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可以用该程序来学习simulink,是最基础的东西(you can use it to study simulink)

regulator matlab simulation PID control is ubiquitous. While simple in theory, design and implementation of PID controllers can be difficult and time consuming in practice. PID control involves several tasks that include:

产生调制信号后，提取调制信号的特征参数的matlab算法。(Generate a modulated signal, the extraction of characteristic parameters of the modulation signal matlab algorithm.)

瑞丽信道mimo的matlab仿真程序，很好的(Ruili channel MIMO matlab simulation program, good)

信号与系统，信号与系统课设的信号加入噪声以及滤波(Signals and Systems, Signals and Systems course set signal to noise and filtering)

This code is very useful to find the hamming distance

WLan-IEEE802.11a modified with MIMO (Multiple transmitter and receiver)

matlab转换c++语言的程序代码，共分享(matlab to c)

说明：  一种改进的自适应滤波器的状态估计，效果非常之好,是用MATLAB编写的，直接在matlab下运行就可以看到完美的滤波效果，自适应算法是sage—husa算法，较传统的滤波器有很大提高(An improved adaptive filter state estimation, the effect is definitely a good thing, is to use MATLAB prepared directly in matlab run will be able to see the effect of a perfect filter, adaptive algorithm is sage-husa algorithm, a more traditional filter machine has greatly improved)

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)