The Experts below are selected from a list of 40566 Experts worldwide ranked by ideXlab platform
Mu Seong Mun - One of the best experts on this subject based on the ideXlab platform.
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A real-time EMG pattern recognition system based on linear-Nonlinear Feature projection for a multifunction myoelectric hand
IEEE Transactions on Biomedical Engineering, 2006Co-Authors: Jun-uk Chu, Inhyuk Moon, Mu Seong MunAbstract:This paper proposes a novel real-time electromyogram (EMG) pattern recognition for the control of a multifunction myoelectric hand from four channel EMG signals. To extract a Feature vector from the EMG signal, we use a wavelet packet transform that is a generalized version of wavelet transform. For dimensionality reduction and Nonlinear mapping of the Features, we also propose a linear-Nonlinear Feature projection composed of principal components analysis (PCA) and a self-organizing Feature map (SOFM). The dimensionality reduction by PCA simplifies the structure of the classifier and reduces processing time for the pattern recognition. The Nonlinear mapping by SOFM transforms the PCA-reduced Features into a new Feature space with high class separability. Finally, a multilayer perceptron (MLP) is used as the classifier. Using an analysis of class separability by Feature projections, we show that the recognition accuracy depends more on the class separability of the projected Features than on the MLP's class separation ability. Consequently, the proposed linear-Nonlinear projection method improves class separability and recognition accuracy. We implement a real-time control system for a multifunction virtual hand. Our experimental results show that all processes, including virtual hand control, are completed within 125 ms, and the proposed method is applicable to real-time myoelectric hand control without an operational time delay
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control of multifunction myoelectric hand using a real time emg pattern recognition
Intelligent Robots and Systems, 2005Co-Authors: Jun-uk Chu, Inhyuk Moon, Shinki Kim, Mu Seong MunAbstract:This paper proposes a novel real-time EMG pattern recognition for the control of a multifunction myoelectric hand from four channel EMG signals. To cope with the nonstationary signal property of the EMG, Features are extracted by wavelet packet transform. For dimensionality reduction and Nonlinear mapping of the Features, we also propose a linear-Nonlinear Feature projection composed of PCA and SOFM. The dimensionality reduction by PCA simplifies the structure of the classifier, and reduces processing time for the pattern recognition. The Nonlinear mapping by SOFM transforms the PCA-reduced Features to a new Feature space with high class separability. Finally a multilayer neural network is employed as the pattern classifier. We implement a real-time control system for a multifunction myoelectric hand. From experimental results, we show that all processes, including myoelectric hand control, are completed within 125 msec, and the proposed method is applicable to real-time myoelectric hand control without an operation time delay.
Jun-uk Chu - One of the best experts on this subject based on the ideXlab platform.
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A real-time EMG pattern recognition system based on linear-Nonlinear Feature projection for a multifunction myoelectric hand
IEEE Transactions on Biomedical Engineering, 2006Co-Authors: Jun-uk Chu, Inhyuk Moon, Mu Seong MunAbstract:This paper proposes a novel real-time electromyogram (EMG) pattern recognition for the control of a multifunction myoelectric hand from four channel EMG signals. To extract a Feature vector from the EMG signal, we use a wavelet packet transform that is a generalized version of wavelet transform. For dimensionality reduction and Nonlinear mapping of the Features, we also propose a linear-Nonlinear Feature projection composed of principal components analysis (PCA) and a self-organizing Feature map (SOFM). The dimensionality reduction by PCA simplifies the structure of the classifier and reduces processing time for the pattern recognition. The Nonlinear mapping by SOFM transforms the PCA-reduced Features into a new Feature space with high class separability. Finally, a multilayer perceptron (MLP) is used as the classifier. Using an analysis of class separability by Feature projections, we show that the recognition accuracy depends more on the class separability of the projected Features than on the MLP's class separation ability. Consequently, the proposed linear-Nonlinear projection method improves class separability and recognition accuracy. We implement a real-time control system for a multifunction virtual hand. Our experimental results show that all processes, including virtual hand control, are completed within 125 ms, and the proposed method is applicable to real-time myoelectric hand control without an operational time delay
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control of multifunction myoelectric hand using a real time emg pattern recognition
Intelligent Robots and Systems, 2005Co-Authors: Jun-uk Chu, Inhyuk Moon, Shinki Kim, Mu Seong MunAbstract:This paper proposes a novel real-time EMG pattern recognition for the control of a multifunction myoelectric hand from four channel EMG signals. To cope with the nonstationary signal property of the EMG, Features are extracted by wavelet packet transform. For dimensionality reduction and Nonlinear mapping of the Features, we also propose a linear-Nonlinear Feature projection composed of PCA and SOFM. The dimensionality reduction by PCA simplifies the structure of the classifier, and reduces processing time for the pattern recognition. The Nonlinear mapping by SOFM transforms the PCA-reduced Features to a new Feature space with high class separability. Finally a multilayer neural network is employed as the pattern classifier. We implement a real-time control system for a multifunction myoelectric hand. From experimental results, we show that all processes, including myoelectric hand control, are completed within 125 msec, and the proposed method is applicable to real-time myoelectric hand control without an operation time delay.
Gustavo Campsvalls - One of the best experts on this subject based on the ideXlab platform.
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signal to noise ratio in reproducing kernel hilbert spaces
Pattern Recognition Letters, 2018Co-Authors: Luis Gomezchova, Raul Santosrodriguez, Gustavo CampsvallsAbstract:Abstract This paper introduces the kernel signal-to-noise ratio (kSNR) for different machine learning and signal processing applications. The kSNR seeks to maximize the signal variance while minimizing the estimated noise variance explicitly in a reproducing kernel Hilbert space (rkHs). The kSNR gives rise to considering complex signal-to-noise relations beyond additive noise models, and can be seen as a useful regularizer for Feature extraction and dimensionality reduction. We show that the kSNR generalizes kernel PCA (and other spectral dimensionality reduction methods), least squares SVM, and kernel ridge regression to deal with cases where signal and noise cannot be assumed independent. We give computationally efficient alternatives based on reduced-rank Nystrom and projection on random Fourier Features approximations, and analyze the bounds of performance and its stability. We illustrate the method through different examples, including Nonlinear regression, Nonlinear classification in channel equalization, Nonlinear Feature extraction from high-dimensional spectral satellite images, and bivariate causal inference. Experimental results show that the proposed kSNR yields more accurate solutions and extracts more noise-free Features when compared to standard approaches.
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learning with the kernel signal to noise ratio
International Workshop on Machine Learning for Signal Processing, 2012Co-Authors: Luis Gomezchova, Gustavo CampsvallsAbstract:This paper presents the application of the kernel signal to noise ratio (KSNR) in the context of Feature extraction to general machine learning and signal processing domains. The proposed approach maximizes the signal variance while minimizes the estimated noise variance in a reproducing kernel Hilbert space (RKHS). The KSNR can be used in any kernel method to deal with correlated (possibly non-Gaussian) noise. We illustrate the method in Nonlinear regression examples, dependence estimation and causal inference, Nonlinear channel equalization, and Nonlinear Feature extraction from high-dimensional satellite images. Results show that the proposed KSNR yields more fitted solutions and extracts more noise-free Features when confronted with standard approaches.
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explicit signal to noise ratio in reproducing kernel hilbert spaces
International Geoscience and Remote Sensing Symposium, 2011Co-Authors: Luis Gomezchova, Allan Aasbjerg Nielsen, Gustavo CampsvallsAbstract:This paper introduces a Nonlinear Feature extraction method based on kernels for remote sensing data analysis. The proposed approach is based on the minimum noise fraction (MNF) transform, which maximizes the signal variance while also minimizing the estimated noise variance. We here propose an alternative kernel MNF (KMNF) in which the noise is explicitly estimated in the reproducing kernel Hilbert space. This enables KMNF dealing with non-linear relations between the noise and the signal Features jointly. Results show that the proposed KMNF provides the most noise-free Features when confronted with PCA, MNF, KPCA, and the previous version of KMNF. Extracted Features with the explicit KMNF also improve hyperspectral image classification.
Inhyuk Moon - One of the best experts on this subject based on the ideXlab platform.
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A real-time EMG pattern recognition system based on linear-Nonlinear Feature projection for a multifunction myoelectric hand
IEEE Transactions on Biomedical Engineering, 2006Co-Authors: Jun-uk Chu, Inhyuk Moon, Mu Seong MunAbstract:This paper proposes a novel real-time electromyogram (EMG) pattern recognition for the control of a multifunction myoelectric hand from four channel EMG signals. To extract a Feature vector from the EMG signal, we use a wavelet packet transform that is a generalized version of wavelet transform. For dimensionality reduction and Nonlinear mapping of the Features, we also propose a linear-Nonlinear Feature projection composed of principal components analysis (PCA) and a self-organizing Feature map (SOFM). The dimensionality reduction by PCA simplifies the structure of the classifier and reduces processing time for the pattern recognition. The Nonlinear mapping by SOFM transforms the PCA-reduced Features into a new Feature space with high class separability. Finally, a multilayer perceptron (MLP) is used as the classifier. Using an analysis of class separability by Feature projections, we show that the recognition accuracy depends more on the class separability of the projected Features than on the MLP's class separation ability. Consequently, the proposed linear-Nonlinear projection method improves class separability and recognition accuracy. We implement a real-time control system for a multifunction virtual hand. Our experimental results show that all processes, including virtual hand control, are completed within 125 ms, and the proposed method is applicable to real-time myoelectric hand control without an operational time delay
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control of multifunction myoelectric hand using a real time emg pattern recognition
Intelligent Robots and Systems, 2005Co-Authors: Jun-uk Chu, Inhyuk Moon, Shinki Kim, Mu Seong MunAbstract:This paper proposes a novel real-time EMG pattern recognition for the control of a multifunction myoelectric hand from four channel EMG signals. To cope with the nonstationary signal property of the EMG, Features are extracted by wavelet packet transform. For dimensionality reduction and Nonlinear mapping of the Features, we also propose a linear-Nonlinear Feature projection composed of PCA and SOFM. The dimensionality reduction by PCA simplifies the structure of the classifier, and reduces processing time for the pattern recognition. The Nonlinear mapping by SOFM transforms the PCA-reduced Features to a new Feature space with high class separability. Finally a multilayer neural network is employed as the pattern classifier. We implement a real-time control system for a multifunction myoelectric hand. From experimental results, we show that all processes, including myoelectric hand control, are completed within 125 msec, and the proposed method is applicable to real-time myoelectric hand control without an operation time delay.
Luis Gomezchova - One of the best experts on this subject based on the ideXlab platform.
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signal to noise ratio in reproducing kernel hilbert spaces
Pattern Recognition Letters, 2018Co-Authors: Luis Gomezchova, Raul Santosrodriguez, Gustavo CampsvallsAbstract:Abstract This paper introduces the kernel signal-to-noise ratio (kSNR) for different machine learning and signal processing applications. The kSNR seeks to maximize the signal variance while minimizing the estimated noise variance explicitly in a reproducing kernel Hilbert space (rkHs). The kSNR gives rise to considering complex signal-to-noise relations beyond additive noise models, and can be seen as a useful regularizer for Feature extraction and dimensionality reduction. We show that the kSNR generalizes kernel PCA (and other spectral dimensionality reduction methods), least squares SVM, and kernel ridge regression to deal with cases where signal and noise cannot be assumed independent. We give computationally efficient alternatives based on reduced-rank Nystrom and projection on random Fourier Features approximations, and analyze the bounds of performance and its stability. We illustrate the method through different examples, including Nonlinear regression, Nonlinear classification in channel equalization, Nonlinear Feature extraction from high-dimensional spectral satellite images, and bivariate causal inference. Experimental results show that the proposed kSNR yields more accurate solutions and extracts more noise-free Features when compared to standard approaches.
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learning with the kernel signal to noise ratio
International Workshop on Machine Learning for Signal Processing, 2012Co-Authors: Luis Gomezchova, Gustavo CampsvallsAbstract:This paper presents the application of the kernel signal to noise ratio (KSNR) in the context of Feature extraction to general machine learning and signal processing domains. The proposed approach maximizes the signal variance while minimizes the estimated noise variance in a reproducing kernel Hilbert space (RKHS). The KSNR can be used in any kernel method to deal with correlated (possibly non-Gaussian) noise. We illustrate the method in Nonlinear regression examples, dependence estimation and causal inference, Nonlinear channel equalization, and Nonlinear Feature extraction from high-dimensional satellite images. Results show that the proposed KSNR yields more fitted solutions and extracts more noise-free Features when confronted with standard approaches.
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explicit signal to noise ratio in reproducing kernel hilbert spaces
International Geoscience and Remote Sensing Symposium, 2011Co-Authors: Luis Gomezchova, Allan Aasbjerg Nielsen, Gustavo CampsvallsAbstract:This paper introduces a Nonlinear Feature extraction method based on kernels for remote sensing data analysis. The proposed approach is based on the minimum noise fraction (MNF) transform, which maximizes the signal variance while also minimizing the estimated noise variance. We here propose an alternative kernel MNF (KMNF) in which the noise is explicitly estimated in the reproducing kernel Hilbert space. This enables KMNF dealing with non-linear relations between the noise and the signal Features jointly. Results show that the proposed KMNF provides the most noise-free Features when confronted with PCA, MNF, KPCA, and the previous version of KMNF. Extracted Features with the explicit KMNF also improve hyperspectral image classification.
