Hopf

采用matlab模型，采用三维纹理绘制的方式，绘制三维模型(Matlab model by using three-dimensional texture drawing method, draw the three-dimensional model)

[W,L,q]=huffman(P) 为huffman编码函数 P为信源的概率矢量，W为编码返回的码字 L为编码返回的平均码字长度，q为编码效率 ">

Dynamic program of unit commitment in power system

在径向基函数拟合、径向基函数神经网络和小波神经网络中的几种常用的基函数。包括高斯基函数、逆二次基函数、小波基函数等。(Several basis functions in radial basis function fitting, RBF neural network and wavelet neural networks. Including Gauss function, inverse quadratic basis functions, wavelet function and so on.)

Morphology, broadly, is the study of form or structure. Folkloristic morphology, then, is the study of the structure of folklore and fairy tales. Folkloristic morphology owes its existence to two seminal researchers and theorists: Russian scholar Vladimir Propp and Finnish folklorist Antti Aarne.

MImo detector , very useful for student

tracking robot by vision sensor

MATLAB demo for paper "Visual Tracking with Online Multiple Instance Learning" by Boris Babenko, Ming-Hsuan Yang, Serge Belongie ,CVPR 2009, Miami, Florida. (MATLAB demo for paper "Visual Tracking with Online Multiple Instance Learning" by Boris Babenko, Ming-Hsuan Yang, Serge Belongie ,CVPR 2009, Miami, Florida.)

MATLAB SVM KM TOOLBox de 的例子(MATLAB SVM KM TOOLBox de examples)

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.)