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A Two Stage Generalized Block Orthogonal Matching Pursuit (TSGBOMP) Algorithm

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2021年 69卷 5846-5858页

作者： Mukhopadhyay, Samrat Chakraborty, Mrityunjoy Indian Sch Mines Indian Inst Technol Dept Elect Engn Dhanbad 826004 Bihar India Indian Inst Technol Dept Elect & Elect Commun Engn Kharagpur 721302 W Bengal India

Recovery of an unknown sparse signal from a few of its projections is the key objective of compressed sensing. Often one comes across signals that are not ordinarily sparse but are sparse blockwise. Existing block sparse recovery algorithms like BOMP make the assumption of uniform block size and known block boundaries, which are, however, not very practical in many applications. This paper addresses this problem and proposes a two step procedure, where the first stage is a coarse block location identification stage while the second stage carries out finer localization of a non-zero cluster within the window selected in the first stage. A detailed convergence analysis of the proposed algorithm is carried out by first defining a so-called pseudoblock-interleaved block RIP for the given generalized block sparse signal and then imposing upper bounds on the corresponding RIC. Simulation results confirm significantly improved performance of the proposed algorithm as compared to BOMP.

关键词： Clustering algorithms Signal processing algorithms Indexes partitioning algorithms Correlation Sensors Matching pursuit algorithms Compressed Sensing (CS) block sparse restricted isometry property (RIP)

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Data Convexity and Parameter Independent Clustering for Biomedical Datasets

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021年第2期18卷 765-772页

作者： Rahman, Md Anisur Ang, Li-Minn Seng, Kah Phooi Charles Sturt Univ NSW Hlth Australia Sydney NSW 2678 Australia Charles Sturt Univ Sch Comp & Math Sydney NSW 2678 Australia Univ Sunshine Coast Sch Sci & Engn Sippy Downs Qld 4556 Australia Univ New South Wales Sch Engn & IT Campbell ACT 2612 Australia

In machine learning, the nature of the dataset itself such as convexity of the data point sets affects the right choice of clustering algorithm to give good performance. This brief paper first focuses on how data convexity influences the clustering performance on biomedical datasets. Then it addresses the main challenges of two well-known clustering groups which are centroid-based and density-based clustering. These techniques typically require a set of parameters to be provided by the user before the algorithms can perform well in terms of good clustering and give the optimal number of clusters. Two parameter independent clustering techniques utilizing unique neighborhood sets (UNSs) called Parameter Independent Convex Centroid-based Clustering (ConvexClust) for convex-dominated datasets and Parameter Independent Non-Convex Density-based Clustering (NonConvexClust) for nonconvex-dominated datasets are introduced. The ConvexClust and NonConvex Clust algorithms are extensively evaluated on real-world biomedical datasets. Their performances are also compared with other clustering algorithms using evaluation criteria such as SSE, entropy and purity. The results have revealed the good performance of the proposed parameter-independent clustering techniques and also shown that most of the biomedical datasets in the experiments demonstrated their tendency towards convex-dominated data point sets.

关键词： Clustering algorithms Biomedical measurement Bioinformatics partitioning algorithms Australia Gene expression Shape Biomedical centroid-based clustering density-based clustering convexity unique neighborhood set

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Entropy K-Means Clustering With Feature Reduction Under Unknown Number of Clusters

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IEEE ACCESS 2021年 9卷 67736-67751页

作者： Sinaga, Kristina P. Hussain, Ishtiaq Yang, Miin-Shen BINUS Univ BINUS Grad Program Dept Master Informat Syst Management Jakarta 10279 Indonesia Chung Yuan Christian Univ Dept Appl Math Taoyuan 32023 Taiwan

The k-means algorithm with its extensions is the most used clustering method in the literature. But, the k-means and its various extensions are generally affected by initializations with a given number of clusters. On the other hand, most of k-means always treat data points with equal importance for feature components. There are several feature-weighted k-means proposed in literature, but, these feature-weighted k-means do not give a feature reduction behavior. In this paper, based on several entropy-regularized terms we can construct a novel k-means clustering algorithm, called Entropy-k-means, such that it can be free of initializations without a given number of clusters, and also has a feature reduction behavior. That is, the proposed Entropy-k-means algorithm can eliminate irrelevant features with feature reduction under free of initializations with automatically finding an optimal number of clusters. Comparisons between the proposed Entropy-k-means and other methods are made. Experimental results and comparisons actually demonstrate these good aspects of the proposed Entropy-k-means with its effectiveness and usefulness in practice.

关键词： Clustering algorithms Entropy Linear programming Indexes Optimization partitioning algorithms Licenses Clustering k-means entropy feature weights feature reduction number of clusters entropy-k-means

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TNS-LPA: An Improved Label Propagation Algorithm for Community Detection Based on Two-Level Neighbourhood Similarity

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IEEE ACCESS 2021年 9卷 23526-23536页

作者： Xu, Guiqiong Guo, Jiawen Yang, Pingle Shanghai Univ Sch Management Dept Informat Management Shanghai 200444 Peoples R China Univ Shanghai Sci & Technol Sch Business Shanghai 200093 Peoples R China Jiangsu Univ Sci & Technol Sch Elect & Informat Engn Zhangjiagang 215600 Peoples R China

Community detection can not only help people understand organizational structure and function of complex networks, but also attributes to many potential applications including targeted advertising and customer relationship management. Due to the low time complexity, the label propagation algorithm is widely used, but there is still room to improve the community quality and the detection stability. Inspired by resource allocation and local path similarity, we first give a new two-level neighbourhood similarity measure called TNS, and on this basis we propose an improved label propagation algorithm for community detection. In this new algorithm, the minimum distance and local centrality index are considered to select the initial community centers, to ensure that they are both important and far away from each other. In the process of forming initial community, we employ the new similarity measure and an optimization strategy of asynchronously updating labels according to node importance. To further improve the accuracy of community division, we introduce the label influence based on the new similarity measure to further optimize the community division of networks. The experimental results on both the artificial network and ten real-world networks show that our proposed algorithm has better comprehensive performance than several existing algorithms in terms of modularity, normalized mutual information and adjusted rand index.

关键词： Indexes partitioning algorithms Clustering algorithms Social networking (online) Prediction algorithms Resource management Heuristic algorithms Complex networks community detection label propagation community kernel local similarity measure

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Partition Beetles Antennae Search Algorithm for Magnetic Sensor Calibration Optimization

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IEEE SENSORS JOURNAL 2021年第5期21卷 5967-5974页

作者： Li, Xiangang Yan, Shenggang Liu, Jianguo Sun, Yang Yan, Youyu Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China

As vector sensors, such as the fluxgate magnetometer, are more and more widely used in magnetic anomaly signal detection and location, researchers find that the error of sensors has become an essential obstacle in the application. To solve this problem, we established a calibration model with three kinds of error. A new optimization algorithm called Partition Beetles Antennae Search is provided to solve the model parameters. Simulation results show that the new algorithm can consider both accuracy and computational complexity. Besides, we set up a single three-axis fluxgate sensor experimental platform for sensor calibration and conducted related experiments. The results further prove the effectiveness and practicability of the method. The work of this article suggests an effective way to calibrate three-axis magnetometers.

关键词： Magnetometers Calibration Sensors Superconducting magnets Magnetic sensors Magnetic field measurement partitioning algorithms Three-axis magnetometers calibration model heuristic algorithms optimization methods parameter estimation beetles antennae search (BAS)

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F1-ECAC: Enhanced Evolutionary Clustering Using an Ensemble of Supervised Classifiers

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IEEE ACCESS 2021年 9卷 134192-134207页

作者： Sainz-Tinajero, Benjamin M. Gutierrez-Rodriguez, Andres E. Ceballos, Hector G. Cantu-Ortiz, Francisco J. Tecnol Monterrey Sch Engn & Sci Atizapan De Zaragoza 52926 Estado De Mexic Mexico Tecnol Monterrey Sch Engn & Sci Toluca De Lerdo 50100 Estado De Mexic Mexico Tecnol Monterrey Sch Engn & Sci Monterrey 64849 Nuevo Leon Mexico

Clustering is an unsupervised learning technique used in data mining for finding groups with increased object similarity within but not between them. However, the absence of a-priori knowledge on the optimal clustering criterion, and the strong bias of traditional algorithms towards clusters with a specific shape, size, or density, raise the need for more flexible solutions to find the underlying structures of the data. As a solution, clustering has been modeled as an optimization problem using meta-heuristics for generating a search space to favor groups of any desired criterion. F1- ECAC is an evolutionary clustering algorithm with an objective function designed as a supervised learning problem, which evaluates the quality of a partition in terms of its generalization degree, or its capability to train an ensemble of classifiers. This algorithm is named after its previous version, ECAC (Evolutionary Clustering Algorithm Using Supervised Classifiers), considering its main point of difference, which is the inclusion of the F1-score instead of the Area Under the Curve metric in the objective function. F1- ECAC shows a significant increase in performance and efficiency to ECAC and is highly competitive to state-of-the-art clustering algorithms. The results demonstrate F1-ECAC's benefits in usability in a wide variety of problems due to its innovative clustering criterion.

关键词： Clustering algorithms partitioning algorithms Linear programming Statistics Sociology Shape Optimization Unsupervised learning clustering evolutionary clustering optimization classifier ensembles

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Group-Based Random Access and Data Transmission Scheme for Massive MTC Networks

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IEEE TRANSACTIONS ON COMMUNICATIONS 2021年第12期69卷 8287-8303页

作者： Wang, Tao Wang, Yichen Wang, Chunfeng Yang, Zihuan Cheng, Julian Xi An Jiao Tong Univ Sch Informat & Commun Engn Xian 710049 Shaanxi Peoples R China China Acad Space Technol Qian Xuesen Lab Space Technol Beijing 100094 Peoples R China Beijing StarPoint Technol Co Ltd Beijing 100102 Peoples R China Univ British Columbia Sch Engn Kelowna BC V1V 1V7 Canada

Massive machine-type communications (mMTC) is one of the three generic services for the fifth-generation (5G) wireless communications system. To utilize fully the high rate transmission feature of the 5G system to support massive MTC devices (MTCDs), we propose a group-based random access and data transmission scheme, where the data packets of MTCDs are first aggregated by the MTC gateways (MTCGs) and then forwarded to the base station. The access process of the MTC network is divided into two phases, namely the intra-group transmission phase and MTCG forwarding phase. The entire resources are also partitioned for the two phases. We employ the discrete-time nonhomogenous Markov model to characterize the joint queue-length evolution of multiple MTCGs, which cannot be analyzed directly due to the exponential complexity and time-nonhomogeneity. To facilitate the analysis, we approximately decompose the joint nonhomogenous queue-length evolution process into multiple independent nonhomogenous queue-length evolution processes with the same state transition probabilities. Then, we establish an equivalent single queue-length evolution based homogenous Markov chain by constructing a virtual queue and determine the corresponding stationary distribution by using the Gauss-Jordan elimination method. An optimization problem is formulated to maximize the average network throughput subject to the constraints on the resource partition for the two phases and the MTCG forwarding threshold. By developing a modified differential evolution algorithm, we provide an efficient solution to the formulated problem, which can be arbitrarily close to the optimal solution. Simulation results show that the proposed scheme can efficiently improve the network performance over the existing schemes.

关键词： Resource management Queueing analysis Markov processes Data communication Analytical models Throughput partitioning algorithms Massive machine-type communication (MTC) resource allocation Markov chain throughput maximization

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Convergence of the Resistive Coupling-Based Waveform Relaxation Method for Chains of Identical and Symmetric Circuits

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2021年第12期68卷 5120-5133页

作者： Menkad, Tarik Dounavis, Anestis Western Univ Dept Elect & Comp Engn London ON N6A 3K7 Canada

The convergence of the waveform relaxation (WR) method is demonstrated for a class of circuits: Chains of identical and symmetrical passive subcircuits. The WR algorithm uses resistive coupling to implement the iteration. Every part is modeled as a symmetric and reciprocal linear two-port network. The iteration matrices of the WR operator are constructed for the Gauss-Jacobi and Gauss-Seidel relaxations in the Fourier domain. An upperbound estimate of the spectral radius of the WR operator is presented. It demonstrates the convergence of the method independently of the number of cascaded parts in the chain and the coupling resistance.

关键词： Couplings Convergence Mathematical models partitioning algorithms Integrated circuit modeling Transmission line matrix methods Resistance Waveform relaxation resistive coupling iteration matrix spectral radius local and global convergence chains high-speed interconnects transient simulation

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Spark-Enabled XDraw Viewshed Analysis

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2021年 14卷 2017-2029页

作者： Zhang, Jianbo Zhao, Subin Ye, Zhuangzhuang China Univ Geosci Sch Geog Informat Engn Wuhan 430074 Peoples R China

Viewshed analysis is an indispensable part of digital terrain analysis and widely used in many application domains. High-resolution raster digital elevation model (DEM) data bring significant computational challenges to the existing viewshed analysis algorithms, which are computationally intensive and require a large memory space and massive computing power. The visibility calculation can be accelerated using Apache Spark. In this article, we present a Spark-based parallel computing approach for the XDraw algorithm, which is composed of a tile-based raster data storing strategy, an equivolume computing strategy, and a stream-merging write-back strategy. The parallel implementation of the XDraw algorithm mainly consists of three parts: partitioning a raster DEM file into square tile sets and reorganizing these tile sets to prevent tile overlap across data divisions of Hadoop Distributed File System, subdividing the DEM into multiple equivolume data sectors according to the viewpoint position, and performing the XDraw algorithm on the corresponding tile sets of each sector independently and writing back the viewshed results efficiently. Experiments on real-world datasets show that the proposed computing approach can achieve higher speedup and efficiency for XDraw viewshed analysis as the raster DEM data volume is dramatically increased. The results also show that the approach has also satisfactory scalability as the number of data nodes in clusters is increased.

关键词： Sparks Clustering algorithms Software algorithms partitioning algorithms Earth Parallel processing Approximation algorithms Parallel computing Spark viewshed analysis XDraw

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A Time-Based Intra-Memory Computing Graph Processor Featuring A* Wavefront Expansion and 2-D Gradient Control

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IEEE JOURNAL OF SOLID-STATE CIRCUITS 2021年第7期56卷 2281-2290页

作者： Everson, Luke R. Sapatnekar, Sachin S. Kim, Chris H. Univ Minnesota Elect & Comp Engn Dept Minneapolis MN 55455 USA

A mixed-signal, time-based 65-nm application-specific integrated circuit is developed for solving shortest-path problems. Digital circuits are collocated with the memory as intra-memory computing. The core follows similar principles from wave routing and, additionally, incorporates a gradient on the periphery of the core to implement the A* algorithm predicted distance heuristic. A leading pulse is propagated from start nodes and is asynchronously latched in neighboring vertex cells and pushed to its four neighbors. Applications include collision avoidance for self-driving cars, shortest path planning, and scientific computing, and are shown to be scalable across many cores. The chip achieves 559 million traversed edges per second at 105 x improved energy efficiency compared with existing platforms such as field-programmable gate array and CPU. The processor operates nominally at 1.79 ns per node with peak power consumption of 26.4 mW.

关键词： Pipelines Routing Random access memory Latches Field programmable gate arrays partitioning algorithms Art A* algorithm graph computing graphs intra-memory computing single-source shortest path (SSSP) time-domain computing time-to-digital converter

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