The Experts below are selected from a list of 207 Experts worldwide ranked by ideXlab platform
Borsen Chen - One of the best experts on this subject based on the ideXlab platform.
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A wavelet time-scale deconvolution filter design for nonstationary Signal transmission systems through a multipath fading channel
IEEE Transactions on Signal Processing, 1999Co-Authors: Borsen Chen, Yue-chiech Chung, Der-feng HuangAbstract:This study attempts to develop a time-scale deconvolution filter for optimal Signal reconstruction of nonstationary processes with a stationary increment transmitted through a multipath fading and colored noisy channel with stochastic tap coefficients. A deconvolution filter based on wavelet analysis/synthesis filter bank is proposed to solve this problem via a three-stage filter bank. A Fractal Signal transmitted through a multipath fading and noisy channel is provided to demonstrate the design procedure's effectiveness and to exhibit the Signal reconstruction performance of the proposed optimal time-scale deconvolution filter.
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deconvolution filter design for Fractal Signal transmission systems a multiscale kalman filter bank approach
IEEE Transactions on Signal Processing, 1997Co-Authors: Borsen ChenAbstract:A deconvolution filtering design is proposed for the 1/f Fractal Signal transmission systems, with its design philosophy being based on multiscale Kalman deconvolution filter bank equipped in the analysis/synthesis wavelet filter bank, The role of wavelet transformation for 1/f Fractal Signal process is exploited as a multiscale whitening filter for removing the properties of self-similarity and long-range dependence from the Fractal Signals.
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Multiscale Wiener filter for the restoration of Fractal Signals: wavelet filter bank approach
IEEE Transactions on Signal Processing, 1994Co-Authors: Borsen ChenAbstract:A filter bank design based on orthonormal wavelets and equipped with a multiscale Wiener filter is proposed in this paper for Signal restoration of 1/f family of Fractal Signals which are distorted by the transmission channel and corrupted by external noise. First, the Fractal Signal transmission process is transformed via the analysis filter bank into multiscale convolution subsystems in time-scale domain based on orthonormal wavelets. Some nonstationary properties, e.g., self-similarity, long-term dependency of Fractal Signals are attenuated in each subband by wavelet multiresolution decomposition so that the Wiener filter bank can be applied to estimate the multiscale input Signals. Then the estimated multiscale input Signals are synthesized to obtain the estimated input Signal. Some simulation examples are given for testing the performance of the proposed algorithm. With this multiscale analysis/synthesis design via the technique of the wavelet filter bank, the multiscale Wiener filter can be applied to treat the Signal restoration problem for nonstationary 1/f Fractal Signals. >
Wieslaw J Staszewski - One of the best experts on this subject based on the ideXlab platform.
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analysis of barkhausen noise using wavelet based Fractal Signal processing for fatigue crack detection
International Journal of Fatigue, 2016Co-Authors: Krzysztof Miesowicz, Wieslaw J Staszewski, Tomasz KorbielAbstract:Abstract The paper presents new approach to Barkhausen noise Signal processing for detection of fatigue crack. Barkhausen noise Signal from mild steel samples under axial fatigue is investigated using Fractal Signal processing, particularly wavelet variance method. Based on repeatability analysis new algorithm is developed and applied to acquired Signals. The influence of fatigue on Fractal characteristics of Barkhausen noise is analyzed. Signal analysis reveals significant and repeatable changes in wavelet variance, spectral parameter and estimated Hurst exponent just after crack initiation. The results demonstrate high potential of Fractal analysis of Surface Barkhausen noise applied to fatigue crack initiation detection.
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lamb wave based structural damage detection using cointegration and Fractal Signal processing
Mechanical Systems and Signal Processing, 2014Co-Authors: Wieslaw J StaszewskiAbstract:Abstract The paper demonstrates how to remove the undesired temperature effect from Lamb wave data in order to detect structural damage accurately. The method used is based on the cointegration technique and Fractal Signal processing. The former relies on the analysis of non-stationary behaviour whereas the latter brings the concept of multi-resolution wavelet decomposition of time series. The results show that self-similar pattern of cointegration residuals is broken when damage is present in the monitored structure. This can be used for effective removal of undesired multiple temperature trends in Lamb waves data. Damage-sensitive features are isolated from temperature variations and damage is effectively detected and classified.
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Fault detection in rolling element bearings using wavelet-based variance analysis and novelty detection
Journal of Vibration and Control, 2014Co-Authors: Aleksandra Ziaja, Wieslaw J Staszewski, Ifigeneia Antoniadou, Tomasz Barszcz, Keith WordenAbstract:Fractal Signal processing and novelty detection are used for fault detection in rolling element bearings. The former applies the concept of self-similarity based on wavelet variance, and the latter is based on machine learning and utilises artificial neural networks. The method is demonstrated using simulated and experimental vibration data. The work presented involves validation both on laboratory test rig data and industrial wind turbine data. The results show that the method can be used successfully for automated fault detection in ball bearings under real operational conditions.
Hongrun Wang - One of the best experts on this subject based on the ideXlab platform.
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PACIIA (1) - Waveform Estimation of Fractal Signals Using Optimum Soft Threshold Technique
2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, 2008Co-Authors: Yongjian Zhao, Hongrun WangAbstract:A new method based on the optimum soft threshold technique is proposed in this paper for wavelet estimation of Fractal Signals which are polluted by external white noise. The optimum soft threshold technique is employed in each scale of wavelet decomposition to estimate the multi-scale input Fractal Signals. The estimates of the input Fractal Signal in each scale are then synthesized to restore the original Signal. Since it doesn?t need to know the parameters of Fractal Signal and the statistical characteristic of external white noise in advance, this method is practical and maneuverable in many situations, besides that, the simulation results also show that this method is robust to the error of SNR estimated.
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Optimum Soft Threshold Technique for Fractal Signals Denoising
2008 Third International Conference on Pervasive Computing and Applications, 2008Co-Authors: Yongjian Zhao, Peng Gong, Hongrun WangAbstract:In this paper, some nonstationary properties, e.g., self-similarity, long-term dependency of wavelet transform coefficients of Fractal Signal and noise at different decomposition scales are analyzed. Based on the minimum mean square error of these wavelet coefficients at each scale, a new method of estimating Fractal Signal from additive white noise is proposed in pervasive computing environment. The parameters of the background noise in this method can be dynamically adapted in runtime to model the variation of both the Signal and the noise. Since it doesn't need to know the parameters of Fractal Signal and the statistical characteristic of added white noise in advance, this method is suitable in various situations. The simulation results show that this method has good performance to be used in pervasive computing environment.
Yongjian Zhao - One of the best experts on this subject based on the ideXlab platform.
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PACIIA (1) - Waveform Estimation of Fractal Signals Using Optimum Soft Threshold Technique
2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, 2008Co-Authors: Yongjian Zhao, Hongrun WangAbstract:A new method based on the optimum soft threshold technique is proposed in this paper for wavelet estimation of Fractal Signals which are polluted by external white noise. The optimum soft threshold technique is employed in each scale of wavelet decomposition to estimate the multi-scale input Fractal Signals. The estimates of the input Fractal Signal in each scale are then synthesized to restore the original Signal. Since it doesn?t need to know the parameters of Fractal Signal and the statistical characteristic of external white noise in advance, this method is practical and maneuverable in many situations, besides that, the simulation results also show that this method is robust to the error of SNR estimated.
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Optimum Soft Threshold Technique for Fractal Signals Denoising
2008 Third International Conference on Pervasive Computing and Applications, 2008Co-Authors: Yongjian Zhao, Peng Gong, Hongrun WangAbstract:In this paper, some nonstationary properties, e.g., self-similarity, long-term dependency of wavelet transform coefficients of Fractal Signal and noise at different decomposition scales are analyzed. Based on the minimum mean square error of these wavelet coefficients at each scale, a new method of estimating Fractal Signal from additive white noise is proposed in pervasive computing environment. The parameters of the background noise in this method can be dynamically adapted in runtime to model the variation of both the Signal and the noise. Since it doesn't need to know the parameters of Fractal Signal and the statistical characteristic of added white noise in advance, this method is suitable in various situations. The simulation results show that this method has good performance to be used in pervasive computing environment.
Tomasz Korbiel - One of the best experts on this subject based on the ideXlab platform.
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analysis of barkhausen noise using wavelet based Fractal Signal processing for fatigue crack detection
International Journal of Fatigue, 2016Co-Authors: Krzysztof Miesowicz, Wieslaw J Staszewski, Tomasz KorbielAbstract:Abstract The paper presents new approach to Barkhausen noise Signal processing for detection of fatigue crack. Barkhausen noise Signal from mild steel samples under axial fatigue is investigated using Fractal Signal processing, particularly wavelet variance method. Based on repeatability analysis new algorithm is developed and applied to acquired Signals. The influence of fatigue on Fractal characteristics of Barkhausen noise is analyzed. Signal analysis reveals significant and repeatable changes in wavelet variance, spectral parameter and estimated Hurst exponent just after crack initiation. The results demonstrate high potential of Fractal analysis of Surface Barkhausen noise applied to fatigue crack initiation detection.
