代码搜索：CMapX
找到约 154 项符合「CMapX」的源代码
代码结果 154
www.eeworm.com/read/138743/5813984
m conjgrad.m
function [fnew,pnew,rnew,Lp] = ConjGrad(f,p,pold,r,u,J0,Lpold);
%Lp = L(u,FWT_Dyad(p,J0));
Lp = L(u.*FWT_ATrou(p,J0));
lambda = (r'*p) / (p'*Lp);
fnew = f + lambda * p;
rnew = r - lam
www.eeworm.com/read/138743/5813986
m calcftheo.m
function ftheo = CalcFtheo(alpha,p1,p2);
x = (log(p2)-log(p1))./(log(p2) + alpha .* log(3));
ftheo = ((x-1).*log(x-1) - x .* log(x)) ./(x .* log(1/3));
% Written by Maureen Clerc and Jerome Kal
www.eeworm.com/read/138743/5813991
m cantor.m
function x = cantor(x,n1,n2,p,a1,a2,b1,b2);
if n2-n1 < 0,
disp('problem')
elseif (n2 - n1 + 1)
www.eeworm.com/read/138743/5814035
m ourphase.m
function phi = ourphase(g,epsilon);
% phi = ourphase(g);
% calculates the complex phase of vector g ;
% coordinates whose moduli are smaller than a defined
% threshold epsilon are associated to p
www.eeworm.com/read/138743/5814036
m analytic.m
function g = Analytic(f);
%
% Takes the Analytic part of signal f
%
f0 = f;
f = f(:);
N = length(f);
hatf = fft(f);
hatg = zeros(N,1);
hatg(1) = hatf(1);
hatg(2:N/2) = 2*hatf(2
www.eeworm.com/read/138743/5814043
m wtch02demo.m
function WTCh02Demo
% WTCh02Demo -- Demo Browser for chapter 2.
% Usage
% WTCh02Demo
% Inputss
% none
% Outputs
% none
%
choice=menu('Chapter 2', 'figure 1', 'Exit Chapter 2');
www.eeworm.com/read/477304/6741460
m gwnoisy.m
function Noisysig = GWNoisy(data,sigma)
% GWNoisy- Addition of a Gaussian White Noise
% Usage
% Noisysig = GWNoisy(sig,sigma)
% Inputs
% sig Input signal
% sigma s.d for add
www.eeworm.com/read/477304/6741464
m gwn.m
function B = GWN(n,beta)
% GWN- Generation of Gaussian White Noise
% Usage
% B=GWN(n,beta)
% Inputs
% n size of datas
% beta standard deviation
% Outputs
% B resulting nois
www.eeworm.com/read/477304/6741467
m snr.m
function value = SNR(sig1,sig2)
% SNR- Signal/Noise ratio
% Usage
% value=SNR(sig1,sig2)
% Inputs
% sig1 Original reference signal
% sig2 Restored or noisy signal
% Outputs
%
www.eeworm.com/read/477304/6741478
m gwn2.m
function B = GWN2(n,beta)
% GWN2- Generation of 2-D Gaussian White NNoise
% Usage
% B=GWN2(n,beta)
% Inputs
% n size of datas
% beta standard deviation
% Outputs
% B resulti