Name_pairs.cpp

新手初次学习windows程序时必看的一部参考书籍，很有帮助的(Novice learner windows program a must-see one reference book, very helpful)

使用MATLAB做图像处理，包括求灰度图、均衡化以及各种直方图，也可进行曲线拟合，求亮度曲面分布，和图像的均值与方差，便于学习(Use MATLAB to do image processing, including seeking grayscale, and a variety of histogram equalization, but also for curve fitting, seeking surface brightness distribution, and the mean and variance of the image, easy to learn)

对一个使用相关器或匹配滤波器的二进制通信系统，用Monte Carlo仿真估计Pe，并画出Pe对SNR的图。(A correlator or matched filter using a binary communication system with Monte Carlo simulation estimates Pe, and Pe draw the map of the SNR.)

很好的压缩感知的国外的书籍材料,对于我们的学习,尤其初学者很有帮助(ON划词翻译ON实时翻译 Very good material of compressed sensing of foreign books, for our learning, especially for beginners very helpful)

说明：  mc389模块at中文指令详解,通过超级终端进行调试.(mc389 module at the Chinese instructions Xiang Jie, through the HyperTerminal for debugging.)

EPS小日本的东西挺好的 EPS小日本的东西挺好的(Good little things EPS of EPS in Japan Japan' s something very good small)

使用MATLAB编写DLL的一些例子，然后用VB进行调用(vb_call_matlab_dll)

本实例将通过ISAPI提供的功能实现访问控制过滤器（Autentication Filter）。可以设定最大访问用户的数量以及授权用户名和密码。(example through the provision of functional ISAPI access control filters (Autentication Filter). The visit can set a maximum number of users and the authorized user name and password.)

基于量子遗传算法的matlab程序，可以在matlab上顺利运行(Based on quantum genetic algorithm matlab program can run smoothly in matlab)

这个Matlab程序实现最速下降算法。最速下降法是一种最基本的算法，它在最优化方法中占有重要地位.最速下降法的优点是工作量小，存储储变量较少,初始点要求不高；缺点是收敛慢，最速下降法适用于寻优过程的前期迭代或作为间插步骤，当接近极值点时，宜选用别种收敛快的算法. (The Matlab program to achieve the steepest descent algorithm. Steepest descent method is a basic algorithm, it occupies an important position in the optimization method. Advantage of the steepest descent method is the small amount of work, less storage reservoir variables, the initial point less demanding drawback is slow convergence, the steepest descent method for the optimization process of the early iterations, or as inserted steps are advised to use other kinds of fast convergence of the algorithm when close to the extreme point.)