Denoising Ultrasonic Echo Signals with Generalized S Transform and Singular Value Decomposition

Zhu, Youqi; Xu, Chunguang; Xiao, Dingguo

doi:10.18280/ts.360203

Cited by 9 publications

(8 citation statements)

References 13 publications

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“…Xu [ 33 ] used the frequency energy spectrum with S transform to identify defects in the concrete structure. Zhu et al [ 34 ] proposed a denoising algorithm based on the generalized S transform and singular value decomposition (SVD) to denoise echo signals of ultrasonic pulse-echo testing.…”

Section: Introductionmentioning

confidence: 99%

High Precision Detection Method for Delamination Defects in Carbon Fiber Composite Laminates Based on Ultrasonic Technique and Signal Correlation Algorithm

Cao

Jiang

et al. 2020

Materials

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This paper presents a method based on signal correlation to detect delamination defects of widely used carbon fiber reinforced plastic with high precision and a convenient process. The objective of it consists in distinguishing defect and non-defect signals and presenting the depth and size of defects by image. A necessary reference signal is generated from the non-defect area by using autocorrelation theory firstly. Through the correlation calculation results, the defect signal and non-defect signal are distinguished by using Euclidean distance. In order to get more accurate time-of-flight, cubic spline interpolation is introduced. In practical automatic ultrasonic A-scan signal processing, signal correlation provide a new way to avoid problems such as signal peak tracking and complex gate setting. Finally, the detection results of a carbon fiber laminate with artificial delamination through ultrasonic phased array C-scan acquired from Olympus OmniScan MX2 and this proposed algorithm are compared, which showing that this proposed algorithm performs well in defect shape presentation and location calculation. The experiment shows that the defect size error is less than 4%, the depth error less than 3%. Compared with ultrasonic C-scan method, this proposed method needs less inspector’s prior-knowledge, which can lead to advantages in automatic ultrasonic testing.

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Section: Introductionmentioning

confidence: 99%

High Precision Detection Method for Delamination Defects in Carbon Fiber Composite Laminates Based on Ultrasonic Technique and Signal Correlation Algorithm

Cao

Jiang

et al. 2020

Materials

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“…The signal-to-noise ratio output [21] ( ) where, ( ) is the pure signal, ^( ) is the impure signal by the noises, ( ) is the filter output and  is the length of the ECG signal, which is the number of samples of each MIT-BIH signal [19]. Five sets of ECG signal from the MIT-BIH arrhythmia database (MLII) are tested and comparative results with using others windowing for a different level of interference are listed in the Table 3 and Table 4.…”

Section: Quantitative Results Of Ecg Denoisingmentioning

confidence: 99%

Implementation of Effective Hybrid Window Function for E.C.G Signal Denoising

Das¹,

Kumar²,

Sahana³

2020

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“…MI Indicates the amount of information of input images to the output image. Peak-Signal-to-Noise ratio (PSNR) and Mean Square Error (MSE) express improved image quality [20].…”

Section: Resultsmentioning

confidence: 99%

Image Fusion of Noisy Images Based on Simultaneous Empirical Wavelet Transform

Rahmani¹,

Almasi²,

Saleh³

et al. 2020

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Fusion image is the method of extracting the relevant information from two or more identical input images into one scene and creating a new image. This method allows the new image to provide comprehensive information about the wand, leading to a visual understanding of the human being. Fusion image application in image processing is an important issue. Applications in many fields such as photography, microscopy, astronomy, medical imaging, satellite imagery, machine vision, biology are monitored. In this study first, an image fusion method, suggested recently based on transform empirical wavelet, was implemented in which coefficients were obtained by processing the input images. Then they were combined by applying the rules. The aim of this study is to investigate the noise effect and to remove the noise in the aforementioned suggested method. First, the noise was added to the images, and the images were decomposed into layers or coefficients. Second, by thresholding the coefficients, the noise was removed. Then the coefficients were combined based on the rules to obtain the final coefficients. In the end, the final coefficients were used to obtain the fused image. The results show that the noise removal of images during image fusion is much better and more effective than denoising before image fusion, and the demonstration of the method is proved by obtaining better results in comparison to some existing methods.

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Denoising Ultrasonic Echo Signals with Generalized S Transform and Singular Value Decomposition

Cited by 9 publications

References 13 publications

High Precision Detection Method for Delamination Defects in Carbon Fiber Composite Laminates Based on Ultrasonic Technique and Signal Correlation Algorithm

High Precision Detection Method for Delamination Defects in Carbon Fiber Composite Laminates Based on Ultrasonic Technique and Signal Correlation Algorithm

Implementation of Effective Hybrid Window Function for E.C.G Signal Denoising

Image Fusion of Noisy Images Based on Simultaneous Empirical Wavelet Transform

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