calmf1111

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)

dea in matlab for windows

说明：  观测器设计 对MATLAB学习帮助很大(Observer design study of great help MATLAB)

ICP shape detection algorithm for 2 mesh

空域滤波器一种--MVDR波束形成器，以《现代信号处理》（张贤达）书上所讲过程所写(Spatial filter one kind- MVDR beamformer to the "Modern Signal Processing" (Zhang XD) wrote the book talking about the process)

model of Inverted Pendulum

无刷电机的仿真模型，取合适的参数，模型仿真结果很逼真,(Simulation model of the brushless motor, take the appropriate parameters, the model simulation results are realistic,)

in tab file with example files

雷达信号处理领域入门与提高的好资料，适合于考前复习与项目使用，对很多概念有深入讲解，易于理解(A good data entry field of radar signal processing and improve the exam review, suitable for use with the project, there are many in-depth explanation of concept, easy to understand)

对符号LMS算法进行性能仿真，并将其和LMS算法的 误差性能和收敛性能比较(The symbol LMS algorithm performance simulation, and error performance and LMS algorithm and convergence performance comparison)