lab

利用龙哥库塔法变步长解决高阶常微分方程的程序(Long Ge Kuta method using variable step procedures for solving higher order ordinary differential equations)

本文介绍在研究生阶段研究的最新卷积程序，希望有用。(This article describes the stages of postgraduate study at the latest convolution process, seek to help.)

灰度拉伸的小仿真，用了多种尝试，希望对大家有帮助(simulation of Gray stretch, i hope it is usefull to you.)

Acceptation rejection Algorithme for Generalized Gaussian Data Generation

I have provided the code for watermarking code in spatial domain and check difference between original and corrupted image.

对数据做eeof处理，得到数据的空间模态和时间系数。(Matlab function of eeof)

内容完整且每章相对独立；是一本简明的MATLAB参考书，既适用于初学者，也适用于高级MATLAB用户。对MATLAB与FORTRAN、C等语言结合的描述更是所有MATLAB书籍中少有的一大特色。(The contents of each chapter is relatively complete and independent it is a simple MATLAB reference books, both for beginners, but also to high-level MATLAB users. Of MATLAB and FORTRAN, C and other languages with a description of all MATLAB books is a major feature of the few.)

基于信息熵的改进型遗传算法，并加入了基于群体浓度更新的操作算子。(based on information entropy IAGA, and to join groups based on the concentration updated Operator.)

Motion Tracking =============== This tarball contains all code required to run the tracking algorithm on a sequence of images. Run the file run_tracker.m in Matlab and follow the instructions. You will need to have a directory of sequentially numbered images available. After entering the path and file types the tracker will begin processing. Once the data window appears the algorithm begins building a background model and attempts to track objects. By clicking on any of the four subwindows you can investigate the background representation (a Mixture of Gaussians) of any pixel. The two windows that then appear display the mixture once as a two-dimensional scatter plot (ignoring the blue colour component), and once as a one-dimensional evolution of the red colour component only. These plots make the internal processing visible and should help determining suitable parameters to be set in mixture_parameters.m.

Superviewshed将计算所引用的地形和目标给予高度观察员矩阵洛杉矶分校。此外，标准的最低高度的地形观察将输出。运行约16倍视域。 (Superviewshed will calculate LOS of a referenced terrain matrices given observer and target altitudes. In addition, the standard minimum height observable of the terrain will be output. Runs approximately 16 times faster than viewshed.)