Sparse deconvolution of ultrasonic NDE traces-a preliminary study

The paper deals with the problem of deconvolving sparse ultrasonic NDE traces with time-varying pulses. A sparse dictionary learning algorithm is utilized to learn a time-varying pulse matrix. Each element of the matrix represents an individual pattern of possible local impulse responses. An ultraso...

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Bibliographic Details
Main Authors: Guang-Ming Zhang, Harvey, D.M., Braden, D.R.
Format: Conference Proceeding
Language:English
Subjects:
Automotive engineering
Bayesian methods
Circuit testing
Deconvolution
Dictionaries
Matrix decomposition
Reflectivity
Sparse matrices
sparse reflectivity sequence
sparse signal representation
Thick films
time-varying pulse estimation
Transmission line matrix methods
ultrasound
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Summary:The paper deals with the problem of deconvolving sparse ultrasonic NDE traces with time-varying pulses. A sparse dictionary learning algorithm is utilized to learn a time-varying pulse matrix. Each element of the matrix represents an individual pattern of possible local impulse responses. An ultrasonic signal is then decomposed into a sparse representation by the sparse Bayesian learning algorithm over the learned pulse matrix. The reflectivity sequence is finally estimated from the resulting sparse representation. The proposed method has been tested using NDE data taken from automotive thick film hybrid circuit boards.
ISSN:1051-0117
DOI:10.1109/ULTSYM.2008.0043