scheduling

matlab 模式识别工具包 希望能对你们有用(pattern recognition matlab toolkit hope to be useful to you)

经典匹配追踪程序。附带经典lena图像、并对其进行处理。(Classic match-tracing program. With classic lena image and its processing.)

说明：  MATLAB信号处理的一个小程序，是关于辅助信号处理技术与应用的，希望对大家会有用(MATLAB signal processing of a small program, on the auxiliary signal processing technology and applications, we want to be useful)

说明：   此函数用于研究Mallet算法及滤波器设计 此函数用于消噪处理 分解的高频系数采用db9较好,即它的消失矩较大 分解的有用信号小波高频系数基本趋于零 对于噪声信号高频分解系数很大，便于阈值消噪处理(this function for research Mallet filter algorithm design and function for Denoising decomposition of high-frequency coefficients Terminal better that it's vanishing moments larger decomposition of useful signal, high-frequency coefficients tends to zero for high-frequency signal noise decomposition coefficient great ease threshold Denoising)

generic algorithm code in matlab

mathews数值分析书的源代码，用matlab编写，此是第一章中的程序。(mathews numerical analysis of the book s source code, using matlab, you can control book-learning.)

基于掺锗sio2的赛蜜儿方程，数值显示其折射率随波长的变化，可作为光纤纤芯加入(Based on germanium doped sio2 tournament Claudel equations, numerical display its refractive index changes with wavelength, can be added as the fiber core)

说明：  基于MATLAB和VC++6.0制作的车牌定位系统，做毕业设计的朋友可以参考参考(MATLAB and VC++6.0 based on production of license plate positioning system, the final project can refer to refer to a friend)

用于自学Matlab的一些小的简单的程序，里边涵盖了许多数学问题(For self-study a number of small simple Matlab program, covers a lot of math problems inside)

MUSIC算法[1]是一种基于矩阵特征空间分解的方法。从几何角度讲，信号处理的观测空间可以分解为信号子空间和噪声子空间，显然这两个空间是正交的。信号子空间由阵列接收到的数据协方差矩阵中与信号对应的特征向量组成，噪声子空间则由协方差矩阵中所有最小特征值（噪声方差）对应的特征向量组成。(MUSIC algorithm [1] is a feature space based on matrix decomposition method. From the geometric point of view, the signal processing can be decomposed observation space the signal subspace and the noise subspace, it is clear that the two spaces are orthogonal. Signal Subspace data received by the array covariance matrix and eigenvectors corresponding to the signal component, the noise subspace from the covariance matrix of all the smallest eigenvalue (noise variance) eigenvector components.)