代码搜索：Matrix
找到约 10,000 项符合「Matrix」的源代码
代码结果 10,000
www.eeworm.com/read/197407/7998615
cpp smatrix2.cpp
// test formula based sparse matrix class
#include
#include "smatrix2.h"
void main(void)
{
try{
SparseMatrix A(2), B(2), C(20);
cin >> A;
cout
www.eeworm.com/read/197407/7998962
cpp smatrix1.cpp
// test formula based sparse matrix class
#include
#include "smatrix1.h"
void main(void)
{
try{
SparseMatrix A(20), B(20), C(20);
cin >> A;
cout
www.eeworm.com/read/398034/8008990
m sa_ex7_15b.m
sa% ESPRIT AOA estimation for a M = 4 element array with noise variance = .1
M = 4; % number of array elements
D = 2; % number of signals
sig2 = .1; % noise variance
th1 = -10*pi/180; %
www.eeworm.com/read/398034/8009016
m sa_fig7_10.m
% Min-Norm AOA estimation for a M = 6 element array with noise variance = .1
figure;
M=6;
D = 2; % number of signals
sig2=.1;
th1=-5*pi/180;
th2=5*pi/180;
a1=[1];
a2=[1];
temp=eye(M);
u
www.eeworm.com/read/398034/8009022
m sa_ex7_11.m
% MUSIC AOA estimation for a M = 6 element array with noise variance = .1
tic
M=6;
D = 2; % number of signals
sig2=.1;
th1=-5*pi/180;
th2=5*pi/180;
a1=[1];
a2=[1];
for i=2:M
a1=[a1
www.eeworm.com/read/398034/8009108
m sa_ex7_15.m
% ESPRIT AOA estimation for a M = 4 element array with noise variance = .1
% use time averages instead of expected values by assuming ergodicity of the mean and
% ergodicity of the correlation.
%
www.eeworm.com/read/397758/8024433
m l2_distance.m
function d = L2_distance(a,b,df)
% L2_DISTANCE - computes Euclidean distance matrix
%
% E = L2_distance(A,B)
%
% A - (DxM) matrix
% B - (DxN) matrix
% df = 1, force diagonals to be ze
www.eeworm.com/read/397758/8024502
m csppstrtrem.m
function X = csppstrtrem(Z,a,b)
% CSPPSTRTREM Projection pursuit structure removal.
%
% X = CSPPSTRTREM(Z,ALPHA,BETA) Removes the structure
% in a projection to the plane spanned by ALPHA and
www.eeworm.com/read/196856/8054171
m removeimpulses.m
function [Y] = RemoveImpulses(X, wha, wva, dha, dva, pfa)
%RemoveImpulses: Two-Dimensional Impulse Removing Filter
%
% [Y] = RemoveImpulses(X,wh,wv,dh,dv,pf)
%
% X Input signal(must be a mat
www.eeworm.com/read/196856/8054186
m removegaussian.m
function [Y] = RemoveGaussian(X, wha, wva, dha, dva, pfa)
%RemoveGaussian: Two-Dimensional Gaussian Noise Removing Filter
%
% [Y] = RemoveGaussian(X,wh,wv,dh,dv,pf)
%
% X Input signal(must b