模式匹配算法及其优化

介绍了几种典型的模式匹配算法,即BM算法、AC算法和AC—BM算法,在此基础上对各算法进行了分析总结,并提出对AC—BM算法进行压缩优化,以节约存储空间,提高匹配效率。     

基于业务流程的企业扁平化组织结构

为了适应经济全球化及剧烈竞争性环境,能对市场的快速变化作出及时反应,企业的组织形式正经历重大转变,由原来垂直的职能部门组织架构转变为横向的、以流程为基础的扁平化的组织结构。结合价值链这一分析工具,阐述了企业通过判定自身竞争优势来进行业务流程再造,从而构建扁平化的组织结构。     

基于直方图相交匹配函数的图像压缩评测准则

常用的基于均方根误差（RMSE）图像保真度准则不能准确地放映一些灰度图像主要敏感细节。本文提出了利用灰度直方图的相似度作为评价重建图像质量的准则。利用JPEG方法对图像进行压缩,并采用直方图相交匹配函数求得图像灰度级的匹配程度。实验结果表明,直方图相交匹配函数是一种有效的图像压缩评测准则。     

基于稀疏表示的人脸识别算法

近年来,人脸识别一直是计算机视觉和模式识别领域一个很热门研究课题。随着压缩感知理论的发展,稀疏表示已经成功应用于人脸识别。文章系统地综述了人脸识别的研究现状、系统,并重点介绍了基于稀疏表示的人脸识别算法及求解,最后对人脸识别算法存在的问题以及发展进行了展望。     

胫骨疲劳性骨折的生物力学分析

在对跑、跳作运动生物力学分析的基础上，提出计算胫骨压缩力的力学模型。结合病例报告，从生物力学角度分析胫骨疲劳性骨折的易发部位以及成因，为本症的诊断、治疗和预防提供理论依据。     

一种有效的基于运动矢量场的压缩域运动对象分割算法

In this paper an efficient compressed domain moving object segmentation algorithm is proposed, in which the motion vector (MV) field parsed from the compressed video is the only cue used for moving object segmentation. First the MV field is temporally and spatially normalized, and then accumulated by an iterative backward projection to enhance salient motions and alleviate noisy MVs. The accumulated MV field is then segmented into motion-homogenous regions using a modified statistical region growing approach. Finally, moving object regions are extracted in turn based on minimization of the joint prediction error using the estimated motion models of two region sets containing the candidate object region and other remaining regions, respectively. Experimental results on several H.264 compressed video sequences demonstrate good segmentation performance.     

能源革命中的物理储能技术

促进储能技术与产业的健康发展,对于提高可再生能源比例、保障能源安全、提高能源利用效率、推动能源革命具有重大的战略意义。物理储能作为最重要的一种储能方式,得到了国内外广泛关注。文章剖析了物理储能技术的重大需求,总结了国内外物理储能技术与产业发展现状与趋势,比较了国内外物理储能技术发展水平,明确了物理储能技术发展问题与挑战,并提出了相应发展建议,旨在为我国物理储能技术和产业的研究与发展提供详实的参考。     

基于压缩感知的SAR图像压缩算法

研究基于压缩感知的合成孔径雷达(SAR)图像压缩算法.根据压缩感知理论,在信号降维方面,提出一种更优化的观测矩阵构造方法;在信号重构方面,提出一种基于微分熵和迭代加权最小二乘的改进重构算法.通过对SAR图像进行压缩和性能比较,得出结论:本文提出的改进算法优于传统的压缩感知算法.     

基于压缩感知的电能质量信号重构算法研究

为解决传统电能质量信号在采样时面临的采样率高,采样资源浪费和硬件成本高的问题,压缩感知理论被引入到电能质量信号的采样与重构过程。信号的稀疏表示是压缩感知理论中的关键问题,一般选择正交基作为压缩感知中的稀疏变换基。基于多重扰动的电能质量信号,本文提出了基于不同干扰的电能质量模型来选择不同的信号稀疏变换基的压缩感知重构算法。实验证明与整个信号采用单一DCT变换基或FFT变换基的压缩感知重构算法相比,本文提出的方法具有更好的信号重构性能。     

Intensive mode delivery of a neuroanatomy unit: Lower final grades but higher student satisfaction

In 2011, Macquarie University moved to a three‐session academic year which included two 13‐week sessions (traditional mode) and one seven‐week session (intensive mode). This study was designed to compare the intensive and traditional modes of delivery in a unit of undergraduate neuroanatomy. The new intensive mode neuroanatomy unit provided the same quantity and quality of material to the same standard, delivered by the same teachers and over the same total hours, but in a shorter timeframe. All students enrolled in session 2 (traditional mode) and session 3 (intensive mode) were invited to participate in this study. The main outcome measures were the final course grades and level of satisfaction with the course. Although there was no significant difference between the two cohorts in self‐rated level of knowledge (P = 0.148), the traditional mode cohort achieved significantly higher final grades compared to the intensive mode cohort (P = 0.001). Similarly, the distribution of final grades was also different between the two cohorts. The two cohorts were equally satisfied with the unit overall, and with the lectures and tutorials. However, the intensive mode cohort was more satisfied with the laboratory practical classes compared to the traditional mode cohort (P < 0.001). Thus this study demonstrates that in the case of neuroanatomy, which is high in content, when the course is taught to the same standards as exist in the traditional mode of delivery, the students do not do as well even though they enjoy the course equally. Anat Sci Educ 6: 286–293. © 2013 American Association of Anatomists.