一种基于小波变换边缘保护的图像融合算法

提出一种基于小波变换的像素级图像融合算法。采用小波系数局部模极大和加权局部能量分析相结合的方法融合高频成分;用加权局部能量分析融合尺度系数。算法获得的融合图像具有很强的视觉表现能力。此算法不需要设置阈值,具有较强的泛化能力。对多聚焦图像进行的融合实验结果表明,基于小波系数局部模极大和局部能量分析相结合的高频融合策略较好地再现了图像中各种边缘信息;基于加权局部能量估计的低频融合策略有效地去除了原图像的模糊。融合后的图像在客观评价和主观视觉效果上均有显著提高。     

一种改进的基于FFT的信号插值算法

在传统运用FFT进行信号插值运算的基础上,提出了一种提高插值精度的改进算法.通过子序列重叠和裁剪,舍弃重建序列边缘误差较大的样点,再将相对准确的样点进行重组,从而大幅提高插值精度.实验结果表明:与Prasad等算法相比,在计算量增加3.1％的情况下,不同子序列长度对应的归一化均方误差平均下降至原来的1/19;在计算量增加2倍的情况下,不同子序列长度对应的归一化均方误差平均下降至原来的1/75.     

An iterative local search approach based on fitness landscapes analysis for the delay-constrained multicast routing problem

This paper presents the first fitness landscape analysis on the delay-constrained least-cost multicast routing problem (DCLC-MRP). Although the problem has attracted an increasing research attention over the past decade in telecommunications and operational research, little research has been conducted to analyze the features of its underlying landscape. Two of the most commonly used landscape analysis techniques, the fitness distance correlation analysis and the autocorrelation analysis, have been applied to analyze the global and local landscape features of DCLC-MRPs. A large amount of simulation experiments on a set of problem instances generated based on the benchmark Steiner tree problems in the OR-library reveals that the landscape of the DCLC-MRP is highly instance dependent with different landscape features. Different delay bounds also affect the distribution of solutions in the search space. The autocorrelation analysis on the benchmark instances of different sizes and delay bounds shows the impact of different local search heuristics and neighborhood structures on the fitness distance landscapes of the DCLC-MRP. The delay bound constraint in the DCLC-MRP has shown a great influence on the underlying landscape characteristic of the problem. Based on the fitness landscape analysis, an iterative local search (ILS) approach is proposed in this paper for the first time to tackle the DCLC-MRP. Computational results demonstrate the effectiveness of the proposed ILS algorithm for the problem in comparison with other algorithms in the literature.     

An Algorithm Based on Tabu Search for Satisfiability Problem

In this paper,a computationally effective algorithm based on tabu search for solving the satisfiability problem(TSSAT)is proposed.Some novel and efficient heuristic strategies for generating candidate neighborhood of the curred assignment and selecting varibables to be flipped are presented. Especially,the aspiration criterion and tabu list tructure of TSSAT are different from those of traditional tabu search.Computational experiments on a class of problem insteances show that,TSSAT,in a reasonable amount of computer time ,yields better results than Novelty which is currently among the fastest known.Therefore TSSAT is feasible and effective.     

A scalable memory efficient multigrid solver for micro-finite element analyses based on CT images

Micro-structural finite element (μFE) analysis based on high-resolution computed tomography represents the current gold standard to predict bone stiffness and strength. Recent progress in solver technology makes possible simulations on large supercomputers that involve billions of degrees of freedom.In this paper we present an improved solver that has a significantly smaller memory footprint compared to the currently used solvers. This new approach fully exploits the information that is contained in the underlying CT image itself. It admits to execute all steps in the underlying multigrid-preconditioned conjugate gradient algorithm in matrix-free form.The reduced memory footprint allows to solve bigger bone models on a given hardware. It is an important step forward to the clinical usage of μFE simulations.The new solver is fully parallel. We show almost perfect scalability up to 8000 cores of a Cray XT-5 supercomputer.     

An Algorithm for Clustering Using L1‐Norm Based on Hyperbolic Smoothing Technique

Cluster analysis deals with the problem of organization of a collection of objects into clusters based on a similarity measure, which can be defined using various distance functions. The use of different similarity measures allows one to find different cluster structures in a data set. In this article, an algorithm is developed to solve clustering problems where the similarity measure is defined using the L₁‐norm. The algorithm is designed using the nonsmooth optimization approach to the clustering problem. Smoothing techniques are applied to smooth both the clustering function and the L₁‐norm. The algorithm computes clusters sequentially and finds global or near global solutions to the clustering problem. Results of numerical experiments using 12 real‐world data sets are reported, and the proposed algorithm is compared with two other clustering algorithms.     

Efficient hardware architecture based on generalized Hebbian algorithm for texture classification

The objective of this paper is to present an efficient hardware architecture for generalized Hebbian algorithm (GHA). In the architecture, the principal component computation and weight vector updating of the GHA are operated in parallel, so that the throughput of the circuit can be significantly enhanced. In addition, the weight vector updating process is separated into a number of stages for lowering area costs and increasing computational speed. To show the effectiveness of the circuit, a texture classification system based on the proposed architecture is designed. It is embedded in a system-on-programmable-chip (SOPC) platform for physical performance measurement. Experimental results show that the proposed architecture is an efficient design for attaining both high speed performance and low area costs.     

一种频谱分方向指纹纹线距离估计的新方法

纹线距离是指纹的固有本质属性，在自动指纹识别中有着重要的作用。然而目前多数纹线距离估计的方法直接在空域作估计，对低质量指纹图像的处理普遍存在较大误差。为了适应低质量指纹图像，提出了一种基于频谱分析的纹线距离估计的新方法，该方法首先通过快速傅立叶变换将指纹图像变换到频域，然后将频谱图像分成n个方向分别求纹线距离，最后作处理从而得到指纹图像的纹线距离。部分典型指纹图像的实验结果表明，该方法具有较强的有效性和鲁棒性。     

基于双特征嵌套注意力的方面词情感分析算法

针对目前方面词情感分析方法忽视了以方面词为核心的局部特征的重要性,并难以有效减小情感干扰项的负面噪声的问题,本文提出了一种带有基于变换器的双向编码器表示技术（bi-directional encoder representations from transformers,BERT）加持的双特征嵌套注意力模型(dual features attention-over-attention with BERT,DFAOA-BERT),首次将AOA(attention-over-attention)与BERT预训练模型结合,并设计了全局与局部特征提取器,能够充分捕捉方面词和语境的有效语义关联。实验结果表明:DFAOA-BERT在SemEval 2014任务4中的餐馆评论、笔记本评论和ACL-14 Twitter社交评论这3个公开数据集上均表现优异,而子模块的有效性实验,也充分证明了DFAOA-BERT各个部分的设计合理性。     

基于图像处理技术的零件孔位尺寸快速测量方法

为提升现有图像测量技术的工程应用价值,针对车身车间现场薄板类零件孔位测量问题,基于二维图像处理技术提出了一种可直接借助智能手机完成零件孔位大小及间距测量的方法;该方法引入测量基准贴纸实现比例尺功能,并且通过手机镜头去畸变算法提升测量精度;借助Canny边缘检测算子得到待测量孔位外边缘轮廓, 使用形态学算法去除干扰;最后计算孔位外围边缘最小外接矩形长宽及中心连线的距离,得到孔位的几何尺寸;两个实验案例表明提出方法所得孔位大小及间距绝对误差小于0.4 mm,能够作为车间现场测量公差较大孔位工作的一种补充手段。     

An approach for visual attention based on biquaternion and its application for ship detection in multispectral imagery

This paper proposes an approach for visual attention based on biquaternion, and investigates its application for ship detection in multispectral imagery. The proposed approach describes high-dimensional data in the form of biquaternion and utilizes the phase spectrum of biquaternion Fourier transform to generate a required saliency map that can be used for salient target detection. In our method, the multidimensional data is processed as a whole, and the features contained in each spectral band can be extracted effectively. Compared with traditional visual attention approaches, our method has very low computational complexity. Experimental results on simulated and real multispectral remote sensing data have shown that the proposed method has excellent performance in ship detection. Furthermore, our method is robust against white noise and almost meets real-time requirements, which has great potentials in engineering applications.     

An algorithm for clustering based on non-smooth optimization techniques

The problem of cluster analysis is formulated as a problem of non‐smooth, non‐convex optimization, and an algorithm for solving the cluster analysis problem based on non‐smooth optimization techniques is developed. We discuss applications of this algorithm in large databases. Results of numerical experiments are presented to demonstrate the effectiveness of this algorithm.     

一种高效的平面点集凸包递归算法

凸包是计算几何的基本结构, 在许多图形图像相关领域得到了广泛应用. 本文提出了一种简单快速的平面点集凸包算法, 使用了主成分分析法(Principle component analysis, PCA)对点集进行预处理, 并研究了适用的排序规则和凸包边缘点判定原则. 该算法已成功应用于一光栅投影三维形貌快速测量系统,对相位干涉图中密集残留点所形成的最小凸包进行提取. 系统将提取的凸包区域进行掩码标记, 从而避免密集残留点造成相位展开错误, 保证了三维形貌重构的准确性. 实验结果表明, 该算法准确可靠, 并且运行效率较高.     

基于尺度空间分析和概率松弛的细胞图像分割算法

提出基于直方图尺度空间分析和概率迭代松弛的混合方法分割背景复杂的细胞图像。首先根据原始图像直方图的尺度空间特性和多尺度滤波结果,选取最佳阈值将图像分为多个类。然后,利用迭代的概率松弛法对粗分结果进行优化,并在后处理阶段中切割粘连细胞。将该方法与最大方差法和区域增长法进行比较,通过实例表明该方法的有效性。     

基于局部特征的自适应加权2DPCA的人脸识别

针对二维主成分分析(2DPCA)提取的是人脸的全局特征,但局部特征对人脸识别的作用非常大,提出了一种基于局部特征的自适应加权2DPCA。该算法首先根据局部特征把人脸图像分为上中下三个独立的子块,2DPCA应用到每个子块,自适应地计算出每个子块对识别的不同预期贡献,并把此预期贡献值作为子块权重加权到分类器中以提高识别率,实验结果证明了此算法的有效性和可行性。     

Self-adaptivity for grid applications. An Efficient Resources Selection model based on evolutionary computation algorithms

Over the last few years, the adaptation ability has become an essential characteristic for grid applications due to the fact that it allows applications to face the dynamic and changing nature of grid systems. This adaptive capability is applied within different grid processes such as resource monitoring, resource discovery, or resource selection. In this regard, the present approach provides a self-adaptive ability to grid applications, focusing on enhancing the resources selection process. This contribution proposes an Efficient Resources Selection model to determine the resources that best fit the application requirements. Hence, the model guides applications during their execution without modifying or controlling grid resources. Within the evaluation phase, the experiments were carried out in a real European grid infrastructure. Finally, the results show that not only a self-adaptive ability is provided by the model but also a reduction in the applications’ execution time and an improvement in the successfully completed tasks rate are accomplished.     

An obstacle avoidance strategy for complex obstacles based on artificial potential field method

When there are obstacles around the target point, the mobile robot cannot reach the target using the traditional artificial potential field (APF). Besides, the traditional APF is prone to local oscillation in complex terrain such as three-point collinear or semiclosed obstacles. Aiming at solving the defects of traditional APF, a novel improved APF algorithm named back virtual obstacle setting strategy-APF has been proposed in this paper. There are two main advantages of the proposed method. First, by redefining the gravitational function as a logarithmic function, the proposed method can make the mobile robot reach the target point when there are obstacles around the target. Second, the proposed method can avoid falling into local oscillation for both three-point collinear and semiclosed obstacles. Compare with APF and other improved APF, the feasibility of the algorithm is proved through software simulation and practical application.     

基于LabWindows/CⅥ的傅里叶变换光谱测量的数据采集系统

介绍一种基于LabWindows/CVI的光谱测量数据采集系统,利用LabWindows/CVI的编程优势,提高了编程效率与光谱仪数据分析的处理能力.给出了傅里叶变换光谱测量方法的设计原理、硬件系统构成以及LabWindows/CVI软件数据采集系统,包括实现干涉图数据采集、实时显示、数据分析处理和光谱分辨率的精确计算等功能.实验结果证明该系统满足设计需求.     

基于局部纹理ASM模型的人脸表情识别

针对主动形状模型(ASM)迭代过程容易陷入局部最优解的不足,提出了一种基于局部纹理模型的改进ASM算法,即EWASM.在局部纹理模型构建中,以每个特征点的中垂线方向搜索其邻域信息以确定最佳匹配位置,对衡量匹配程度的马氏距离加以推广,进而得到改进的扩展加权局部纹理模型,它由中心局部纹理模型、前局部纹理模型和后局部纹理模型共3个子模型加权组成,并对加权参数进行实验优化,使各个特征点之间的联系更加紧密,模型的鲁棒性更好.通过表情识别实验对提出的EWASM算法和传统ASM算法进行对比,选用RBF神经网络分类器进行表情分类,实验结果表明EWASM算法收敛速度更快,识别率也得以提高,并解决了局部最小问题,能更有效地表征表情.     

基于改进的ISODATA的超球覆盖仿生模式分类算法

现有仿生模式识别分类器难以解决含有多个聚集点、非线性和稀疏性样本的分类问题。因此,引入特征分类贡献度,提出了基于改进的迭代自组织数据分析(M-ISODATA)的超球覆盖仿生模式识别算法。首先引入马氏距离对自组织数据分析方法(ISODATA)的欧氏距离替换,并引入熵权法对马氏距离进行加权以赋予各特征不同的贡献度;同时为了去除干扰样本点,引入改进的局部离群因子检测方法(M-LOF)对样本进行训练,减少了不同类别流形之间的重叠区域。再利用改进的自组织数据分析方法(M-ISODATA)对每类训练样本点动态聚类,寻找到同一类的多个小类覆盖区中心后,用超球进行该类的有效覆盖,并对落入重叠区域的测试样本点进行二次划分,实现测试样本的正确分类。最后在iris数据集上验证该算法的有效性,并将该算法应用于雷达辐射源信号的分类识别。实验结果表明,该算法具有很好的拒识、免重训能力,对于雷达信号的识别率能达到97.29%,相比于传统典型模式识别算法具有更好的识别能力。