Weiran Yuan¹, Pu Chen^1,2, Kaishin Liu^1,3

CMES-Computer Modeling in Engineering & Sciences, Vol.17, No.2, pp. 115-134, 2007, DOI:10.3970/cmes.2007.017.115

Abstract In this paper we propose a direct solution method for the quasi-unsymmetric sparse matrix (QUSM) arising in the Meshless Local Petrov-Galerkin method (MLPG). QUSM, which is conventionally treated as a general unsymmetric matrix, is unsymmetric in its numerical values, but nearly symmetric in its nonzero distribution of upper and lower triangular portions. MLPG employs trial and test functions in different functional spaces in the local domain weak form of governing equations. Consequently the stiffness matrix of the resultant linear system is a QUSM. The new solver for QUSM conducts a two-level unrolling technique for LDU factorization method and can be… More >

Gang Xu^1,*, Yufan Zhu¹, Lishan Deng¹, Guozhao Wang², Bojian Li¹, Kin-chuen Hui³

CMC-Computers, Materials & Continua, Vol.58, No.3, pp. 879-892, 2019, DOI:10.32604/cmc.2019.03752

Abstract In this paper, we propose an efficient method to construct energy-minimizing B-spline curves by using discrete mask method. The linear relations between control points are firstly derived for different energy-minimization problems, then the construction of B-spline curve with minimal internal energy can be addressed by solving a sparse linear system. The existence and uniqueness of the solution for the linear system are also proved. Experimental results show the efficiency of the proposed approach, and its application in G¹ blending curve construction is also presented. More >

Yu Liu¹, Zhong Yang², Xiaoyong Yan³, Guangchi Liu⁴, Bo Hu^5,*

CMC-Computers, Materials & Continua, Vol.58, No.1, pp. 79-100, 2019, DOI:10.32604/cmc.2019.03626

Abstract Node location estimation is not only the promise of the wireless network for target recognition, monitoring, tracking and many other applications, but also one of the hot topics in wireless network research. In this paper, the localization algorithm for wireless network with unevenly distributed nodes is discussed, and a novel multi-hop localization algorithm based on Elastic Net is proposed. The proposed approach is formulated as a regression problem, which is solved by Elastic Net. Unlike other previous localization approaches, the proposed approach overcomes the shortcomings of traditional approaches assume that nodes are distributed in regular areas without holes or obstacles,… More >

Rui Wang¹, Miaomiao Shen^1,*, Yanping Li¹, Samuel Gomes²

CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 25-48, 2018, DOI:10.32604/cmc.2018.02408

Abstract Recently, sparse representation classification (SRC) and fisher discrimination dictionary learning (FDDL) methods have emerged as important methods for vehicle classification. In this paper, inspired by recent breakthroughs of discrimination dictionary learning approach and multi-task joint covariate selection, we focus on the problem of vehicle classification in real-world applications by formulating it as a multi-task joint sparse representation model based on fisher discrimination dictionary learning to merge the strength of multiple features among multiple sensors. To improve the classification accuracy in complex scenes, we develop a new method, called multi-task joint sparse representation classification based on fisher discrimination dictionary learning, for… More >

Jixin Liu^1,*, Ning Sun^1,2, Xiaofei Li¹, Guang Han¹, Haigen Yang¹, Quansen Sun³

CMC-Computers, Materials & Continua, Vol.55, No.3, pp. 435-446, 2018, DOI: 10.3970/cmc.2018.02177

Abstract Rare bird has long been considered an important in the field of airport security, biological conservation, environmental monitoring, and so on. With the development and popularization of IOT-based video surveillance, all day and weather unattended bird monitoring becomes possible. However, the current mainstream bird recognition methods are mostly based on deep learning. These will be appropriate for big data applications, but the training sample size for rare bird is usually very short. Therefore, this paper presents a new sparse recognition model via improved part detection and our previous dictionary learning. There are two achievements in our work: (1) after the… More >