DUOJING

说明：  一个小小作业~解双曲线微分方程 大家要下就下吧呵呵~(~ Solutions for a small job, under the hyperbolic differential equation we have to lower it Ha ha ~)

说明：  MATLAB编写的经典粒子群程序，实现最基本的函数优化功能。(MATLAB program written in the classical PSO, to achieve the most basic function optimization.)

文件可以实现ofdm的仿真，是程序很全的文件，包括二-十进制转换，16QPSK调制解调，QAM调制解调等，实现了仿真的整个过程(Ofdm simulation file can be achieved, is the program is the whole file, including two- decimal conversion, 16QPSK modulation and demodulation, QAM modulation and demodulation, etc., to achieve a simulation of the whole process)

遗传算法-优化算法-模板-可以供学习参考(Genetic algorithms- Optimization- Template)

稀疏分解信号重建程序，利用匹配追踪原理不断对信号匹配，最后达到重建信号目的。(sparse decomposition signal reconstruction procedures, the use of matching principle constantly tracking the signal matching, Finally signal to the redevelopment purposes.)

白噪声产生程序，使用C++编程，适合初级系统辨识的研究人员使用(whilte noise)

说明：  经验模态分解的优化，达到更好的去燥效果。(The optimization of empirical mode decomposition can achieve better drying effect)

MATLAB软件的回归分析教程和MATLAB的具体程序说明(Regression Analysis MATLAB software tutorials and MATLAB description of the specific procedures)

this is a code about dispersion diagram af a photonic crystal laser diod

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)