rect2grid

最近在编制的一个求解微分方程的poission程序(Recently in the preparation of a program to solve differential equations poission)

基于matlab的数字信号的实现，包括单位阶跃，单位脉冲，正弦和指数序列(Based on the realization of matlab digital signal, including unit step, unit impulse, sinusoidal and index sequence)

实现了L=3，h=0.5,M,RC升余弦CPM调制(Achieve L = 3, RC raised cosine CPM modulation)

本程序采用matlab编程，通过设计训练一个人工神经网络来达到其对一个4传感器实时检测输出结果融合判断的模拟，文中涉及人工圣经网络和多传感器融合两们学科的基本知识。(the procedures used Matlab programming, through the design of a training artificial neural network to achieve its right to a four-detection sensor fusion output judgment simulation , the text involving artificial Bible network and multi-sensor fusion of the two disciplines have basic knowledge.)

基于matlab前馈补偿的PID控制程序(Feedforward compensation of PID control based on)

windbg的使用文档.目前而言,本人认为是最全面的文档了(use of windbg documentation. now, I think is the most comprehensive documentation of the)

说明：  小波变换在多尺度下检测分解重构图像，以及融合边缘算法。(Under the multi-scale wavelet decomposition of the reconstructed image detection, and edge fusion algorithm.)

关于GPS的MATLAB源程序，经运行验证，算法简单，结果有效。(MATLAB source code on the GPS, verified by running the algorithm is simple, and effective.)

matlab视频处理书籍，详细介绍了matlab下的视频处理(matlab video process)

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)