message-signal

matlab6.5参考手册，是英文版的。里面含有simulink的参考手册。(matlab6.5 Reference Manual is in English. Simulink which contains a reference manual.)

说明：  利用平稳随机信号各态历经性，用周期图法对随机信号进行功率谱估计(The use of smooth ergodic random signal, and use periodogram of random signals, power spectrum estimation)

C-CMethod.m C-C法主函数，调用方式见函数体 correlation_integral.m 关联积分求解（被C-C法主函数调用） disjoint.m 将时间序列分为t个不相交的向量 heaviside.m 判断函数>0为1，<0为0 reconstitution.m 相空间重构函数 lorenz_phase.m 实验函数，用lorenz系统测试C-C法 lorenz_sim.mdl 用simulink建立的lorenz系统(C-CMethod.m CC method main function, see the body of the function invocation correlation_integral.m correlation integral solution (CC method is the primary function call) disjoint.m time series is divided into t disjoint vector heaviside.m determine the function> 0 1, < 0 0 reconstitution.m experimental phase space reconstruction function lorenz_phase.m function lorenz system test with CC method lorenz_sim.mdl established lorenz system with simulink)

Calculated Powers in 3 Phase

通过频域合成和复图像合成，实现一维信号的带宽合成，从而提高回波信号距离向的分辨率。(Through the frequency domain synthesis and complex image synthesis, realize a d signal bandwidth synthesis, so as to improve the echo signal to the resolution of the distance. )

包含不同条件下的MAB问题 仿真，还有仿真结果图例(include MAB PROBLEM policyBayesUCB.m policyCPUCB.m policyDMED.m policyGittins.m policyKLUCB.m )

多目标的线性调频连续波回波信号的正交匹配追踪算法OMP稀疏分解,matlab语言编写。(OMP method for signal compression)

包括图像分析的四部分代码：matlab扩散和高斯函数，线性扩散，线性复扩散，非线性扩散。(It contains four parts： [1]MATLAB function:diffusion.m gauss.m [2]Linear diffusion Applying linear diffusion to images creating linear scale-space. MATLAB code: demo_lin.m Image: haifa1.bmp [3]Linear complex diffusion Applying linear complex diffusion creating Gaussian and Laplacian scale-spaces. MATLAB code: demo_cmplin.m [4]Nonlinear diffusions: Perona-Malik ["Anisotropic diffusion"] Catte et al. regularization of P-M Complex ramp-preserving diffusion Nonlinear edge preserving diffusions.The classical Perona-Malik process: the value of the diffusion coefficient is reduced near edges estimated by the first derivative. Best applied to step edges. A regularized version by Catte et al- the gradient estimation for controlling the process is smoothed by a Gaussian. Proved to be mathematically well-posed. Ramp preserving complex diffusion- best for ramp-type edges. Results are smoother with almost no staircasing effects. MATLAB code: demo_nldif.m Image: ct_scan.bmp)

使用matlab读取CSV文件，本例能够很好的对其读取方法进行展示，给学习带来帮助 (Using matlab to read CSV file, in this case to read a good method of its display, to bring help to learn)

蚁群算法的MATLAB实现,有详细的注释（ACO_VRP.m）(ACO_VRP)