• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.10A
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• pp.988-995
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• 2006

In this Paper, we Present a nonharmonic may geometry design method using Euclidan minimum distance function in difference Phase spaces for 2-D (azimuth/elevation) multiple baseline antenna may which has a way to reduce the number of sensor antennas while maintaining accurate DOA estimate. The major advantages of our approach is that even the shortest interelement spacing can be larger than half-wavelength and is not limit13d to linear and it can be applied successfully to any array configuration. In multiple signals impinging situation, the performance simulation results of superresolution algorithms shows the effectiveness of the proposed method. Also the 2-D asymmetric may using the Proposed method is designed and the Performance of the manufactured away through the experimental test is verified.

• Journal of IKEEE
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• v.22 no.4
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• pp.1140-1146
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• 2018

Recently, a methodology for analyzing complex bio-signals using a deep learning model has emerged among studies that recognize human emotions. At this time, the accuracy of emotion classification may be changed depending on the evaluation method and reliability depending on the kind of data to be learned. In the case of biological signals, the reliability of data is determined according to the noise ratio, so that the noise detection method is as important as that. Also, according to the methodology for defining emotions, appropriate emotional evaluation methods will be needed. In this paper, we propose a wavelet -based noise threshold setting algorithm for verifying the reliability of data for multimodal bio-signal data labeled Valence and Arousal and a method for improving the emotion recognition rate by weighting the evaluation data. After extracting the wavelet component of the signal using the wavelet transform, the distortion and kurtosis of the component are obtained, the noise is detected at the threshold calculated by the hampel identifier, and the training data is selected considering the noise ratio of the original signal. In addition, weighting is applied to the overall evaluation of the emotion recognition rate using the euclidean distance from the median value of the Valence-Arousal plane when classifying emotional data. To verify the proposed algorithm, we use ASCERTAIN data set to observe the degree of emotion recognition rate improvement.