Wavelet basis selection and feature extraction for shift invariant ultrasound foreign body classification

Tsui, Patrick P. C.; Basir, Otman

doi:10.1016/j.ultras.2006.05.214

Cited by 31 publications

(10 citation statements)

References 41 publications

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“…In the ultrasonic measurement of residual stress, according to the characteristics of the LCR wave signal, the selected wavelet base should have tight support in the time domain [27]. Besides, to ensure the local analysis ability of the wavelet base in the frequency domain, the wavelet base is also required to have a fast attenuation in the frequency domain.…”

Section: Selection Of the Optimal Wavelet Base And Decomposition Level Of Lcr Wave Signalmentioning

confidence: 99%

Denoising of LCR Wave Signal of Residual Stress for Rail Surface Based on Lifting Scheme Wavelet Packet Transform

Pan

et al. 2021

Coatings

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According to the acousto elastic effect, the residual stress on the surface of the rail can be evaluated by measuring the change in the propagation velocity of ultrasonic waves, such as longitudinal critically refracted (LCR) waves on the surface of the rail. The LCR wave signal is often polluted by a variety of noise sources, coupled with the influence of the poor surface condition of the inspected component, which greatly reduces the detectability and online measurement ability of the LCR wave signal. This paper proposes the application of the lifting scheme wavelet packet transform (LSWPT) denoising method to solve the noise suppression problem of LCR wave signal. The traditional wavelet transform (WT), wavelet packet transform (WPT), as well as the lifting scheme wavelet transform (LSWT) and lifting scheme wavelet packet transform are compared and analyzed in the soft thresholding and hard thresholding processing of denoising ability and efficiency of the noisy LCR wave signal. The experimental results show that the LSWPT method has the characteristics of fast calculation speed and a good denoising effect, and it is an efficient method of denoising signals for on-line ultrasonic measurement of residual stress on the rail surface.

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Section: Selection Of the Optimal Wavelet Base And Decomposition Level Of Lcr Wave Signalmentioning

confidence: 99%

Denoising of LCR Wave Signal of Residual Stress for Rail Surface Based on Lifting Scheme Wavelet Packet Transform

Pan

et al. 2021

Coatings

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“…2). The PQ events include magnitude variation in the range of [0, 4] pu, frequency variation in the range of [0, 10] kHz, harmonics of order {5, 7,11,13,17,19,23,25,29,31, …} and the event duration from 0.5 to 30 cycles. Further, to investigate the effects of measurement noise, PQ events were added with zero-mean Gaussian white noise.…”

Section: Pq Eventsmentioning

confidence: 99%

“…In this study, three well-known quantitative criteria have been included [22][23][24][25]: (i) maximum relative wavelet energy (ii) minimum entropy and (iii) ratio of energy-to-entropy. The main rationale behind these criteria is that the ideal base wavelet is similar to the signal of interest and therefore it leads to a maximum convolution at particular 'scale'.…”

Section: Indirect Approachmentioning

confidence: 99%

“…Several quantitative criteria (referred here as the indirect approach) have been proposed to determine the optimum base wavelet for applications in image-processing, bio-medical, partial discharge (PD) detection and mechanical fault diagnostic [21][22][23][24][25][26]. These indirect approaches have been developed on the notion that ideal base wavelet is similar to the signal being analysed.…”

Section: Introductionmentioning

confidence: 99%

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Identification of power quality events: selection of optimum base wavelet and machine learning algorithm

Hafiz¹,

Swain²,

Naik³

et al. 2019

IET Science, Measurement & Technology

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“…Similarly, we need to select several parameters for DWT transformation for applications, such as wavelet basis, scale and coefficient. There are many literatures concerning about such selections [3]. However when facing dimensionality reduction problems, we usually choose first few coefficients to approximate after certain hierarchical decomposition.…”

Section: Background and Related Workmentioning

confidence: 99%

Large Margin Dimensionality Reduction for Time Series

2010

2010 First International Conference on Pervasive Computing, Signal Processing and Applications

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Dimensionality reduction techniques are widely used in time series data mining. Dimensionality reduction can not only speed up the computation but also lead to improved performance. Most available techniques implement the reduction process without supervised information. This operation can be used to de-noise the insignificance detail, or blur the discriminative information which is important for supervised learning. To solve the problem, we design a framework of dimensionality reduction method, called the Large Margin Dimensionality Reduction (LMDR), based on large margin criterion. It is shown empirically that the LMDR significantly improves the performance in terms of time series data mining.

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Wavelet basis selection and feature extraction for shift invariant ultrasound foreign body classification

Cited by 31 publications

References 41 publications

Denoising of LCR Wave Signal of Residual Stress for Rail Surface Based on Lifting Scheme Wavelet Packet Transform

Denoising of LCR Wave Signal of Residual Stress for Rail Surface Based on Lifting Scheme Wavelet Packet Transform

Identification of power quality events: selection of optimum base wavelet and machine learning algorithm

Large Margin Dimensionality Reduction for Time Series

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