Masato Katayama

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A novel filter is proposed by applying back propagation neural network (BPNN) ensemble where the noisy signal and the reference one are the same. The neural network(NN) ensemble filter not only well reduces additive and multiplicative white noise inside signals, but also preserves signals' characteristics. It is proved that while power of noise is larger,(More)
In this paper we describe an Independent Component Analysis (ICA) method for computing the brain signals of unknown source parameters for the inverse problem. First, a method is applied to estimate the number of dipoles beforehand and reduce dimensionality which can reduce the ICA complexity and improve the unmixing accuracy. We apply Blind Source(More)
A novel filter is proposed by applying back propagation neural network (BPNN) ensemble where the noisy signal and the reference one are the same in a learning process. This neural network (NN) ensemble filter not only well reduces additive and multiplicative white noise inside signals, but also preserves signals' characteristics. It is proved that the(More)
We report a low-voltage and submillisecond-response polymer-stabilized hyper-twisted-nematic (HTN) liquid crystal cell with a large dielectric anisotropy host mixture. To correct the measured voltage-dependent transmittance, we have to take the voltage shielding effect of the alignment layers into consideration. Both Kerr effect and flexoelectro-optic(More)
We tested the localization accuracy of electroencephalograph (EEG) for an inert region in a simulation at sizes ranging from 1 to 8 cm at 1 cm intervals. We used international 10-20 system electrodes placements and three concentric shell model to calculate forward problems. From using the data, neural network could be used to solve inverse problems. In this(More)
Evaluation of biomedical signals is important in the diagnosis of neurology diseases, such as dementia, in neurology through the use of electroencephalograms (EEG). While automated techniques exist for EEG analysis, it is likely that additional information can be extracted from EEG signal through the use of new methods. We describe a method for identifying(More)
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