De-impulsenoise

在模式识别中线性判别函数的MATLAB实现，与程序分析(Linear discriminant function in pattern recognition of the MATLAB implementation, and program analysis)

说明：  系统的介绍了独立分析原理，并有相关的代码解释(System introduced the principle of independent analysis and code interpretation)

音乐合成 需要自己载入音乐和声音 合成比为7：3(music combine)

该程序是一个简单的递推最小二乘法（rls）、共轭梯度法（grad）以及黄金分割法，输入数据，能够计算出来(The program is a simple recursive least squares (RLS), the conjugate gradient method (grad) and golden section method, the input data, can be calculated)

单相逆变器的双环系统matlab仿真（阻性,感性性,容性负载）(Single-phase inverter bicyclic system matlab simulation (resistive, inductive, capacitive load))

svm预测时间序列，源码在txt文件里，需要的请自行输入到MATLAB里实现(svm time series forecasting, source code in txt file, need to enter on their own to achieve in MATLAB)

针对高精度的畸变图像,提出了一种基于最小二乘影像匹配的高精度畸变图像矫正算 法. 算法首先利用特征提取与边缘检测对图像进行预处理,并且将特征匹配与最小二乘算法相 结合,从而实现了图像与模板之间精确的子像素定位与匹配. 实验表明,该算法较好的解决了目前高精度畸变图像矫正算法中普遍存在的定位和匹配精度较差的缺陷,图像矫正效果良好,是一种有效的畸变图像矫正算法.(Image distortion for high-precision, a least-squares image matching based on high-precision image distortion correction algorithm. First of all, the use of feature extraction algorithm for edge detection and image pre-processing, and will feature matching with a combination of least-squares algorithm in order to achieve the image and the template between the sub-pixel accurate positioning and matching. The experimental results show that the algorithm has better accuracy of the solution to the current distortion prevalent image correction algorithm to match the accuracy of the positioning and less defects, image correction effect well, is an effective algorithm to correct image distortion.)

image watermarkingusing matlab

一本关于Matlab仿真通信系统的书，很系统全面地讲解了各个通信系统部分，及其matlab仿真，并附有相应的Matlab练习题。(a book about simulating communication systems using matlab. Very comprehensive explanation and corresponding matlab code, and exercises.)

　计算机人脸识别技术( Face Reocgnition)就利用计算机分析人脸图像,从中提取出有效的识别信息,用来辨认身份的一门技术。[ 1 ]即对已知人脸进行标准化处理后,通过某种方法和数据库中的人脸样本进行匹配,寻找库中对应人脸及该人脸相关信息。人脸自动识别系统有两个主要技术环节,一是人脸定位,即从输入图像中找到人脸存在的位置,将人脸从背景中分割出来,二是对标准化后的人脸图像进行特征提取和识别。本文中介绍的PCA (特征脸)方法就是一种常用的人脸 特征提取方法。(Computer Face Recognition Technology (Face Reocgnition) on the use of computer analysis of facial image, to extract the valid identification information used to identify the status of a technology. [1] that is known to standardize treatment of face, through a method and a database of face samples for matching, search library, the corresponding face and the face-related information. Automatic face recognition system has two main technical aspects, first, face location, that is, from the input image to find the location of the face there, the faces will be split out from the background, the second is, the standard features of face images extraction and recognition. Described in this paper PCA (Eigenfaces) method is a common facial feature extraction method.)