs	https://doi.org/10.1109/34.946991
State	Published - Aug 1 2001
Externally published	Yes

Access to Document

10.1109/34.946991

Cite this

@article{73d3e5936f6f4a889530fd3f73bb2d9b,

title = "Genetic algorithm wavelet design for signal classification",

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.",

author = "Eric Jones and Paul Runkle and Nilanjan Dasgupta and Luise Couchman and Lawrence Carin",

note = "Generated from Scopus record by KAUST IRTS on 2021-02-09",

year = "2001",

month = aug,

day = "1",

doi = "10.1109/34.946991",

language = "English (US)",

volume = "23",

pages = "890--895",

journal = "IEEE Transactions on Pattern Analysis and Machine Intelligence",

issn = "0162-8828",

publisher = "Institute of Electrical and Electronics Engineers",

number = "8",

}

TY - JOUR

T1 - Genetic algorithm wavelet design for signal classification

AU - Jones, Eric

AU - Runkle, Paul

AU - Dasgupta, Nilanjan

AU - Couchman, Luise

AU - Carin, Lawrence

N1 - Generated from Scopus record by KAUST IRTS on 2021-02-09

PY - 2001/8/1

Y1 - 2001/8/1

N2 - 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.

AB - 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.

UR - http://ieeexplore.ieee.org/document/946991/

UR - http://www.scopus.com/inward/record.url?scp=0035422809&partnerID=8YFLogxK

U2 - 10.1109/34.946991

DO - 10.1109/34.946991

M3 - Article

SN - 0162-8828

VL - 23

SP - 890

EP - 895

JO - IEEE Transactions on Pattern Analysis and Machine Intelligence

JF - IEEE Transactions on Pattern Analysis and Machine Intelligence

IS - 8

ER -

Genetic algorithm wavelet design for signal classification

Abstract

Access to Document

Other files and links

Fingerprint

Cite this