PERFORMANCE ANALYSIS OF

关于语音识别的Matlab程序，包括Matlab下的work里面所有的东西(Matlab on the speech recognition process, including work under Matlab everything inside)

基于个数判断脉冲噪声的中值滤波器在matlab下的仿真实现，整个程序可以运行(adp_spa_denoise)

有关无线传感器网络中节能算法的协议，LEACH算法的详细介绍(The energy-saving algorithm in wireless sensor networks protocol, LEACH Algorithm Details)

说明：  本文详细而系统地介绍了Matlab与labview两种应用程序混合编程的方法和应用特点。(In this paper, a detailed and systematic introduction to Matlab and LabVIEW applications two hybrid programming methods and application characteristics.)

Block Matching Algorithms For Motion Estimation

数值积分方法练习与函数调用：利用ode23或ode45求解线性时不变系统微分方程 =Ay(t)并绘出y（t）的曲线，式中 =0， =4 (Using ode45 ode23 or to solve the linear differential equation when the same system = Ay (t) and draw y (t) curve, type = 0, = 4 )

模拟退火算法，常用的数值优化算法，适合初学者，非常方便(simulated annealing method, commonly used numerical optimization algorithm, for beginners, a very convenient)

用小波包包信号五层分解，得出每个频带重构信号，分析信号(xiao bo bao fen jie)

IDL语言的基础学习案例编程，便于初学者掌握； matlab语言的基础学习案例编程，便于初学者掌握；GIS开发技巧及实例；GEODATABASE教程，便与初学者学习，循序渐进了解GEODATABASE(IDL programming language based learning cases, easy for beginners to master matlab programming language based learning cases, easy for beginners to master GIS development skills and examples GEODATABASE tutorials, and beginners will learn, step by step to understand GEODATABASE)

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)