bpn

擅 蔓：以人脸的表情识别为实验背景， 分析了在对人脸表情的识别过程中，单个独立分薰对识别率的影响，由此进一步总结了在表情识 别中如何更有效地选取独立子空间，以实现在不影响识别率的前提下，减少用于构成独立子空问所需的独立分量的个数。 独立成分分析；表情识别；独立子空间 (Good man: Face to face identification for the experimental background, the analysis of facial expressions in the identification process, a single smoked separate the impact of the recognition rate, thus further summarized in the expression recognition of how to more effectively select the independent sub-space in order to achieve the recognition rate does not affect the premise, for constituting a separate sub-reducing space required for the number of independent component. Independent component analysis expression recognition independent subspace)

本人经过实际验证的比其他计算lyapunov指数更准确。。代码不足200行。。非常清晰明确(I read the actual verification than other lyapunov index calculation is more accurate. . Code less than 200 lines. . Very clear)

Dandn文件给出了输入参数的名称及格式，即在调用prim前先输入邻接矩阵D和节点个数n输入prim得到两行的矩阵T,将上下两行数字对应的节点相连即可(Dandn file gives the name and format of the input parameters, namely, the input before calling the prim adjacency matrix D and the number of nodes n prim get two lines of the input matrix T, the bottom two lines connected to the node numbers corresponding)

进行通信编程时候需要参考使用，还比较方便(Communication program to use when you need a reference, but also more convenient)

given a line function, compute the intersection of the line with the perimeter of a 2D box so that the line can be plotted. called by perceptron.m.

this source code is transormers 2 code

参数可控的分数阶傅里叶变换，可通过改变参数来进行傅里叶变换(Parameters controllable fractional Fourier transform, by changing the parameters to the Fourier transform)

本算法用来实现图像的首数字规则特性，首数字规则在1881年就被发现，他能检测图像的所有像素点的AC系数是否符合首数字规则(The first algorithm is used to achieve image characteristic figure rule, the first rule number 1881 was found that he can detect all the pixels in the image whether the First Digital AC coefficient rules)

主成分做排名的matlab代码，运用主成分分析计算教练排名得分的方法。( Principal component rankings do matlab code, using principal component analysis method to calculate the coach ranking score.)

EMD的Toolbox及使用方法 经验模态分解（Empirical Mode Decomposition, 简称EMD）是由美国NASA的黄锷博士提出的一种信号分析方法.它依据数据自身的时间尺度特征来进行信号分解, 无须预先设定任何基函数。这一点与建立在先验性的谐波基函数和小波基函数上的傅里叶分解与小波分解方法具有本质性的差别。正是由于这样的特点, EMD 方法在理论上可以应用于任何类型的信号的分解, 因而在处理非平稳及非线性数据上, 具有非常明显的优势。所以, EMD方法一经提出就在不同的工程领域得到了迅速有效的应用, 例如用在海洋、大气、天体观测资料与地震记录分析、机械故障诊断、密频动力系统的阻尼识别以及大型土木工程结构的模态参数识别方面。本工具书主要分以下几个部分：导论、非稳态信号介绍、一维时频信号分解、二维时频信号分解、时频影像信息提取等。 (Time frequency toolbox for use with MATLAB The Time-Frequency Toolbox has been mainly developed under the auspices of the French CNRS (Centre National de la Recherche Scientifique). It results from a research effort conducted within its Groupements de Recherche ”Traitement du Signal et Images” (O. Macchi) and ”Information, Signal et Images” (J.-M. Chassery). Parts of the Toolbox have also been developed at Rice University, when one of the authors (PG) was visiting the Department of Electrical and Computer Engineering, supported by NSF. Supporting institutions are gratefully acknowledged, as well as M. Guglielmi, M. Najim, R. Settineri, R.G. Baraniuk, M. Chausse, D. Roche, E. Chassande-Mottin, O. Michel and P. Abry for their help at different phases of the development.)