• Title/Summary/Keyword: Matching pursuit algorithm

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Magnetic Resonance Imaging Using Matching Pursuit (Matching Pursuit 방법을 이용한 MR영상법에 관한 연구)

  • Ro, Y.M.;Zakhora, Avideh
    • Proceedings of the KOSOMBE Conference
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    • v.1997 no.05
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    • pp.230-234
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    • 1997
  • The matching pursuit (MP) algorithm developed by S. Mallat and Z. Zhang is applied to magnetic resonance (MR) imaging. Since matching pursuit is a greedy algorithm to find waveforms which are the best match for an object-signal, the signal can be decomposed with a few iterations. In this paper, we propose an application of the MP algorithm to the MR imaging to reduce imaging time. Inner products of residual signals and selected waveforms in the MP algorithm are derived from the MR signals by excitation of RF pulses which are fourier transforms of selected waveforms. Results from computer simulations demonstrate that the imaging time is reduced by using the MP algorithm and further a progressive reconstruction can be achieved.

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Fine Granular Scalable Coding using Matching Pursuit with Multi-Step Search (다단계 탐색 기반 Matching Pursuit을 이용한 미세 계층적 부호화 기법)

  • 최웅일
    • Journal of Broadcast Engineering
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    • v.6 no.3
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    • pp.225-233
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    • 2001
  • Real-time video communication applications over Internet should be able to support such functionality as scalability because of the unpredictable and varying channel bandwidth between server and client. To accommodatethe wide variety of channel bitrates, a new scalable coding tool, namely the Fine Granular Scalability (FGS) coding tool has been reduce the adopted In the MPEG-4 video standard. This paper presentsa new FGS algorithm with matching Pursuit that can reduce the computational complexity of ordinal matching pursuit-based algorithm. The Proposed coding algorithm can make trade-off between Picture Quality and computationalcomplexity. Our simulation result shows that the proposed algorithm can reduce the computational cumplexity up to 1/5 compared to the conventional FGS method while retaining a similar picture quality.

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Multiple Candidate Matching Pursuit (다중 후보 매칭 퍼슛)

  • Kwon, Seokbeop;Shim, Byonghyo
    • Journal of Broadcast Engineering
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    • v.17 no.6
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    • pp.954-963
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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 this paper, we multiple candidate matching pursuit (MuCaMP), which builds up candidate support set in every iteration and uses the minimum residual at last iteration. Using the restricted isometry property (RIP), we derive the sufficient condition for MuCaMP to recover the sparse signal exactly. The MuCaMP guarantees to reconstruct the K-sparse signal when the sensing matrix satisfies the RIP constant ${\delta}_{N+K}<\frac{\sqrt{N}}{\sqrt{K}+3\sqrt{N}}$. In addition, we show a recovery performance both noiseless and noisy measurements.

Computational performance and accuracy of compressive sensing algorithms for range-Doppler estimation (거리-도플러 추정을 위한 압축 센싱 알고리즘의 계산 성능과 정확도)

  • Lee, Hyunkyu;Lee, Keunhwa;Hong, Wooyoung;Lim, Jun-Seok;Cheong, Myoung-Jun
    • The Journal of the Acoustical Society of Korea
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    • v.38 no.5
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    • pp.534-542
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    • 2019
  • In active SONAR, several different methods are used to detect range-Doppler information of the target. Compressive sensing based method is more accurate than conventional methods and shows superior performance. There are several compressive sensing algorithms for range-Doppler estimation of active sonar. The ability of each algorithm depends on algorithm type, mutual coherence of sensing matrix, and signal to noise ratio. In this paper, we compared and analyzed computational performance and accuracy of various compressive sensing algorithms for range-Doppler estimation of active sonar. The performance of OMP (Orthogonal Matching Pursuit), CoSaMP (Compressive Sampling Matching Pursuit), BPDN (CVX) (Basis Pursuit Denoising), LARS (Least Angle Regression) algorithms is respectively estimated for varying SNR (Signal to Noise Ratio), and mutual coherence. The optimal compressive sensing algorithm is presented according to the situation.

Stagewise Weak Orthogonal Matching Pursuit Algorithm Based on Adaptive Weak Threshold and Arithmetic Mean

  • Zhao, Liquan;Ma, Ke
    • Journal of Information Processing Systems
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    • v.16 no.6
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    • pp.1343-1358
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    • 2020
  • In the stagewise arithmetic orthogonal matching pursuit algorithm, the weak threshold used in sparsity estimation is determined via maximum iterations. Different maximum iterations correspond to different thresholds and affect the performance of the algorithm. To solve this problem, we propose an improved variable weak threshold based on the stagewise arithmetic orthogonal matching pursuit algorithm. Our proposed algorithm uses the residual error value to control the weak threshold. When the residual value decreases, the threshold value continuously increases, so that the atoms contained in the atomic set are closer to the real sparsity value, making it possible to improve the reconstruction accuracy. In addition, we improved the generalized Jaccard coefficient in order to replace the inner product method that is used in the stagewise arithmetic orthogonal matching pursuit algorithm. Our proposed algorithm uses the covariance to replace the joint expectation for two variables based on the generalized Jaccard coefficient. The improved generalized Jaccard coefficient can be used to generate a more accurate calculation of the correlation between the measurement matrixes. In addition, the residual is more accurate, which can reduce the possibility of selecting the wrong atoms. We demonstrate using simulations that the proposed algorithm produces a better reconstruction result in the reconstruction of a one-dimensional signal and two-dimensional image signal.

Fast Matching Pursuit Method Using Property of Symmetry and Classification for Scalable Video Coding

  • Oh, Soekbyeung;Jeon, Byeungwoo
    • Proceedings of the IEEK Conference
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    • 2000.07a
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    • pp.278-281
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    • 2000
  • Matching pursuit algorithm is a signal expansion technique whose efficiency for motion compensated residual image has already been demonstrated in the MPEG-4 framework. However, one of the practical concerns related to applying matching pursuit algorithm to real-time scalable video coding is its massive computation required for finding dictionary elements. In this respective, this paper proposes a fast algorithm, which is composed of three sub-methods. The first method utilizes the property of symmetry in 1-D dictionary element and the second uses mathematical elimination of inner product calculation in advance, and the last one uses frequency property of 2-D dictionary. Experimental results show that our algorithm needs about 30% computational load compared to the conventional fast algorithm using separable property of 2-D gabor dictionary with negligible quality degradation.

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Generalized Orthogonal Matching Pursuit (일반화된 직교 매칭 퍼슛 알고리듬)

  • Kwon, Seok-Beop;Shim, Byong-Hyo
    • 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.

Fast Matching Pursuit based on Vector Length Comparison (벡터길이 비교를 이용한 고속 Matching Pursuit)

  • O, Seok-Byeong;Jeon, Byeong-U
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.38 no.2
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    • pp.129-137
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    • 2001
  • Matching pursuit algorithm was successfully demonstrated useful in low bit-rate video coding, However, one of the practical concerns related to applying the matching pursuit algorithm to application is its massive computation required for finding bases whose weighted sum best approximates the given input image. The main contribution of this paper is that we provide a new method that can drastically reduce the computational load without any degradation of image quality. Its main idea is based on reducing the number of inner product calculation required for finding best bases because the complexity of matching pursuit algorithm is due to the exhaustive local inner product calculation. As the first step, we compute a matrix which is the 1-D inner product of the given motion-compensated error input image with the 1-D vertical Gabor functions using the separable property of Gabor bases. In the second step, we calculate length of each vector in the matrix that corresponds to 1-D horizontal Gabor function, and compare the length with the current maximum absolute inner product value so far. According to the result of this comparison, one can decide whether or not to calculate the inner product. Since most of them do not need to calculate the inner product value, one can significantly reduce the computational load. Experimental results show that proposed method reduces about 70% of inner product calculation compared to the Neff's fast algorithm without any degradation of image quality.

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Massive MIMO Channel Estimation Algorithm Based on Weighted Compressed Sensing

  • Lv, Zhiguo;Wang, Weijing
    • Journal of Information Processing Systems
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    • v.17 no.6
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    • pp.1083-1096
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    • 2021
  • Compressed sensing-based matching pursuit algorithms can estimate the sparse channel of massive multiple input multiple-output systems with short pilot sequences. Although they have the advantages of low computational complexity and low pilot overhead, their accuracy remains insufficient. Simply multiplying the weight value and the estimated channel obtained in different iterations can only improve the accuracy of channel estimation under conditions of low signal-to-noise ratio (SNR), whereas it degrades accuracy under conditions of high SNR. To address this issue, an improved weighted matching pursuit algorithm is proposed, which obtains a suitable weight value uop by training the channel data. The step of the weight value increasing with successive iterations is calculated according to the sparsity of the channel and uop. Adjusting the weight value adaptively over the iterations can further improve the accuracy of estimation. The results of simulations conducted to evaluate the proposed algorithm show that it exhibits improved performance in terms of accuracy compared to previous methods under conditions of both high and low SNR.

Channel estimation of OFDM System using Matching Pursuit method (Matching Pursuit 방식을 이용한 OFDM 시스템의 채널 추정)

  • Choi Jae Hwan;Lim Chae Hyun;Han Dong Seog;Yoon Dae Jung
    • Journal of Broadcast Engineering
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    • v.10 no.2
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    • pp.166-173
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    • 2005
  • In this paper, we propose a mobile channel estimation algorithm using matching pursuit algorithm for orthogonal frequency division multiplexing (OFDM) systems. Least square (LS) algorithm, which is used as a conventional channel estimation algorithm for OFDM systems, has error probability of channel estimation affected by effects of noise. By estimating the channel of sparse type, the proposed algorithm reduces effects of noise during time intervals that multi-path signal doesn't exist. The proposed algorithm estimates a mobile receivingchannel using pilot information transmitted consequently. We compare performance of the proposed algorithm with the LS algorithm by measuring symbol error rate with 64QAM under a mobile multi-path fading channel model.