The Experts below are selected from a list of 34752 Experts worldwide ranked by ideXlab platform
Yasushi Yagi - One of the best experts on this subject based on the ideXlab platform.
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Phase Estimation of a Single Quasi-Periodic Signal
IEEE Transactions on Signal Processing, 2014Co-Authors: Yasushi Makihara, Ngo Thanh Trung, Yasuhiro Mukaigawa, Muhammad Rasyid Aqmar, Hajime Nagahara, Ryusuke Sagawa, Yasushi YagiAbstract:We propose a method for phase estimation of a single non-parametric quasi-Periodic Signal. Assuming Signal intensities should be equal among samples of the same phase, such corresponding samples are obtained by self-dynamic time warping between a quasi-Periodic Signal and a Signal with multiple-period shifts applied. A phase sequence is then estimated in a sub-sampling order using an optimization framework incorporating 1) a data term derived from the correspondences and 2) a smoothness term of the local phase evolution under 3) a monotonic-increasing constraint on the phase. Such a phase estimation is, however, ill-posed because of combination ambiguity between the phase evolution and the normalized Periodic Signal, and hence can result in a biased solution. Therefore, we introduce into the optimization framework 4) a bias correction term, which imposes zero-bias from the linear phase evolution. Analysis of the quasi-Periodic Signals from both simulated and real data indicate the effectiveness and also potential applications of the proposed method.
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phase registration of a single quasi Periodic Signal using self dynamic time warping
Asian Conference on Computer Vision, 2010Co-Authors: Yasushi Makihara, Ngo Thanh Trung, Yasuhiro Mukaigawa, Hajime Nagahara, Ryusuke Sagawa, Yasushi YagiAbstract:This paper proposes a method for phase registration of a single non-parametric quasi-Periodic Signal. After a short-term period has been detected for each sample by normalized autocorrelation, Self Dynamic Time Warping (Self DTW) between a quasi-Periodic Signal and that with multiple-period shifts is applied to obtain corresponding samples of the same phase. A phase sequence is finally estimated by the optimization framework including the data term derived from the correspondences, the regularization term derived from short-term periods, and a monotonic increasing constraint of the phase. Experiments on quasiPeriodic Signals from both simulated and real data show the effectiveness of the proposed method.
Yasushi Makihara - One of the best experts on this subject based on the ideXlab platform.
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Phase Estimation of a Single Quasi-Periodic Signal
IEEE Transactions on Signal Processing, 2014Co-Authors: Yasushi Makihara, Ngo Thanh Trung, Yasuhiro Mukaigawa, Muhammad Rasyid Aqmar, Hajime Nagahara, Ryusuke Sagawa, Yasushi YagiAbstract:We propose a method for phase estimation of a single non-parametric quasi-Periodic Signal. Assuming Signal intensities should be equal among samples of the same phase, such corresponding samples are obtained by self-dynamic time warping between a quasi-Periodic Signal and a Signal with multiple-period shifts applied. A phase sequence is then estimated in a sub-sampling order using an optimization framework incorporating 1) a data term derived from the correspondences and 2) a smoothness term of the local phase evolution under 3) a monotonic-increasing constraint on the phase. Such a phase estimation is, however, ill-posed because of combination ambiguity between the phase evolution and the normalized Periodic Signal, and hence can result in a biased solution. Therefore, we introduce into the optimization framework 4) a bias correction term, which imposes zero-bias from the linear phase evolution. Analysis of the quasi-Periodic Signals from both simulated and real data indicate the effectiveness and also potential applications of the proposed method.
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phase registration of a single quasi Periodic Signal using self dynamic time warping
Asian Conference on Computer Vision, 2010Co-Authors: Yasushi Makihara, Ngo Thanh Trung, Yasuhiro Mukaigawa, Hajime Nagahara, Ryusuke Sagawa, Yasushi YagiAbstract:This paper proposes a method for phase registration of a single non-parametric quasi-Periodic Signal. After a short-term period has been detected for each sample by normalized autocorrelation, Self Dynamic Time Warping (Self DTW) between a quasi-Periodic Signal and that with multiple-period shifts is applied to obtain corresponding samples of the same phase. A phase sequence is finally estimated by the optimization framework including the data term derived from the correspondences, the regularization term derived from short-term periods, and a monotonic increasing constraint of the phase. Experiments on quasiPeriodic Signals from both simulated and real data show the effectiveness of the proposed method.
Hajime Nagahara - One of the best experts on this subject based on the ideXlab platform.
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Phase Estimation of a Single Quasi-Periodic Signal
IEEE Transactions on Signal Processing, 2014Co-Authors: Yasushi Makihara, Ngo Thanh Trung, Yasuhiro Mukaigawa, Muhammad Rasyid Aqmar, Hajime Nagahara, Ryusuke Sagawa, Yasushi YagiAbstract:We propose a method for phase estimation of a single non-parametric quasi-Periodic Signal. Assuming Signal intensities should be equal among samples of the same phase, such corresponding samples are obtained by self-dynamic time warping between a quasi-Periodic Signal and a Signal with multiple-period shifts applied. A phase sequence is then estimated in a sub-sampling order using an optimization framework incorporating 1) a data term derived from the correspondences and 2) a smoothness term of the local phase evolution under 3) a monotonic-increasing constraint on the phase. Such a phase estimation is, however, ill-posed because of combination ambiguity between the phase evolution and the normalized Periodic Signal, and hence can result in a biased solution. Therefore, we introduce into the optimization framework 4) a bias correction term, which imposes zero-bias from the linear phase evolution. Analysis of the quasi-Periodic Signals from both simulated and real data indicate the effectiveness and also potential applications of the proposed method.
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phase registration of a single quasi Periodic Signal using self dynamic time warping
Asian Conference on Computer Vision, 2010Co-Authors: Yasushi Makihara, Ngo Thanh Trung, Yasuhiro Mukaigawa, Hajime Nagahara, Ryusuke Sagawa, Yasushi YagiAbstract:This paper proposes a method for phase registration of a single non-parametric quasi-Periodic Signal. After a short-term period has been detected for each sample by normalized autocorrelation, Self Dynamic Time Warping (Self DTW) between a quasi-Periodic Signal and that with multiple-period shifts is applied to obtain corresponding samples of the same phase. A phase sequence is finally estimated by the optimization framework including the data term derived from the correspondences, the regularization term derived from short-term periods, and a monotonic increasing constraint of the phase. Experiments on quasiPeriodic Signals from both simulated and real data show the effectiveness of the proposed method.
Ngo Thanh Trung - One of the best experts on this subject based on the ideXlab platform.
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Phase Estimation of a Single Quasi-Periodic Signal
IEEE Transactions on Signal Processing, 2014Co-Authors: Yasushi Makihara, Ngo Thanh Trung, Yasuhiro Mukaigawa, Muhammad Rasyid Aqmar, Hajime Nagahara, Ryusuke Sagawa, Yasushi YagiAbstract:We propose a method for phase estimation of a single non-parametric quasi-Periodic Signal. Assuming Signal intensities should be equal among samples of the same phase, such corresponding samples are obtained by self-dynamic time warping between a quasi-Periodic Signal and a Signal with multiple-period shifts applied. A phase sequence is then estimated in a sub-sampling order using an optimization framework incorporating 1) a data term derived from the correspondences and 2) a smoothness term of the local phase evolution under 3) a monotonic-increasing constraint on the phase. Such a phase estimation is, however, ill-posed because of combination ambiguity between the phase evolution and the normalized Periodic Signal, and hence can result in a biased solution. Therefore, we introduce into the optimization framework 4) a bias correction term, which imposes zero-bias from the linear phase evolution. Analysis of the quasi-Periodic Signals from both simulated and real data indicate the effectiveness and also potential applications of the proposed method.
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phase registration of a single quasi Periodic Signal using self dynamic time warping
Asian Conference on Computer Vision, 2010Co-Authors: Yasushi Makihara, Ngo Thanh Trung, Yasuhiro Mukaigawa, Hajime Nagahara, Ryusuke Sagawa, Yasushi YagiAbstract:This paper proposes a method for phase registration of a single non-parametric quasi-Periodic Signal. After a short-term period has been detected for each sample by normalized autocorrelation, Self Dynamic Time Warping (Self DTW) between a quasi-Periodic Signal and that with multiple-period shifts is applied to obtain corresponding samples of the same phase. A phase sequence is finally estimated by the optimization framework including the data term derived from the correspondences, the regularization term derived from short-term periods, and a monotonic increasing constraint of the phase. Experiments on quasiPeriodic Signals from both simulated and real data show the effectiveness of the proposed method.
Ryusuke Sagawa - One of the best experts on this subject based on the ideXlab platform.
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Phase Estimation of a Single Quasi-Periodic Signal
IEEE Transactions on Signal Processing, 2014Co-Authors: Yasushi Makihara, Ngo Thanh Trung, Yasuhiro Mukaigawa, Muhammad Rasyid Aqmar, Hajime Nagahara, Ryusuke Sagawa, Yasushi YagiAbstract:We propose a method for phase estimation of a single non-parametric quasi-Periodic Signal. Assuming Signal intensities should be equal among samples of the same phase, such corresponding samples are obtained by self-dynamic time warping between a quasi-Periodic Signal and a Signal with multiple-period shifts applied. A phase sequence is then estimated in a sub-sampling order using an optimization framework incorporating 1) a data term derived from the correspondences and 2) a smoothness term of the local phase evolution under 3) a monotonic-increasing constraint on the phase. Such a phase estimation is, however, ill-posed because of combination ambiguity between the phase evolution and the normalized Periodic Signal, and hence can result in a biased solution. Therefore, we introduce into the optimization framework 4) a bias correction term, which imposes zero-bias from the linear phase evolution. Analysis of the quasi-Periodic Signals from both simulated and real data indicate the effectiveness and also potential applications of the proposed method.
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phase registration of a single quasi Periodic Signal using self dynamic time warping
Asian Conference on Computer Vision, 2010Co-Authors: Yasushi Makihara, Ngo Thanh Trung, Yasuhiro Mukaigawa, Hajime Nagahara, Ryusuke Sagawa, Yasushi YagiAbstract:This paper proposes a method for phase registration of a single non-parametric quasi-Periodic Signal. After a short-term period has been detected for each sample by normalized autocorrelation, Self Dynamic Time Warping (Self DTW) between a quasi-Periodic Signal and that with multiple-period shifts is applied to obtain corresponding samples of the same phase. A phase sequence is finally estimated by the optimization framework including the data term derived from the correspondences, the regularization term derived from short-term periods, and a monotonic increasing constraint of the phase. Experiments on quasiPeriodic Signals from both simulated and real data show the effectiveness of the proposed method.
