lqr-F16

description on Matlab of JEPG compression

This Program is used for calculating surface wave group velocity(This Program is used for calculating surfa ce wave group velocity)

68872607AGA自适应遗传算法 求解函数最小值.rar(Adaptive genetic algorithm for minimum function of function)

阵列麦克风中的music算法，用matlab仿真的(Music algorithm in array microphone, matlab simulation)

Hurst数计算，用于算法交易，非常有用(Hurst index)

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,适合于分析非线性、非平稳信号序列，具有很高的信噪比。(EMD decomposition method in theory can be applied to any type of signal, which in the non-stationary and nonlinear data, with a very distinct advantage, suitable for analyzing nonlinear and non-stationary signal sequence, with a high signal to noise ratio .)

SOlving hyperbolic equation in Matlab

关于支持向量机和Doa估计的，讲的很详细，还有自己理解弄的代码(With regard to support vector machines and Doa estimation, and speak in great detail, as well as his own understanding, get the code)

Matlab小波分析工具箱原理与应用 电子版书籍(Matlab wavelet analysis toolbox principle and application of the electronic books)

基于奇异值分解的人脸识别方法 梁毅雄 龚卫国 潘英俊 李伟红 刘嘉敏 张红梅 提出了一种将傅里叶变换和奇异值分解相结合的人脸自动识别方法.首先对人脸图像进行傅里叶变换,得到其具有位移不变特性的振幅谱表征.其次,从所有训练图像样本的振幅谱表征中给定标准脸并对其进行奇异值分解,求出标准特征矩阵,再将人脸的振幅谱表征投影到标准特征矩阵后得到的投影系数作为该人脸的模式特征.然后,对经典的最近邻分类器算法进行了改进,并采用模式特征之间的欧式距离作为相似性度量,从而完成对未知人脸的识别.采用ORL (Olivetti Research Laboratory)人脸库对本文提出的人脸识别方法进行验证,获得了100.00 的识别率.实验结果表明,本方法优于现有的基于奇异值分解的人脸识别方法,且对表情、姿态变换等具有一定的鲁棒性. (Face recognition based on singular value decomposition method Deliberate simultaneously Gong Weiguo Li Wei Hung Stephen Lau, Hong-Mei Zhang Ying-Jun Pan Paper, a Fourier transform and singular value decomposition of the combination of automatic face recognition. First of all, the face image by Fourier transformation, it has the same characteristics of the displacement amplitude spectra. Secondly, all training The amplitude spectrum of the sample images given in standard face representation and its singular value decomposition, find the standard characteristic matrix, then the amplitude of spectral characterization of human faces projected onto the standard characteristic matrix of projection coefficients obtained as the face of the model features . Then, the classical nearest neighbor classifier is improved, and the use of Euclidean distance between pattern features as the similarity measure, thus completing the identification of unknown human faces. using ORL (Olivetti Research La)