AP

bm3d图像去噪算法，3个文件 用于处理灰度图像 x64(v 1.7.3 x64)

用MATLAB程序实现卫星位置解算、用户位置解算（最小二乘法）、导航电文读取、坐标转换。(MATLAB program to achieve the satellite position solver, solver user location, navigation message reads, GPS positioning simulation (implemented by the least squares method).)

说明：  matlab光线追迹例子，用于三片式光学结构追迹仿真(matlab for optic ray tracing)

CRC校验，实现基本的CRC校验功能，当mask不同时，修改也相当方便(CRC checksum, CRC checksum to achieve the basic function, when the mask is not the same time, changes are also very convenient)

this code is used to find autocovariance of two discrete signals

说明：  从别的地方下载的源文件PLL的，希望能帮到你(Downloaded from somewhere else PLL source file, and hope you can help)

说明：  珍藏版，可实现，新K均值聚类算法，分为如下几个步骤： 一、初始化聚类中心 1、根据具体问题，凭经验从样本集中选出C个比较合适的样本作为初始聚类中心。 2、用前C个样本作为初始聚类中心。 3、将全部样本随机地分成C类，计算每类的样本均值，将样本均值作为初始聚类中心。 二、初始聚类 1、按就近原则将样本归入各聚类中心所代表的类中。 2、取一样本，将其归入与其最近的聚类中心的那一类中，重新计算样本均值，更新聚类中心。然后取下一样本，重复操作，直至所有样本归入相应类中。 三、判断聚类是否合理 采用误差平方和准则函数判断聚类是否合理，不合理则修改分类。循环进行判断、修改直至达到算法终止条件。(NewK-means clustering algorithm ，Divided into the following several steps: A, initialize clustering center 1, according to the specific problems, from samples with experience selected C a more appropriate focus the sample as the initial clustering center. 2, with former C a sample as the initial clustering center. 3, will all samples randomly divided into C, calculate the sample mean, each the sample mean as the initial clustering center. Second, initial clustering 1, according to the sample into the nearest principle clustering center represents the class. 2, as this, take the its recent as clustering center of that category, recount the sample mean, update clustering center. And then taking off, as this, repeated operation until all samples into the corresponding class. Three, judge clustering is reasonable Adopt error squares principles function cluster analysis.after clustering whether reasonable, no reasonable criterion revisio)

matlab讲义资料，教你如何简单的掌握matlab的使用方法(matlab lecture materials, teaching you how to master the simple use of matlab)

SVM 2D Classification Comparing Gaussian and polynomial Kernels (SVM 2D Classification Comparing Gaussian and polynomial Kernels)

matlab files fro the water fall curves