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模糊神经解耦,比较长,这个程序对做解耦控制有帮助.(Fuzzy Neural Decoupling, relatively long, the process of decoupling control so helpful.)

PWM整流器仿真模型图 具有一定代表性(PWM)

mean shift算法提取图像边缘和图像分割(mean shift algorithm for image edge extraction and image segmentation)

二维变系数分数阶偏微分方程有限差分格式,拥有周期边界条件，和零初始条件(finite difference scheme for two-D fractional PDE)

七单元天线阵MUSIC DOA估计： d=1 ， 天线阵元的间距； lma=2， 信号中心波长； 四输入信号； A=[A1,A2,A3,A4]， 得出A矩； 四信号的频率d=[1.3*cos(v1*n) 1*sin(v2*n) 1*sin(v3*n) 1*sin(v4*n)] 构造输入信号矢量 U=A*d 总的输入信号 总输入信号的协方差矩阵 [s,h]=eig(c) 求协方差的特征矢量及特征值 取出与零特征值对应的特征矢量 求协方差矩阵的逆矩阵 应用Music法估计输出 绘出各波达方向图(Seven-element antenna array MUSIC DOA estimates: d = 1, Antenna Array pitch LMA = 2 signal center wavelength four input signals A = [A1, A2, A3, and A4], drawn A moment tetra-frequency of the signal D = [1.3* cos (V1* n) 1* ⁢ sin (v2* n) 1* sin (v3* n) 1* sin (V4* n)] constructed input signal vector U = A* D of the total input signal of the total input signal covariance matrix [S] = EIG (c) seeking covariance feature vector and the feature value removing and corresponding to the zero eigenvalues ​ ​ characterized vector seeking covariance matrix inverse matrix Applications Music estimate output plotted DOA Figure)

A progress bar in a single line of code with ETA

Largest of 2 no. C++

implementation of arnold transform

小波包分解重构程序，克服小波分解在高频段的频率分辨率较差(Wavelet packet decomposition and reconstruction program, overcome the wavelet decomposition in the poor frequency resolution in high frequency )

重绘窗口，实现360右下角通知窗口模式demo(repaint window, you can get the representation like the way 360 do )