RINEXcode

说明：  递推最小二乘法，根据最小二乘法改变，可以辨识参数(LSM)

说明：  盲源分离，基于最大化负熵的盲源分离方法； 算法及调试(blind source separation)

There is a simulation of Orthogonal Frequency Division Multiplexing (OFDM) in by multipath fading channels. (There is a simulation of Orthogonal Frequency Division Multiplexing (OFDM) in by multipath fading channels.)

k-means clustering for image classification

Heat equation with variable coeficients using Point Jacobi Method

神经网络函数拟合图形，调节权重值得到较好的图形(Neural network function fitting graphics, adjust the weights worth to better graphics)

说明：  可实现自动分析，偏最小而成分析，结果可得相关性和RMSEC值(Automatic analysis can be realized, and analysis based on minimum)

Add for matlab.Can do orienting plus shraping.

一个很简单的m序列产生过程，很容易掌握的。(m-sequences generated by matlab procedures, good procedures, hard to find, and can be downloaded.)

三维点云法向量估计综述由于获取方便、表示简单、灵活等优势，点云逐渐成为常用的三维模型表示方法之一。法向量作为点云必不可少的属性 之一，其估计方法在点云处理中具有重要的位置。另一方面，由于点云获取过程中不可避免的噪声、误差和遮挡，点云中通常含 有噪声、外点和空洞，并且部分采样模型如CAD模型，也会存在尖锐特征，这些都给法向量估计提出了挑战。对当前已有的点云 法向量估计算法进行综述，分析其原理及关键技术，着重分析它们在处理噪声、外点和尖锐特征等方面的能力并给出比较，最后 为未来研究提供了一些建议(The three-dimensional point cloud normal vector estimation summarized obtain convenient, represents a simple, flexible, and becoming one of the methods commonly used three-dimensional model represents the point cloud. Normal vector of one of the as point clouds essential attributes, its estimation method has important position in the point cloud processing. On the other hand, the noise is inevitable in the process to obtain because of the point cloud, error and occlusion, point cloud typically contains noise outside point and empty, and part of the sampling model such as CAD models, there will still be sharp features, which gave the normal vector estimated that presents a challenge. Overview of current existing point cloud normal vector estimation algorithm, analysis of the principle and key technologies, and analyzed their ability to deal with the noise, the outer points and sharp features and gives comparison, and finally provide some suggestions for future research)