balanis_example

π／4DQPSK抗噪声性能仿真与分析,基于扩频和CDMA系统(π/4DQPSK anti-noise performance simulation and analysis, based on spread spectrum and CDMA systems)

ofdm仿真参考，仅仅是个参考，望给大家带来帮助(The correlation)

TV 重建工具箱，用于处理图像的去噪，去模糊，修补等，是很好的学习工具。(TV rebuild Toolbox for denoising image to blur, repair, etc., is a good learning tool.)

模糊神经网络的预测算法,嘉陵江水质评价,MATLAB的经典算法(Fuzzy neural network prediction algorithm, Jialing River water quality assessment, MATLAB classical algorithm)

用于二维平面的闭合曲线三次插值，通过给定的几个坐标点，完成闭合曲线的三次插值函数(Closed curve cubic interpolation for two-dimensional plane, through a given number of coordinate points, complete the closed curve cubic interpolation function)

说明：  带约束多变量DMC控制，能处理带约束的多变量DMC的控制问题(Constrained multivariable DMC control, able to handle constrained multivariable control problem of DMC)

这是一个ESPRIT 算法的matlab程序，非常好用(This is a ESPRIT algorithm matlab program, very easy to use)

mtalab中最好 理解的文章 最好是新手必看的！(mtalab the best understanding of the article preferably新手必看!)

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)

bch decoder using Berlekamp-Massey and Chien search standard bch decoder simulation