The Experts below are selected from a list of 12249 Experts worldwide ranked by ideXlab platform
Yan-da Li - One of the best experts on this subject based on the ideXlab platform.
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Adaptive identification of nonminimum phase ARMA models using higher order cumulants alone
IEEE Transactions on Signal Processing, 1996Co-Authors: Xian-da Zhang, Yu Song, Yan-da LiAbstract:An adaptive identification algorithm for causal nonminimum phase ARMA models in additive colored Gaussian noise is proposed. The algorithm utilizes higher order cumulants of the Observed Signal alone. It estimates the AR and MA parameters successively in each iteration without computing the residual time series. The steepest descent method is used for parameter updating.
Marco Congedo - One of the best experts on this subject based on the ideXlab platform.
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EMBC - A non-orthogonal SVD-based decomposition for phase invariant error-related potential estimation
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and, 2011Co-Authors: Ronald Phlypo, Nisrine Jrad, Sandra Rousseau, Marco CongedoAbstract:The estimation of the Error Related Potential from a set of trials is a challenging problem. Indeed, the Error Related Potential is of low amplitude compared to the ongoing electroencephalographic activity. In addition, simple summing over the different trials is prone to errors, since the waveform does not appear at an exact latency with respect to the trigger. In this work, we propose a method to cope with the discrepancy of these latencies of the Error Related Potential waveform and offer a framework in which the estimation of the Error Related Potential waveform reduces to a simple Singular Value Decomposition of an analytic waveform representation of the Observed Signal. The followed approach is promising, since we are able to explain a higher portion of the variance of the Observed Signal with fewer components in the expansion.
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A non-orthogonal SVD-based decomposition for phase invariant error-related potential estimation
2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011Co-Authors: Ronald Phlypo, Nisrine Jrad, Sandra Rousseau, Marco CongedoAbstract:The estimation of the Error Related Potential from a set of trials is a challenging problem. Indeed, the Error Related Potential is of low amplitude compared to the ongoing electroencephalographic activity. In addition, simple summing over the different trials is prone to errors, since the waveform does not appear at an exact latency with respect to the trigger. In this work, we propose a method to cope with the discrepancy of these latencies of the Error Related Potential waveform and offer a framework in which the estimation of the Error Related Potential waveform reduces to a simple Singular Value Decomposition of an analytic waveform representation of the Observed Signal. The followed approach is promising, since we are able to explain a higher portion of the variance of the Observed Signal with fewer components in the expansion.
D.b. Williams - One of the best experts on this subject based on the ideXlab platform.
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A noise reduction method for chaotic Signals
1995 International Conference on Acoustics Speech and Signal Processing, 1995Co-Authors: Lee Chungyong, D.b. WilliamsAbstract:An iterative method for reducing noise in contaminated chaotic Signals is proposed. This method estimates the deviation of the Observed Signal from the nearest noise-free Signal satisfying the system dynamics in order to get a noise-reduced (or enhanced) Signal. To calculate the deviation we minimize a cost function composed of two parts: one containing information that represents how close the enhanced Signal is to the Observed Signal and another including constraints that fit the dynamics of the system. This method has a simple structure and is flexible in the choice of the parts of the cost function. The proposed method is compared with Farmer's method which is known to have good performance in mild Signal-to-noise ratios but has a more complex structure.
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ICASSP - A noise reduction method for chaotic Signals
1995 International Conference on Acoustics Speech and Signal Processing, 1995Co-Authors: Lee Chungyong, D.b. WilliamsAbstract:An iterative method for reducing noise in contaminated chaotic Signals is proposed. This method estimates the deviation of the Observed Signal from the nearest noise-free Signal satisfying the system dynamics in order to get a noise-reduced (or enhanced) Signal. To calculate the deviation we minimize a cost function composed of two parts: one containing information that represents how close the enhanced Signal is to the Observed Signal and another including constraints that fit the dynamics of the system. This method has a simple structure and is flexible in the choice of the parts of the cost function. The proposed method is compared with Farmer's method which is known to have good performance in mild Signal-to-noise ratios but has a more complex structure.
Pascal Frossard - One of the best experts on this subject based on the ideXlab platform.
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ICME - Distributed SVM Applied to Image Classification
2006 IEEE International Conference on Multimedia and Expo, 2006Co-Authors: Effrosyni Kokiopoulou, Pascal FrossardAbstract:This paper proposes an algorithm for distributed classification, based on a SVM scheme. The contribution of each support vector is approximated by low complexity distributed thresholding over sub-dictionaries, whose union forms a redundant dictionary of atoms that spans the space of the Observed Signal. Redundant dictionaries allow for sparse representation of the Observed Signal, hence a good approximation of the support vector contributions, which is moreover robust to noise. The algorithm is applied to distributed image classification, in the context of handwritten digit recognition in a sensor network. The experimental results indicate that the proposed method is capable of achieving the same classification performance as the standard (non distributed) SVM, with an increased resiliency to noise.
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Distributed SVM applied to image classification
2006 IEEE International Conference on Multimedia and Expo ICME 2006 - Proceedings, 2006Co-Authors: Effrosyni Kokiopoulou, Pascal FrossardAbstract:This paper proposes an algorithm for distributed classification. based on a SVM scheme. The contribution of each support vector is approximated by low complexity distributed thresholding over sub-dictionaries, whose union forms a redundant dictionary of atoms that spans the space of the Observed Signal. Redundant dictionaries allow for sparse representation of the Observed Signal, hence a good approximation of the support vector contributions, which is moreover robust to noise. The algorithm is applied to distributed image classification, in the context of handwritten digit recognition in a sensor network. The experimental results indicate that the proposed method is capable of achieving the same classification performance as the standard (non distributed) SVM, with an increased resiliency to noise.
Hiroshi Yasukawa - One of the best experts on this subject based on the ideXlab platform.
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Anomalous Signal Detection in ELF Band Electromagnetic Wave using Multi-layer Neural Network with Wavelet Decomposition
Ieej Transactions on Fundamentals and Materials, 2009Co-Authors: Akitoshi Itai, Ichi Takumi, Hiroshi Yasukawa, Masayasu HataAbstract:It is well known that electromagnetic waves radiated from the earth's crust are useful for predicting earthquakes. We analyze the electromagnetic waves received at the extremely low frequency band of 223Hz. These Observed Signals contain the seismic radiation from the earth's crust, but also include several undesired Signals. Our research focuses on the Signal detection technique to identify an anomalous Signal corresponding to the seismic radiation in the Observed Signal. Conventional anomalous Signal detections lack a wide applicability due to their assumptions, e.g. the digital data have to be Observed at the same time or the same sensor. In order to overcome the limitation related to the Observed Signal, we proposed the anomalous Signals detection based on a multi-layer neural network which is trained by digital data Observed during a span of a day. In the neural network approach, training data do not need to be recorded at the same place or the same time. However, some noises, which have a large amplitude, are detected as the anomalous Signal. This paper develops a multi-layer neural network to decrease the false detection of the anomalous Signal from the electromagnetic wave. The training data for the proposed network is the decomposed Signal of the Observed Signal during several days, since the seismic radiations are often recorded from several days to a couple of weeks. Results show that the proposed neural network is useful to achieve the accurate detection of the anomalous Signal that indicates seismic activity.
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Detection of Anomalous Environmental Electromagnetic Wave by Statistical Property in Magnetic Field Azimuth
IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium, 2008Co-Authors: Tokiyasu Sato, Ichi Takumi, Masayasu Hata, Hiroshi YasukawaAbstract:Anomalous radiation of environmental electromagnetic waves is reported as a portent of earthquakes. Then, we are observing environmental electromagnetic waves of the ELF range all over Japan. We have been regarding the high amplitude in the Observed Signal as anomalous radiation. However, the Observed Signal contains a lot of low amplitude anomalies hidden by background noise. These anomalies also have the possibility with significant information of a portent of an earthquake. In this paper, we propose the method of detecting anomalous radiation using statistical property in magnetic field azimuth which doesn't depend on the amplitude level. Moreover, we show that an anomalous radiation is the portent of earthquakes by calculating an azimuthal cross-correlation among two or more observation sites.
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Anomaly detection of environmental electromagnetic wave based on time fluctuation and cross-correlation in magnetic field azimuth
2008 International Symposium on Information Theory and Its Applications, 2008Co-Authors: Tokiyasu Sato, Ichi Takumi, Masayasu Hata, Hiroshi YasukawaAbstract:Anomalous radiation of environmental electromagnetic waves is reported as a portent of earthquakes. Then, we are observing environmental electromagnetic waves of the ELF range all over Japan. We have been regarding the high amplitude in the Observed Signal as anomalous radiation. However, the Observed Signal contains a lot of low amplitude anomalies hidden by background noise. These anomalies also have the possibility with significant information of a portent of an earthquake. In this paper, we propose the method of detecting anomalous radiation using time fluctuation in magnetic field azimuth which doesn't depend on the amplitude level. Moreover, we show that an anomalous radiation is the portent of earthquakes by calculating an azimuthal cross-correlation among two or more observation sites.
