The Experts below are selected from a list of 1209 Experts worldwide ranked by ideXlab platform
A. Gualandi - One of the best experts on this subject based on the ideXlab platform.
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Blind Source Separation Problem in GPS time series
Journal of Geodesy, 2016Co-Authors: A. Gualandi, E. Serpelloni, M. E. BelardinelliAbstract:A critical point in the analysis of ground displacement time series, as those recorded by space geodetic techniques, is the development of data-driven methods that allow the different Sources of deformation to be discerned and characterized in the space and time domains. Multivariate statistic includes several approaches that can be considered as a part of data-driven methods. A widely used technique is the principal component analysis (PCA), which allows us to reduce the dimensionality of the data space while maintaining most of the variance of the dataset explained. However, PCA does not perform well in finding the solution to the so-called blind Source Separation (BSS) Problem, i.e., in recovering and separating the original Sources that generate the observed data. This is mainly due to the fact that PCA minimizes the misfit calculated using an $$L_{2}$$ L 2 norm ( $$\chi ^{2})$$ χ 2 ) , looking for a new Euclidean space where the projected data are uncorrelated. The independent component analysis (ICA) is a popular technique adopted to approach the BSS Problem. However, the independence condition is not easy to impose, and it is often necessary to introduce some approximations. To work around this Problem, we test the use of a modified variational Bayesian ICA (vbICA) method to recover the multiple Sources of ground deformation even in the presence of missing data. The vbICA method models the probability density function (pdf) of each Source signal using a mix of Gaussian distributions, allowing for more flexibility in the description of the pdf of the Sources with respect to standard ICA, and giving a more reliable estimate of them. Here we present its application to synthetic global positioning system (GPS) position time series, generated by simulating deformation near an active fault, including inter-seismic, co-seismic, and post-seismic signals, plus seasonal signals and noise, and an additional time-dependent volcanic Source. We evaluate the ability of the PCA and ICA decomposition techniques in explaining the data and in recovering the original (known) Sources. Using the same number of components, we find that the vbICA method fits the data almost as well as a PCA method, since the $$\chi ^{2}$$ χ 2 increase is less than 10 % the value calculated using a PCA decomposition. Unlike PCA, the vbICA algorithm is found to correctly separate the Sources if the correlation of the dataset is low ( $$6$$ M w > 6 occurred). We also provide a cookbook for the use of the vbICA algorithm in analyses of position time series for tectonic and non-tectonic applications.
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blind Source Separation Problem in gps time series
Journal of Geodesy, 2016Co-Authors: A. Gualandi, E. Serpelloni, M. E. BelardinelliAbstract:A critical point in the analysis of ground displacement time series, as those recorded by space geodetic techniques, is the development of data-driven methods that allow the different Sources of deformation to be discerned and characterized in the space and time domains. Multivariate statistic includes several approaches that can be considered as a part of data-driven methods. A widely used technique is the principal component analysis (PCA), which allows us to reduce the dimensionality of the data space while maintaining most of the variance of the dataset explained. However, PCA does not perform well in finding the solution to the so-called blind Source Separation (BSS) Problem, i.e., in recovering and separating the original Sources that generate the observed data. This is mainly due to the fact that PCA minimizes the misfit calculated using an $$L_{2}$$ norm ( $$\chi ^{2})$$ , looking for a new Euclidean space where the projected data are uncorrelated. The independent component analysis (ICA) is a popular technique adopted to approach the BSS Problem. However, the independence condition is not easy to impose, and it is often necessary to introduce some approximations. To work around this Problem, we test the use of a modified variational Bayesian ICA (vbICA) method to recover the multiple Sources of ground deformation even in the presence of missing data. The vbICA method models the probability density function (pdf) of each Source signal using a mix of Gaussian distributions, allowing for more flexibility in the description of the pdf of the Sources with respect to standard ICA, and giving a more reliable estimate of them. Here we present its application to synthetic global positioning system (GPS) position time series, generated by simulating deformation near an active fault, including inter-seismic, co-seismic, and post-seismic signals, plus seasonal signals and noise, and an additional time-dependent volcanic Source. We evaluate the ability of the PCA and ICA decomposition techniques in explaining the data and in recovering the original (known) Sources. Using the same number of components, we find that the vbICA method fits the data almost as well as a PCA method, since the $$\chi ^{2}$$ increase is less than 10 % the value calculated using a PCA decomposition. Unlike PCA, the vbICA algorithm is found to correctly separate the Sources if the correlation of the dataset is low ( $$<$$ 0.67) and the geodetic network is sufficiently dense (ten continuous GPS stations within a box of side equal to two times the locking depth of a fault where an earthquake of $$M_\mathrm{{w}} >6$$ occurred). We also provide a cookbook for the use of the vbICA algorithm in analyses of position time series for tectonic and non-tectonic applications.
M. E. Belardinelli - One of the best experts on this subject based on the ideXlab platform.
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Blind Source Separation Problem in GPS time series
Journal of Geodesy, 2016Co-Authors: A. Gualandi, E. Serpelloni, M. E. BelardinelliAbstract:A critical point in the analysis of ground displacement time series, as those recorded by space geodetic techniques, is the development of data-driven methods that allow the different Sources of deformation to be discerned and characterized in the space and time domains. Multivariate statistic includes several approaches that can be considered as a part of data-driven methods. A widely used technique is the principal component analysis (PCA), which allows us to reduce the dimensionality of the data space while maintaining most of the variance of the dataset explained. However, PCA does not perform well in finding the solution to the so-called blind Source Separation (BSS) Problem, i.e., in recovering and separating the original Sources that generate the observed data. This is mainly due to the fact that PCA minimizes the misfit calculated using an $$L_{2}$$ L 2 norm ( $$\chi ^{2})$$ χ 2 ) , looking for a new Euclidean space where the projected data are uncorrelated. The independent component analysis (ICA) is a popular technique adopted to approach the BSS Problem. However, the independence condition is not easy to impose, and it is often necessary to introduce some approximations. To work around this Problem, we test the use of a modified variational Bayesian ICA (vbICA) method to recover the multiple Sources of ground deformation even in the presence of missing data. The vbICA method models the probability density function (pdf) of each Source signal using a mix of Gaussian distributions, allowing for more flexibility in the description of the pdf of the Sources with respect to standard ICA, and giving a more reliable estimate of them. Here we present its application to synthetic global positioning system (GPS) position time series, generated by simulating deformation near an active fault, including inter-seismic, co-seismic, and post-seismic signals, plus seasonal signals and noise, and an additional time-dependent volcanic Source. We evaluate the ability of the PCA and ICA decomposition techniques in explaining the data and in recovering the original (known) Sources. Using the same number of components, we find that the vbICA method fits the data almost as well as a PCA method, since the $$\chi ^{2}$$ χ 2 increase is less than 10 % the value calculated using a PCA decomposition. Unlike PCA, the vbICA algorithm is found to correctly separate the Sources if the correlation of the dataset is low ( $$6$$ M w > 6 occurred). We also provide a cookbook for the use of the vbICA algorithm in analyses of position time series for tectonic and non-tectonic applications.
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blind Source Separation Problem in gps time series
Journal of Geodesy, 2016Co-Authors: A. Gualandi, E. Serpelloni, M. E. BelardinelliAbstract:A critical point in the analysis of ground displacement time series, as those recorded by space geodetic techniques, is the development of data-driven methods that allow the different Sources of deformation to be discerned and characterized in the space and time domains. Multivariate statistic includes several approaches that can be considered as a part of data-driven methods. A widely used technique is the principal component analysis (PCA), which allows us to reduce the dimensionality of the data space while maintaining most of the variance of the dataset explained. However, PCA does not perform well in finding the solution to the so-called blind Source Separation (BSS) Problem, i.e., in recovering and separating the original Sources that generate the observed data. This is mainly due to the fact that PCA minimizes the misfit calculated using an $$L_{2}$$ norm ( $$\chi ^{2})$$ , looking for a new Euclidean space where the projected data are uncorrelated. The independent component analysis (ICA) is a popular technique adopted to approach the BSS Problem. However, the independence condition is not easy to impose, and it is often necessary to introduce some approximations. To work around this Problem, we test the use of a modified variational Bayesian ICA (vbICA) method to recover the multiple Sources of ground deformation even in the presence of missing data. The vbICA method models the probability density function (pdf) of each Source signal using a mix of Gaussian distributions, allowing for more flexibility in the description of the pdf of the Sources with respect to standard ICA, and giving a more reliable estimate of them. Here we present its application to synthetic global positioning system (GPS) position time series, generated by simulating deformation near an active fault, including inter-seismic, co-seismic, and post-seismic signals, plus seasonal signals and noise, and an additional time-dependent volcanic Source. We evaluate the ability of the PCA and ICA decomposition techniques in explaining the data and in recovering the original (known) Sources. Using the same number of components, we find that the vbICA method fits the data almost as well as a PCA method, since the $$\chi ^{2}$$ increase is less than 10 % the value calculated using a PCA decomposition. Unlike PCA, the vbICA algorithm is found to correctly separate the Sources if the correlation of the dataset is low ( $$<$$ 0.67) and the geodetic network is sufficiently dense (ten continuous GPS stations within a box of side equal to two times the locking depth of a fault where an earthquake of $$M_\mathrm{{w}} >6$$ occurred). We also provide a cookbook for the use of the vbICA algorithm in analyses of position time series for tectonic and non-tectonic applications.
Akira Morimoto - One of the best experts on this subject based on the ideXlab platform.
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Application of the Source reduction method with multiwavelets to image Separation
2012 International Conference on Wavelet Analysis and Pattern Recognition, 2012Co-Authors: Akira Morimoto, Ryuichi Ashino, Takeshi MandaiAbstract:The blind image Source Separation Problem is to estimate the number of the unknown Source images and to separate the original images from superpositions of Source images. The coefficients of the superposition are also unknown. Our proposed Source reduction method has a potential to overcome the difficulty of previously proposed methods. In this paper, we give an algorithm of the method with continuous multiwavelet transform for the image Separation. Various numerical experiments using the proposed algorithm ensure that our proposed Source reduction method performs well for image Separation. An example of numerical experiments is presented.
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Source reduction method using wavelets for the Source Separation Problem
2012 International Conference on Wavelet Analysis and Pattern Recognition, 2012Co-Authors: Akira Morimoto, Ryuichi Ashino, Takeshi MandaiAbstract:The blind Source Separation Problem is to estimate the number of the unknown Source signals and to separate the original Source signals from the several observed signals, which are assumed to be linear superpositions of the Sources. The coefficients of the superposition are also unknown. In this paper, in order to overcome a difficulty in previously proposed methods, a new approach to solve the Problem, named Source reduction method, using continuous wavelet transform is proposed.
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BLIND Source Separation OF SPATIO-TEMPORAL MIXED SIGNALS USING PHASE INFORMATION OF ANALYTIC WAVELET TRANSFORM
International Journal of Wavelets Multiresolution and Information Processing, 2010Co-Authors: Ryuichi Ashino, Takeshi Mandai, Akira MorimotoAbstract:The cocktail party Problem deals with the specialized human listening ability to focus one's listening attention on a single talker among a cacophony of conversations and background noises. The blind Source Separation Problem is how to enable computers to solve the cocktail party Problem in a satisfactory manner. The simplest version of spatio-temporal mixture Problem, which is a type of blind Source Separation Problem, has been solved by a generalized version of the quotient signal estimation method based on the analytic wavelet transform, under the assumption that the time delays are integer multiples of the sampling period. The analytic wavelet transform is used to represent time-frequency information of observed signals. Without the above assumption, improved algorithms, utilizing phase information of the analytic wavelet transforms of the observed signals, are proposed. A series of numerical simulations is presented.
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blind Source Separation of spatio temporal mixed signals using time frequency analysis
Applicable Analysis, 2009Co-Authors: Ryuichi Ashino, Akira Morimoto, Takeshi Mandai, Fumio SasakiAbstract:The cocktail party Problem deals with the specialized human listening ability to focus one's listening attention on a single talker among a cacophony of conversations and background noise. The blind Source Separation Problem corresponds to a way to enable computers to solve the cocktail party Problem in a satisfactory manner. The simplest version of spatio-temporal mixture Problem, which is a type of blind Source Separation Problems, is solved using time-frequency analysis. The analytic wavelet transform is used to represent time-frequency information and a numerical simulation is given. †Dedicated to Professor Hideo Soga on the occasion of his 60th birthday.
Takeshi Mandai - One of the best experts on this subject based on the ideXlab platform.
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Application of the Source reduction method with multiwavelets to image Separation
2012 International Conference on Wavelet Analysis and Pattern Recognition, 2012Co-Authors: Akira Morimoto, Ryuichi Ashino, Takeshi MandaiAbstract:The blind image Source Separation Problem is to estimate the number of the unknown Source images and to separate the original images from superpositions of Source images. The coefficients of the superposition are also unknown. Our proposed Source reduction method has a potential to overcome the difficulty of previously proposed methods. In this paper, we give an algorithm of the method with continuous multiwavelet transform for the image Separation. Various numerical experiments using the proposed algorithm ensure that our proposed Source reduction method performs well for image Separation. An example of numerical experiments is presented.
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Source reduction method using wavelets for the Source Separation Problem
2012 International Conference on Wavelet Analysis and Pattern Recognition, 2012Co-Authors: Akira Morimoto, Ryuichi Ashino, Takeshi MandaiAbstract:The blind Source Separation Problem is to estimate the number of the unknown Source signals and to separate the original Source signals from the several observed signals, which are assumed to be linear superpositions of the Sources. The coefficients of the superposition are also unknown. In this paper, in order to overcome a difficulty in previously proposed methods, a new approach to solve the Problem, named Source reduction method, using continuous wavelet transform is proposed.
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BLIND Source Separation OF SPATIO-TEMPORAL MIXED SIGNALS USING PHASE INFORMATION OF ANALYTIC WAVELET TRANSFORM
International Journal of Wavelets Multiresolution and Information Processing, 2010Co-Authors: Ryuichi Ashino, Takeshi Mandai, Akira MorimotoAbstract:The cocktail party Problem deals with the specialized human listening ability to focus one's listening attention on a single talker among a cacophony of conversations and background noises. The blind Source Separation Problem is how to enable computers to solve the cocktail party Problem in a satisfactory manner. The simplest version of spatio-temporal mixture Problem, which is a type of blind Source Separation Problem, has been solved by a generalized version of the quotient signal estimation method based on the analytic wavelet transform, under the assumption that the time delays are integer multiples of the sampling period. The analytic wavelet transform is used to represent time-frequency information of observed signals. Without the above assumption, improved algorithms, utilizing phase information of the analytic wavelet transforms of the observed signals, are proposed. A series of numerical simulations is presented.
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blind Source Separation of spatio temporal mixed signals using time frequency analysis
Applicable Analysis, 2009Co-Authors: Ryuichi Ashino, Akira Morimoto, Takeshi Mandai, Fumio SasakiAbstract:The cocktail party Problem deals with the specialized human listening ability to focus one's listening attention on a single talker among a cacophony of conversations and background noise. The blind Source Separation Problem corresponds to a way to enable computers to solve the cocktail party Problem in a satisfactory manner. The simplest version of spatio-temporal mixture Problem, which is a type of blind Source Separation Problems, is solved using time-frequency analysis. The analytic wavelet transform is used to represent time-frequency information and a numerical simulation is given. †Dedicated to Professor Hideo Soga on the occasion of his 60th birthday.
Clive Cheong Took - One of the best experts on this subject based on the ideXlab platform.
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ICASSP - Novel quaternion matrix factorisations
2016 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2016Co-Authors: Shirin Enshaeifar, Clive Cheong Took, Saeid Sanei, Danilo P. MandicAbstract:The recent introduction of η-Hermitian matrices A = AηH has opened a new avenue of research in quaternion signal processing. However, the exploitation of this matrix structure has been limited, perhaps due to the lack of joint diagonalisation methodologies of these matrices. As such, we propose novel decompositions of η-Hermitian matrices to address this shortcoming in the literature. As an application, we consider a blind Source Separation Problem in the form of an Alamouti-based communication system. Simulation studies demonstrate the effectiveness of our proposed joint diago-nalisation technique and indicate that our approach is particularly useful when the Sources are correlated.
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Underdetermined Blind Source Separation of Temporomandibular Joint Sounds
IEEE transactions on bio-medical engineering, 2006Co-Authors: Clive Cheong Took, Saeid Sanei, J.a. Chambers, Stephen DunneAbstract:The underdetermined blind Source Separation Problem using a filtering approach is addressed. An extension of the FastICA algorithm is devised which exploits the disparity in the kurtoses of the underlying Sources to estimate the mixing matrix and thereafter achieves Source recovery by employing the lscr 1-norm algorithm. Besides, we demonstrate how promising FastICA can be to extract the Sources. Furthermore, we illustrate how this scenario is particularly appropriate for the Separation of temporomandibular joint (TMJ) sounds
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ICASSP (3) - A Filtering Approach to Underdetermined Blind Source Separation With Application to Temporomandibular Disorders
2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, 1Co-Authors: Clive Cheong Took, Saeid Sanei, J.a. ChambersAbstract:This paper addresses the underdetermined blind Source Separation Problem, using a filtering approach. We have developed an extension of the FastICA algorithm which exploits the disparity in the kurtoses of the underlying Sources to estimate the mixing matrix and thereafter the recovery of the Sources is achieved by employing the ℓ1-norm algorithm. Also, we demonstrate how promising FastICA can be to extract the Sources, without utilizing the ℓ1-norm algorithm. Furthermore, we illustrate how this scenario is particularly suitable to the Separation of the temporomandibular joint (TMJ) sounds, crucial in the diagnosis of temporomandibular disorders (TMDs). © 2006 British Crown Copyright