网络篮球2k20赛事直播比分 (关于网络篮球2k20赛事直播比分 玩法)

网络篮球2k20赛事直播比分 是关于信号处理中的小波变换分析，用matlab命令实现的

% FWT_DB.M;% 此示意程序用DWT实现二维小波变换% 编程时间2004-4-10，编程人沙威%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%clear;clc;T=256; % 图像维数SUB_T=T/2; % 子图维数%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 1.调原始图像矩阵load wbarb; % 下载图像f=X; % 原始图像%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 2.进行二维小波分解l=wfilters('db10','l'); % db10（消失矩为10)低通分解滤波器冲击响应（长度为20）L=T-length(l);l_zeros=[l,zeros(1,L)]; % 矩阵行数与输入图像一致，为2的整数幂h=wfilters('db10','h'); % db10（消失矩为10)高通分解滤波器冲击响应（长度为20）h_zeros=[h,zeros(1,L)]; % 矩阵行数与输入图像一致，为2的整数幂for i=1:T; % 列变换 row(1:SUB_T,i)=dyaddown( ifft( fft(l_zeros).*fft(f(:,i)') ) ).'; % 圆周卷积<->FFT row(SUB_T+1:T,i)=dyaddown( ifft( fft(h_zeros).*fft(f(:,i)') ) ).'; % 圆周卷积<->FFTend;for j=1:T; % 行变换 line(j,1:SUB_T)=dyaddown( ifft( fft(l_zeros).*fft(row(j,:)) ) ); % 圆周卷积<->FFT line(j,SUB_T+1:T)=dyaddown( ifft( fft(h_zeros).*fft(row(j,:)) ) ); % 圆周卷积<->FFTend;decompose_pic=line; % 分解矩阵% 图像分为四块lt_pic=decompose_pic(1:SUB_T,1:SUB_T); % 在矩阵左上方为低频分量--fi(x)*fi(y)rt_pic=decompose_pic(1:SUB_T,SUB_T+1:T); % 矩阵右上为--fi(x)*psi(y)lb_pic=decompose_pic(SUB_T+1:T,1:SUB_T); % 矩阵左下为--psi(x)*fi(y)rb_pic=decompose_pic(SUB_T+1:T,SUB_T+1:T); % 右下方为高频分量--psi(x)*psi(y) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 3.分解结果显示figure(1);colormap(map);subplot(2,1,1);image(f); % 原始图像 title('original pic');subplot(2,1,2);image(abs(decompose_pic)); % 分解后图像title('decomposed pic');figure(2);colormap(map);subplot(2,2,1);image(abs(lt_pic)); % 左上方为低频分量--fi(x)*fi(y)title('Phi(x)*Phi(y)');subplot(2,2,2);image(abs(rt_pic)); % 矩阵右上为--fi(x)*psi(y)title('Phi(x)*Psi(y)');subplot(2,2,3);image(abs(lb_pic)); % 矩阵左下为--psi(x)*fi(y)title('Psi(x)*Phi(y)');subplot(2,2,4);image(abs(rb_pic)); % 右下方为高频分量--psi(x)*psi(y)title('Psi(x)*Psi(y)'); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 5.重构源图像及结果显示% construct_pic=decompose_matrix'*decompose_pic*decompose_matrix;%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%l_re=l_zeros(end:-1:1); % 重构低通滤波l_r=circshift(l_re',1)'; % 位置调整h_re=h_zeros(end:-1:1); % 重构高通滤波h_r=circshift(h_re',1)'; % 位置调整 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%top_pic=[lt_pic,rt_pic]; % 图像上半部分t=0;for i=1:T; % 行插值低频 if (mod(i,2)==0) topll(i,:)=top_pic(t,:); % 偶数行保持 else t=t+1; topll(i,:)=zeros(1,T); % 奇数行为零 endend;for i=1:T; % 列变换 topcl_re(:,i)=ifft( fft(l_r).*fft(topll(:,i)') )'; % 圆周卷积<->FFTend; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%bottom_pic=[lb_pic,rb_pic]; % 图像下半部分t=0;for i=1:T; % 行插值高频 if (mod(i,2)==0) bottomlh(i,:)=bottom_pic(t,:); % 偶数行保持 else bottomlh(i,:)=zeros(1,T); % 奇数行为零 t=t+1; endend;for i=1:T; % 列变换 bottomch_re(:,i)=ifft( fft(h_r).*fft(bottomlh(:,i)') )'; % 圆周卷积<->FFTend; construct1=bottomch_re+topcl_re; % 列变换重构完毕 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%left_pic=construct1(:,1:SUB_T); % 图像左半部分t=0;for i=1:T; % 列插值低频 if (mod(i,2)==0) leftll(:,i)=left_pic(:,t); % 偶数列保持 else t=t+1; leftll(:,i)=zeros(T,1); % 奇数列为零 endend;for i=1:T; % 行变换 leftcl_re(i,:)=ifft( fft(l_r).*fft(leftll(i,:)) ); % 圆周卷积<->FFTend; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%right_pic=construct1(:,SUB_T+1:T); % 图像右半部分t=0;for i=1:T; % 列插值高频 if (mod(i,2)==0) rightlh(:,i)=right_pic(:,t); % 偶数列保持 else rightlh(:,i)=zeros(T,1); % 奇数列为零 t=t+1; endend;for i=1:T; % 行变换 rightch_re(i,:)=ifft( fft(h_r).*fft(rightlh(i,:)) ); % 圆周卷积<->FFTend;%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%construct_pic=rightch_re+leftcl_re; % 重建全部图像%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 结果显示figure(3);colormap(map);subplot(2,1,1);image(f); % 源图像显示title('original pic');subplot(2,1,2);image(abs(construct_pic)); % 重构源图像显示title('reconstructed pic');error=abs(construct_pic-f); % 重构图形与原始图像误值figure(4);mesh(error); % 误差三维图像title('absolute error display'); clearclc%在噪声环境下语音信号的增强%语音信号为读入的声音文件%噪声为正态随机噪声sound=wavread('c12345.wav');count1=length(sound);noise=0.05*randn(1,count1);for i=1:count1signal(i)=sound(i);endfor i=1:count1y(i)=signal(i)+noise(i);end%在小波基'db3'下进行一维离散小波变换[coefs1,coefs2]=dwt(y,'db3'); %[低频 高频]count2=length(coefs1);count3=length(coefs2);energy1=sum((abs(coefs1)).^2);energy2=sum((abs(coefs2)).^2);energy3=energy1+energy2;for i=1:count2recoefs1(i)=coefs1(i)/energy3;endfor i=1:count3recoefs2(i)=coefs2(i)/energy3;end%低频系数进行语音信号清浊音的判别zhen=160;count4=fix(count2/zhen);for i=1:count4n=160*(i-1)+1:160+160*(i-1);s=sound(n);w=hamming(160);sw=s.*w;a=aryule(sw,10);sw=filter(a,1,sw);sw=sw/sum(sw);r=xcorr(sw,'biased');corr=max(r);%为清音（unvoice）时，输出为1；为浊音（voice）时，输出为0if corr>=0.8output1(i)=0;elseif corr<=0.1output1(i)=1;endendfor i=1:count4n=160*(i-1)+1:160+160*(i-1);if output1(i)==1switch abs(recoefs1(i))case abs(recoefs1(i))<=0.002recoefs1(i)=0;case abs(recoefs1(i))>0.002 & abs(recoefs1(i))<=0.003recoefs1(i)=sgn(recoefs1(i))*(0.003*abs(recoefs1(i))-0.000003)/0.002;otherwise recoefs1(i)=recoefs1(i);endelseif output1(i)==0recoefs1(i)=recoefs1(i);endend%对高频系数进行语音信号清浊音的判别count5=fix(count3/zhen);for i=1:count5n=160*(i-1)+1:160+160*(i-1);s=sound(n);w=hamming(160);sw=s.*w;a=aryule(sw,10);sw=filter(a,1,sw);sw=sw/sum(sw);r=xcorr(sw,'biased');corr=max(r);%为清音（unvoice）时，输出为1；为浊音（voice）时，输出为0if corr>=0.8output2(i)=0;elseif corr<=0.1output2(i)=1;endendfor i=1:count5n=160*(i-1)+1:160+160*(i-1);if output2(i)==1switch abs(recoefs2(i))case abs(recoefs2(i))<=0.002recoefs2(i)=0;case abs(recoefs2(i))>0.002 & abs(recoefs2(i))<=0.003recoefs2(i)=sgn(recoefs2(i))*(0.003*abs(recoefs2(i))-0.000003)/0.002;otherwise recoefs2(i)=recoefs2(i);endelseif output2(i)==0recoefs2(i)=recoefs2(i);endend%在小波基'db3'下进行一维离散小波反变换 output3=idwt(recoefs1, recoefs2,'db3');%对输出信号抽样点值进行归一化处理maxdata=max(output3);output4=output3/maxdata;%读出带噪语音信号，存为'101.wav'wavwrite(y,5500,16,'c101'); %读出处理后语音信号，存为'102.wav'wavwrite(output4,5500,16,'c102'); function [I_W , S] = func_DWT(I, level, Lo_D, Hi_D);%通过这个函数将I进行小波分解，并将分解后的一维向量转换为矩阵形式% Matlab implementation of SPIHT (without Arithmatic coding stage)% Wavelet decomposition% input: I : input image% level : wavelet decomposition level% Lo_D : low-pass decomposition filter% Hi_D : high-pass decomposition filter% output: I_W : decomposed image vector% S : corresponding bookkeeping matrix% please refer wavedec2 function to see more [C,S] = func_Mywavedec2(I,level,Lo_D,Hi_D); S(:,3) = S(:,1).*S(:,2); % dim of detail coef nmatrices 求低频和每个尺度中高频的元素个数%st=S(1,3)+S(2,3)*3+S(3,3)*3;%%%%对前两层加密%C(1:st)=0;L = length(S); %a求S的列数I_W = zeros(S(L,1),S(L,2));%设一个与原图像大小相同的全零矩阵% approx partI_W( 1:S(1,1) , 1:S(1,2) ) = reshape(C(1:S(1,3)),S(1,1:2)); %将LL层从C中还原为S(1,1)*S(1,2)的矩阵for k = 2 : L-1 %将C向量中还原出HL,HH,LH 矩阵 rows = [sum(S(1:k-1,1))+1:sum(S(1:k,1))]; columns = [sum(S(1:k-1,2))+1:sum(S(1:k,2))]; % horizontal part c_start = S(1,3) + 3*sum(S(2:k-1,3)) + 1; c_stop = S(1,3) + 3*sum(S(2:k-1,3)) + S(k,3); I_W( 1:S(k,1) , columns ) = reshape( C(c_start:c_stop) , S(k,1:2) ); % vertical part c_start = S(1,3) + 3*sum(S(2:k-1,3)) + S(k,3) + 1; c_stop = S(1,3) + 3*sum(S(2:k-1,3)) + 2*S(k,3); I_W( rows , 1:S(k,2) ) = reshape( C(c_start:c_stop) , S(k,1:2) ); % diagonal part c_start = S(1,3) + 3*sum(S(2:k-1,3)) + 2*S(k,3) + 1; c_stop = S(1,3) + 3*sum(S(2:k,3)); I_W( rows , columns ) = reshape( C(c_start:c_stop) , S(k,1:2) );end %%%%%%%mallat algorithm%%%%% clc; clear;tic; %%%%original signal%%%% f=100;%%frequence ts=1/800;%%抽样间隔 N=1:100;%%点数 s=sin(2*ts*pi*f.*N);%%源信号 figure(1) plot(s);%%%源信号s title('原信号'); grid on; %%%%小波滤波器%%%% ld=wfilters('db1','l');%%低通 hd=wfilters('db1','h');%%高通 figure(2) stem(ld,'r');%%%低通 grid on; figure(3) stem(hd,'b')%%%高通 grid on; %%%%% tem=conv(s,ld);%%低通和原信号卷积 ca1=dyaddown(tem);%%抽样 figure(4) plot(ca1); grid on; tem=conv(s,hd);%%高通和原信号卷积 cb1=dyaddown(tem);%%抽样 figure(5) plot(cb1); grid on; %%%%%%%% %[ca3,cb3]=dwt(s,'db1');%%小波变换 %%%%%%%% [lr,hr]=wfilters('db1','r');%%重构滤波器 figure(6) stem(lr); figure(7) stem(hr); tem=dyadup(cb1);%%插值 tem=conv(tem,hr);%%卷积 d1=wkeep(tem,100);%%去掉两头的分量 %%%%%%%%% tem=dyadup(ca1);%%插值 tem=conv(tem,lr);%%卷积 a1=wkeep(tem,100);%%去掉两头的分量 a=a1+d1;%%%重构原信号 %%%%%%%%% %a3=idwt(ca3,cb3,'db1',100);%%%小波逆变换 %%%%%%%%% figure(8) plot(a,'.b'); hold on; plot(s,'r'); grid on; title('重构信号和原信号的比较');toc; %figure(9) %plot(a3,'.b'); %hold on; %plot(s,'r'); %grid on; %title('重构信号和原信号的比较');

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