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Implements a directional Gaussian filter. it can be used to evaluate the first directional derivative of an image.

matlab code used for analyzing trace files from ns2

lena 图像的线性插值 对图像处理的理解很有帮助(lena bilinear)

说明：  遗传算法matlab工具 1.工具箱文件拷贝至Matlab文件夹下，具体路径为：C:\program files\MATLAB\R2009a\toolbox。 2.工具箱路径加入Matlab文件路径之中。 流程为：File-->Set Path-->Add Folder。即，将C:\program files\MATLAB\R2009a\toolbox\文件夹加入该路径系统中。 3 重新启动Matlab，运行即可。 4 一般情况下，会出现如下情况。 安装遗传工具箱后出现问题：Warning: Name is nonexistent or not a directory: \afs\eos\info\ie\ie589k_info 解决方案： 将C:\program files\MATLAB\R2009a\toolbox\文件夹下的startup.m文件中的路径“\afs\eos\info\ie\ie589k_info\”改为goat当前所在的目录，“C:\program files\MATLAB\R2009a\toolbox\”即可。(Genetic algorithm matlab tool 1. Matlab toolbox files are copied to the folder, the specific path is: C: \ program files \ MATLAB \ R2009a \ toolbox. 2. Matlab toolbox add the file path into the path. Process is: File-&gt Set Path-&gt Add Folder. That is, the C: \ program files \ MATLAB \ R2009a \ toolbox \ folder by adding the path system. 3 Restart Matlab, can be run. 4 Under normal circumstances, there will be the following. Problems after installing genetic toolbox: Warning: Name is nonexistent or not a directory: \ afs \ eos \ info \ ie \ ie589k_info \ Solution: C: \ program files \ MATLAB \ R2009a \ toolbox \ startup folder under the . m file path &quot \ afs \ eos \ info \ ie \ ie589k_info \&quot to the current directory where the goat, &quot C: \ program files \ MATLAB \ R2009a \ toolbox \&quot can be.)

说明：  ofdm传输系统仿真源程序代码。供初学者参考。(ofdm transmission system simulation source code. For advanced users.)

Matlab编写的自适应线谱增强算法（ALE）(An adptive line enhancer code written with matlab)

GTK C exemple, creating table of image, using GTK API, very usefull to create an follow tools

公司最近做的一个项目数字上变频DUC simulink建模 花了我两个礼拜心血啊 (this is a project about digital up-converter,it is bulit with Simulink.)

matlab program for digital communication

用于kalman的设计，为初学者提供一个很好地基础程序，能很好的理解Kalman算法。(For the design of Kalman, provides a very good basic program for beginners, can be a good understanding of Kalman algorithm.)