MxianxintiaopA

数字水印源码，基于matlab的，可实现，共同分享。(Digital watermarking source code, based on the matlab can be realized.)

仿真程序：首先需要用一个随机发生器产生（0.1）内的均匀随机数，然后再将该序列映射到对应的幅度电平｛Am｝。然后将这个范围再分成4个相等的区间，这些子区间分别对应于4个信号比特的符号00，01，10，11。检测器观察到r=Am+n，并且计算r和4种可能传输的信号幅度之间的距离，它的输出Bm就是相应于最小距离的信号电平。Bm与真正的的传输信号幅度比较，差错计数器用来对检测器产生的差错计数。(Simulation program: first need to use a random generator (0.1) the uniform random number, then the sequence is mapped to the corresponding amplitude level {Am}. Then this range has been divided into four equal intervals, these sub-intervals corresponding to four signal bits of the symbol 00,01,10,11. Detector observed r = Am+ n, and calculate r and 4 possible transmission distance between the signal amplitude, its output corresponding to the minimum distance Bm is the signal level. Bm and the real rate of transmission of the signal comparison, error detection counter is used to generate the error count.)

利用matlab实现光纤的模式分布图的模拟。(Using matlab to achieve mode fiber distribution simulations.)

To decode SIRC data when someone want to build some device remote control.

K-d树实现。采用matalb和C++混合编译的方式实现。(kdtree provides a minimalistic implementation of kd-tree. The implementation can be used either inside MATLAB by means of MEX calls, or as a standalone tool, directly a C/C++ program. )

morphological operation such as erosion is very suitable to compress the size of the cell by using SE.

山上有m只猴子要选大王,选举办法如下：所有猴子从1到m进行编号并围坐一圈,从第一号开始按顺序1,2,...n继续报数,凡是报n号的猴子都退出到圈外,照此循环报数,直到圈内只剩下一只猴子时,这只猴子就是大王.输出大王的编号。 (Mountains have m of the monkeys to elect king, method of election are as follows: All monkeys are numbered from 1 to m, and sitting around a circle, to begin from the first order 1,2, ... n continue to report the number of those who reported that n No. The monkeys are out to outsiders the basis of this report the number of cycles until the circle only one monkey, the monkey is king. Output King number.)

VC和MATLAB混合编程 使用MATLAB工具函数计算(VC and hybrid programming using MATLAB tool MATLAB function to calculate)

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Haar+AdaBoost分类器训练总结(Haar+ AdaBoost classifier training summary)