The Experts below are selected from a list of 16452 Experts worldwide ranked by ideXlab platform
Naveed Ur Rehman - One of the best experts on this subject based on the ideXlab platform.
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Wavelet based multivariate Signal Denoising using Mahalanobis distance and EDF statistics
2020Co-Authors: Naveed Khuram, Naveed Ur RehmanAbstract:A multivariate Signal Denoising method is proposed which employs a novel multivariate goodness of fit (GoF) test that is applied at multiple data scales obtained from discrete wavelet transform (DWT). In the proposed multivariate GoF test, we first utilize squared Mahalanobis distance (MD) measure to transform input multivariate data residing in M-dimensional space $\mathcal{R}^M$ to a single-dimensional space of positive real numbers $\mathcal{R}_+$, i.e., $\mathcal{R}^M \rightarrow \mathcal{R}_+$, where $M > 1$. Owing to the properties of the MD measure, the transformed data in $\mathcal{R}_+$ follows a distinct distribution. That enables us to apply the GoF test using statistic based on empirical distribution function (EDF) on the resulting data in order to define a test for multivariate normality. We further propose to apply the above test locally on multiple input data scales obtained from discrete wavelet transform, resulting in a multivariate Signal Denoising framework. Within the proposed method, the reference cumulative distribution function (CDF) is defined as a quadratic transformation of multivariate Gaussian random process. Consequently, the proposed method checks whether a set of DWT coefficients belong to multivariate reference distribution or not; the coefficients belonging to the reference distribution are discarded. The effectiveness of our proposed method is demonstrated by performing extensive simulations on both synthetic and real world datasets
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A Statistical Approach to Signal Denoising Based on Data-driven Multiscale Representation
2020Co-Authors: Naveed Khuram, Akhtar, Muhammad Tahir, Siddiqui, Muhammad Faisal, Naveed Ur RehmanAbstract:We develop a data-driven approach for Signal Denoising that utilizes variational mode decomposition (VMD) algorithm and Cramer Von Misses (CVM) statistic. In comparison with the classical empirical mode decomposition (EMD), VMD enjoys superior mathematical and theoretical framework that makes it robust to noise and mode mixing. These desirable properties of VMD materialize in segregation of a major part of noise into a few final modes while majority of the Signal content is distributed among the earlier ones. To exploit this representation for Denoising purpose, we propose to estimate the distribution of noise from the predominantly noisy modes and then use it to detect and reject noise from the remaining modes. The proposed approach first selects the predominantly noisy modes using the CVM measure of statistical distance. Next, CVM statistic is used locally on the remaining modes to test how closely the modes fit the estimated noise distribution; the modes that yield closer fit to the noise distribution are rejected (set to zero). Extensive experiments demonstrate the superiority of the proposed method as compared to the state of the art in Signal Denoising and underscore its utility in practical applications where noise distribution is not known a priori
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a joint framework for multivariate Signal Denoising using multivariate empirical mode decomposition
Signal Processing, 2017Co-Authors: H L Wang, Naveed Ur RehmanAbstract:In this paper, a novel multivariate Denoising scheme using multivariate empirical mode decomposition (MEMD) is proposed. Unlike previous EMD-based Denoising methods, the proposed scheme can align common frequency modes across multiple channels of a multivariate data, thus, facilitating direct multichannel data Denoising. The key idea in this work is to extend our earlier MEMD based Denoising method for univariate Signal in Hao et al. (2016) [19] to the multivariate data. The MEMD modes (known as intrinsic mode functions) for separating noise components are first adaptively selected on the basis of a similarity measure between the probability density function (pdf) of the input multivariate Signal and that of each mode by Frobenius norm. The selected modes are then denoised further by a local interval thesholding procedure followed by reconstruction of the thresholded IMFs. The resulting method operates directly in multidimensional space where input Signal resides, owing to MEMD, and also benefits from its mode-alignment property. Furthermore, subspace projection is introduced within the framework of the proposed method to exploit the inter-channel dependence among IMFs with the same index, enabling the diversity reception of the Signal. Performance of the proposed method against standard multiscale Denoising schemes is demonstrated on both synthetic and real world data. A Signal Denoising framework is proposed for multivariate data.Data adaptive and multiscale Signal representation via MEMD algorithm is adopted.Similarity between the pdfs of Signal and noise is computed via Frobenius norm.Subspace projection is used in combination with the multiscale Denoising approach.
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translation invariant multi scale Signal Denoising based on goodness of fit tests
Signal Processing, 2017Co-Authors: Naveed Ur Rehman, Syed Zain Abbas, Anum Asif, Anum Javed, Khuram Naveed, Danilo P MandicAbstract:A novel Signal Denoising method based on discrete wavelet transform (DWT) and goodness of fit (GOF) statistical tests employing empirical distribution function (EDF) statistics is proposed. We cast the Denoising problem into a hypothesis testing problem with a null hypothesis H 0 corresponding to the presence of noise, and an alternative hypothesis H 1 representing the presence of only desired Signal in the samples being tested. The decision process involves GOF tests, employing statistics based on EDF, which is applied directly on multiple scales obtained from DWT. The resulting coefficients found to be belonging to noise are discarded while the remaining coefficients - corresponding to the desired Signal - are retained. The cycle spinning approach is next employed on the denoised data to introduce translation invariance into the proposed method. The performance of the resulting method is evaluated against standard and modern wavelet shrinkage Denoising methods through extensive repeated simulations performed on standard test Signals. Simulation results on real world noisy images are also presented to demonstrate the effectiveness of the proposed method. HighlightsA novel multi-scale Signal Denoising algorithm is presented.It employs goodness-of-fit tests on multiple data scales to identify noise.Anderson-Darling test statistic is employed within the GOF framework.Experimental results have been reported and analyzed for both 1D and 2D Signals.
Hailin Feng - One of the best experts on this subject based on the ideXlab platform.
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stress wave Signal Denoising using ensemble empirical mode decomposition and an instantaneous half period model
Sensors, 2011Co-Authors: Yiming Fang, Hailin FengAbstract:Stress-wave-based techniques have been proven to be an accurate nondestructive test means for determining the quality of wood based materials and they been widely used for this purpose. However, the results are usually inconsistent, partially due to the significant difficulties in processing the nonlinear, non-stationary stress wave Signals which are often corrupted by noise. In this paper, an ensemble empirical mode decomposition (EEMD) based approach with the aim of Signal Denoising was proposed and applied to stress wave Signals. The method defined the time interval between two adjacent zero-crossings within the intrinsic mode function (IMF) as the instantaneous half period (IHP) and used it as a criterion to detect and classify the noise oscillations. The waveform between the two adjacent zero-crossings was retained when the IHP was larger than the predefined threshold, whereas the waveforms with smaller IHP were set to zero. Finally the estimated Signal was obtained by reconstructing the processed IMFs. The details of threshold choosing rules were also discussed in the paper. Additive Gaussian white noise was embedded into real stress wave Signals to test the proposed method. Butterworth low pass filter, EEMD-based low pass filter and EEMD-based thresholding filter were used to compare filtering performance. Mean square error between clean and filtered stress waves was used as filtering performance indexes. The results demonstrated the excellent efficiency of the proposed method.
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stress wave Signal Denoising using ensemble empirical mode decomposition and an instantaneous half period model
Sensors, 2011Co-Authors: Yiming Fang, Hailin Feng, Jian Li, Guanghui LiAbstract:Stress-wave-based techniques have been proven to be an accurate nondestructive test means for determining the quality of wood based materials and they been widely used for this purpose. However, the results are usually inconsistent, partially due to the significant difficulties in processing the nonlinear, non-stationary stress wave Signals which are often corrupted by noise. In this paper, an ensemble empirical mode decomposition (EEMD) based approach with the aim of Signal Denoising was proposed and applied to stress wave Signals. The method defined the time interval between two adjacent zero-crossings within the intrinsic mode function (IMF) as the instantaneous half period (IHP) and used it as a criterion to detect and classify the noise oscillations. The waveform between the two adjacent zero-crossings was retained when the IHP was larger than the predefined threshold, whereas the waveforms with smaller IHP were set to zero. Finally the estimated Signal was obtained by reconstructing the processed IMFs. The details of threshold choosing rules were also discussed in the paper. Additive Gaussian white noise was embedded into real stress wave Signals to test the proposed method. Butterworth low pass filter, EEMD-based low pass filter and EEMD-based thresholding filter were used to compare filtering performance. Mean square error between clean and filtered stress waves was used as filtering performance indexes. The results demonstrated the excellent efficiency of the proposed method.
Yiming Fang - One of the best experts on this subject based on the ideXlab platform.
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stress wave Signal Denoising using ensemble empirical mode decomposition and an instantaneous half period model
Sensors, 2011Co-Authors: Yiming Fang, Hailin FengAbstract:Stress-wave-based techniques have been proven to be an accurate nondestructive test means for determining the quality of wood based materials and they been widely used for this purpose. However, the results are usually inconsistent, partially due to the significant difficulties in processing the nonlinear, non-stationary stress wave Signals which are often corrupted by noise. In this paper, an ensemble empirical mode decomposition (EEMD) based approach with the aim of Signal Denoising was proposed and applied to stress wave Signals. The method defined the time interval between two adjacent zero-crossings within the intrinsic mode function (IMF) as the instantaneous half period (IHP) and used it as a criterion to detect and classify the noise oscillations. The waveform between the two adjacent zero-crossings was retained when the IHP was larger than the predefined threshold, whereas the waveforms with smaller IHP were set to zero. Finally the estimated Signal was obtained by reconstructing the processed IMFs. The details of threshold choosing rules were also discussed in the paper. Additive Gaussian white noise was embedded into real stress wave Signals to test the proposed method. Butterworth low pass filter, EEMD-based low pass filter and EEMD-based thresholding filter were used to compare filtering performance. Mean square error between clean and filtered stress waves was used as filtering performance indexes. The results demonstrated the excellent efficiency of the proposed method.
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stress wave Signal Denoising using ensemble empirical mode decomposition and an instantaneous half period model
Sensors, 2011Co-Authors: Yiming Fang, Hailin Feng, Jian Li, Guanghui LiAbstract:Stress-wave-based techniques have been proven to be an accurate nondestructive test means for determining the quality of wood based materials and they been widely used for this purpose. However, the results are usually inconsistent, partially due to the significant difficulties in processing the nonlinear, non-stationary stress wave Signals which are often corrupted by noise. In this paper, an ensemble empirical mode decomposition (EEMD) based approach with the aim of Signal Denoising was proposed and applied to stress wave Signals. The method defined the time interval between two adjacent zero-crossings within the intrinsic mode function (IMF) as the instantaneous half period (IHP) and used it as a criterion to detect and classify the noise oscillations. The waveform between the two adjacent zero-crossings was retained when the IHP was larger than the predefined threshold, whereas the waveforms with smaller IHP were set to zero. Finally the estimated Signal was obtained by reconstructing the processed IMFs. The details of threshold choosing rules were also discussed in the paper. Additive Gaussian white noise was embedded into real stress wave Signals to test the proposed method. Butterworth low pass filter, EEMD-based low pass filter and EEMD-based thresholding filter were used to compare filtering performance. Mean square error between clean and filtered stress waves was used as filtering performance indexes. The results demonstrated the excellent efficiency of the proposed method.
Ivan Prebil - One of the best experts on this subject based on the ideXlab platform.
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non linear multivariate and multiscale monitoring and Signal Denoising strategy using kernel principal component analysis combined with ensemble empirical mode decomposition method
Mechanical Systems and Signal Processing, 2011Co-Authors: Matej žvokelj, Samo Zupan, Ivan PrebilAbstract:Abstract The article presents a novel non-linear multivariate and multiscale statistical process monitoring and Signal Denoising method which combines the strengths of the Kernel Principal Component Analysis (KPCA) non-linear multivariate monitoring approach with the benefits of Ensemble Empirical Mode Decomposition (EEMD) to handle multiscale system dynamics. The proposed method which enables us to cope with complex even severe non-linear systems with a wide dynamic range was named the EEMD-based multiscale KPCA (EEMD-MSKPCA). The method is quite general in nature and could be used in different areas for various tasks even without any really deep understanding of the nature of the system under consideration. Its efficiency was first demonstrated by an illustrative example, after which the applicability for the task of bearing fault detection, diagnosis and Signal denosing was tested on simulated as well as actual vibration and acoustic emission (AE) Signals measured on purpose-built large-size low-speed bearing test stand. The positive results obtained indicate that the proposed EEMD-MSKPCA method provides a promising tool for tackling non-linear multiscale data which present a convolved picture of many events occupying different regions in the time–frequency plane.
Yuichi Tanaka - One of the best experts on this subject based on the ideXlab platform.
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graph Signal Denoising via trilateral filter on graph spectral domain
IEEE Transactions on Signal and Information Processing over Networks, 2016Co-Authors: Masaki Onuki, Masao Yamagishi, Yuichi TanakaAbstract:This paper presents a graph Signal Denoising method with the trilateral filter defined in the graph spectral domain. The original trilateral filter (TF) is a data-dependent filter that is widely used as an edge-preserving smoothing method for image processing. However, because of the data-dependency, one cannot provide its frequency domain representation. To overcome this problem, we establish the graph spectral domain representation of the data-dependent filter, i.e., a spectral graph TF (SGTF). This representation enables us to design an effective graph Signal Denoising filter with a Tikhonov regularization. Moreover, for the proposed graph Denoising filter, we provide a parameter optimization technique to search for a regularization parameter that approximately minimizes the mean squared error w.r.t. the unknown graph Signal of interest. Comprehensive experimental results validate our graph Signal processing-based approach for images and graph Signals.
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m channel oversampled perfect reconstruction filter banks for graph Signals
International Conference on Acoustics Speech and Signal Processing, 2014Co-Authors: Yuichi Tanaka, Akie SakiyamaAbstract:This paper proposes M-channel oversampled filter banks for graph Signals. The filter set satisfies the perfect reconstruction condition. A method of designing oversampled graph filter banks is presented which allows us to design filters with arbitrary parameters, unlike the conventional critically-sampled graph filter banks. The practical performance of the proposed filter banks is validated through graph Signal Denoising experiments.
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$M$ -Channel Oversampled Graph Filter Banks
IEEE Transactions on Signal Processing, 2014Co-Authors: Yuichi Tanaka, Akie SakiyamaAbstract:This paper proposes M-channel oversampled filter banks for graph Signals. The filter set satisfies the perfect reconstruction condition. A method of designing oversampled graph filter banks is presented that allows us to design filters with arbitrary parameters, unlike the conventional critically sampled graph filter banks. The oversampled graph Laplacian matrix is also introduced with a discussion of the entire redundancy of the oversampled graph Signal processing system. The practical performance of the proposed filter banks is validated through graph Signal Denoising experiments.
