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Improving the reconstruction efficiency of sparsity adaptive matching pursuit based on the Wilkinson matrix

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Frontiers of Information Technology & Electronic Engineering 2018年第4期19卷 503-512页

作者： Rasha SHOITAN Zaki NOSSAIR I.I.IBRAHIM Ahmed TOBAL Computer and Systems Department Electronic Research Institute Electronics Communicationsand Computer DepartmentHelwan University

Sparsity adaptive matching pursuit(SAMP)is a greedy reconstruction algorithm for compressive sensing *** reconstructs signals without prior information of sparsity and presents better reconstruction performance for noisy signals compared to other greedy ***,SAMP still suffers from relatively poor reconstruction quality especially at high compression *** the proposed research,the Wilkinson matrix is used as a sensing matrix to improve the reconstruction quality and to increase the compression ratio of the SAMP ***,the idea of block compressive sensing(BCS)is combined with the SAMP technique to improve the performance of the SAMP *** simulations have been conducted to evaluate the proposed BCS-SAMP technique and to compare its results with those of several compressed sensing *** results show that the proposed BCS-SAMP technique improves the reconstruction quality by up to six decibels(d B)relative to the conventional SAMP *** addition,the reconstruction quality of the proposed BCS-SAMP is highly comparable to that of iterative ***,the computation time of the proposed BCS-SAMP is less than that of the iterative techniques,especially at lower measurement fractions.

关键词： Block compressive sensing Sparsity adaptive matching pursuit greedy algorithm Wilkinson matrix

algorithm for source recovery in underdetermined blind source separation based on plane pursuit

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Journal of Systems Engineering and Electronics 2018年第2期29卷 223-228页

作者： FU Weihong WEI Juan LIU Naian CHEN Jiehu State Key Lab of Integrated Service Networks Xidian University No.10 Research Institution China Electronics Technology Group Corporation

In order to achieve accurate recovery signals under the underdetermined circumstance in a comparatively short time,an algorithm based on plane pursuit(PP) is proposed. The proposed algorithm selects the atoms according to the correlation between received signals and hyper planes, which are composed by column vectors of the mixing matrix, and uses these atoms to recover source signals. Simulation results demonstrate that the PP algorithm has low complexity and higher accuracy as compared with basic pursuit(BP), orthogonal matching pursuit(OMP), and adaptive sparsity matching pursuit(ASMP) algorithms.

关键词： underdetermined blind source separation(UBSS) source recovery greedy algorithm plane pursuit

A Simulated Annealing algorithm for Training Empirical Potential Functions of Protein Folding

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Chemical Research in Chinese Universities 2005年第1期21卷 73-77页

作者： WANGYu-hong LIWei DepartmentofMolecularBiology dilinUniversityChangchun130023P.R.China

In this paper are reported the local minimum problem by means of current greedy algorithm for training the empirical potential function of protein folding on 8623 non-native structures of 31 globular proteins and a solution of the problem based upon the simulated annealing algorithm. This simulated annealing algorithm is indispensable for developing and testing highly refined empirical potential functions.

关键词： Empirical potential function of protein folding Training Simulated annealing greedy algorithm

The Minimum Feature Subset Selection Problem

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Journal of Computer Science & Technology 1997年第2期12卷 145-153页

作者：陈彬洪家荣王亚东 DepartmentofComputerScience HarbinInstituteofTechnologyHarbin150001

In applications of learning from examples to real-world tasks, feature subset selection is important to speed up training and to improve generalization performance. ideally, an inductive algorithm should use subset of features as small as possible. In this paper however, the authors show that the problem of selecting the minimum subset of features is NP-hard. The paper then presents a greedy algorithm for feature subset selection. The result of running the greedy algorithm on hand-written numeral recognition problem is also given.

关键词： Learning from examples NP-hard greedy algorithm

Beam Scheduling with Various Mission Demands in Data Relay Satellite Systems

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Journal of Communications and Information Networks 2021年第4期6卷 396-410页

作者： Boyu Deng Chunxiao Jiang Jingchao Wang Linling Kuang The Department of Electronic Engineering Tsinghua UniversityBeijing 100084China Tsinghua Space Center Tsinghua UniversityBeijing 100084China Peng Cheng Laboratory Shenzhen 518055China

Beam scheduling is one of the most important issues regarding data relay satellite systems,which can improve the utilization efficiency of limited system resources by programming beam allocation for relay *** ever-increasing relay missions create a substantial challenge for beam scheduling due to an increase in different mission *** cooperative usage of different beams further increases the complexity of this ***,we develop a novel optimization method to solve the beam scheduling problem for the scenario of various mission demands in the data relay satellite system(DRSS).Based on the analysis of mission demands and resource features,we first construct a heterogeneous parallel machines scheduling model to formulate the beam scheduling problem in the *** solve this complicated model,we investigate the matching method between mission demands and beam resources,and introduce two concepts,the loose duration and the number of available beams,to make the matching process more ***,the following three algorithms are *** first approach,the maximized completion probability algorithm(MCPA),applies a greedy strategy based on the new concepts to allocate beams for missions;and two improved versions of this algorithm are also presented,which employ the strategies of mission insertion optimization and mission sequence optimization,*** simulation results show that the proposed algorithms are superior to the existing algorithms in terms of the scheduled missions,the weight of scheduled missions,and the processing time,which significantly improves the performance of beam scheduling in the DRSS.

关键词： DRSS beam scheduling various mission demands loose duration available beams greedy algorithm

Near-field time-frequency localization method using sparse representation

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The Journal of China Universities of Posts and Telecommunications 2012年第6期19卷 29-34页

作者： WANG Bo LIU Juan-juan SUN Xiao-ying ZHANG Yan-jun College of Communication Engineering Jilin University

This paper presents a novel near-field source localization method based on the time-frequency sparse model. Firstly, the method converts the time domain data of array output into time-frequency domain by time-frequency transform; then constructs sparse localization model by utilizing the specially selected time-frequency points, and finally the greedy algorithms are chosen to solve the sparse problem to localize the source. When the coherent sources exist, we propose an additional iterative selection procedure to improve the estimation performance. The proposed method is suitable for uncorrelated and coherent sources, moreover, the improved estimation accuracy and the robustness to low signal to noise ratio （SNR） are achieved. Simulations results verify the efficiency of the proposed algorithm

关键词： near-field source time-fi'equency distribution sparse representation DOA estimation range estimation greedy algorithm

Recursive Dictionary-Based Simultaneous Orthogonal Matching Pursuit for Sparse Unmixing of Hyperspectral Data

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Transactions of Nanjing University of Aeronautics and Astronautics 2017年第4期34卷 456-464页

作者： Kong Fanqiang Guo Wenjun Shen Qiu Wang Dandan College of Astronautics Nanjing University of Aeronautics and Astronautics

The sparse unmixing problem of greedy algorithms still remains a great challenge at finding an optimal subset of endmembers for the observed data from the spectral library,due to the usually high correlation of the spectral *** such circumstances,a novel greedy algorithm for sparse unmixing of hyperspectral data is presented,termed the recursive dictionary-based simultaneous orthogonal matching pursuit(RD-SOMP).The algorithm adopts a block-processing strategy to divide the whole hyperspectral image into several *** each iteration of the block,the spectral library is projected into the orthogonal subspace and renormalized,which can reduce the correlation of the spectral *** RD-SOMP selects a new endmember with the maximum correlation between the current residual and the orthogonal subspace of the spectral *** endmembers picked in all the blocks are associated as the endmember sets of the whole hyperspectral ***,the abundances are estimated using the whole hyperspectral data with the obtained endmember *** can be proved that RD-SOMP can recover the optimal endmembers from the spectral library under certain *** results demonstrate that the RD-SOMP algorithm outperforms the other algorithms,with a better spectral unmixing accuracy.

关键词： hyperspectral unmixing greedy algorithm simultaneous sparse representation sparse unmixing

An algorithm for the Inverse Problem of Matrix Processing: DNA Chains, Their Distance Matrices and Reconstructing

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Journal of Biosciences and Medicines 2023年第5期11卷 310-320页

作者： Boris F. Melnikov Ye Zhang Dmitrii Chaikovskii Faculty of Computational Mathematics and Cybernetics Shenzhen MSU-BIT University Shenzhen China School of Mathematics and Statistics Beijing Institute of Technology Beijing China

We continue to consider one of the cybernetic methods in biology related to the study of DNA chains. Exactly, we are considering the problem of reconstructing the distance matrix for DNA chains. Such a matrix is formed on the basis of any of the possible algorithms for determining the distances between DNA chains, as well as any specific object of study. At the same time, for example, the practical programming results show that on an average modern computer, it takes about a day to build such a 30 × 30 matrix for mnDNAs using the Needleman-Wunsch algorithm;therefore, for such a 300 × 300 matrix, about 3 months of continuous computer operation is expected. Thus, even for a relatively small number of species, calculating the distance matrix on conventional computers is hardly feasible and the supercomputers are usually not available. Therefore, we started publishing our variants of the algorithms for calculating the distance between two DNA chains, then we publish algorithms for restoring partially filled matrices, i.e., the inverse problem of matrix processing. Previously, we used the method of branches and boundaries, but in this paper we propose to use another new algorithm for restoring the distance matrix for DNA chains. Our recent work has shown that even greater improvement in the quality of the algorithm can often be achieved without improving the auxiliary heuristics of the branches and boundaries method. Thus, we are improving the algorithms that formulate the greedy function of this method only. .

关键词： DNA Chains Distance Matrix Optimization Problem Restoring algorithm greedy algorithm Heuristics

Quality of experience based scheduling algorithm in LTE network with various traffics

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Journal of Beijing Institute of Technology 2016年第4期25卷 547-552页

作者：吴志坤费泽松王飞巩世琪李娜 Research Institute of Communication Technology School of Information and ElectronicsBeijing Institute of Technology

Quality of experience （ QoE ） based scheduling algorithm of long term evalution （ LTE ） network with various traffics is studied. Utility functions are adopted to estimate mean opinion score （MOS） for different traffics and a new MOS metric called normalized MOS is defined. A scheduling algorithm based on normalized MOS and greedy algorithm is proposed, aiming at maximizing the entirety MOS level of the whole users in the cell. We compare the performance of the proposed algorithm with other typical scheduling algorithms and the simulation results show that the algorithm pro- posed outperform other ones in term of QoE and fairness.

关键词： quality of experience ( QoE ) long term evolution ( LTE ) multi-application schedu-ling mean opinion score (MOS) greedy algorithm