lyapunovfractal2

此软件的基本功能主要分为2大模块:一个是数字图像处理模块，另一个是数字图像匹配模块。在数字图像处理模块中，用户可以对数字图像进行简单的处理，可以对图像进行放大，缩小，翻转，灰度处理。在数字图像匹配模块中，用户可以对两张图像进行匹配并显示匹配结果。(The basic function of this software is divided into two major modules: one is the digital image processing module, and the other is the digital image matching module. In digital image processing module, users can conduct a simple digital image processing, the right image to enlarge, reduce, flip, gray scale processing. In the digital image matching module, the user can match two images and displays matching results.)

说明：  基于遗传算法的优化bp神经网络的源代码，希望对大家有所帮助(Bp neural network genetic algorithm optimization)

非常好的关于粒子滤波的matlab源程序，值得拥有(Very good source on the particle filter, is worth having)

this zip when released for the applications of modeling of some severe body mechanical sudies inorder to be able to tackle some hard mechanical issues in the doamin of aerodynamics

按字节32 位对输入信息进行CRC校验编码的Matlab 程序(CRC redundancy code according to the byte 32-bit with MATLAB)

有效的多用户情况下MIMO-OFDM自适应资源分配算法(Efficient multi-user MIMO-OFDM case, adaptive resource allocation algorithm)

matla窗口制作，滤波，频谱分析等功能的很多程序(window matla production, filtering, spectral analysis of the many procedures and other functions)

用于移动通信系统的仿真，是对瑞丽衰落信道的仿真！(communication system )

analyze data association algorithms (Nearest Neighbour and JCBB) in SLAM. Data association is basically a combinatorial optimization problem whose goal is to find the “best” set of associations between the observations and predictions according to a particular criterion (e.g. individual compatibility). Form this point of view, near- est neighbour (NN, a.k.a. ML) is simply a “greedy” algorithm in which the “compatibility” of individual associations with each other is not examined. On the contrary, JCBB, by considering both the individual and joint compatibility, is able to yield much better results. JCBB uses the branch and bound technique in order to prune the search tree

This MATLAB code is an example of how to train the GCRF model described in "Learning Gaussian Conditional Random Fields for Low-Level Vision" by M.F. Tappen, C. Liu, E.H. Adelson, and W.T. Freeman in CVPR 2007. If you use this code in your research, please cite this paper