zantai

自由电子激光，超短束团，滑移计算，matlab模拟(free-electron laser, ultrashort bunch, slip, Matlab simulation)

有限时域差分源程序，边界条件为各向异性完全匹配层（Upml）(FDTD source, boundary conditions for the anisotropic perfectly matched layer (Upml))

说明：  利用M序列实现系统的结构辨识和参数辨识，有些很好的辨识效果(Use of M sequence to achieve the system' s structure identification and parameter identification, and some very good recognition results)

谱估计 (1)修正协方差法 (2)多重信号分类（MUSIC）算法 (3)ESPRIT算法 (4)皮萨论科（Pisarenko）谐波分解法 并对各算法进行分析。 解压后即可在MATLAB上运行(Spectral estimation (1) modified covariance method (2) multiple signal classification (MUSIC) algorithm (3) ESPRIT algorithm (4) pizza on the Branch (Pisarenko) harmonic decomposition method and algorithm analysis. After decompression can be run in MATLAB)

多任务压缩感知算法的相关代码。代码良好，可以运行。(Multitasking compressed sensing algorithm related code)

MATLAB 与C++混合编程。 MATLAB5.3的提供了C/C++数学库，其中的C++数学库功能很强，使用它可以用类似MATLAB 的语法编写C++程序，十分方便。(MATLAB and C++ mixed programming. MATLAB5.3 to provide a C/C++ math library, which C++ Math Library function strong, use it to write syntax similar to MATLAB C++ program, is very convenient.)

多变量灰色预测模型算法的Matlab实现，一个网络流量模型与算法(Multivariate prediction model gray Matlab implementation of the algorithm, a network traffic model and algorithm)

Solving the LP problem using Simplex method

Source separation problems in digital signal processing are those which are used to separate useful signal a mix of signals of interest, other signals and noise. The typical example of a source separation problem is the cocktail party problem, where many number of peoples are talking simultaneously in a room and a listener is trying to follow one of the discussions. The human brain can separate the voices of different people (source separation) using neurons in the auditory region. But source separation using digital signals is difficult task with the available techniques. Several approaches have been proposed for solving this issue but most of the techniques are still in development stages. Some of the successfully tested approaches are principal components analysis (PCA) and Independent components analysis (ICA). Both the approaches work well when there are no delays or echoes present

全自动GM模型代码，能自动判断是否适合灰色预测，并给出残差预测模型代码(Fully automatic GM model code can automatically judge whether it is suitable for grey prediction and give residual prediction model code.)