clear;clc; [image,pt,ft]=freadenvi('CSU_MS'); image=double(image); image1=zeros(488,310,3); for i=1:3 image1(:,:,i)=reshape(image(:,i),488,310); end times=2/0.5; J1=zeros(488*times,310*times,3); for i=1:3 J1(:,:,i)=imresize(image1(:,:,i),times,'bicubic'); end J2=Normalize3(J1);%归一化 0到1 double %转换成IHS IHS=rgbtoihs(J2(:,:,3:-1:1)); %将RGB变换到IHS空间，生成的IHS图像中，1通道对应H，2通道对应S，3通道对应I %读全色图像输出pan 0-1double类型 [ori_pan,pt,ft]=freadenvi('CSU_PAN'); pan=zeros(1952,1240); pan=reshape(ori_pan,1952,1240)/max(ori_pan); %用pan代替IHS的I分量 IHS(:,:,3)=pan; %逆IHS变换，构建融合图象 fusion=ihstorgb(IHS); figure,imshow(Normalize3(image1(:,:,3:-1:1))); title('original') figure,imshow(fusion); title('IHS'); enviwrite(fusion,1952,1240,3,'C:\Users\Administrator\Documents\MATLAB\weiboyaogan_test4\fusion');

clc clear all %% img_in = imread('ILSVRC2017_test_00000237.jpg'); m = size(img_in,1); n = size(img_in,2); img_lab = rgb2lab(img_in); img_L_mean = mean(mean(img_lab(:,:,1))); img_a_mean = mean(mean(img_lab(:,:,2))); img_b_mean = mean(mean(img_lab(:,:,3))); %% %高斯滤波 img_R = img_in(:,:,1); img_G = img_in(:,:,2); img_B = img_in(:,:,3); w = fspecial('gaussian',[7 7]); img_R_blur = imfilter(img_R,w); img_G_blur = imfilter(img_G,w); img_B_blur = imfilter(img_B,w); img_blur = cat(3,img_R_blur,img_G_blur,img_B_blur); figure('name','滤波') imshow(img_blur) img_lab_blur = rgb2lab(img_blur); %% %计算显著图 Sd = zeros(m,n); for i = 1:m for j = 1:n Sd(i,j) = sqrt((img_L_mean - img_lab_blur(i,j,1))^2 + (img_a_mean - img_lab_blur(i,j,2))^2 + (img_b_mean - img_lab_blur(i,j,3))^2); end end %归一化 Sd_normalized = figure_normalize(Sd); imwrite(Sd_normalized,'FT_saliency.jpg') figure imshow(Sd_normalized) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function Out_image = figure_normalize(In_image) % 归一化至0-1 o_max_image = max(max(In_image)); o_min_image = min(min(In_image)); Out_image = double(In_image - o_min_image)/double(o_max_image - o_min_image); end改进该代码使其能在matlab上运行

clc; clear all; img_in = imread('ILSVRC2017_test_00000237.jpg'); m = size(img_in,1); n = size(img_in,2);...Out_image = double(In_image - o_min_image)/double(o_max_image - o_min_image); end

clear all; close all; clc; tic bits_options = [0,1,2]; noise_option = 1; b = 4; NT = 2; SNRdBs =[0:2:20]; sq05=sqrt(0.5); nobe_target = 500; BER_target = 1e-3; raw_bit_len = 2592-6; interleaving_num = 72; deinterleaving_num = 72; N_frame = 1e8; for i_bits=1:length(bits_options) bits_option=bits_options(i_bits); BER=zeros(size(SNRdBs)); for i_SNR=1:length(SNRdBs) sig_power=NT; SNRdB=SNRdBs(i_SNR); sigma2=sig_power10^(-SNRdB/10)noise_option; sigma1=sqrt(sigma2/2); nobe = 0; Viterbi_init for i_frame=1:1:N_frame switch (bits_option) case {0}, bits=zeros(1,raw_bit_len); case {1}, bits=ones(1,raw_bit_len); case {2}, bits=randi(1,raw_bit_len,[0,1]); end encoding_bits = convolution_encoder(bits); interleaved=[]; for i=1:interleaving_num interleaved=[interleaved encoding_bits([i:interleaving_num:end])]; end temp_bit =[]; for tx_time=1:648 tx_bits=interleaved(1:8); interleaved(1:8)=[]; QAM16_symbol = QAM16_mod(tx_bits, 2); x(1,1) = QAM16_symbol(1); x(2,1) = QAM16_symbol(2); if rem(tx_time-1,81)==0 H = sq05(randn(2,2)+jrandn(2,2)); end y = Hx; if noise_option==1 noise = sqrt(sigma2/2)(randn(2,1)+jrandn(2,1)); y = y + noise; end W = inv(H'H+sigma2diag(ones(1,2)))H'; X_tilde = Wy; X_hat = QAM16_slicer(X_tilde, 2); temp_bit = [temp_bit QAM16_demapper(X_hat, 2)]; end deinterleaved=[]; for i=1:deinterleaving_num deinterleaved=[deinterleaved temp_bit([i:deinterleaving_num:end])]; end received_bit=Viterbi_decode(deinterleaved); for EC_dummy=1:1:raw_bit_len, if bits(EC_dummy)~=received_bit(EC_dummy), nobe=nobe+1; end if nobe>=nobe_target, break; end end if (nobe>=nobe_target) break; end end = BER(i_SNR) = nobe/((i_frame-1)raw_bit_len+EC_dummy); fprintf('bits_option:%d,SNR:%d dB,BER:%1.4f\n',bits_option,SNRdB,BER(i_SNR)); end figure; semilogy(SNRdBs,BER); xlabel('SNR(dB)'); ylabel('BER'); title(['Bits_option:',num2str(bits_option)]); grid on; end将这段代码改为有噪声的情况

sigma1=sqrt(sigma2/2); nobe = 0; Viterbi_init for i_frame=1:1:N_frame switch (bits_option) case {0}, bits=zeros(1,raw_bit_len); case {1}, bits=ones(1,raw_bit_len); case {2}, bits=randi(1,raw...

clear all; close all; clc;tic its_option =2; hoise_option=1; =4;NT=2; SNRdBs=[0:2:20];sq05=sqrt(0.5); obe_target =500; BER_target =1e-3; taw_bit_len= 2592-6; nterleaving_num = 72; deinterleaving_num = 72; _frame = 1e8; or i_SNR=1:length(SNRdBs) sig_power=NI;SNRdB=SNRdBs(i_SNR); sigma2=sig_power10°(-SNRdB/10)noise_option;sigmal=sqrt(sigma2/2); nobe = 0; Viterbi_init for i_frame=1:1:N_frame I switch (bits_option) case (0】, bits=zeros(1,raw_bit_len); case (11, bits=ones(1,raw_bit_len); casef2), bits=randint(1,raw_bit_len); case (2), bits=randi(1,1,raw_bit_len)-1; end encoding_bits= convolution_encoder(bits);interleaved=[]; for i=l:interleaving_mum interleaved=[interleavedencoding_bits([i:interleaving_mum:end])];解释一下上述代码中构造sq05的原因

由于标准差为1的正态分布随机变量的方差为1，因此其均方根值也为1。而由于sq05=sqrt(0.5)，因此sq05的平方等于0.5，即标准差为1的正态分布随机变量的方差为0.5，因此在使用sq05作为系数时，需要将接收信号乘以sqrt(2...

clear all; close all; clc;ticits_option = 2;noise_option = 1;raw_bit_len = 2592-6;interleaving_num = 72;deinterleaving_num = 72;N_frame = 1e4;SNRdBs = [0:2:20];sq05 = sqrt(0.5);bits_options = [0, 1, 2]; % 三种bits-option情况obe_target = 500;BER_target = 1e-3;for i_bits = 1:length(bits_options) bits_option = bits_options(i_bits); BER = zeros(size(SNRdBs)); for i_SNR = 1:length(SNRdBs) sig_power = 1; SNRdB = SNRdBs(i_SNR); sigma2 = sig_power * 10^(-SNRdB/10); sigma = sqrt(sigma2/2); nobe = 0; for i_frame = 1:N_frame switch bits_option case 0 bits = zeros(1, raw_bit_len); case 1 bits = ones(1, raw_bit_len); case 2 bits = randi([0,1], 1, raw_bit_len); end encoding_bits = convolution_encoder(bits); interleaved = []; for i = 1:interleaving_num interleaved = [interleaved encoding_bits([i:interleaving_num:end])]; end temp_bit = []; for tx_time = 1:648 tx_bits = interleaved(1:8); interleaved(1:8) = []; QAM16_symbol = QAM16_mod(tx_bits, 2); x(1,1) = QAM16_symbol(1); x(2,1) = QAM16_symbol(2); if rem(tx_time - 1, 81) == 0 H = sq05 * (randn(2,2) + j * randn(2,2)); end y = H * x; if noise_option == 1 noise = sigma * (randn(2,1) + j * randn(2,1)); y = y + noise; end W = inv(H' * H + sigma2 * diag(ones(1,2))) * H'; K_tilde = W * y; x_hat = QAM16_slicer(K_tilde, 2); temp_bit = [temp_bit QAM16_demapper(x_hat, 2)]; end deinterleaved = []; for i = 1:deinterleaving_num deinterleaved = [deinterleaved temp_bit([i:deinterleaving_num:end])]; end received_bit = Viterbi_decode(deinterleaved); for EC_dummy = 1:1:raw_bit_len if nobe >= obe_target break; end if received_bit(EC_dummy) ~= bits(EC_dummy) nobe = nobe + 1; end end if nobe >= obe_target break; end end BER(i_SNR) = nobe / (i_frame * raw_bit_len); fprintf('bits-option: %d, SNR: %d dB, BER: %1.4f\n', bits_option, SNRdB, BER(i_SNR)); end figure; semilogy(SNRdBs, BER); xlabel('SNR (dB)'); ylabel('BER'); title(['Bits-Option: ', num2str(bits_option)]); grid on;end注释这段matlab代码

bits = zeros(1, raw_bit_len); case 1 bits = ones(1, raw_bit_len); case 2 bits = randi([0,1], 1, raw_bit_len); end encoding_bits = convolution_encoder(bits); % 卷积编码 % 扰码器 ...

clc, close, clear, % 帧结构参数 subframe_length = 10; % 子帧长度 subframe_sync = 'EB90'; % 子帧同步码 subframe_data = randi([0 1], subframe_length, 1); % 随机生成子帧数据 frames_per_superframe = 4; % 每副帧包含的帧数 frame_length = subframe_length * frames_per_superframe; % 副帧长度 subframe_sync_bits = hexToBinaryVector(subframe_sync); % 将同步码转换为二进制向量 % 构造副帧数据 frame_data = zeros(frame_length, 1); for i = 1:frames_per_superframe start_index = (i-1)subframe_length + 1; end_index = isubframe_length; frame_data(start_index:end_index) = subframe_data; sync_index = start_index + find(subframe_sync_bits) - 1; frame_data(sync_index) = xor(frame_data(sync_index), 1); % 同步码取反 end % 生成子帧同步码 subframe_sync_bits = [subframe_sync_bits; zeros(subframe_length-length(subframe_sync_bits), 1)]; subframe_sync_bits = repmat(subframe_sync_bits, frames_per_superframe, 1); subframe_sync_bits = [subframe_sync_bits; zeros(frame_length-length(subframe_sync_bits), 1)]; subframe_sync_bits = circshift(subframe_sync_bits, [0 subframe_length]); subframe_sync = binaryVectorToHex(subframe_sync_bits); % 将同步码转换为十六进制字符串 date1=hexToBinaryVector(frame_date); % 输出结果 disp(['子帧长度为 ', num2str(subframe_length), ', 子帧同步码为 ', subframe_sync]); disp(['副帧长度为 ', num2str(frame_length), ', 同步码为']); disp('帧数据：'); disp(data);

而不是 date1=zeros(frame_length,1); 2. 在第29行，应该把 frame_date 改为 frame_data。 3. 在第31行，应该把 data 改为 frame_data。请将这些错误更正，代码应该可以正常运行。另外，您可能需要...

clear clc pathname = uigetdir; name_list=dir(pathname); for i=3:22 name_list(i).num=zeros(20,1); name_list(i).num(i-2)=1; end

这段代码的作用是获取用户选择的文件夹路径，然后遍历该文件夹下的所有文件和文件夹（除去当前目录和上一级目录），将每个文件名存入一个结构体数组中，并为每个文件名添加一个数字编号，编号从1开始，直到数组中...

逐行翻译以下代码： clc clear Data_test=readtable(“附件2：调查数据”); Data_cell=table2cell(Data_test); LabelStr = Data_test.Properties.VariableDescriptions; [m,n] = size(Data_test); Data_out = zeros(m,n); Data_out(:,1) = 1:1:m; for i = 2:22 DataLabel = Data_cell(:,i); DataLabel_cat = categorical(DataLabel); DataLabel_int = double(DataLabel_cat); Data_out(:,i) = DataLabel_int; end mutiStr = {[a,b,c,d,e,f,g,h],[aa,bb,cc],[aaa,bbb,ccc,ddd],[aaaa,bbbb,cccc,dddd],[o,p,q,r,s,t],[oo,pp,qq],[ooo,ppp,qqq,rrr,sss],[w,x,y,z]}; Data_out2 = Data_out; Data_muti = cell(m,length(mutiStr));

clc % 清空命令窗口 clear % 清除工作空间变量 Data_test = readtable("附件2：调查数据"); % 读取名为“附件2：调查数据”的表格文件，将数据存储在Data_test中 Data_cell = table2cell(Data_test); % 将Data_test...

解释代码：clear,clc k=0.082; r=300; c=1377; dt=0.1;dx=0.0001; alpha=kdt/r/c/dx/dx; A=alpha/2;B=1+alpha; T_ahead=zeros(1,101); T_ahead(1:end)=37; T_ahead(1)=100; n=1:3000; T50=zeros(1,3000); T50(1:end)=37; K=zeros(1,100); K_real=zeros(1,99); F=zeros(99); FF=zeros(99); FF(logical(eye(size(F))))=A; F(logical(eye(size(F))))=-B; AA=[zeros(98,1) FF(1:98,1:98);zeros(1,99)]; C=[zeros(1,99);FF(1:98,:)]; Total=F+AA+C; for time=1:3000 for i=2:100 K(i)=-T_ahead(i)-A(T_ahead(i+1)-2T_ahead(i)+T_ahead(i-1)); K_real(i-1)=K(i); K_real(1)=K(2)-AT_ahead(1); K_real(99)=K(100)-AT_ahead(end); end resolve=inv(Total)K_real'; x_real=[T_ahead(1);resolve;T_ahead(end)]; T_ahead=x_real; T_ahead(1)=100; T_ahead(end)=37; x=1:101; y=1:2; P=meshgrid(T_ahead,y); subplot(1,3,1) surf(x,y,P),title('三维视图') subplot(1,3,2) surf(x,y,P),view(0,0),title('正视图') subplot(1,3,3) plot(n,T50),title('中心点温度历程') xlabel('时间/s'),ylabel('温度'),axis([0 3000 35 60]) set(gcf,'unit','centimeters','position',[2 5 30 8]) drawnow T50(time)=T_ahead(50); T_ahead(50) end

1. clear, clc：清除命令窗口和工作区中的所有变量。 2. k=0.082; r=300; c=1377; dt=0.1; dx=0.0001;：定义了一些常数，包括热导率k、密度r、比热容c、时间步长dt和空间步长dx。 3. alpha=k*dt/r/c/dx/dx;...

clear;clear;clc; fs = 8000; % 采样频率 t = 0:1/fs:0.01-1/fs;% 采样时间 f = 2000; % 方波信号频率 x = square(2pif*t); % 生成方波信号 N = length(x)-1; % 信号长度保证为正整数 % 计算基4FFT和频谱 X = fft(x, N/4); %找到X_mag中的最大值，然后将X_mag除以该最大值得到X_mag_norm X_mag = abs(X); X_mag_norm = X_mag / max(X_mag); % 计算谐波失真度和谐波的归一化振幅 harmonics = 5; % 要计算的谐波次数 thd = 0; harmonic_amp_norm = zeros(1, harmonics);%表示要处理的谐波分量的数量 for k = 1:harmonics harmonic_amp_norm(k) = X_mag_norm(k4+1); thd = thd + harmonic_amp_norm(k)^2; end thd = sqrt(thd) / harmonic_amp_norm(1) 100; 修改代码错位

harmonic_amp_norm = zeros(1, harmonics); % 表示要处理的谐波分量的数量 for k = 1:harmonics harmonic_amp_norm(k) = X_mag_norm(k*4+1); thd = thd + harmonic_amp_norm(k)^2; end thd = sqrt(thd) / ...

clc clear load fisheriris; X = meas(:,3:4); Y = species; cv = cvpartition(Y,'holdout',0.3); % 30% 的数据用于测试 Y_train = grp2idx(Y_train); Y_test = grp2idx(Y_test); X_train = X(cv.training,:); Y_train = Y(cv.training,:); X_test = X(cv.test,:); Y_test = Y(cv.test,:); svm_model = fitcecoc(X_train, Y_train); Y_pred = predict(svm_model,X_test); accuracy = sum(Y_pred==Y_test)/length(Y_test); 未定义函数或变量 'Y_train'。

clc clear load fisheriris; X = meas(:,3:4); Y = species; cv = cvpartition(Y,'holdout',0.3); % 30% 的数据用于测试 Y_train = grp2idx(Y(cv.training)); Y_test = grp2idx(Y(cv.test)); X_train = X(cv....

clc;clear; se=[1,1,1,1,1,1,1;1,1,1,1,1,1,1;1,1,1,1,1,1,1;1,1,1,1,1,1,1;1,1,1,1,1,1,1;1,1,1,1,1,1,1;1,1,1,1,1,1,1]; %Structuring Element I0=imread("word_bw.bmp"); I=double(I0); I=im2gray(I); [im_height,im_width]=size(I); [se_height,se_width]=size(se); halfheight=floor(se_height/2); halfwidth=floor(se_width/2); [se_origin]=floor((size(se)+1)/2); image_dilation=padarray(I,se_origin,0,'both'); %Image to be used for dilation image_erosion=padarray(I,se_origin,256,'both'); %Image to be used for erosion %膨胀操作 for k=se_origin(1)+1:im_height+se_origin(1) for kk=se_origin(2)+1:im_width+se_origin(2) dilated_image(k-se_origin(1),kk-se_origin(2))=max(max(se+image_dilation(k-se_origin(1):k+halfheight-1,kk-se_origin(2):kk+halfwidth-1))); end end中每句话的作用以及[se_origin]=floor((size(se)+1)/2);记录的是哪个点

最终得到的输出图像即为膨胀后的图像dilated_image。 [se_origin]=floor((size(se)+1)/2)这一行代码的作用是计算结构元素se的中心点坐标（以该点为中心进行卷积操作）。其中，size(se)用于获取结构元素se的大小，加...

这段代码优化巡行时间：clc;clear; tic %遍历循环读取所有tiff文件 file_path = 'D:\JIANGXiaoYu\5_20230709_24dpf\';% 图像文件夹路径 img_path_list = dir(strcat(file_path,'.tiff'));%获取该文件夹中所有TIFF格式的图像 img_num = length(img_path_list);%获取图像总数量 Output_path='D:\JIANGXiaoYu\5_20230709_24dpf_tif\';%文件夹的路径 for jj = 1:img_num image_name = img_path_list(jj).name; % 图像名 image=imread(strcat(file_path,img_path_list(jj).name)); Info=imfinfo(image_name); Slice=size(Info,1); %%获取图片z向帧数 Width=Info.Width; Height=Info.Height; Image=zeros(Height,Width,Slice); for i=1:Slice Image(:,:,i)=imread(strcat(file_path,img_path_list(jj).name),i); %%一层一层的读入图像 J=uint8(Image(:,:,i)); %%一层一层写出图像 imwrite(J,[Output_path,num2str(209(jj-1)+i,'%04d'),'.tif']);%2G209/4G419 end fprintf(' %d %s\n',jj,strcat(file_path,image_name));% 显示正在处理的图像名 end toc disp(['运行时间: ',num2str(toc)]); %遍历循环读取所有tiff文件

clc; clear; tic file_path = 'D:\JIANGXiaoYu\5_20230709_24dpf\'; img_path_list = dir(fullfile(file_path, '*.tiff')); img_num = length(img_path_list); output_path = 'D:\JIANGXiaoYu\5_20230709_24dpf_...

帮我翻译一下代码 clc image_data=imread('D:\STUDIES\Projects\DSP Project\Brain Tumor\1.jpg'); figure, imshow(image_data); title('Brain MRI Image'); %% ------------------------------------------------------------------------ image_data = imresize(image_data,[224,224]);Ix = imfilter(double(image_data), hx, 'replicate'); gradmag = sqrt(Ix.^2 + Iy.^2); L = watershed(gradmag); Lrgb = label2rgb(L); figure, imshow(Lrgb); title('Watershed segmented image'); image_data= rgb2gray(image_data); % Gray Scaling image_data = medfilt2(image_data); % Median Filter image_data= im2bw(image_data,.6); figure, imshow(image_data); title('Thresholded Image'); %% ------------------------------------------------------------------------ hy = -fspecial('sobel'); hx = hy'; Iy = imfilter(double(image_data), hy, 'replicate'); %% ----------------------------------------------------------------- se = strel('disk', 20); % se - Structuring Element Io = imopen(image_data, se); Ie = imerode(image_data, se); Iobr = imreconstruct(Ie, image_data); Iobrd = imdilate(Iobr, se); Iobrcbr = imreconstruct(imcomplement(Iobrd), imcomplement(Iobr)); Iobrcbr = imcomplement(Iobrcbr); I2 = image_data; fgm = imregionalmax(Iobrcbr); I2(fgm) = 255; se2 = strel(ones(5,5)); fgm2 = imclose(fgm, se2); fgm3 = imerode(fgm2, se2); fgm4 = bwareaopen(fgm3, 20); I3 = imerode(image_data, se); bw = im2bw(Iobrcbr); figure imshow(bw); title('only tumor'); %% ------------------------

首先，使用imread函数读取1.jpg图像，然后用imshow在窗口中显示该图像。接下来，使用imresize函数将图像大小调整为224x224。然后，使用sobel算子对图像进行滤波，计算梯度幅值，再使用watershed算法对图像进行分割...

clc; clear; close all; tic; N=128; M=[4 16 32 64]; D=5; c=0.15; nt=0.1289; nr=0.9500; N_ofdm=1000; snr_dB=1:18; SNR=10.^(snr_dB./10); for kk=1:length(snr_dB) N_fft=N2+2; for jj=1:length(M) base_data=randi([0 1],1,NN_ofdmlog2(M(jj))); data_temp1= reshape(base_data,log2(M(jj)),[])'; data_temp2= bi2de(data_temp1); mod_data = qammod(data_temp2,M(jj)); data=reshape(mod_data,N,[])'; H_data=zeros(N_ofdm,N_fft); H_data(:,2:N_fft/2)= data; H_data(:,N_fft/2+2:N_fft)= conj(fliplr(data)); ifft_data=ifft(H_data,[],2); ifft_data=ifft_data+0.02ones(size(ifft_data)); Noise=awgn(ifft_data,SNR(kk),'measured')-ifft_data; Rx_data=ifft_datantnrexp(-cD)+Noise; Rx_data=Rx_data/(ntnrexp(-cD)) fft_data=fft(Rx_data,[],2); Rx_psk_data=fft_data(:,2:N_fft/2); demodulation_data = qamdemod(Rx_psk_data',M(jj)); demodulation_data= reshape(demodulation_data,[],1); temp1=de2bi(demodulation_data); err(kk,jj)=sum(sum((temp1~=data_temp1))); end BER(kk,:)=err(kk,:)./(NN_ofdmlog2(M(jj))); end figure(); for a=1:length(M) semilogy(snr_dB,BER(:,a),'-','LineWidth',1.5);hold on; end代码翻译

clc; clear; close all; tic; % 清空变量和图形窗口，记录程序开始时间 N=128; % 子载波数 M=[4 16 32 64]; % 调制阶数 D=5; % 距离 c=0.15; % 衰落系数 nt=0.1289; % 发射天线数 nr=0.9500; % 接收天线数 N_ofdm=...

MATLAB基础：clear, clc, clf与format函数详解及plot用法

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