• The Journal of the Acoustical Society of Korea
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• v.35 no.5
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• pp.395-402
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• 2016

In this paper, a wideband adaptive beamforming approach for an acoustic vector sensor linear array is presented. It is a very important issue to estimate the stable covariance matrix for adaptive beamforming. In the conventional wideband adaptive beamforming based on coherent signal-subspace (CSS) processing, the error of bearing estimates is resulted from the focusing matrix estimation and the large number of data snapshot is necessary. To alleviate the estimation error and snapshot deficiency in estimating covariance matrix, the steered covariance matrix method in the pressure sensor is extended to the vector sensor array, and the subarray technique is incorporated. By this technique, more accurate azimuth estimates and a stable covariance matrix can be obtained with a small number of data snapshot. Through simulation, the azimuth estimation performance of the proposed beamforming method and a wideband adaptive beamforming based on CSS processing are assessed.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.1
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• pp.139-149
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• 2010

Random scalar countermeasures, which carry out the scalar multiplication by the ephemeral secret key, against the differential power analysis of ECIES and ECDH have been known to be secure against various power analyses. However, if an attacker can find this ephemeral key from the one power signal, these countermeasures can be analyzed. In this paper, we propose a new power attack method which can do this analysis. Proposed attack method can be accomplished while an attacker compares the elliptic curve doubling operations and we use the principle component analysis in order to ease this comparison. When we have actually carried out the proposed power analysis, we can perfectly eliminate the error of existing function for the comparison and find a private key from this elimination of the error.

• 전자공학회논문지 IE
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• v.48 no.1
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• pp.25-29
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• 2011

Ryu et al. proposed a multiple target angle-tracking algorithm with efficient equation for angular innovation, and Ryu's algorithm has good feature that it has no data association problem. Ryu's algorithm is only applicable to linear sensor array, because its efficient equation for angular innovation is derived in case of using a linear sensor array. In a many fields studying multiple target angle-tracking, the various shapes of sensor array are used. In sonar, a cylindrical sensor array is as much used as a linear sensor array, a example is hull mounted sonar. In this paper, Ryu's algorithm is modified to be applicable to cylindrical sensor array, and the tracking performance of a modified algorithm is verified by various computer simulations.

• The Journal of the Acoustical Society of Korea
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• v.20 no.4
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• pp.35-41
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• 2001

This paper describes Linear Discriminant Analysis and common vector extraction for speech recognition. Voice signal contains psychological and physiological properties of the speaker as well as dialect differences, acoustical environment effects, and phase differences. For these reasons, the same word spelled out by different speakers can be very different heard. This property of speech signal make it very difficult to extract common properties in the same speech class (word or phoneme). Linear algebra method like BT (Karhunen-Loeve Transformation) is generally used for common properties extraction In the speech signals, but common vector extraction which is suggested by M. Bilginer et at. is used in this paper. The method of M. Bilginer et al. extracts the optimized common vector from the speech signals used for training. And it has 100％ recognition accuracy in the trained data which is used for common vector extraction. In spite of these characteristics, the method has some drawback-we cannot use numbers of speech signal for training and the discriminant information among common vectors is not defined. This paper suggests advanced method which can reduce error rate by maximizing the discriminant information among common vectors. And novel method to normalize the size of common vector also added. The result shows improved performance of algorithm and better recognition accuracy of 2％ than conventional method.