Testing for Nongaussian Fluctuations in Grain Noise

Neal, Steven P.; Donohue, Kevin D.

doi:10.1007/978-1-4613-0383-1_103

Cited by 6 publications

(9 citation statements)

References 9 publications

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“…The given results are based on the assumption of true model order, (3,2). However, the algorithm was tried with various orders and following general conclusions were deduced.…”

Section: Effect Of Model Ordermentioning

confidence: 98%

“…Conventional deconvolution techniques (CDT) such as least square, Wiener filter, and minimum variance deconvolution [2] are based on a priori knowledge of second order statistics (SOS) of the noise and the input signal. In practice however, the acoustic noise due to scattering from the grains inside the propagation medium does not have a readily known statistic [3]. Higher order spectra (known as polyspectra), defined in terms of higher order statistics (HOS) of a signal, do contain such information.…”

Section: Introductionmentioning

confidence: 99%

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Defect deconvolution using 3<sup>rd</sup> order statistics for Ultrasonic Nondestructive Testing

Qidwai

Bettayeb

Yamani

2007

2007 9th International Symposium on Signal Processing and Its Applications

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Ultrasonic Nondestructive Testing (NDT) is primarily based upon the detection and classification of a defect in the field of industrial materials. This information is useful in making administrative decisions in terms of maintenance and replacement. The technique presented in this paper utilizes the concept of defect induction as a convolution process between the clean sample and the defect signature. Hence, to identify the type of defect a deconvolution approach can be useful. Due to several similarities between the ultrasonic echoes and the usual modulated sinusoids, a motivation is present to use 2 nd and higher order statistics for completely defining the waveform. Such a definition, when compared with standard defects, will provide useful insight in terms of defect classifications and understanding.

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“…The given results are based on the assumption of true model order, (3,2). However, the algorithm was tried with various orders and following general conclusions were deduced.…”

Section: Effect Of Model Ordermentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Defect deconvolution using 3<sup>rd</sup> order statistics for Ultrasonic Nondestructive Testing

Qidwai

Bettayeb

Yamani

2007

2007 9th International Symposium on Signal Processing and Its Applications

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“…( 1) via the frequency domain is given by [3] k(w) = Y(w). x*(w) Mw)12 +uw%(d (2) where X(w), Y(w) and h(w) are the Fourier transforms of x(t), y(t) and 6(t), respectively, whereas S,(w) and S,(w) are the power spectral densities of N(t) and h(t), respectively. It can be seen from Eq.…”

Section: Wienerjilteringmentioning

confidence: 99%

High-Order Spectra-Based Deconvolution of Ultrasonic NDT Signals for Defect Identification

Yamani

Bettayeb

Ghouti

et al. 1999

Review of Progress in Quantitative Nondestructive Evaluation

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In ultrasonic nondestructive testing (NDT) of materials, pulse-echo measurements are masked by the characteristics of the measuring instruments, the propagation paths taken by the ultrasonic pulses, and noise. This measured pulse-echo signal is modeled by the convolution of the defect impulse response and the measurement system response, added to noise. The deconvolution operation, therefore, seeks to undo the effect of the convolution and extract the defect impulse response which is essential for defect identification. In this contribution, we show that the defect ultrasonic model can be formulated in the higherorder-spectra (HOS) domain in which the processing is more suitable to unravel the effect of the measurement system and the additive Gaussian noise. In addition, a new technique is developed to faithfully recover the impulse response signal from its HOS. Synthesized ultrasonic signals as well as real signals obtained from artificial defects are used to show that the proposed technique is superior to conventional second-order statistics-based deconvolution techniques commonly used in NDT. 0 1997 Elsevier Science B.V.

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“…The problem is to extract an estimate of A(oo), written as A(oo). We will use a Wiener filter following Neal et al [1,[16][17][18][19][20]. For a zero mean scattering amplitude ensemble, the filter gives an optimal estimate of A on the average as…”

Section: Wiener Filter Based Flaw Signature Estimationmentioning

confidence: 99%

“…That is, the autocorrelation function of the noise in time does not behave as a delta function, but falls off rather slowly [22]. In addition, grain noise is often zero mean Gaussian in the time domain [20,22]. A forward Fourier transform (FT) of the real valued time domain noise signal yields a complex noise signal in the frequency domain where the real part and the imaginary part are each zero mean Gaussian and are uncorrelated from frequency to frequency.…”

Section: Wavelet Signal Processingmentioning

confidence: 99%

An Application of Wavelet Signal Processing to Ultrasonic Nondestructive Evaluation

Nevel

DeFacio

Neal

1996

Review of Progress in Quantitative Nondestructive Evaluation

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Testing for Nongaussian Fluctuations in Grain Noise

Cited by 6 publications

References 9 publications

Defect deconvolution using 3<sup>rd</sup> order statistics for Ultrasonic Nondestructive Testing

Defect deconvolution using 3<sup>rd</sup> order statistics for Ultrasonic Nondestructive Testing

High-Order Spectra-Based Deconvolution of Ultrasonic NDT Signals for Defect Identification

An Application of Wavelet Signal Processing to Ultrasonic Nondestructive Evaluation

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