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Structure noise reduction and deconvolution of ultrasonic data using wavelet decomposition

From National Research Council Canada

Alternative titleStructure noise reduction and deconvolution of ultrasonic data using wavelet decomposition (ultrasonic flaw detection)
DOIResolve DOI: https://doi.org/10.1109/ULTSYM.1989.67167
AuthorSearch for: 1; Search for: 1
Affiliation
  1. National Research Council of Canada
FormatText, Article
ConferenceIEEE Ultrasonics Symposium, October 3-6, 1989, Montréal, Québéc, Canada
Subjectnoise reduction; deconvolution; microstructure; Gaussian processes; signal to noise ratio; filters; bandwidth; time frequency analysis; signal processing; composite materials
Abstract
The split-spectrum processing (SSP) technique is known to improve flaw detection in materials in which the coarse microstructure produces broadband noise of large amplitude, which masks useful signals. It is shown that the spectral decomposition used in the SSP is equivalent to the time-frequency Gabor decomposition. A generalized SSP based on a wavelet decomposition, in which the received signal is decomposed over a basis of elementary wavelets translated in frequency and dilated in time, and followed as in the conventional SSP by an optimization algorithm is introduced. The flexibility in the choice of the wavelet basis allows implementation of efficient signal decomposition. Three wavelet bases are presented: Gaussian-shaped wavelets, binary wavelets, and autoregressive wavelets. Applications of the SSP with wavelet decomposition to noise reduction and deconvolution are illustrated with experimental and simulated data.
Publication date
PublisherIEEE
In
  • Proceedings of the IEEE Ultrasonics Symposium (6 October 1989): 1139–1144.
LanguageEnglish
Peer reviewedYes
NPARC number23004236
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Record identifierf23e28dc-078e-41d8-8895-f44fdd8c5c5b
Record created2018-10-12
Record modified2023-06-15
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