shuzhi

很好的matlab学习资料，由浅到深的解析，附带源码(Matlab good learning materials, from shallow to deep analysis, with source code)

数字图像处理中matlab环境先的几个实用的滤波函数(With the aid of examples such as histogram equalization analysis and spatial filtering, this article explains the application of matlab in image processing.)

simple simulink model of MSK

说明：  duffing方程相关程序，方程求解，时间历程绘制，相图及庞加莱映射图(duffing, Poincare,phase diagram)

数值分析中，三次样条差值实例，使用matlab编程。(Numerical analysis, cubic spline difference instance, the use of Matlab programming.)

基于改进的蚁群算法利用matlab对VRP问题的求解(Based on improved ant colony algorithm using matlab to solve the VRP problem)

对图像进行基于SIFT算法的特征提取并对图像特征并实现特征匹配(the implement of the sift algorithm and matching based on the feature extracted the image)

In his 1960 famous publication (“A new approach to linear filtering and prediction problems”, Trans.ASME J. Basic Engineering., vol 82, March 1960, pp 34-45), Rudolf Kalman based the construction of the state estimation filter on probability theory, and more specifically, on the properties of conditional Gaussian random variables. The criterion he proposed to minimize is the state vector covariance norm, yielding to the classical recursion : the new state estimate is deduced from the previous estimation by addition of a correction term proportional to the prediction error (or the innovation of the measured signal).

灰色系统知道系统的一些简单信息，但是并没有完全了解该系统，只能根据统计推断或某种逻辑思维来研究该系统。(The gray system know some simple information system, but did not fully understand the system, to study the system can only be based on statistical inference, or some kind of logical thinking.)

均匀圆阵仿真:m=8 阵元数 azimuth=[-55 0 40] 方位角 p=length(azimuth) 信号源个数 snap=1000 最大快拍数 drl=0.5 圆阵半径与波长之比 snr=15 信噪比dB 流程：1、构造信号2、构造噪声阵列流型矩阵3、观测信号4、观测信号协方差矩阵5、求解特征值与特征向量6、显示图谱。(The uniform circular array simulation: m = 8 element azimuth = [-55 0 40] azimuth p = length (azimuth) number of source snap = 1000 maximum number of snapshots drl = 0.5 ratio of the radius of the circular array wavelength snr = 15 SNR dB process: 1, constructed signal structure noise array manifold matrix 3, the observed signals observed signals covariance matrix 5 eigenvalue and eigenvector 6, display the map.)