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function varargout = imageprocess(varargin) % IMAGEPROCESS M-file for imageprocess.fig % IMAGEPROCESS, by itself, creates a new IMAGEPROCESS or raises the existing % singleton*. % % H =

function [x,y] = arembed(Z,p,m); % function [x,y] = arembed(Z,p,m); % Create embedded data for use in, for example, an AR model % Z univariate time series % p order of model % m

% row product % function Z=rprod(X,Y) function Z=rprod(X,Y) [n m]=size(X); if(length(Y(:,1)) ~=n | length(Y(1,:)) ~=1) disp('Error in RPROD'); return; end Z=X.*(Y*ones(1,m));

function [x]=sampgauss(m,C,N) % % x=SAMPGAUSS(m,C,N) % % samples N-times from an multi-dimensional gaussian distribution % with covariance matrix C and mean m. Dimensionality is implied % in the

m=[1 2 3 4;5 6 7 8;9 10 11 12] p=[1 1 1 1 2 2 2 2 3 3 3 3] a=[] b=zeros(2,3) c=ones(2,3) d=eye(2,3) e=eye(3,3)

m=101; x=-5:10/(m-1):5; y=1./(1+x.^2);z=0*x; plot(x,z,'r',x,y,'LineWidth',1.5), gtext('y=1/(1+x^2)'),pause n=3; x0=-5:10/(n-1):5; y0=1./(1+x0.^2); y1=lagr1(x0,y0,x); hold on,plot(x,y1,'b'),

function y=lagr1(x0,y0,x) n=length(x0); m=length(x); for i=1:m z=x(i); s=0.0; for k=1:n p=1.0; for j=1:n if j~=k p=p*(z-x0(j))/(x0(k)-x0(j));

M = 16; %M进制 x = randint(5000,1,M); y=modulate(modem.qammod(M),x); %调制 ynoisy = awgn(y,15,'measured'); %加噪声 scatterplot(y); scatterplot(ynoisy); z=demodulate(modem.qamdemod(M),ynoisy); %解调

%impinvar_bilinear.m %chebyshev1模拟原型滤波器 Omegap=0.2*pi;Rp=1; %设计指标 Omegas=0.3*pi;As=16; [n1,Wn1]=cheb1ord(Omegap,Omegas,Rp,As,'s'); %获得chebyshev1的阶次和截止频率 [B1,A1]=cheby1(n1,0.5,Wn1,'s');

M=512; %滤波器长度 L=M; %每个数据块的长度 N=2*M; %FFT的长度 %% 需要添加u, y,d,e等数据，该程序就能给出类似于11.4节LMS算法的仿真图 w=zeros(1,M); wf=fft([w,zeros(1,M)]); %频域的抽头系数 for k=0:blocknum-1; %blocknum为数据块