• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.3
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• pp.237-247
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• 2014

This paper describes a 2D discrete-time wavelet transform for which the Q-factor is easily specified. Hence, the transform can be tuned according to the oscillatory behavior of the image signal to which it is applied. The tunable Q-factor wavelet transform (TQWT) is a fully-discrete wavelet transform for which the Q-factor, Q, of the underlying wavelet and the asymptotic redundancy (over-sampling rate), r, of the transform are easily and independently specified. In particular, the specified parameters Q and r can be real-valued. Therefore, by tuning Q, the oscillatory behavior of the wavelet can be chosen to match the oscillatory behavior of the signal of interest, so as to enhance the sparsity of a sparse signal representation. The TQWT is well suited to fast algorithms for sparsity-based inverse problems because it is a Parseval frame, easily invertible, and can be efficiently implemented. The TQWT can also be used as an easily-invertible discrete approximation of the continuous wavelet transform. The transform is based on a real valued scaling factor (dilation-factor) and is implemented using a perfect reconstruction over-sampled filter bank with real-valued sampling factors. The transform is parameterized by its Q-factor and its oversampling rate (redundancy), with modest oversampling rates (e. g. 3-4 times overcomplete) being sufficient for the analysis/synthesis functions to be well localized. Therefore, This method services good performance in image processing fields.

• ETRI Journal
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• v.36 no.3
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• pp.460-468
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• 2014

This paper addresses the problem of joint time of arrival (TOA) and direction of arrival (DOA) estimation in impulse radio ultra-wideband systems with a two-antenna receiver and links the joint estimation of TOA and DOA to the sparse representation framework. Exploiting this link, an orthogonal matching pursuit algorithm is used for TOA estimation in the two antennas, and then the DOA parameters are estimated via the difference in the TOAs between the two antennas. The proposed algorithm can work well with a single measurement vector and can pair TOA and DOA parameters. Furthermore, it has better parameter-estimation performance than traditional propagator methods, such as, estimation of signal parameters via rotational invariance techniques algorithms matrix pencil algorithms, and other new joint-estimation schemes, with one single snapshot. The simulation results verify the usefulness of the proposed algorithm.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.49 no.1
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• pp.52-58
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• 2000

Signal propagation in nonlinear optical fiber is analyzed numerically by using SS-FEM (Split-Step Finite Element Method). By adopting cubic element function in FEM, soliton equation of which exact solution was well known, has been solved. Also, accuracy of numerical results and computing times are compared with those of Fourier method, and we have found that solution obtained from using FEM was very relatively accurate. Especially, to reduce CPU time in matrix computation in each step, the matrix imposed by the boundary condition is approximated as a sparse matrix. As a result, computation time was shortened even with the same or better accuracy when compared to those of the conventional FEM and Fourier method.

• ETRI Journal
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• v.41 no.3
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• pp.298-307
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• 2019

This paper investigates the use of the inverse-free sparse Bayesian learning (SBL) approach for peak-to-average power ratio (PAPR) reduction in orthogonal frequency-division multiplexing (OFDM)-based multiuser massive multiple-input multiple-output (MIMO) systems. The Bayesian inference method employs a truncated Gaussian mixture prior for the sought-after low-PAPR signal. To learn the prior signal, associated hyperparameters and underlying statistical parameters, we use the variational expectation-maximization (EM) iterative algorithm. The matrix inversion involved in the expectation step (E-step) is averted by invoking a relaxed evidence lower bound (relaxed-ELBO). The resulting inverse-free SBL algorithm has a much lower complexity than the standard SBL algorithm. Numerical experiments confirm the substantial improvement over existing methods in terms of PAPR reduction for different MIMO configurations.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.9
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• pp.952-958
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• 2014

In this paper, we propose a parametric sparse recovery algorithm(SRA) applied to a radar signal model, based on the compressive sensing(CS), for the ISAR(Inverse Synthetic Aperture Radar) image reconstruction from an incomplete radar-cross-section(RCS) data and for the estimation of rotation rate of a target. As the SRA, the iteratively-reweighted-least-square(IRLS) is combined with the radar signal model including chirp components with unknown chirp rate in the cross-range direction. In addition, the particle swarm optimization(PSO) technique is considered for searching correct parameters related to the rotation rate. Therefore, the parametric SRA based on the IRLS can reconstruct ISAR image and estimate the rotation rate of a target efficiently, although there exists missing data in observed RCS data samples. The performance of the proposed method in terms of image entropy is also compared with that of the traditional interpolation methods for the incomplete RCS data.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.6
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• pp.445-451
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• 2021

Due to the recent spread and increasing damage of COVID-19, the most important measure to prevent infection is to find infected people early. Group testing which introduced half a century ago, can be used as a diagnostic method for COVID-19 and has become very efficient method. In this paper, we review the fundamental principles of existing group testing algorithms. In addition, the sparse signal reconstruction approach proposed by compressed sensing is improved and presented as a solution to group testing. Compressed sensing and group testing differ in computational methods, but are similar in that they find sparse signals. The our simulation results show the superiority of the proposed sparse signal reconstruction method. It is noteworthy that the proposed method shows performance improvement over other algorithms in the group testing schemes. It also shows performance improvement when finding a large number of defective samples.

• Journal of Broadcast Engineering
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• v.1 no.1
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• pp.44-54
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• 1996

In this paper, we propose a new 3-D deinterlacing algorithm based on wide sparse vector correlations and a vertical edge based motion detection algorithm. which is an extension of the deinterlacing algorithm proposed in [10. llJ by the authors. The prooised algorithm is developed mainly for the format conversion problem encountered in current HDTV system, but can also be aplicable to the double scan conversion problesm frequently encountered in ths NTSC systems. By exploiting the edge oriented spatial interpolation based on the wide vector correlations, visually annoying artifiacts caused by interlacing such as a serrate line. line crawling, a line flicker, and a large area flicker can be remarkably reduced since the use of the wide vectors increases the range of the edge orientations that can be detected, and by exploiting sparse vectors correlations the HjW complexity for realizing the algorithm in applications cam be significantly simplified. Simulations are provided indicating thet the proposed algorithm results in a high performance comparable to the performance of the deinterlacing algorithm. based on the wide vector correlations.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.07a
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• pp.317-318
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• 2012

Sparse한 신호 복원 방법으로 underdetemined system에서 11-minimization을 이용한 compressive sensing의 연구와 함께, 11-minimization 비해 간단한 greed 알고리듬도 활발히 연구되고 있다. 이에 본 논문은 greed 알고리듬의 대표적인 orthogonal matching pursuit기법에서 iteration 마다 support 선택 개수에 따른 성능을 연구한다. 모의 실험을 통해 OMP의 iteration 단계에서 하나의 support만 선택하는 것보다 다수의 support를 선택하는 것이 더 낮은 sparsity의 신호를 복원할 수 있고 더 낮은 계산량의 이득을 가져오는 것을 확인 할 수 있다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.5
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• pp.538-542
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• 2018

Echo generated in digital hearing aid vent give rise to user's discomfort. For cancelling feedback echo in vent, it is required to estimate vent impulse response exactly. The vent impulse response has time varying and sparse characteristics. The IPNLMS has been known a useful adaptive algorithm to estimate vent impulse response with these characteristics. In this paper, subband sparse adaptive filter which applying IPNLMS to subband hearing aid structure is proposed to cancel echo of vent by estimating sparse vent impulse response. In the propose method, the decomposition of input signal to subband can pre-whiten each subband signal, so adaptive filter convergence speed can be improved. And the poly phase component decomposition of adaptive filter increases sparsity of each components, and the better echo cancellation can be possible without additional computation. To derive coefficients update equation of the adaptive filter, by defining the cost function based weight NLMS is defined, and the coefficient update equation of each subband is derived. For verifying performances of the adaptive filter, convergence speed, and steady state error by white signal input, and echo cancelling results by real speech input are evaluated by comparing conventional adaptive filters.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.7
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• pp.560-565
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• 2018

The SPICE (Sparse Iterative Covariance-based Estimation) algorithm estimates the azimuth angle by applying a sparse recovery method to the covariance matrix in the time domain. In this paper, we show how the SPICE algorithm, which was originally formulated in the time domain, can be extended to the frequency domain. Furthermore, we demonstrate, through numerical results, that the performance of the proposed algorithm is superior to that of the conventional frequency domain algorithm.