Performance Comparision of Wavelet Threshold Estimators for ECG Signal Denoising

Jaffery, Zainul Abdin; Ahmad, Khalid; Afroz,

doi:10.1109/artcom.2010.86

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…The basic principle of wavelet denoising depends on signal preservation by reducing the smaller amplitude components irrespective of frequency. By altering the wavelet coefficients, denoising can be achieved [42,43]. Image transformation from the spatial domain to the frequency domain using DWT is performed by hierarchical functions [44].…”

Section: Image Processing Techniquementioning

confidence: 99%

Defect Estimation in Non-Destructive Testing of Composites by Ultrasonic Guided Waves and Image Processing

et al. 2019

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The estimation of the size and location of defects in multi-layered composite structures by ultrasonic non-destructive testing using guided waves has attracted the attention of researchers for the last few decades. Although extensive signal processing techniques are available, there are only a few studies available based on image processing of the ultrasonic B-scan image to extract the size and location of defects via the process of ultrasonic non-destructive testing. This work presents an image processing technique for ultrasonic B-scan images to improve the estimation of the location and size of disbond-type defects in glass fiber-reinforced plastic materials with 25-mm and 51-mm diameters. The sample is a segment of a wind turbine blade with a variable thickness ranging from 3 to 24 mm. The experiment is performed by using a low-frequency ultrasonic system and a pair of contact-type piezoceramic transducers kept apart by a 50-mm distance and embedded on a moving mechanical panel. The B-scan image acquired by the ultrasonic pitch-catch technique is denoised by utilizing features of two-dimensional discrete wavelet transform. Thereafter, the normalized pixel densities are compared along the scanned distance on the region of interest of the image, and a −3 dB threshold is applied to the locations and sizes the defects in the spatial domain.

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Section: Image Processing Techniquementioning

confidence: 99%

Defect Estimation in Non-Destructive Testing of Composites by Ultrasonic Guided Waves and Image Processing

et al. 2019

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“…In the process of wavelet noising, the information of the signal is preserved while reducing the signal components of smaller amplitudes without consideration of frequencies. The wavelet coefficients are altered or discarded for the noise removal using DWT [ 38 , 39 ]. The best way for the manipulation of wavelet coefficients in the case of correlated noise is a soft-threshold technique with universal threshold [ 40 ].…”

Section: Overview Of Gw Signal Processing Techniquesmentioning

confidence: 99%

Identification and Characterization of Defects in Glass Fiber Reinforced Plastic by Refining the Guided Lamb Waves

Tiwari

Raišutis

2018

Materials

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In this paper, the disbond-type defect presented on glass fiber reinforced plastic material is analyzed by refining the guided Lamb wave signals. A segment of wind turbine blade is considered as a test sample. The low-frequency ultrasonic measurement system is used for the non-destructive testing of the test sample using guided waves. The P-1 type macro-fiber composite transducer as a transmitter and contact-type piezoceramic transducer as a receiver are used for the testing of a sample. The disbond type defect having a diameter of 81 mm is detected from the experimental results. To improve the accuracy in locating and sizing the defects and estimation of the time of flight and phase velocity of ultrasonic guided waves in defective region, signal processing algorithm is developed by utilizing the promising properties of various ultrasonic signal processing techniques such as wavelet transform, amplitude detection, two-dimensional Fast-Fourier transform, Hilbert transform and variational mode decomposition. The discrete wavelet transform is used to denoise the guided wave signals and then, the size and location of defects are estimated by amplitude detection. The reflected wave signals from the opposite edge of the sample are removed by applying the two-dimensional Fast-Fourier transform to the experimental B-scan signal. Afterwards, variational mode decomposition and Hilbert transform are used for the phase velocity and time-delay estimation by comparing the instantaneous amplitudes of the defective and defect-free signal. The validation and the demonstration of reproducibility of the algorithm is performed by extracting the features of a 51 mm defect from another experimental B-scan.

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“…There are many ways by which the wavelet coefficients can be manipulated or discarded. 30,31 In the case of ultrasonic signals with correlated noise, the soft-threshold method by applying a universal threshold is an efficient way to improve the signal-to-noise ratio. 32 There are many wavelet families, but there is no universal rule for the selection of appropriate wavelet family.…”

Section: Overview Of Signal Processing Of Ultrasonic Signalsmentioning

confidence: 99%

Post-processing of ultrasonic signals for the analysis of defects in wind turbine blade using guided waves

Tiwari

Raišutis

2018

The Journal of Strain Analysis for Engineering Design

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In this work, the most promising ultrasonic signal processing methods-discrete wavelet transform, variational mode decomposition and Hilbert transform-are applied for the analysis of disbond-type defects in the segment of wind turbine blade. Two disbond-type artificial defects having diameters of 81 and 25 mm were located on the main spar of wind turbine blade. The low-frequency ultrasonic system developed by Ultrasound Research Institute of the Kaunas University of Technology was used for the experimental investigation of wind turbine blade using guided waves and only one side of the blade segment was accessed. Two contact type ultrasonic transducers separated by 50 mm distance and fixed on a movable mechanical panel were used as a transmitter-receiver pair during the experiment for the ultrasonic signals recording up to the scanning distance of 250 mm with the scanning step of 1 mm. Both types of defects were marginally detected from the conventional experimental B-scan and therefore appropriate signal processing techniques were used to improve the accuracy of the analysis of defects. The discrete wavelet transform was combined with the amplitude detection method for estimating the size and location of defects. Finally, the variational mode decomposition is combined with the Hilbert transform to compare the instantaneous frequencies and amplitudes of the defect-free and defective signals as well as for the measurement of time-delays between the defect-free and defective signals.

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Performance Comparision of Wavelet Threshold Estimators for ECG Signal Denoising

Cited by 6 publications

References 14 publications

Defect Estimation in Non-Destructive Testing of Composites by Ultrasonic Guided Waves and Image Processing

Defect Estimation in Non-Destructive Testing of Composites by Ultrasonic Guided Waves and Image Processing

Identification and Characterization of Defects in Glass Fiber Reinforced Plastic by Refining the Guided Lamb Waves

Post-processing of ultrasonic signals for the analysis of defects in wind turbine blade using guided waves

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