A Comparative Simulation Study of Wavelet Based Denoising Algorithms

Rosas-Orea, M.C.E.; Hernández-Dıaz, Marıa E.; Alarcón-Aquino, Vicente; Guerrero-Ojeda, L.G.

doi:10.1109/coniel.2005.6

Cited by 21 publications

(10 citation statements)

References 9 publications

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“…The solution would be to filter the maximum quantity of noise while keeping as much of the effective signal frequency spectrum as possible. Wavelet denoising algorithms have been received extensive consideration in the processing of white Gaussian noise in biological signals, especially for the Electrocardiogram [12]- [14]. Most wavelet based denoising literatures suggest the use of the Donoho's method [15], [16], that makes an estimation of the thresholds by maximizing a risk function in terms of quadratic loss at the sample points.…”

Section: Introductionmentioning

confidence: 99%

Baseline Adaptive Wavelet Thresholding Technique for sEMG Denoising

Bartolomeo¹,

Zecca

Sessa³

et al. 2011

AIP Conference Proceedings

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Section: Introductionmentioning

confidence: 99%

Baseline Adaptive Wavelet Thresholding Technique for sEMG Denoising

Bartolomeo¹,

Zecca

Sessa³

et al. 2011

AIP Conference Proceedings

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“…The variances and amplitudes of the details of noise at the various levels decrease regularly as the level increases. On the other hand, the amplitudes and variances of wavelet transform of the available signal are not related to the change of scale [18][19]. According to this property, by selecting an appropriate threshold, the noise can be eliminated.…”

Section: B Wavelet Threshold Denoisingmentioning

confidence: 99%

“…Daubechies wavelet 'db8' is selected as the wavelet function. The adaptive threshold th in equation (4) is calculated via the principle of stein's unbiased risk estimate [19][20]. Finally, denoised signal can be obtained through the combination of denoised sub-band signals.…”

Section: Hilbert-huang Transformmentioning

confidence: 99%

Underwater sound classification based on Gammatone filter bank and Hilbert-Huang transform

Zeng

Wang

2014

2014 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)

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The variable acoustic environment makes it harder for the application of underwater sound recognition system. However, human auditory system has remarkable ability on dealing with complex acoustic conditions. A robust underwater noise target classification system is expected if this ability can be simulated. Aimed at this purpose, a robust underwater sound classification algorithm which employs Gammatone filter bank and Hilbert-Huang transform is studied in this paper. Gammatone filter bank is used for the simulation of nonlinear dividing of human ears. Then the wavelet denoising procedure is applied on the divided sub-bands. At last, Hilbert-Huang transform is used as the time-frequency analysis tool for the feature extraction. With the help of Hilbert-Huang transform, instantaneous features are extracted, and then used for the built of feature vector. Experimental results indicated the expected efficiency of the proposed algorithm. key words-Gammatone filter bank, Hilbert-Huang transform, Time-frequency analysis.

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“…In signal processing, utilizing the wavelet coefficients capability [13] of spare distribution and auto-zooming in time domain and frequency domain can deal with the non-stationary signal for acquiring a higher S/N ratio. In time domain analysis, the used features in VMS includes a mean value for indicating the trend, standard derivation value for measuring variance, max peak-to-peak for presenting difference of peaks, root mean square (RMS) for indicating the weighting effect of variances so that …”

mentioning

confidence: 99%

A novel virtual metrology scheme for predicting machining precision of machine tools

Yang

Hung

Cheng

2013

2013 IEEE International Conference on Robotics and Automation

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Because virtual metrology (VM) can achieve real-time and on-line total inspection, it is a promising way for measuring machining precision of machine tools. However, the machining processes possess the characteristics of severe vibrations. Thus, how to effectively handle signals with low signal/noise ratios and extract key features from them is a challenging issue for successfully applying VM to the machine tools. In this paper, a novel VM scheme for predicting machining precision of machine tools is proposed based on several previously developed methods for data quality evaluation, model reliance evaluation, and machining precision prediction. Besides, for data preprocess, we propose a Wavelet-based de-noising method to improve the S/N ratio of sensor data. In addition, we base on the stepwise technique to develop an automatic feature selection method that can extract key features related to machining operations in time, frequency, and time-frequency domains, and can reduce the dimension of essential features. Testing results of a 3-axis CNC machine center machining standard workpieces show that the VMS can achieve the performance that the maximum average error of machining-precision conjecture is less than 2 um and the conjecture of 20 machining-precision items can be completed within 3.8 sec.

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A Comparative Simulation Study of Wavelet Based Denoising Algorithms

Cited by 21 publications

References 9 publications

Baseline Adaptive Wavelet Thresholding Technique for sEMG Denoising

Baseline Adaptive Wavelet Thresholding Technique for sEMG Denoising

Underwater sound classification based on Gammatone filter bank and Hilbert-Huang transform

A novel virtual metrology scheme for predicting machining precision of machine tools

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