the-robot-sentinel-control

ansys分析技术指南，详细介绍了拓扑优化和自适应网格划分(ansys analysis technique guide, detailing the topology optimization and adaptive meshing)

这个matlab小程序实现了基于11ac射频协议测试中的频谱掩膜比较(This small program to achieve the matlab based on 11 ac rf spectrum mask comparison of protocol testing )

在通信中的仿真，很有用，涉及到通信技术中关键技术的仿真(In communication simulation, very useful, related to communication technologies in key technology simulation)

说明：  遗传算法在优化路径中的应用研究，MATLAB程序。

卡尔曼滤波器范例，在matlab当中可以实现卡尔曼滤波的功能，通过修改参数可得不同的结果(Example of the Kalman filter, Kalman filter function in matlab which can get different results by modifying the parameters)

基于matlab的有限元分析源程序，用于结构静力分析(Finite element analysis of source code based on matlab for structural static analysis)

风力发电中MPPT算法中Cp系数的实现方法，亲测有效(Implementation of wind power in MPPT algorithm Cp coefficient, effective pro-test)

Affine Scale Invariant Feature transform..

matlab的中文使用文档，超链接目录，非常好用方便。matlab入门比较好的一本文档(Chinese use of matlab files, directory hyperlink is very easy to use convenience. matlab entry documents a relatively good)

In this session, I will focus on the gridding method for fast image reconstruction from non- Cartesian data [1-5]. While gridding is not the only game in town, it is the reconstruction of choice when reconstruction time is an issue. Other reconstruction techniques include rBURS [6] and non-Cartesian iterative methods [7,8]. Non-Cartesian iterative methods generally use gridding and inverse gridding as their core operation, so even when gridding is not the right solution for an application, an understanding of gridding can be useful. I will cover the main steps of the gridding algorithm and then discuss some implementation tips that can signicantly reduce the computation time. Finally, I will present some illustrations and viewpoints that I have found to aid in an intuitive understanding of the gridding method, which can be helpful when designing the density compensation function and judging the appropriateness of gridding as a solution for a particular application.