• Smart Structures and Systems
• /
• 제25권3호
• /
• pp.369-384
• /
• 2020

A wavefield sparse reconstruction technique based on compressed sensing is developed in this work to dramatically reduce the number of measurements. Firstly, a severely underdetermined representation of guided wavefield at a snapshot is established in the spatial domain. Secondly, an optimal compressed sensing model of guided wavefield sparse reconstruction is established based on l₁-norm penalty, where a suite of discrete cosine functions is selected as the dictionary to promote the sparsity. The regular, random and jittered undersampling schemes are compared and selected as the undersampling matrix of compressed sensing. Thirdly, a gradient projection method is employed to solve the compressed sensing model of wavefield sparse reconstruction from highly incomplete measurements. Finally, experiments with different excitation frequencies are conducted on an aluminum plate to verify the effectiveness of the proposed sparse reconstruction method, where a scanning laser Doppler vibrometer as the true benchmark is used to measure the original wavefield in a given inspection region. Experiments demonstrate that the missing wavefield data can be accurately reconstructed from less than 12% of the original measurements; The reconstruction accuracy of the jittered undersampling scheme is slightly higher than that of the random undersampling scheme in high probability, but the regular undersampling scheme fails to reconstruct the wavefield image; A quantified mapping relationship between the sparsity ratio and the recovery error over a special interval is established with respect to statistical modeling and analysis.

• Smart Structures and Systems
• /
• 제25권2호
• /
• pp.123-133
• /
• 2020

Compressive sensing (CS) is a newly developed data acquisition and processing technique that takes advantage of the sparse structure in signals. Normally signals in their primitive space or format are reconstructed from their compressed measurements for further treatments, such as modal analysis for vibration data. This approach causes problems such as leakage, loss of fidelity, etc., and the computation of reconstruction itself is costly as well. Therefore, it is appealing to directly work on the compressed data without prior reconstruction of the original data. In this paper, a direct approach for modal analysis of damped systems is proposed by decomposing the compressed measurements with an appropriate dictionary. The damped free vibration function is adopted to form atoms in the dictionary for the following sparse decomposition. Compared with the normally used Fourier bases, the damped free vibration function spans a space with both the frequency and damping as the control variables. In order to efficiently search the enormous two-dimension dictionary with frequency and damping as variables, a two-step strategy is implemented combined with the Orthogonal Matching Pursuit (OMP) to determine the optimal atom in the dictionary, which greatly reduces the computation of the sparse decomposition. The performance of the proposed method is demonstrated by a numerical and an experimental example, and advantages of the method are revealed by comparison with another such kind method using POD technique.

• 한국콘텐츠학회:학술대회논문집
• /
• 한국콘텐츠학회 2018년도 춘계 종합학술대회 논문집
• /
• pp.485-486
• /
• 2018

In this paper, we consider a binary recovery framework of the Compressed Sensing (CS) problem. We derive an upper bound for $L_0$ recovery performance of a binary sparse signal in terms of the dimension N and sparsity K of signals, the number of measurements M. We show that the upper bound obtained from this work goes to the limit bound when the sensing matrix sufficiently become dense. In addition, for perfect recovery performance, if the signals are very sparse, the sensing matrices required for $L_0$ recovery are little more dense.

• 대한전자공학회논문지TC
• /
• 제49권5호
• /
• pp.59-65
• /
• 2012

본 논문에서는 super-RENS 디스크의 채널 모델링을 위하여 압축 센싱 알고리즘에 기반한 sparse Volterra 필터에 대해 연구하였다. Super-RENS 디스크 시스템에서 심한 비선형 심벌간 간섭(ISI)이 발생하는 것은 익히 알려진 사실이다. 메모리를 가진 비선형 시스템은 Volterra 급수로 모델링할 수 있다. 또한, 압축 센싱은 측정치로부터 성긴 또는 압축된 신호를 복원할 수 있다. 이러한 이유로 super-RENS의 성긴 특성을 갖는 read-out 채널을 예측하기 위해 압축 센싱 알고리즘을 사용하였다. 평가 결과는 압축 센싱 알고리즘으로 super-RENS의 read-out 채널을 위한 sparse Volterra 모델을 효과적으로 구성할 수 있음을 보여준다.

• Journal of Information Processing Systems
• /
• 제15권2호
• /
• pp.410-421
• /
• 2019

Distributed compressed sensing (DCS) states that we can recover the sparse signals from very few linear measurements. Various studies about DCS have been carried out recently. In many practical applications, there is no prior information except for standard sparsity on signals. The typical example is the sparse signals have block-sparse structures whose non-zero coefficients occurring in clusters, while the cluster pattern is usually unavailable as the prior information. To discuss this issue, a new algorithm, called backtracking-based adaptive orthogonal matching pursuit for block distributed compressed sensing (DCSBBAOMP), is proposed. In contrast to existing block methods which consider the single-channel signal reconstruction, the DCSBBAOMP resorts to the multi-channel signals reconstruction. Moreover, this algorithm is an iterative approach, which consists of forward selection and backward removal stages in each iteration. An advantage of this method is that perfect reconstruction performance can be achieved without prior information on the block-sparsity structure. Numerical experiments are provided to illustrate the desirable performance of the proposed method.

• 대한전자공학회논문지SP
• /
• 제48권2호
• /
• pp.134-140
• /
• 2011

압축 센싱 (Compressed Sensing) 기술을 통해 $M{\times}N$ 측정 행렬의 원소들이 특정의 독립적인 확률 분포에서 뽑혀 identically 분포의 성질을 가지고 있을 때 $M{\ll}N$의 경우에도 스파스 (sparse) 신호를 높은 확률로 정확하게 복원할 수 있다. $L_1$-최소화 알고리즘이 불완전한 측정에 대해서도 스파스 (sparse) 신호를 복원할 수 있다는 것은 잘 알려진 사실이다. 본 논문에서는 OMP를 변형시킨 support 검출과 가중치 기법을 이용한 $L_1$-최소화 방법을 통하여 스파스 (sparse) 신호의 복원 성능을 향상시키는 알고리즘을 제안하고자 한다.

• 한국통신학회논문지
• /
• 제40권6호
• /
• pp.1024-1031
• /
• 2015

압축 센싱은 신호가 성긴 (Sparse) 특성을 지니며 선형 측정된 값들이 Incoherent 할 때, 나이퀴스트율 이하로 표본화된 신호를 원본 신호로 정확하게 복구할 수 있는 새로운 신호 획득 이론이다. 본 논문에서는 원본 신호의 Sparse한 정도에 따라 성능이 변화하는 압축 센싱을 이용한 효율적인 신호 검출 및 추정 기법을 제안하며, 이론적 분석과 함께 모의 실험 결과를 보여준다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제9권3호
• /
• pp.1014-1034
• /
• 2015

Location information of sensor nodes plays a critical role in many wireless sensor network (WSN) applications and protocols. Although many localization algorithms have been proposed in recent years, they usually target at dense networks and perform poorly in sparse networks. In this paper, we propose two component-based localization algorithms that can localize many more nodes in sparse networks than the state-of-the-art solution. We first develop the Basic Common nodes-based Localization Algorithm, namely BCLA, which uses both common nodes and measured distances between adjacent components to merge components. BCLA outperforms CALL, the state-of-the-art component-based localization algorithm that uses only distance measurements to merge components. In order to further improve the performance of BCLA, we further exploit the angular information among nodes to merge components, and propose the Component-based Localization with Angle and Distance information algorithm, namely CLAD. We prove the merging conditions for BCLA and CLAD, and evaluate their performance through extensive simulations. Simulations results show that, CLAD can locate more than 90 percent of nodes in a sparse network with average node degree 7.5, while CALL can locate only 78 percent of nodes in the same scenario.

• Journal of Communications and Networks
• /
• 제18권5호
• /
• pp.699-712
• /
• 2016

Recently, greedy algorithm has received much attention as a cost-effective means to reconstruct the sparse signals from compressed measurements. Much of previous work has focused on the investigation of a single candidate to identify the support (index set of nonzero elements) of the sparse signals. Well-known drawback of the greedy approach is that the chosen candidate is often not the optimal solution due to the myopic decision in each iteration. In this paper, we propose a tree search based sparse signal recovery algorithm referred to as the tree search matching pursuit (TSMP). Two key ingredients of the proposed TSMP algorithm to control the computational complexity are the pre-selection to put a restriction on columns of the sensing matrix to be investigated and the tree pruning to eliminate unpromising paths from the search tree. In numerical simulations of Internet of Things (IoT) environments, it is shown that TSMP outperforms conventional schemes by a large margin.

• 한국방송∙미디어공학회:학술대회논문집
• /
• 한국방송공학회 2010년도 하계학술대회
• /
• pp.61-63
• /
• 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.