• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.969-974
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• 2004

Although guided-waves are very efficient for long-range nondestructive damage inspection, it is not easy to extract meaningful pulses of small magnitude out of noisy signals. The ultimate goal of this research is to develop an efficient signal processing technique for the current guided-wave technology. The specific contribution of this investigation towards achieving this goal, a two-stage Gabor pulse-based matching pursuit algorithm is proposed : rough approximations with a set for predetermined parameters characterizing the Gabor pulse and fine adjustments of the parameters by optimization. The parameters estimated from the measured signal are then used to assess not only the location but also the size of a crack existing in a rod. To validate the effectiveness of the proposed method, the longitudinal wave-based damage detection in rods is considered. To estimate the crack size, Love's theory for the dispersion of longitudinal waves is employed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.9
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• pp.2087-2093
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• 2014

In this paper, an orthogonal matching pursuit (OMP) method combined with genetic algorithm (GA), named GAOMP, is proposed for sparse signal recovery. Some recent greedy algorithms such as SP, CoSaMP, and gOMP improved the reconstruction performance by deleting unsuitable atoms at each iteration. However they still often fail to converge to the solution because the support set could not avoid the local minimum during the iterations. Mutating the candidate support set chosen by the OMP algorithm, GAOMP is able to escape from the local minimum and hence recovers the sparse signal. Experimental results show that GAOMP outperforms several OMP based algorithms and the $l_1$ optimization method in terms of exact reconstruction probability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2400-2413
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• 2019

Doubly-selective (DS) fading channel is often occurred in many orthogonal frequency division multiplexing (OFDM) communication systems, such as high-speed rail communication systems and underwater acoustic (UWA) wireless networks. It is challenging to provide an accurate and fast estimation over the doubly-selective channel, due to the strong Doppler shift. This paper addresses the doubly selective channel estimation problem based on complex exponential basis expansion model (CE-BEM) in OFDM systems from the perspective of distributed compressive sensing (DCS). We propose a novel DCS-based improved sparsity adaptive matching pursuit (DCS-IMSAMP) algorithm. The advantage of the proposed algorithm is that it can exploit the joint channel sparsity information using dynamic threshold, variable step size and tailoring mechanism. Simulation results show that the proposed algorithm achieves 5dB performance gain with faster operation speed, in comparison with traditional DCS-based sparsity adaptive matching pursuit (DCS-SAMP) algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.2
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• pp.583-599
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• 2015

We apply the Bayesian matching pursuit (BMP) algorithm to the estimation of time-frequency selective channels in orthogonal frequency division multiplexing (OFDM) systems. By exploiting prior statistics and sparse characteristics of propagation channels, the Bayesian method provides a more accurate and efficient detection of the channel status information (CSI) than do conventional sparse channel estimation methods that are based on compressive sensing (CS) technologies. Using a reasonable approximation of the system model and a skillfully designed pilot arrangement, the proposed estimation scheme is able to address the Doppler-induced inter-carrier interference (ICI) with a relatively low complexity. Moreover, to further reduce the computational cost of the channel estimation, we make some modifications to the BMP algorithm. The modified algorithm can make good use of the group-sparse structure of doubly selective channels and thus reconstruct the CSI more efficiently than does the original BMP algorithm, which treats the sparse signals in the conventional manner and ignores the specific structure of their sparsity patterns. Numerical results demonstrate that the proposed Bayesian estimation has a good performance over rapidly time-varying channels.

• ETRI Journal
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• v.42 no.3
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• pp.376-387
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• 2020

Massive computation of the reconstruction algorithm for compressive sensing (CS) has been a major concern for its real-time application. In this paper, we propose a novel high-speed architecture for the orthogonal matching pursuit (OMP) algorithm, which is the most frequently used to reconstruct compressively sensed signals. The proposed design offers a very high throughput and includes an innovative pipeline architecture and scheduling algorithm. Least-squares problem solving, which requires a huge amount of computations in the OMP, is implemented by using systolic arrays with four new processing elements. In addition, a distributed-arithmetic-based circuit for matrix multiplication is proposed to counterbalance the area overhead caused by the multi-stage pipelining. The results of logic synthesis show that the proposed design reconstructs signals nearly 19 times faster while occupying an only 1.06 times larger area than the existing designs for N = 256, M = 64, and m = 16, where N is the number of the original samples, M is the length of the measurement vector, and m is the sparsity level of the signal.

• Journal of the Optical Society of Korea
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• v.20 no.4
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• pp.515-523
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• 2016

Spectral reflectance is sparse in space, and while the traditional spectral-reconstruction algorithm does not make full use of this characteristic sparseness, the compressive sensing algorithm can make full use of it. In this paper, on the basis of analyzing compressive sensing based on the orthogonal matching pursuit algorithm, a new algorithm based on the Dice matching criterion is proposed. The Dice similarity coefficient is introduced, to calculate the correlation coefficient of the atoms and the residual error, and is used to select the atoms from a library. The accuracy of Spectral reconstruction based on the pseudo-inverse method, Wiener estimation method, OMP algorithm, and DOMP algorithm is compared by simulation on the MATLAB platform and experimental testing. The result is that spectral-reconstruction accuracy based on the DOMP algorithm is higher than for the other three methods. The root-mean-square error and color difference decreases with an increasing number of principal components. The reconstruction error decreases as the number of iterations increases. Spectral reconstruction based on the DOMP algorithm can improve the accuracy of color-information replication effectively, and high-accuracy color-information reproduction can be realized.

• The Journal of the Acoustical Society of Korea
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• v.24 no.7
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• pp.402-409
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• 2005

In this paper. we propose a coding method using a matching pursuit algorithm in a strongly periodic highband signal. Also. we propose an efficient quantizer for the estimated parameters : spectral magnitude and phase. Based on the error concealment principle and sinusoidal model. the MP algorithm requires the high-precision pitch period estimation. To estimate more accurate pitch period. the refined pitch obtained from lowband speech is used. which increases the efficiency of bit allocation. The spectral magnitude parameters are quantized by the method which is combined with MDCT (Modified Discrete Cosine Transform) and multi-stage structure. The spectral phase quantizer uses the $2{\pi}$ modular characteristic of phases and the weighted function by spectral magnitudes. To evaluate the efficiency of the proposed method. we applied it to analysis-by-synthesis system. Furthermore we suggest the possibillity of scalable wideband speech codecs based on band-split structure.

• Journal of IKEEE
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• v.17 no.3
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• pp.339-345
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• 2013

In this paper, the probabilistic exclusion based orthogonal matching pursuit (PEOMP) algorithm for the sparse signal reconstruction is proposed. Some of recent greedy algorithms such as CoSaMP, gOMP, BAOMP improved the reconstruction performance by deleting unsuitable atoms at each iteration. They still often fail to converge to the solution because the support set could not escape from a local minimum. PEOMP helps to escape by excluding a random atom in the support set according to a well-chosen probability function. Experimental results show that PEOMP outperforms several OMP based algorithms and the $l_1$ optimization method in terms of exact reconstruction probability.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.15 no.4 s.97
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• pp.382-387
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• 2005

For successful guided-wave damage inspection, the appropriate signal processing of measured wave signals is very important. The objective of this paper is to introduce an efficient signal processing technique especially suitable for the guided-waves used for damage detection. The key idea of this technique is to model guided-waves by chirp functions of special form considering the dispersion phenomenon. To determine the parameter of the chirp functions simulating guided-waves, the matching pursuit algorithm is employed. The damage information in waveguides can be extracted by pulse-characterizing parameters. The effectiveness of present method is checked with the guided wave-based damage inspection.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.11a
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• pp.615-618
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• 2004

For successful guided-wave damage inspection, the appropriate signal processing of measured wave signals is very important. The objective of this paper is to introduce an efficient signal processing technique especially suitable for the guided-waves used for damage detection. The key idea of this technique is to model guided-waves by chirp functions of special form considering the dispersion phenomenon. To determine the parameter of the chirp functions simulating guided-waves, the matching pursuit algorithm is employed. The damage information in waveguides can be extracted by pulse-characterizing parameters. The effectiveness of present method is checked with the longitudinal wave-based damage inspection.