• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.9
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• pp.3559-3571
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• 2015

This paper addresses the problem of signal acquisition with a sparse representation in a given orthonormal basis using fewer noisy measurements. The authors formulate the problem statement for randomly measuring with strong signal noise. The impact of white Gaussian signals noise on the recovery performance is analyzed to provide a theoretical basis for the reasonable design of the measurement matrix. With the idea that the measurement matrix can be adapted for noise suppression in the adaptive CS system, an adapted selective compressive sensing (ASCS) scheme is proposed whose measurement matrix can be updated according to the noise information fed back by the processing center. In terms of objective recovery quality, failure rate and mean-square error (MSE), a comparison is made with some nonadaptive methods and existing CS measurement approaches. Extensive numerical experiments show that the proposed scheme has better noise suppression performance and improves the support recovery of sparse signal. The proposed scheme should have a great potential and bright prospect of broadband signals such as biological signal measurement and radar signal detection.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.07a
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• pp.61-63
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• 2010

Recent work in compressed sensing theory shows that m${\times}$n independent and identically distributed sensing matrices whose entries are drawn independently from certain probability distributions guarantee exact recovery of a sparse signal with high probability even if m${\ll}$n. In particular, it is well understood that the L1 minimization algorithm is able to recover sparse signals from incomplete measurements. In this paper, we propose a novel sparse signal reconstruction method that is based on the reweighted L1 minimization via support recovery.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.360-369
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• 2017

In this paper, a new iterative algorithm for reconstructing block sparse signals, called block backtracking-based adaptive orthogonal matching pursuit (BBAOMP) method, is proposed. Compared with existing methods, the BBAOMP method can bring some flexibility between computational complexity and reconstruction property by using the backtracking step. Another outstanding advantage of BBAOMP algorithm is that it can be done without another information of signal sparsity. Several experiments illustrate that the BBAOMP algorithm occupies certain superiority in terms of probability of exact reconstruction and running time.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.2
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• pp.122-129
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• 2012

As a greedy algorithm reconstructing the sparse signal from underdetermined system, orthogonal matching pursuit (OMP) algorithm has received much attention in recent years. In this paper, we present an extension of OMP for pursuing efficiency of the index selection. Our approach, referred to as generalized OMP (gOMP), is literally a generalization of the OMP in the sense that multiple (N) columns are identified per step. Using the restricted isometry property (RIP), we derive the condition for gOMP to recover the sparse signal exactly. The gOMP guarantees to reconstruct sparse signal when the sensing matrix satisfies the RIP constant ${\delta}_{NK}$ < $\frac{\sqrt{N}}{\sqrt{K}+2\sqrt{N}}$. In addition, we show recovery performance and the reduced number of iteration required to recover the sparse signal.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.6
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• pp.628-630
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• 2016

Compressive sensing can recover an original sparse signal from a few measurements. Its performance is affected by the number of non-zero elements in the signal. The knowledge of partial locations of non-zero elements can improve the recovery performance. In this paper, we apply the partial location knowledge to the multipath matching pursuit. The numerical results show it improves the signal recovery performance and the channel estimation performance in the ITU-VB channel.

• Journal of Digital Contents Society
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• v.16 no.5
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• pp.805-813
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• 2015

Many researchers expect the compressive sensing and sparse recovery problem can overcome the limitation of conventional digital techniques. However, these new approaches require to solve the l1 norm optimization problems when it comes to signal reconstruction. In the signal reconstruction process, the transform computation by multiplication of a random matrix and a vector consumes considerable computing power. To address this issue, parallel processing is applied to the optimization problems. In particular, due to huge size of original signal, it is hard to store the random matrix directly in memory, which makes one need to design a procedural approach in handling the random matrix. This paper presents a new parallel algorithm to calculate random partial Haar wavelet transform based on Single Instruction Multiple Threads (SIMT) platform.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.7
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• pp.424-430
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• 2014

In this paper, we propose a compressed sensing-based signal recovery technique for an uplink multi-user spatial modulation (MU-SM) system. In the MU-SM system, only one antenna among $N_t$ antennas of each user becomes active by nature. Thus, this characteristics is exploited for signal recovery at a base station. We modify the conventional orthogonal matching pursuit (OMP) algorithm which has been widely used for sparse signal recovery in literature for the MU-SM system, which is called MU-OMP. We also propose a parallel OMP algorithm for the MU-SM system, which is called MU-POMP. Specifically, in the proposed algorithms, antenna indices of a specific user who was selected in the previous iteration are excluded in the next iteration of the OMP algorithm. Simulation results show that the proposed algorithms outperform the conventional OMP algorithm in the MU-SM system.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.2
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• pp.134-140
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• 2011

Recent work in compressed sensing theory shows that $M{\times}N$ independent and identically distributed sensing matrix whose entries are drawn independently from certain probability distributions guarantee exact recovery of a sparse signal with high probability even if $M{\ll}N$. In particular, it is well understood that the $L_1$-minimization algorithm is able to recover sparse signals from incomplete measurements. In this paper, we propose a novel sparse signal reconstruction method that is based on the reweighted $L_1$-minimization via support detection.

• The Journal of the Acoustical Society of Korea
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• v.42 no.6
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• pp.529-535
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• 2023

Noise originating from incipient propeller cavitation is assumed to come from a limited number of sources emitting a broadband signal. Conventional methods for cavitation localization have limitations because they cannot distinguish adjacent sound sources effectively due to low accuracy and resolution. On the other hand, sparse Bayesian learning technique demonstrates high-resolution restoration performance for sparse signals and offers greater resolution compared to conventional cavitation localization methods. In this paper, an incipient propeller cavitation localization method using sparse Bayesian learning is proposed and shown to be superior to the conventional method in terms of accuracy and resolution through experimental data from a model ship.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.5103-5115
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• 2015

In radar imaging, a target is usually consisted of a few strong scatterers which are sparsely distributed. In this paper, an improved sparse signal recovery algorithm based on smoothed l₀ (SL0) norm method is proposed to achieve high resolution ISAR imaging with limited pulse numbers. Firstly, one new smoothed function is proposed to approximate the l₀ norm to measure the sparsity. Then a single loop step is used instead of two loop layers in SL0 method which increases the searching density of variable parameter to ensure the recovery accuracy without increasing computation amount, the cost function is undated in every loop for the next loop until the termination is satisfied. Finally, the new set of solution is projected into the feasible set. Simulation results show that the proposed algorithm is superior to the several popular methods both in terms of the reconstruction performance and computation time. Real data ISAR imaging obtained by the proposed algorithm is competitive to several other methods.