kafangcanshuguji

说明：  MATLAB在六维腕力传感器系统标定中的应用，学习Matlab的好东西。(MATLAB in the six-axis wrist force sensor system calibration of the application, learning the good things Matlab.)

对倒立摆系统采用非线性控制方法进行研究的仿真实验结果，取得了预期的效果，程序运行正确。(right inverted pendulum system uses nonlinear control methods to conduct studies on the experimental results, and achieved the desired results, procedures correctly.)

PSS的各种各样的模型，以及各种模型的参数和功能的简介(the function of pss)

MATLAB BASED IMAGE PROCESSING INPAINTING

视频水印，在每一帧视频帧上嵌入水印，不可见性良好(video waterMarking,embed the watermark on each frame of video,invisibility is mediocre)

EMTP-ATP软件包使用说明书.rar电力系统暂态稳态分析(EMTP- ATP package instruction manual. Rar power system transient state analysis )

用matlab编写的64位的正交幅度调制，可运行，且会出图形！(64QAM)

一本的电磁数值分析方面的，集有限差分法，有限元法和矩量法为一体的基础教材。(An electromagnetic numerical analysis, set finite difference method, finite element method and the method of moments as one basis for teaching materials.)

说明：  matlab对连续信号的抽样，对初学matlab者很有用(matlab sampling of continuous signals, is useful for beginners who matlab)

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)