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Relevant bibliographies by topics / Compressive sampling matching pursuit algorithm

Academic literature on the topic 'Compressive sampling matching pursuit algorithm'

Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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Journal articles on the topic "Compressive sampling matching pursuit algorithm"

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Zhao, Yijiu, Xiaoyan Zhuang, Zhijian Dai, and Houjun Wang. "Wavelet Compressive Sampling Signal Reconstruction Using Upside-Down Tree Structure." Mathematical Problems in Engineering 2011 (2011): 1–10. http://dx.doi.org/10.1155/2011/606974.

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This paper suggests an upside-down tree-based orthogonal matching pursuit (UDT-OMP) compressive sampling signal reconstruction method in wavelet domain. An upside-down tree for the wavelet coefficients of signal is constructed, and an improved version of orthogonal matching pursuit is presented. The proposed algorithm reconstructs compressive sampling signal by exploiting the upside-down tree structure of the wavelet coefficients of signal besides its sparsity in wavelet basis. Compared with conventional greedy pursuit algorithms: orthogonal matching pursuit (OMP) and tree-based orthogonal matching pursuit (TOMP), signal-to-noise ratio (SNR) using UDT-OMP is significantly improved.

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Zhao, Huihuang, Yaonan Wang, Zhijun Qiao, and Bin Fu. "Solder joint imagery compressing and recovery based on compressive sensing." Soldering & Surface Mount Technology 26, no. 3 (May 27, 2014): 129–38. http://dx.doi.org/10.1108/ssmt-09-2013-0024.

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Purpose – The purpose of this paper is to develop an improved compressive sensing algorithm for solder joint imagery compressing and recovery. The improved algorithm can improve the performance in terms of peak signal to noise ratio (PSNR) of solder joint imagery recovery. Design/methodology/approach – Unlike the traditional method, at first, the image was transformed into a sparse signal by discrete cosine transform; then the solder joint image was divided into blocks, and each image block was transformed into a one-dimensional data vector. At last, a block compressive sampling matching pursuit was proposed, and the proposed algorithm with different block sizes was used in recovering the solder joint imagery. Findings – The experiments showed that the proposed algorithm could achieve the best results on PSNR when compared to other methods such as the orthogonal matching pursuit algorithm, greedy basis pursuit algorithm, subspace pursuit algorithm and compressive sampling matching pursuit algorithm. When the block size was 16 × 16, the proposed algorithm could obtain better results than when the block size was 8 × 8 and 4 × 4. Practical implications – The paper provides a methodology for solder joint imagery compressing and recovery, and the proposed algorithm can also be used in other image compressing and recovery applications. Originality/value – According to the compressed sensing (CS) theory, a sparse or compressible signal can be represented by a fewer number of bases than those required by the Nyquist theorem. The findings provide fundamental guidelines to improve performance in image compressing and recovery based on compressive sensing.

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Rao, M. Sreenivasa, K. Krishna Naik, and K. Maheshwara Reddy. "Radar Signal Recovery using Compressive Sampling Matching Pursuit Algorithm." Defence Science Journal 67, no. 1 (December 23, 2016): 94. http://dx.doi.org/10.14429/dsj.67.9906.

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In this study, we propose compressive sampling matching pursuit (CoSaMP) algorithm for sub-Nyquist based electronic warfare (EW) receiver system. In compressed sensing (CS) theory time-frequency plane localisation and discretisation into a N×N grid in union of subspaces is established. The train of radar signals are sparse in time and frequency can be under sampled with almost no information loss. The CS theory may be applied to EW digital receivers to reduce sampling rate of analog to digital converter; to improve radar parameter resolution and increase input bandwidth. Simulated an efficient approach for radar signal recovery by CoSaMP algorithm by using a set of various sample and different sparsity level with various radar signals. This approach allows a scalable and flexible recovery process. The method has been satisfied with data in a wide frequency range up to 40 GHz. The simulation shows the feasibility of our method.

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Zhao, Huihuang, Jianzhen Chen, Shibiao Xu, Ying Wang, and Zhijun Qiao. "Compressive sensing for noisy solder joint imagery based on convex optimization." Soldering & Surface Mount Technology 28, no. 2 (April 4, 2016): 114–22. http://dx.doi.org/10.1108/ssmt-09-2014-0017.

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Purpose The purpose of this paper is to develop a compressive sensing (CS) algorithm for noisy solder joint imagery compression and recovery. A fast gradient-based compressive sensing (FGbCS) approach is proposed based on the convex optimization. The proposed algorithm is able to improve performance in terms of peak signal noise ratio (PSNR) and computational cost. Design/methodology/approach Unlike traditional CS methods, the authors first transformed a noise solder joint image to a sparse signal by a discrete cosine transform (DCT), so that the reconstruction of noisy solder joint imagery is changed to a convex optimization problem. Then, a so-called gradient-based method is utilized for solving the problem. To improve the method efficiency, the authors assume the problem to be convex with the Lipschitz gradient through the replacement of an iteration parameter by the Lipschitz constant. Moreover, a FGbCS algorithm is proposed to recover the noisy solder joint imagery under different parameters. Findings Experiments reveal that the proposed algorithm can achieve better results on PNSR with fewer computational costs than classical algorithms like Orthogonal Matching Pursuit (OMP), Greedy Basis Pursuit (GBP), Subspace Pursuit (SP), Compressive Sampling Matching Pursuit (CoSaMP) and Iterative Re-weighted Least Squares (IRLS). Convergence of the proposed algorithm is with a faster rate O(k*k) instead of O(1/k). Practical implications This paper provides a novel methodology for the CS of noisy solder joint imagery, and the proposed algorithm can also be used in other imagery compression and recovery. Originality/value According to the CS theory, a sparse or compressible signal can be represented by a fewer number of bases than those required by the Nyquist theorem. The new development might provide some fundamental guidelines for noisy imagery compression and recovering.

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Aziz, Ahmed, Karan Singh, Ahmed Elsawy, Walid Osamy, and Ahmed M. Khedr. "GWRA: grey wolf based reconstruction algorithm for compressive sensing signals." PeerJ Computer Science 5 (September 2, 2019): e217. http://dx.doi.org/10.7717/peerj-cs.217.

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The recent advances in compressive sensing (CS) based solutions make it a promising technique for signal acquisition, image processing and other types of data compression needs. In CS, the most challenging problem is to design an accurate and efficient algorithm for reconstructing the original data. Greedy-based reconstruction algorithms proved themselves as a good solution to this problem because of their fast implementation and low complex computations. In this paper, we propose a new optimization algorithm called grey wolf reconstruction algorithm (GWRA). GWRA is inspired from the benefits of integrating both the reversible greedy algorithm and the grey wolf optimizer algorithm. The effectiveness of GWRA technique is demonstrated and validated through rigorous simulations. The simulation results show that GWRA significantly exceeds the greedy-based reconstruction algorithms such as sum product, orthogonal matching pursuit, compressive sampling matching pursuit and filtered back projection and swarm based techniques such as BA and PSO in terms of reducing the reconstruction error, the mean absolute percentage error and the average normalized mean squared error.

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Jarrah, Amin, and Mohsin M. Jamali. "Reconfigurable FPGA/GPU-Based Architecture of Block Compressive Sampling Matching Pursuit Algorithm." Journal of Circuits, Systems and Computers 24, no. 04 (March 4, 2015): 1550055. http://dx.doi.org/10.1142/s0218126615500553.

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The signals in reality are sparse signal where a few numbers of samples are non-zero. So, a compression technique must be applied to reduce the overhead of processing, storing, and transmission. Blocking compressive sampling matching pursuit (BCoSaMP) algorithm is a recursive algorithm which provides an accurate reconstruction of sparse signal from a small number of noisy samples. It doesn't assume that the noise is Gaussian or bounded but it uses information about the noise magnitude for stopping criterion. However, BCoSaMP is a computationally intensive algorithm. So, BCoSaMP algorithm has been implemented on both field-programmable gate array (FPGA) and graphic processing units (GPU) by exploiting parallel and pipelining approaches. A new software tool called radar signal processing tool (RSPT) is also presented. It allows the designer to auto-generate fully optimized VHDL representation of BCoSaMP by specifying many user input parameters through graphical user interface (GUI). Moreover, it provides the designer a feedback on various performance parameters. This offer the designer the ability to make any adjustments to the BCoSaMP component until gets the desired performance of the overall system-on-chip (SoC). Our simulation results indicate that the achieved speed-up of FPGA and GPU over the sequential one is improved by up to 14 and 10.7, respectively.

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Ruiz, Milton, Manuel Jaramillo, Alexander Aguila, Leony Ortiz, and Silvana Varela. "A Novel Data Compression Methodology Focused on Power Quality Signals Using Compressive Sampling Matching Pursuit." Energies 15, no. 24 (December 9, 2022): 9345. http://dx.doi.org/10.3390/en15249345.

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In this research a new data compression technique for electrical signals was proposed. The methodology combined wavelets and compressed sensing techniques. Two algorithms were proposed; the first one was designed to find specific characteristics of any type of energy quality signal such as the number of samples per cycle, zero-crossing indices, and signal amplitude. With the data obtained, the second algorithm was designed to apply a biorthogonal wavelet transform resulting in a shifted signal, and its amplitude was modified with respect to the original. The errors were rectified with the attributes found in the early stage, and the application of filters was conducted to reduce the ripple attached. Then, the third algorithm was designed to apply Compressive Sampling Matching Pursuit, which is a greedy algorithm that creates a dictionary with orthogonal bases representing the original signal in a sparse vector. The results exhibited excellent features of quality and were accomplished by the suggested compression and reconstruction technique. These results were a compression ratio of 1020:1, that is, the signal was compressed by 99.90% with respect to the original one. The quality indicators achieved were RTE = 0.9938, NMSE = 0.0098, and COR = 0.99, exceeding the results of the most relevant research papers published in Q1 high-impact journals that were further discussed in the introduction section.

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Yunfeng, Hu, and Zhao Liquan. "A Fuzzy Selection Compressive Sampling Matching Pursuit Algorithm for its Practical Application." IEEE Access 7 (2019): 144101–24. http://dx.doi.org/10.1109/access.2019.2941725.

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Li, Guo Zhu, De Qiang Wang, Zi Kai Zhang, and Zhi Yong Li. "A Weighted OMP Algorithm for Compressive UWB Channel Estimation." Applied Mechanics and Materials 392 (September 2013): 852–56. http://dx.doi.org/10.4028/www.scientific.net/amm.392.852.

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We investigate ultra-wideband (UWB) channel estimation based on compressive sampling (CS), where the orthogonal matching pursuit (OMP) algorithm is employed to recover the channel waveform from noisy measurements. In order to boost the robustness of OMP in the presence of additive Gaussian noise (AWGN), we propose a weighted OMP (WOMP) algorithm. For a given sparse dictionary, weighting factors are assigned to the atoms and a weighted matching process is performed by WOMP. Simulation results show that the proposed WOMP is more robust than the original OMP and can be used to gain better channel estimation precision.

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Zhou, Huiyuan, and Ram M. Narayanan. "A DUAL-MESH MICROWAVE RECONSTRUCTION METHOD BASED ON COMPRESSIVE SAMPLING MATCHING PURSUIT ALGORITHM." Progress In Electromagnetics Research 166 (2019): 43–57. http://dx.doi.org/10.2528/pier19090203.

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Dissertations / Theses on the topic "Compressive sampling matching pursuit algorithm"

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邱聖友. "Progressive Orthogonal Matching Pursuit Algorithm for Compressive Sensing Reconstruction." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/08495215768700257889.

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Book chapters on the topic "Compressive sampling matching pursuit algorithm"

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Liu, Xiang-pu, Feng Yang, Xiang Yi, and Li-li Guo. "A Sparsity Adaptive Compressive Sampling Matching Pursuit Algorithm." In Proceedings of the 6th International Asia Conference on Industrial Engineering and Management Innovation, 177–87. Paris: Atlantis Press, 2015. http://dx.doi.org/10.2991/978-94-6239-145-1_18.

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C., George, and T. Justin. "Applications of the Orthogonal Matching Pursuit/ Nonlinear Least Squares Algorithm to Compressive Sensing Recovery." In Applications of Digital Signal Processing. InTech, 2011. http://dx.doi.org/10.5772/25842.

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Conference papers on the topic "Compressive sampling matching pursuit algorithm"

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Lu, Tingting, Rui Wang, Jian Zhao, Chao Zhang, Weiwen Su, Jian Jia, and Shunli Zhang. "A Novel and Fast Adaptive Compressive Sampling Matching Pursuit Algorithm." In International Conference on Information System and Management Engineering. SCITEPRESS - Science and and Technology Publications, 2016. http://dx.doi.org/10.5220/0006449603120317.

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Yu, Qi, and Jubo Zhu. "An adaptive matching pursuit algorithm for linear frequency modulation signal compressive sampling." In 2014 7th International Congress on Image and Signal Processing (CISP). IEEE, 2014. http://dx.doi.org/10.1109/cisp.2014.7003897.

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Lin, Huibin, Jianmeng Tang, and Chris Mechefske. "Roller Bearing Fault Feature Extraction Based on Compressive Sensing." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-85196.

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Compressive sensing (CS) theory allows measurement of sparse signals with a sampling rate far lower than the Nyquist sampling frequency. This could reduce the burden of local storage and remote transmitting. The periodic impacts generated in rolling element bearing local faults are obviously sparse in the time domain. According to this sparse feature, a rolling element bearing fault feature extraction method based on CS theory is proposed in the paper. Utilizing the shift invariant dictionary learning algorithm and the periodic presentation characteristic of local faults of roller bearings, a shift-invariant dictionary of which each atom contains only one impact pattern is constructed to represent the fault impact as sparsely as possible. The limited degree of sparsity is utilized to reconstruct the feature components based on compressive sampling matching pursuit (CoSaMP) method, realizing the diagnosis of the roller bearing impact fault. A simulation was used to analyze the effects of parameters such as sparsity, SNR and compressive rate on the proposed method and prove the effectiveness of the proposed method.

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Zobly, Sulieman M. S., and Yasser M. Kadah. "Orthogonal matching pursuit & compressive sampling matching pursuit for Doppler ultrasound signal reconstruction." In 2012 Cairo International Biomedical Engineering Conference (CIBEC). IEEE, 2012. http://dx.doi.org/10.1109/cibec.2012.6473336.

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Ambat, Sooraj K., Saikat Chatterjee, and K. V. S. Hari. "On selection of search space dimension in Compressive Sampling Matching Pursuit." In TENCON 2012 - 2012 IEEE Region 10 Conference. IEEE, 2012. http://dx.doi.org/10.1109/tencon.2012.6412345.

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Gao, Y., W. Peng, Y. Qu, and J. Ding. "Through-the-wall imaging based on modified compressive sampling matching pursuit." In 2017 Sixth Asia-Pacific Conference on Antennas and Propagation (APCAP). IEEE, 2017. http://dx.doi.org/10.1109/apcap.2017.8420403.

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Ambat, Sooraj K., Shree Ranga Raju N. M., and K. V. S. Hari. "Gini index based search space selection in Compressive Sampling Matching Pursuit." In 2014 Annual IEEE India Conference (INDICON). IEEE, 2014. http://dx.doi.org/10.1109/indicon.2014.7030517.

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Sathyabama, B., S. G. Siva Sankari, and S. Nayagara. "Fusion of satellite images using Compressive Sampling Matching Pursuit (CoSaMP) method." In 2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG). IEEE, 2013. http://dx.doi.org/10.1109/ncvpripg.2013.6776256.

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Lodhi, Muhammad Asad, Sergey Voronin, and Waheed U. Bajwa. "YAMPA: Yet Another Matching Pursuit Algorithm for compressive sensing." In Compressive Sensing V: From Diverse Modalities to Big Data Analytics, edited by Fauzia Ahmad. SPIE, 2016. http://dx.doi.org/10.1117/12.2224334.

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Yue Shen, Hanwen Zhang, Guohai Liu, Hui Liu, Hongxuan Wu, and Wei Xia. "A novel method on harmonics detection based on compressive sampling matching pursuit." In 2014 11th World Congress on Intelligent Control and Automation (WCICA). IEEE, 2014. http://dx.doi.org/10.1109/wcica.2014.7053667.

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