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demo Optical communication in underwater spectrum

fuzzy logic ,makes an application that is used to control temperature and humidity of orchid.

We see that HTH matrix is almost diagonal as the off diagonal elements are nearly zero. So we are getting exact estimation in case of PRN INPUT due to diagonality of HTH matrix. As seen that the unknown inpulse response ( h ) and estimated impulse response ( hth ) are coming out to be exactly same. The estimation even gets better for large N .as we get the HTH matrix perfect diagonal. So we come to know that we can estimate the unknown parameters exactly when PRN input is used.

这是基于约束最大期望算法的matlab程序，对参数估计及图像分割有较大作用(This is based on expectations of the biggest constraint algorithm Matlab procedures, parameter estimation and image segmentation greater role)

分别实现由MVDR和RLS实现了单波束形成（signal beamfroming using MVDR and RLS）(Were implemented by MVDR and RLS achieve a single beam forming (signal beamfroming using MVDR and RLS))

高斯有色噪声生成，有色噪声的谱估计及其在雷达中的应用(gauss noise generation)

ofdm粗频偏估计程序，包含5个模块，基于PN序列的同步(ofdm coarse frequency offset estimation procedures, including the five modules)

说明：  用matlab画蜂窝网络,用蜂窝覆盖给定的圆域(drawing cellular network using matlab )

该程序使用MATLAB实现了车牌识别，已通过测试，使用方法有边缘检测，腐蚀等操作(The program use MATLAB the license plate identification, already through the test, and method of use have edge detection, corrosion and other operational)

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)