L6.1

基于主元分析(pca)的elman,rbf神经网络的matlab程序,运算时间短,程序简单(Based on principal component analysis (pca) of elman, rbf neural network matlab procedures, computing time is short, simple procedures)

交流电机控制matlab建模仿真（空间矢量控制算法）(Ac motor control)

说明：  CT头像重建算法matlab源代码，利用反卷积投影重建算法实现CT数据的重建(The CT the Avatar reconstruction algorithm matlab source code using deconvolution projection reconstruction algorithm reconstruction of CT data)

realizacion de benchmarking a aguas residuales

This example shows a model of a solid oxide fuel cell (SOFC) which can be utilized in SimPowerSystems™ .

用matlab实现16-QAM调制与解调，在simulink里面实现，能观察16QAM信号，星座映射图，毕业设计很好的参考资料。(Using matlab to achieve 16-QAM modulation and demodulation, which in simulink realization can be observed 16QAM signal constellation map, a good reference for graduation. )

说明：  matlab gui 设计的实例 代码(matlab gui design examples of code)

《MATLAB遗传算法工具箱及应用》书籍源程序(MATLAB genetic algorithm toolbox and application source code)

MATLAB学习资料，新手和有经验的都可以使用，英文文章，适合英文好的人使用(MATLAB learning materials, novice and experienced can use the English articles, suitable for the use of good English)

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)