Simulation-GPS

基于RSSI的测距定位算法，可以直接自己写demo然后进行测试，十分的方便(RSSI-based distance positioning algorithm can write your own demo and then test, very convenient)

在系统辨识中，应用多级最小二乘可以很好的估计出噪声系数(In system identification, the application can be multi-stage least squares estimates of the noise figure)

说明：  小波去噪程序，在别人的程序上有一定的修改，对于任意声音文件a.wav进行小波去噪。(Wavelet denoising procedure, in others there are certain procedural changes, for any sound file a.wav wavelet denoising.)

MATLAB用于数据处理分类，包括广义线性模型，logist模型(MATLAB for Data Processing classification, including generalized linear models, logist model)

用来在matalab上直接使用的surf代码(Surf code to be used directly on MATALAB)

小球落地弹跳过程仿真。 可以在界面中输入小球的初始高度，初速度，落地弹跳后的能量损失，重力加速度等参数来显示小球的运动轨迹。(bouncing ball landed process simulation. The input interface of the initial ball high early speed, bouncing landing after the energy losses gravity parameters to show the ball trajectory.)

本程序适用于将一段长语音文件分割为多个所需小段，长度大小可调节，写该蚕食即可(This procedure applies to a long voice file into small pieces more than the required length of the size can be adjusted, you can write the encroachment)

功能：输入一页文字，程序可以统计出文字、数字、空格的个数功能：输入一页文字，程序可以统计出文字、数字、空格的个数(code)

Spectrogram implementation in matlab environment

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)