Reflash MiniPro

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纯CSS3方块跳动加载动画特效是一款适用于网页动态加载样式代码。(Pure CSS3 box jump loading animation effect is a dynamic loading style code for web pages.)

冒泡法,快速排序等排序的实现-Bubble, rapid sequencing order as the realization

高品质DVDRip制作进阶教程 个人认为很不错的一个教程,从第一次压DVD以来一直都以这个教程为蓝本.推荐给想用DVD压出高质量AVI的朋友. (Production of high-quality DVDRip Advanced Tutorial personally think that' s a very good tutorial from the first press DVD with this tutorial has been modeled. Recommend to want to use high-quality DVD compression AVI friend.)

Install windown server 2008 exporss

该文档详细介绍了高速PCB布线的一些概念及注意点，本人看后感觉帮助比较大，不敢独享-the document in detail the high-speed PCB and the attention of some of the concepts, I look and feel more help, not monopoly

xletview, for UNIX file and directory names are case sensitive. The path to the project CVSROOT must be specified using lowercase characters (i.e. /cvsroot/xletview) -xletview. for UNIX file and directory names are case sensi tive. The path to the project CVSROOT must be spe cified using lowercase characters (i.e./cvsr oot/xletview)

一个MATLAB小波分析的程序，这是一个信号处理程序，用于故障诊断。-SOUCE CODE

登录系统的时候，打开功能快速调用。在过滤中输入，明细功能的首拼或汉字，列表会自动检索你输入的数据，按上下键选择你想要打开的功能(直接在过滤文本框按上下键即可)，然后按回车即可调出功能。(When you log on to the system, turn on the function to call quickly. Input in the filter, the first spelling of detailed functions or Chinese characters, the list will automatically retrieve the data you entered, press the upper and lower keys to select the function you want to open (just press the upper and lower keys in the filter text box), and then press Enter to call out the function.)

常用的盲分离算法有二阶统计量方法、高阶累积量方法、信息最大化（ Infomax ）以及独 立成分分析（ ICA ）等。这些方法取得最佳性能的条件总是与源信号的概率密度函数假设有关， 一旦假设的概率密度与实际信号的密度函数相差甚远，分离性能将大大降低。本文提出采用 核函数密度估计的方法进行任意信号源的盲分离，并通过典型算例与几种盲分离算法进行了 性能比较，验证了方法的可行性。(The commonly used blind separation algorithms include two order statistics, higher order cumulants, and maximization of information Infomax) and independent component analysis (ICA). The conditions for obtaining the best performance of these methods are always related to the hypothesis of the probability density function of the source signal. The separation performance will be greatly reduced when the assumed probability density is very different from the density function of the actual signal. In this paper, a method of kernel function density estimation for blind separation of arbitrary signal sources is proposed. The performance of several blind separation algorithms is compared with some typical examples, and the feasibility of the method is verified.)