• Proceedings of the Acoustical Society of Korea Conference
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• 1998.06e
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• pp.189-192
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• 1998

본 논문에서는 희소어레이 구조를 응용한 선형제약형 적응어레이 처리기를 제안하였다. 다경로 환경하에서 간섭신호의 제거에 효과적인 공간유화방법을 사용하여 제안된 어레이 처리기의 성능을 분석평가하였다. 선형어레이와 최적화된 센서간격으로 이루어진 선형어레이와 정상어레이 간의 성능을 비교하였다. 실험결과 최적화된 희소어레이를 이용한 선형제약형 적응어레이의 성능이 정상어레이의 성능에 버금가는 것으로 나타났다.

• The Journal of the Acoustical Society of Korea
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• v.36 no.4
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• pp.285-291
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• 2017

Many methods have been studied to estimate the time delay between incoming signals to two receivers. In the case of the method based on the channel estimation technique, the relative delay between the input signals of the two receivers is estimated as an impulse response of the channel between the two signals. In this case, the characteristic of the channel has sparsity. Most of the existing methods do not take advantage of the channel sparseness. In this paper, we propose a time delay estimation method using BPD (Basis Pursuit Denoising) optimization technique, which is one of the sparse signal optimization methods, in order to utilize the channel sparseness. Compared with the existing GCC (Generalized Cross Correlation) method, adaptive eigen decomposition method and RZA-LMS (Reweighted Zero-Attracting Least Mean Square), the proposed method shows that it can mitigate the threshold phenomenon even under a white Gaussian source, a colored signal source and oceanic mammal sound source.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.4
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• pp.295-303
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• 2008

In general, transform domain adaptive filters show faster convergence speed than the time domain adaptive filters, but the amount of calculation increases dramatically as the filter order increases. This problem can be solved by making use of the subband structure in transform domain adaptive filters. In this paper, to increase the convergence speed on the generalized subband decomposition FIR adaptive filters, a structure of the adaptive filter with subfilter of dyadic sparsity factor in wavelet transform domain is designed. And, in this adaptive filter, the equivalent input in transform domain is derived and, by using the input, the convergence properties for the LMS algorithm is analyzed and evaluated. By using this sub band adaptive filter, the inverse system modeling and the periodic noise canceller were designed, and, by computer simulation, the convergence speeds of the systems on LMS algorithm were compared with that of the subband adaptive filter using DFT(discrete Fourier transform).

• Journal of IKEEE
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• v.23 no.3
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• pp.910-916
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• 2019

Non-local similarity of natural images is one of highly exploited features in various applications dealing with images. Unique edges, texture, and pattern of the images are frequently repeated over the entire image. Once the similar image blocks are classified into a cluster, representative features of the image blocks can be extracted from the cluster. The bigger the size of the cluster is the better the additive white noise can be separated. Denoising is one of major research topics in the image processing field suppressing the additive noise. In this paper, a denoising algorithm is proposed which first clusters the noisy image blocks based on similarity, extracts the feature of the cluster, and finally recovers the original image. Performance experiments with several images under various noise strengths show that the proposed algorithm recovers the details of the image such as edges, texture, and patterns while outperforming the previous methods in terms of PSNR in removing the additive Gaussian noise.

• The Journal of the Acoustical Society of Korea
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• v.36 no.4
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• pp.279-284
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• 2017

The localization of sources has a numerous number of applications. To estimate the position of sources, the relative time delay between two or more received signals for the direct signal must be determined. Although the GCC (Generalized Cross-Correlation) method is the most popular technique, an approach based on CCA (Canonical Correlation Analysis) was also proposed for the TDE (Time Delay Estimation). In this paper, we propose a new adaptive algorithm based on CCA in order to utilized the sparsity in the eigenvector of CCA based time delay estimator. The proposed algorithm uses the eigenvector corresponding to the maximum eigenvalue with log-sum regularization in order to utilize the sparsity in the eigenvector. We have performed simulations for several SNR(signal to noise ratio)s, showing that the new CCA based algorithm can estimate the time delays more accurately than the conventional CCA and GCC based TDE algorithms.