signal-spectrum-estimation-

有限差分解光波传播方程Finite Difference Beam Propagation Method (Finite Difference Beam Propagation Method)

webserver-ssl library This is part of revision 226 of the Stellaris SSL Web Server.

彩色图像分解与重构，非常简单易懂，需要灰度图像的可以下载(caisetuxiangfenjiechonggou)

MATLAB 教学视频（史峰）配套ppt讲义。( MATLAB（Shi—Feng） teaching video supporting ppt lecture notes.)

本文件是用matlab编写的俄罗斯方块，还挺好玩的哦(This document is written using matlab Tetris kinda fun!)

this is the Digital Signal Processing Laboratory using Matlab Sanjit K Mitra book for digital systems and signal processing.....

说明：  关于机动目标跟踪的α，β滤波算法，需要调用模型函数，自己编写一个简单的就可以。(Maneuvering target tracking on the α, β filtering algorithm, the need to call the model function, I have written a simple can be.)

0-1整数规划有很广泛的应用背景，比如指派问题，背包问题等等，实际上TSP问题也是一个0-1问题，当然这些问题都是NP问题，对于规模较大的问题用穷举法是没有办法在可接受的时间内求得最优解的，本程序只不过是一个练习，得意之处是用递归法把所有解都排列出来。另：胡运权所著的《运筹学基础及应用（第三版）》第97页的例3，我用本程序求解得到的结果是：最优解是x*=(1,0, 0, 0, 0)，最优值是f(x*)=8，但书求得最优解是x*=(1,0, 1, 0, 0)，最优值是f(x*)=4，是不是书中写错了，请大家验证。以下是源程序，大家可以任意使用无版权问题，另外，如果大家有大规模的0-1规划的问题也希望提供给我，谢谢。变量个数至少是3个(0-1 Integer Programming is a very broad background, such as assignment, bags and so on. actually TSP is a 0-1, of course, these problems are NP, for larger problem with the law is not exhaustive approach in an acceptable time frame to obtain the optimal solution. this procedure is only one practice, farewell tour is the method used recursive all of the solutions to show. Another : Hu Yun-author of "operational research, and application base (third edition)" Article 97 of the three cases, I use this procedure to solve the result is : is the optimal solution x = (1,0, 0, 0, 0), the optimal values of f (x*) = 8, but the book is to find the optimum solution x = (1,0, 1, 0, 0), the optimal values of f (x*) = 4, the book is not a mistake, please certification. Following is the source)

This is a matlab function that help you to compare some personal images and detect the age difference between two subjects

采用bayes方法进行模型的校正，先验分布和似然函数采用时间序列模型，压箱底的东西(Using Bayes method for the calibration of the model, the prior distribution and the likelihood function using time series models, bottom pressure thing)