ifft2

说明：  主要介绍一种滤波器的实现及其方法，维纳滤波器是声音去噪中应用较多的一种！(Describes the realization of a filter and method, Wiener filter is the sound of a denoising application of more!)

使用robotic toolbox 可自行定义1至7自由度的机器人，并显示和移动 可以输入任意机器人参数，注意sigma=0是旋转关节，sigma=1为平移关节(Use robotic toolbox definable 1-7 DOF robot, and display and mobile robot can enter any parameters, note sigma = 0 is rotating joints, sigma = 1 for translational joints)

坐标转换（四参数） [row col]=size(source) for i=1:row u(i)=source(i,2) v(i)=source(i,3) x(i)=source(i,4) y(i)=source(i,5) B(2*i-1,1)=1 B(2*i-1,2)=0 B(2*i-1,3)=u(i) B(2*i-1,4)=-v(i) B(2*i,1)=0 B(2*i,2)=1 B(2*i,3)=v(i) B(2*i,4)=u(i) L(2*i-1,1)=x(i) L(2*i,1)=y(i) end X=inv(B *B)*(B *L) Txy=[X(1) X(2)] xzjz(1,1)=X(3) xzjz(1,2)=-X(4) xzjz(2,1)=X(4) xzjz(2,2)=X(3) [row1 col1]=size(target) for i=1:row1 u1(i)=target(i,2) v1(i)=target(i,3) d=[u1(i) v1(i)] md=Txy+xzjz*d end end (Coordinate conversion： [row col]=size(source) for i=1:row u(i)=source(i,2) v(i)=source(i,3) x(i)=source(i,4) y(i)=source(i,5) B(2*i-1,1)=1 B(2*i-1,2)=0 B(2*i-1,3)=u(i) B(2*i-1,4)=-v(i) B(2*i,1)=0 B(2*i,2)=1 B(2*i,3)=v(i) B(2*i,4)=u(i) L(2*i-1,1)=x(i) L(2*i,1)=y(i) end X=inv(B *B)*(B *L) Txy=[X(1) X(2)] xzjz(1,1)=X(3) xzjz(1,2)=-X(4) xzjz(2,1)=X(4) xzjz(2,2)=X(3) [row1 col1]=size(target) for i=1:row1 u1(i)=target(i,2) v1(i)=target(i,3) d=[u1(i) v1(i)] md=Txy+xzjz*d end end )

Hi ,it s a simple code

aic bic信息准则的使用，AR模型，确定阶数，等顶顶顶(AIC BIC)

simple application of newton s method to solve nonlinear equation

Flow to compute the incompressible flow in a lid driven cavity

Particle Filter 粒子滤波 MATLAB程序 Sampling Importance Resampling 粒子滤波，应用于跟踪，图像处理(Particle filter, applied to tracking, image processing)

准确计算语音信号或者噪声信号的三分之一倍频程。(Accurately calculate the speech signal, or one-third octave noise signal.)

癌细胞的识别，实现阈值分割，细胞核分割，膨胀与腐蚀，形态学分类识别(Identification of cancer cells to achieve threshold segmentation, nuclear division, expansion and corrosion, morphological classification)