一个包含若干不连续点的一维信号，利用小波变换的模极大提取它的阶梯型边界点

matlabe code for mumford......

实现音频中添加数字水印的matlab程序(Add audio digital watermarking matlab program)

说明：  连续信号的相关于褶积。本程序在matlab上已经测试，可以直接调用。(Continuous signal related to the convolution. This procedure has been tested in matlab can be called directly.)

《智能控制》第二版（刘金琨著）程序源码第五章(" Intelligent control" , Second Edition (Liu Jinkun a) program source code Chapter)

常用的一些图像处理Matlab源代码,比如数字图像的傅立叶变换，二维离散余弦变换的图像压缩，采用灰度变换的方法增强图像的对比度，直方图均匀化，图像滤波等(Some commonly used image processing Matlab source code, such as digital image Fourier transform two-dimensional discrete cosine transform image compression method using gray transform image contrast enhancement, histogram equalization, image filtering, etc.)

Harris角点检测，用于图像的匹配等！ 直接运行，没有错误！(Harris corner detection for image matching! Run directly error!)

Q-learning (Watkins, 1989) is a simple way for agents to learn how to act optimally in controlled Markovian domains. It amounts to an incremental method for dynamic programming which imposes limited computational demands. It works by successively improving its evaluations of the quality of particular actions at particular states. This paper presents and proves in detail a convergence theorem for Q,-learning based on that outlined in Watkins (1989). We show that Q-learning converges to the optimum action-values with probability 1 so long as all actions are repeatedly sampled in all states and the action-values are represented discretely. We also sketch extensions to the cases of non-discounted, but absorbing, Markov environments, and where many Q values can be changed each iteration, rather than just one.

说明：  利用matlab仿真实现载波的平方环载波同步,costas环载波同步和符号同步(The square loop carrier synchronization, Costas loop carrier synchronization and symbol synchronization are realized by MATLAB simulation)

说明：  蚁群算法的优化计算-二维路径 经过优化，效果比较好(Two dimensional ant colony algorithm for path optimization After optimization, the effect is better)

资源描述 train_data_m：人脸训练集的矩阵形式，其中每个人脸都没有拉成向量形式，每个人脸图片都是矩阵，最后组合成的大矩阵。 train_data_c：每张人脸图片都被拉成了一个列向量，最后由所有的图片的列向量  train_label：每个训练图像的类标组成的一个行向量  P：包含--P.hang=hang（每张图片的行）;P.lie=lie（每张人脸图片的列）;P.num_person=num_person（人脸库包含的人数）; P.num=num_each_person/2;（我取的是一半作为训练，一半作为测试） P.num_train=num_train;训练样本数 P.num_test=num_test;测试样本数