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Spectral histogram using the minimization algorithm-theory and applications to flaw detection
Journal article

Spectral histogram using the minimization algorithm-theory and applications to flaw detection

X Li, N.M Bilgutay and R Murthy
IEEE transactions on ultrasonics, ferroelectrics, and frequency control, v 39(2), pp 279-284
Mar 1992
PMID: 18263148

Abstract

Filtering Frequency diversity Histograms Minimization methods Narrowband Signal to noise ratio Statistical distributions Transfer functions Wideband Wiener filter
In ultrasonic flaw detection in large grained materials, backscattered grain noise often masks the flaw signal. To enhance the flaw visibility, a frequency diverse statistical filtering technique known as split-spectrum processing has been developed. This technique splits the received wideband signal into an ensemble of narrowband signals exhibiting different signal-to-noise ratios (SNR). Using a minimization algorithm, SNR enhancement can be obtained at the output. The nonlinear properties of the frequency diverse statistic filter are characterized based on the spectral histogram, which is the statistical distribution of the spectral windows selected by the minimization algorithm. The theoretical analysis indicates that the spectral histogram is similar in nature to the Wiener filter transfer function. Therefore, the optimal filter frequency region can be determined adaptively based on the spectral histogram without prior knowledge of the signal and noise spectra.< >

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Web of Science research areas
Acoustics
Engineering, Electrical & Electronic
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