代码搜索：共模滤波
找到约 10,000 项符合「共模滤波」的源代码
代码结果 10,000
www.eeworm.com/read/168453/9912549
m afd_elip.m
function [b,a] = afd_elip(Wp,Ws,Rp,As);
% 椭圆型模拟低通滤波器设计
% --------------------------------------
% [b,a] = afd_elip(Wp,Ws,Rp,As);
% b = Ha(s) 分子的系数
% a = Ha(s) 分母的系数
% Wp = 以弧度/秒为单位的通带边缘频率; Wp
www.eeworm.com/read/168453/9912558
m parfiltr.m
function y = parfiltr(C,B,A,x);
% IIR 滤波器的并行型实现
% ----------------------------------------
% [y] = parfiltr(C,B,A,x);
% y = 输出序列
% C =当 M >= N时(FIR) 的多项式部分
% B = 包含各bk的K乘2维实系数矩阵
% A = 包含各
www.eeworm.com/read/168453/9912578
m u_chb2ap.m
function [b,a] = u_chb2ap(N,As,Omegac);
% 未归一化的切比雪夫-2型模拟低通滤波器原型
% --------------------------------------------------------
% [b,a] = u_chb2ap(N,As,Omegac);
% b = Ha(s) 分子多项式的系数
% a = Ha(s) 分母
www.eeworm.com/read/168453/9912793
m ex080200.m
% 第八章: 例 8.2
% 给定|Ha(jW)|求巴特沃斯滤波器的 Ha(s)
%
N = 3; OmegaC = 0.5;
[b,a] = u_buttap(N,OmegaC);
[C,B,A] = sdir2cas(b,a)
%%C = 0.1250
%%B = 0 0 1
%%A = 1.0000 0.5000 0.2500
%%
www.eeworm.com/read/165568/10057244
m huadongpingjunlbq.m
clear all %滑动平均滤波器
n=0:1:20;
sn=2.*(n.*0.9.^n);%signal
subplot(311)
stem(n,sn)
axis([-5 25 -0.5 10])
dn=randn(1,21);%噪声
xn=sn+dn;
subplot(312)
stem(n,xn)
ax
www.eeworm.com/read/423914/10526837
m ex080200.m
% 第八章: 例 8.2
% 给定|Ha(jW)|求巴特沃斯滤波器的 Ha(s)
%
N = 3; OmegaC = 0.5;
[b,a] = u_buttap(N,OmegaC);
[C,B,A] = sdir2cas(b,a)
%%C = 0.1250
%%B = 0 0 1
%%A = 1.0000 0.5000 0.2500
%%
www.eeworm.com/read/422226/10653862
m kalman_filter.m
function XE=Kalman_filter(d,Flag,zx,zy,N)
% Kalman_filter 采用Kalman滤波方法，从观测数值中得到航迹的估计
% d 噪声的标准差值
% Flag 判断计算x轴或y轴数据,'0'--x,'1'--y
%zx zy
www.eeworm.com/read/276400/10743211
txt readme.txt
//20031226
1.实时监测加入Range_Hi , Range_Low //相应修改RT.par文件
2.修改实时曲线显示
3.修改远程通信接口定义，在读取实时数据中加入报警上下限
4.加入滤波程序
www.eeworm.com/read/350382/10746027
m 7-13.m
%例程7－13自适应陷波滤波器
%自适应陷波器仿真
clear all
clc
N=400; %总采样长度
t=0:N-1;
s=sin(2*pi*t/20); %原始正弦信号
A=0.5; %干扰信号的幅值
fai=pi/3; %干扰
www.eeworm.com/read/350382/10746066
m 7-1.m
%例程7－1 噪声图像维纳滤波
% e.g.7-1.m for example7-1;
%test the function of weina filter.
RGB = imread('saturn.png');
I = rgb2gray(RGB);
J = imnoise(I,'gaussian',0,0.005);
figure, imshow(J);
K = wien