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一个目标跟踪的实例，用的是交互式多模型仿真方法。(Examples of tracking a target, using a method of interactive multi-model simulation.)

DC TO DC converter (boost) simulation with matlab

自己在网上看到的一个关于平方根的求值方式，可以参考下(Themselves in line to see a square root to be evaluated on, you can refer to the following)

Implementation of Quadrature Amplitude Modulation-Demodulation

本程序采用自相关法求混沌时间序列重构的时间延迟。(This procedure using the autocorrelation method for chaotic time series reconstruction time delay.)

基于MATLAB对模拟非线性调制系统的仿真和性能分析(Based on the simulation of MATLAB simulation and performance analysis of nonlinear modulation system)

MUSIC算法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算法就是利用这两个互补空间之间的正交特性来估计空间信号的方位。噪声子空间的所有向量被用来构造谱，所有空间方位谱中的峰值位置对应信号的来波方位。MUSIC算法大大提高了测向分辨率，同时适应于任意形状的天线阵列，但是原型MUSIC算法要求来波信号是不相干的。(MUSIC algorithm is a matrix decomposition method based on feature space. From the geometric point of view, the observed spatial signal processing can be decomposed into signal subspace and noise subspace, it is clear that the two spaces are orthogonal. The signal received by the array to the subspace of the covariance matrix of the signal component corresponding eigenvectors, the noise subspace of the covariance matrix of all by the smallest eigenvalue (noise variance) eigenvectors corresponding to the composition. MUSIC algorithm is the use of orthogonal properties between these two complementary space to estimate the spatial orientation of the signal. Noise subspace of all vectors are used to construct the spectrum, all the spatial orientation of the spectrum corresponding to the peak position of the signal wave direction. MUSIC algorithm greatly improves the resolution measurements, while the antenna array adapted to any shape, but the prototype MUSIC algorithm requires to wave sign)

初学CS，自己编了很多算法，试试发出来大家一起交流，基于CS的OMP算法(OMP algorithm based on CS)

matlab的zernike矩算法，可计算R(matlab zernike)

MATLAB R2008数字图像处理与分析实例教程源程序 众多实例(MATLAB R2008 digital image processing and analysis of many instances tutorial source instance)