RandomDistribute_simu

ｌｄｐｃ的论文，希望对初学者有帮助，物品也是刚开始的。(LDPC papers, and they hope to be helpful for beginners, is also the beginning of articles.)

《精通Matlab6.5版》张志勇等编著，书本api相关的文件("proficient Matalab 6.5 version of the" Written by Zhang Zhiyong, books api relevant documents)

滤波代码，通过虚拟的动态窗口迭代过滤，剔除地物保留地表信息。(filter code，keep the ground information through a virture moving window.)

这是一个综合性的仿真程序。包括：用matlab生成一个FIR滤波器，得到滤波器系数及阶数；再用C语言产生一个混合频率的数字信号，之后将生成的数据样点送入到DSP主程序进行数字滤波，DSP主程序算法用的是循环缓冲区算法，这一部分在CCS上实现。(This is a comprehensive simulation program. Comprising: generating a matlab FIR filter, to obtain the filter coefficients and order then mixed to produce a C-frequency digital signal, and then the resulting data samples into the main program to the DSP for digital filtering, DSP main algorithm using a circular buffer algorithm, this part of the CCS to achieve.)

这个是关于小波变换，对图像增强介绍的文章，共大家参考研究、使用。(This is the wavelet transform, image enhancement described in the article on the total of your reference and use.)

matlab环境下编写的求解等熵涡问题，属于CFD入门级代码(matlab environment prepared isentropic vortex problem solving, is the entry-level CFD code)

mitigation of sags and swells

The purpose of this tutorial is to gain familiarity with MATLAB’s Image Processing Toolbox. This tutorial does not contain all of the functions available in MATLAB. It is very useful to go to HelpMATLAB Help in the MATLAB window if you have any questions not answered by this tutorial. Many of the examples in this tutorial are modified versions of MATLAB’s help examples. The help tool is especially useful in image processing applications, since there are numerous filter examples.

本程序是用来解决0-1规划问题的，匈牙利算法的MATLAB程序（用以解决分配（指派）问题） 本程序的用法是：输入效率矩阵 marix 为方阵，调用自编指令[z,ans]=fenpei(marix) 输出z为最优解，ans为最优分配矩阵。(This procedure usage is: input efficiency marix phalanx, calls for matrix maozhi instructions, [z] = fenpei (ans marix) output z for optimal solution, ans for optimal distribution matrix.)

The Gauss-Seidel technique is intuitive and easy to use on large or small problems. However, you should beware that the convergence tolerance must be carefully selected. Because the convergence criterion is based on the error between iterations, the same convergence criteria may lead to very different results. The direct solution technique presented in Section 2.6.2 is generally preferred over an iterative technique like Gauss-Seidel iteration if a tool such as MATLAB that provides a computationally efficient means of solving sparse matrices is available.