I would like to prototype software-defined radio (SDR) systems using MATLAB & Simulink using the Xilinx Virtex-6 and Spartan-6 FPGA boards. Is there an available Support Package for Xilinx FPGA Radio?I would like to prototype software-defined radio (SDR) systems using MATLAB & Simulink using the Xilinx Virtex-6 and Spartan-6 FPGA boards. Is there an available Support Package for Xilinx FPGA Radio? I would like to prototype software-defined radio (SDR) systems using MATLAB & Simulink using the Xilinx Virtex-6 and Spartan-6 FPGA boards. Is there an available Support Package for Xilinx FPGA Radio? xilinx, fpga, support, package, communications, system, toolbox, zynq, sdr MATLAB Answers — New Questions

​

Hello everyone, I’m very new to MATLAB. Could you help me with this question please?
How to write codes that calculate e^x by serializing the x value entered from the keyboard into a series equal to the number of terms (N) entered from the keyboard?Hello everyone, I’m very new to MATLAB. Could you help me with this question please?
How to write codes that calculate e^x by serializing the x value entered from the keyboard into a series equal to the number of terms (N) entered from the keyboard? Hello everyone, I’m very new to MATLAB. Could you help me with this question please?
How to write codes that calculate e^x by serializing the x value entered from the keyboard into a series equal to the number of terms (N) entered from the keyboard? #e^x #maclaurin #taylorseries MATLAB Answers — New Questions

​

I have a parameter in a model reference that I would like to tune while the simulation is running in accelerator mode. How do I do this?I have a parameter in a model reference that I would like to tune while the simulation is running in accelerator mode. How do I do this? I have a parameter in a model reference that I would like to tune while the simulation is running in accelerator mode. How do I do this?  MATLAB Answers — New Questions

​

Hi there,
I have been getting different outputs from Matlab to JASP and SPSS, both of which agree that Matlab is wrong, which I think is due to the intercept being included as an interaction term in the model spec.

I have run a 2b*6w*2w Mixed ANOVA in Matlab. (Between-2=Consciousness, Within-6 = Conguency, Within-2 =Target) However I am getting the intercept as an interaction term in my ranova table:
Matlab Output

The factors Congruency and Target should be reported only as a main effects, not as an interaction with the intercept.

I’m including a JASP output so to compare the difference between the two softwares.

Could you take a look at my code, I think I’ve done something wrong in the Wilkinson notation of the model specification, and tell me how I stop it using the intercept as an interaction term.

OverallDataTable=readtable(‘Data.xlsx’)

WithinDesign=table(categorical({‘Related Hand’,’Related Foot’,’Unrelated Hand’,’Unrelated Foot’, …
‘Neutral Hand’,’Neutral Foot’,’Related Hand’,’Related Foot’,’Unrelated Hand’,’Unrelated Foot’, …
‘Neutral Hand’,’Neutral Foot’}’),categorical({‘BP’,’BP’,’BP’,’BP’,’BP’,’BP’,’O’,’O’,’O’,’O’,’O’,’O’}’), …
‘VariableNames’,{‘Congruency’,’Target’});

OverallMixedANOVA=fitrm(OverallDataTable,’BP_RH_Con-O_NF_Sub~Consciousness’,WithinDesign=WithinDesign);

OverallMixedANOVATable=ranova(OverallMixedANOVA,"WithinModel",’Congruency*Target’)

Thanks for any and all advice

DanHi there,
I have been getting different outputs from Matlab to JASP and SPSS, both of which agree that Matlab is wrong, which I think is due to the intercept being included as an interaction term in the model spec.

I have run a 2b*6w*2w Mixed ANOVA in Matlab. (Between-2=Consciousness, Within-6 = Conguency, Within-2 =Target) However I am getting the intercept as an interaction term in my ranova table:
Matlab Output

The factors Congruency and Target should be reported only as a main effects, not as an interaction with the intercept.

I’m including a JASP output so to compare the difference between the two softwares.

Could you take a look at my code, I think I’ve done something wrong in the Wilkinson notation of the model specification, and tell me how I stop it using the intercept as an interaction term.

OverallDataTable=readtable(‘Data.xlsx’)

WithinDesign=table(categorical({‘Related Hand’,’Related Foot’,’Unrelated Hand’,’Unrelated Foot’, …
‘Neutral Hand’,’Neutral Foot’,’Related Hand’,’Related Foot’,’Unrelated Hand’,’Unrelated Foot’, …
‘Neutral Hand’,’Neutral Foot’}’),categorical({‘BP’,’BP’,’BP’,’BP’,’BP’,’BP’,’O’,’O’,’O’,’O’,’O’,’O’}’), …
‘VariableNames’,{‘Congruency’,’Target’});

OverallMixedANOVA=fitrm(OverallDataTable,’BP_RH_Con-O_NF_Sub~Consciousness’,WithinDesign=WithinDesign);

OverallMixedANOVATable=ranova(OverallMixedANOVA,"WithinModel",’Congruency*Target’)

Thanks for any and all advice

Dan Hi there,
I have been getting different outputs from Matlab to JASP and SPSS, both of which agree that Matlab is wrong, which I think is due to the intercept being included as an interaction term in the model spec.

I have run a 2b*6w*2w Mixed ANOVA in Matlab. (Between-2=Consciousness, Within-6 = Conguency, Within-2 =Target) However I am getting the intercept as an interaction term in my ranova table:
Matlab Output

The factors Congruency and Target should be reported only as a main effects, not as an interaction with the intercept.

I’m including a JASP output so to compare the difference between the two softwares.

Could you take a look at my code, I think I’ve done something wrong in the Wilkinson notation of the model specification, and tell me how I stop it using the intercept as an interaction term.

OverallDataTable=readtable(‘Data.xlsx’)

WithinDesign=table(categorical({‘Related Hand’,’Related Foot’,’Unrelated Hand’,’Unrelated Foot’, …
‘Neutral Hand’,’Neutral Foot’,’Related Hand’,’Related Foot’,’Unrelated Hand’,’Unrelated Foot’, …
‘Neutral Hand’,’Neutral Foot’}’),categorical({‘BP’,’BP’,’BP’,’BP’,’BP’,’BP’,’O’,’O’,’O’,’O’,’O’,’O’}’), …
‘VariableNames’,{‘Congruency’,’Target’});

OverallMixedANOVA=fitrm(OverallDataTable,’BP_RH_Con-O_NF_Sub~Consciousness’,WithinDesign=WithinDesign);

OverallMixedANOVATable=ranova(OverallMixedANOVA,"WithinModel",’Congruency*Target’)

Thanks for any and all advice

Dan fitrm, statistics, mixed anova, ranova, wilkinson notation MATLAB Answers — New Questions

​

Hi,
The full error is:
"Error: File: builtin.m Line: 1 Column: 24
Invalid text character. Check for unsupported symbol, invisible character, or pasting of non-ASCII characters."
I get it whenever I try to do anything, including close Matlab. The file name changes slightly depending on what I do: for example, when I try to open a new file, I get the following error:
"Error in matlab.unittest.internal.ui.toolstrip.getFileInfoForToolstrip (line 8)
isClassBasedTest = false;",
preceded by the above text with "false.m" as the file name.

I think the problem started when I tried to set the path? I got the following error:
"Unable to run P-code file. The file header is damaged.
Error in pathtool (line 13)
isDesktop = desktop(‘-inuse’);"
But I’m not sure. Any help that you can give me would be appreciated!Hi,
The full error is:
"Error: File: builtin.m Line: 1 Column: 24
Invalid text character. Check for unsupported symbol, invisible character, or pasting of non-ASCII characters."
I get it whenever I try to do anything, including close Matlab. The file name changes slightly depending on what I do: for example, when I try to open a new file, I get the following error:
"Error in matlab.unittest.internal.ui.toolstrip.getFileInfoForToolstrip (line 8)
isClassBasedTest = false;",
preceded by the above text with "false.m" as the file name.

I think the problem started when I tried to set the path? I got the following error:
"Unable to run P-code file. The file header is damaged.
Error in pathtool (line 13)
isDesktop = desktop(‘-inuse’);"
But I’m not sure. Any help that you can give me would be appreciated! Hi,
The full error is:
"Error: File: builtin.m Line: 1 Column: 24
Invalid text character. Check for unsupported symbol, invisible character, or pasting of non-ASCII characters."
I get it whenever I try to do anything, including close Matlab. The file name changes slightly depending on what I do: for example, when I try to open a new file, I get the following error:
"Error in matlab.unittest.internal.ui.toolstrip.getFileInfoForToolstrip (line 8)
isClassBasedTest = false;",
preceded by the above text with "false.m" as the file name.

I think the problem started when I tried to set the path? I got the following error:
"Unable to run P-code file. The file header is damaged.
Error in pathtool (line 13)
isDesktop = desktop(‘-inuse’);"
But I’m not sure. Any help that you can give me would be appreciated! matlab, set path MATLAB Answers — New Questions

​

I have a pretty large script that analyzes a somewhat large data file (~5gbs). My script works fine when I first run it but then when I go to run a codeblock or even just try to get the output from a single simple variable in the command window AFTER I loaded everything into my workspace, MATLAB will stall for a minute or even more before starting to run whatever command I gave it. I’ve been monitoring my PC resources and it doesn’t seem like I am running out of RAM or anything (working with 64gbs). I even went through and cleared many larger variables that were not needed for later parts of the script and the problem persists. I do not recieve any errors, it is just very slow to do simple things. Once it starts executing the command, it runs at the expected speed (I’ve verfied with some manual progress bars I coded in).
The data that I load is from a single .MAT file which has a structure in it with all of my data. I’ve also ran this script on 3 other PCs and had the same issue.I have a pretty large script that analyzes a somewhat large data file (~5gbs). My script works fine when I first run it but then when I go to run a codeblock or even just try to get the output from a single simple variable in the command window AFTER I loaded everything into my workspace, MATLAB will stall for a minute or even more before starting to run whatever command I gave it. I’ve been monitoring my PC resources and it doesn’t seem like I am running out of RAM or anything (working with 64gbs). I even went through and cleared many larger variables that were not needed for later parts of the script and the problem persists. I do not recieve any errors, it is just very slow to do simple things. Once it starts executing the command, it runs at the expected speed (I’ve verfied with some manual progress bars I coded in).
The data that I load is from a single .MAT file which has a structure in it with all of my data. I’ve also ran this script on 3 other PCs and had the same issue. I have a pretty large script that analyzes a somewhat large data file (~5gbs). My script works fine when I first run it but then when I go to run a codeblock or even just try to get the output from a single simple variable in the command window AFTER I loaded everything into my workspace, MATLAB will stall for a minute or even more before starting to run whatever command I gave it. I’ve been monitoring my PC resources and it doesn’t seem like I am running out of RAM or anything (working with 64gbs). I even went through and cleared many larger variables that were not needed for later parts of the script and the problem persists. I do not recieve any errors, it is just very slow to do simple things. Once it starts executing the command, it runs at the expected speed (I’ve verfied with some manual progress bars I coded in).
The data that I load is from a single .MAT file which has a structure in it with all of my data. I’ve also ran this script on 3 other PCs and had the same issue. structures MATLAB Answers — New Questions

​

I’m working on a MATLAB plot where I want to achieve the following functionality:
Change the color of a line when it’s clicked.
Enable panning by dragging while clicking off the line.
Enable zooming using the scroll wheel.
Here’s the simple code I’m using:

% Plotting a simple line
h = plot([0,1],[0,1],’LineWidth’,3);

% Setting the ButtonDownFcn to change line color (placeholder code)
h.ButtonDownFcn = @(~,~) disp(h);

In this example, clicking on the line displays its handle h in the Command Window, which is a placeholder for my actual code to change the line color.

The Problem:

Assigning a ButtonDownFcn to the line object seems to override MATLAB’s built-in pan and zoom functionalities. After setting the ButtonDownFcn, I’m unable to pan by clicking and dragging off the line, and the scroll wheel zoom no longer works. It appears that the custom callback interferes with the default interactive behaviors, even when I’m not interacting directly with the line.

My Questions:

Why does setting the ButtonDownFcn on the line object disable panning and zooming, even when interacting off the line?
Is there a way to have both the custom click behavior (changing the line color when clicked) and retain the default pan and zoom functionalities when interacting elsewhere on the plot?I’m working on a MATLAB plot where I want to achieve the following functionality:
Change the color of a line when it’s clicked.
Enable panning by dragging while clicking off the line.
Enable zooming using the scroll wheel.
Here’s the simple code I’m using:

% Plotting a simple line
h = plot([0,1],[0,1],’LineWidth’,3);

% Setting the ButtonDownFcn to change line color (placeholder code)
h.ButtonDownFcn = @(~,~) disp(h);

In this example, clicking on the line displays its handle h in the Command Window, which is a placeholder for my actual code to change the line color.

The Problem:

Assigning a ButtonDownFcn to the line object seems to override MATLAB’s built-in pan and zoom functionalities. After setting the ButtonDownFcn, I’m unable to pan by clicking and dragging off the line, and the scroll wheel zoom no longer works. It appears that the custom callback interferes with the default interactive behaviors, even when I’m not interacting directly with the line.

My Questions:

Why does setting the ButtonDownFcn on the line object disable panning and zooming, even when interacting off the line?
Is there a way to have both the custom click behavior (changing the line color when clicked) and retain the default pan and zoom functionalities when interacting elsewhere on the plot? I’m working on a MATLAB plot where I want to achieve the following functionality:
Change the color of a line when it’s clicked.
Enable panning by dragging while clicking off the line.
Enable zooming using the scroll wheel.
Here’s the simple code I’m using:

% Plotting a simple line
h = plot([0,1],[0,1],’LineWidth’,3);

% Setting the ButtonDownFcn to change line color (placeholder code)
h.ButtonDownFcn = @(~,~) disp(h);

In this example, clicking on the line displays its handle h in the Command Window, which is a placeholder for my actual code to change the line color.

The Problem:

Assigning a ButtonDownFcn to the line object seems to override MATLAB’s built-in pan and zoom functionalities. After setting the ButtonDownFcn, I’m unable to pan by clicking and dragging off the line, and the scroll wheel zoom no longer works. It appears that the custom callback interferes with the default interactive behaviors, even when I’m not interacting directly with the line.

My Questions:

Why does setting the ButtonDownFcn on the line object disable panning and zooming, even when interacting off the line?
Is there a way to have both the custom click behavior (changing the line color when clicked) and retain the default pan and zoom functionalities when interacting elsewhere on the plot? callback, plot MATLAB Answers — New Questions

​

function [y, signalT, notch_intensity] = signalfunccycle(Gridlength, startfreq, endfreq, notchstart1, notchend1, datalength)
close all;
clear all;
PIx2 = 2 * pi; % used to calculate the frequency values of the output signal
fstart = 0;
Gridlength = 200e-3;
startfreq = 40e3;
endfreq = 200e3;
stimeStep = 5e-8;
datalength = round(Gridlength / stimeStep);

notchstart1 = 116000;
notchend1 = 119000;
dataN = datalength;
Stimelength = Gridlength;
fend = 10^6;
Fs = dataN / Gridlength;
NFFT = dataN; % Number of points used in the FFT analysis
fstep = (Fs – fstart) / NFFT; % Point step

T = 1 / Fs;
Stime = linspace(0, Stimelength, dataN);
t = (0:dataN-1) * T; % Length of time
signal = zeros(1, length(t));
f1 = notchstart1;
f2 = notchend1;
f3 = notchstart1;
f4 = notchend1;

for freq = startfreq:0.5*10^3:endfreq
if (f1 <= freq && freq <= f2) || (f3 <= freq && freq <= f4)
continue;
end
signal = sin(PIx2 * freq * t + pi) + signal;
end

%% Time-domain signal graphs
figure(1);
plot(Stime, signal);
grid on;
title(‘signal’);
xlabel(‘time’);
ylabel(‘amplitude’);

%% FFT
fMax = NFFT / 2 + 1; % The maximum frequency to plot
signalFFT0 = fft(signal, NFFT); % FFT of the signal
signalFFT_phase = angle(fft(signal, NFFT)); % Phase of the FFT result
signalFFT = abs(fft(signal, NFFT)); % Absolute value (modulus) of the FFT result
signalFFTShow = 2 * signalFFT / dataN; % Actual amplitude values
signalFFTShow(1) = signalFFTShow(1) / 2; % DC component
f = Fs / 2 * linspace(0, 1, fMax); % Actual frequencies

% Plot FFT of the signal
figure(2);
plot(f, signalFFTShow(1:fMax), ‘r’, ‘LineWidth’, 3); % Red color, line width set to 3
grid on; % Display grid lines
title(‘FFT Signal’);
xlabel(‘Frequency’);
ylabel(‘Amplitude’);

%% Phase function modulation
signalstart = startfreq;
signalend = endfreq;
Istart = round(signalstart / fstep);
Iend = round(signalend / fstep);
Itotal = Iend – Istart;

N1start1 = round(f1);
N1end1 = round(f2);
N1start2 = round(f3);
N1end2 = round(f4);
signalphase(1:NFFT) = 0;

for I = 1:NFFT
if (I <= N1end1 && I >= N1start1) || (I <= N1end2 && I >= N1start2)
signalphase(I) = 0;
else
signalphase(I) = pi / 2 * (I – Istart) + 1 * pi / Itotal / 2 * (I – Istart)^2;
end
end

signalF = 1/2 * signalFFTShow .* exp(1i * (signalphase + signalFFT_phase));
signalT = ifft(NFFT * signalF);
t1 = (0:NFFT-1) * T;

%% Phase spectrogram
x1 = 1:NFFT;
figure(3);
plot(x1, signalphase)
xlabel(‘frequency’);
ylabel(‘Phase’);
title(‘Phase spectrum’);

%% Signal waveform after modulation
signaltime = (0:NFFT-1) * T;
figure(4);
plot(signaltime, real(signalT));
xlabel(‘time’);
ylabel(‘amplitude’);
signalT_real = real(signalT);
savefile = ‘signalT_real.txt’;
x = Stime’;
y = signalT_real’;
save(savefile, ‘x’, ‘y’, ‘-ASCII’)

%% STFT Calculation
NN = 10;
win_size = round(datalength / NN);
overlap = 0;
fft_size = win_size * 2 + 1;
stft_result = [];

for i = 1:win_size*(1-overlap):length(signalT)-win_size
window = signalT(i:i+win_size-1);
fft_result = fft(window, fft_size+1);
stft_result = [stft_result; abs(fft_result(1:fft_size))];
end

time_axis = linspace(0, Gridlength, size(stft_result, 1));
freq_axis = linspace(fstart, fend, fft_size);

% Extract intensity for notch frequencies (116 to 119 kHz)
notch_indices = find(freq_axis >= 116000 & freq_axis <= 119000);
notch_intensity = mean(stft_result(:, notch_indices), 2);

% Plot the STFT spectrogram
figure(5);
imagesc(time_axis * 1000, freq_axis / 1000, stft_result.’);
axis([Gridlength * 950 Gridlength * 1050 110e3 / 1000 125e3 / 1000]);
colorbar;
ylabel(‘Frequency (kHz)’);
xlabel(‘Time (ms)’);

% % Plot the intensity over time for the notch frequencies
% figure(6);
% plot(time_axis * 1000, notch_intensity, ‘LineWidth’, 2);
% xlabel(‘Time (ms)’);
% ylabel(‘Intensity’);
% title(‘Intensity at Notch Frequencies (116 to 119 kHz)’);
% grid on;
endfunction [y, signalT, notch_intensity] = signalfunccycle(Gridlength, startfreq, endfreq, notchstart1, notchend1, datalength)
close all;
clear all;
PIx2 = 2 * pi; % used to calculate the frequency values of the output signal
fstart = 0;
Gridlength = 200e-3;
startfreq = 40e3;
endfreq = 200e3;
stimeStep = 5e-8;
datalength = round(Gridlength / stimeStep);

notchstart1 = 116000;
notchend1 = 119000;
dataN = datalength;
Stimelength = Gridlength;
fend = 10^6;
Fs = dataN / Gridlength;
NFFT = dataN; % Number of points used in the FFT analysis
fstep = (Fs – fstart) / NFFT; % Point step

T = 1 / Fs;
Stime = linspace(0, Stimelength, dataN);
t = (0:dataN-1) * T; % Length of time
signal = zeros(1, length(t));
f1 = notchstart1;
f2 = notchend1;
f3 = notchstart1;
f4 = notchend1;

for freq = startfreq:0.5*10^3:endfreq
if (f1 <= freq && freq <= f2) || (f3 <= freq && freq <= f4)
continue;
end
signal = sin(PIx2 * freq * t + pi) + signal;
end

%% Time-domain signal graphs
figure(1);
plot(Stime, signal);
grid on;
title(‘signal’);
xlabel(‘time’);
ylabel(‘amplitude’);

%% FFT
fMax = NFFT / 2 + 1; % The maximum frequency to plot
signalFFT0 = fft(signal, NFFT); % FFT of the signal
signalFFT_phase = angle(fft(signal, NFFT)); % Phase of the FFT result
signalFFT = abs(fft(signal, NFFT)); % Absolute value (modulus) of the FFT result
signalFFTShow = 2 * signalFFT / dataN; % Actual amplitude values
signalFFTShow(1) = signalFFTShow(1) / 2; % DC component
f = Fs / 2 * linspace(0, 1, fMax); % Actual frequencies

% Plot FFT of the signal
figure(2);
plot(f, signalFFTShow(1:fMax), ‘r’, ‘LineWidth’, 3); % Red color, line width set to 3
grid on; % Display grid lines
title(‘FFT Signal’);
xlabel(‘Frequency’);
ylabel(‘Amplitude’);

%% Phase function modulation
signalstart = startfreq;
signalend = endfreq;
Istart = round(signalstart / fstep);
Iend = round(signalend / fstep);
Itotal = Iend – Istart;

N1start1 = round(f1);
N1end1 = round(f2);
N1start2 = round(f3);
N1end2 = round(f4);
signalphase(1:NFFT) = 0;

for I = 1:NFFT
if (I <= N1end1 && I >= N1start1) || (I <= N1end2 && I >= N1start2)
signalphase(I) = 0;
else
signalphase(I) = pi / 2 * (I – Istart) + 1 * pi / Itotal / 2 * (I – Istart)^2;
end
end

signalF = 1/2 * signalFFTShow .* exp(1i * (signalphase + signalFFT_phase));
signalT = ifft(NFFT * signalF);
t1 = (0:NFFT-1) * T;

%% Phase spectrogram
x1 = 1:NFFT;
figure(3);
plot(x1, signalphase)
xlabel(‘frequency’);
ylabel(‘Phase’);
title(‘Phase spectrum’);

%% Signal waveform after modulation
signaltime = (0:NFFT-1) * T;
figure(4);
plot(signaltime, real(signalT));
xlabel(‘time’);
ylabel(‘amplitude’);
signalT_real = real(signalT);
savefile = ‘signalT_real.txt’;
x = Stime’;
y = signalT_real’;
save(savefile, ‘x’, ‘y’, ‘-ASCII’)

%% STFT Calculation
NN = 10;
win_size = round(datalength / NN);
overlap = 0;
fft_size = win_size * 2 + 1;
stft_result = [];

for i = 1:win_size*(1-overlap):length(signalT)-win_size
window = signalT(i:i+win_size-1);
fft_result = fft(window, fft_size+1);
stft_result = [stft_result; abs(fft_result(1:fft_size))];
end

time_axis = linspace(0, Gridlength, size(stft_result, 1));
freq_axis = linspace(fstart, fend, fft_size);

% Extract intensity for notch frequencies (116 to 119 kHz)
notch_indices = find(freq_axis >= 116000 & freq_axis <= 119000);
notch_intensity = mean(stft_result(:, notch_indices), 2);

% Plot the STFT spectrogram
figure(5);
imagesc(time_axis * 1000, freq_axis / 1000, stft_result.’);
axis([Gridlength * 950 Gridlength * 1050 110e3 / 1000 125e3 / 1000]);
colorbar;
ylabel(‘Frequency (kHz)’);
xlabel(‘Time (ms)’);

% % Plot the intensity over time for the notch frequencies
% figure(6);
% plot(time_axis * 1000, notch_intensity, ‘LineWidth’, 2);
% xlabel(‘Time (ms)’);
% ylabel(‘Intensity’);
% title(‘Intensity at Notch Frequencies (116 to 119 kHz)’);
% grid on;
end function [y, signalT, notch_intensity] = signalfunccycle(Gridlength, startfreq, endfreq, notchstart1, notchend1, datalength)
close all;
clear all;
PIx2 = 2 * pi; % used to calculate the frequency values of the output signal
fstart = 0;
Gridlength = 200e-3;
startfreq = 40e3;
endfreq = 200e3;
stimeStep = 5e-8;
datalength = round(Gridlength / stimeStep);

notchstart1 = 116000;
notchend1 = 119000;
dataN = datalength;
Stimelength = Gridlength;
fend = 10^6;
Fs = dataN / Gridlength;
NFFT = dataN; % Number of points used in the FFT analysis
fstep = (Fs – fstart) / NFFT; % Point step

T = 1 / Fs;
Stime = linspace(0, Stimelength, dataN);
t = (0:dataN-1) * T; % Length of time
signal = zeros(1, length(t));
f1 = notchstart1;
f2 = notchend1;
f3 = notchstart1;
f4 = notchend1;

for freq = startfreq:0.5*10^3:endfreq
if (f1 <= freq && freq <= f2) || (f3 <= freq && freq <= f4)
continue;
end
signal = sin(PIx2 * freq * t + pi) + signal;
end

%% Time-domain signal graphs
figure(1);
plot(Stime, signal);
grid on;
title(‘signal’);
xlabel(‘time’);
ylabel(‘amplitude’);

%% FFT
fMax = NFFT / 2 + 1; % The maximum frequency to plot
signalFFT0 = fft(signal, NFFT); % FFT of the signal
signalFFT_phase = angle(fft(signal, NFFT)); % Phase of the FFT result
signalFFT = abs(fft(signal, NFFT)); % Absolute value (modulus) of the FFT result
signalFFTShow = 2 * signalFFT / dataN; % Actual amplitude values
signalFFTShow(1) = signalFFTShow(1) / 2; % DC component
f = Fs / 2 * linspace(0, 1, fMax); % Actual frequencies

% Plot FFT of the signal
figure(2);
plot(f, signalFFTShow(1:fMax), ‘r’, ‘LineWidth’, 3); % Red color, line width set to 3
grid on; % Display grid lines
title(‘FFT Signal’);
xlabel(‘Frequency’);
ylabel(‘Amplitude’);

%% Phase function modulation
signalstart = startfreq;
signalend = endfreq;
Istart = round(signalstart / fstep);
Iend = round(signalend / fstep);
Itotal = Iend – Istart;

N1start1 = round(f1);
N1end1 = round(f2);
N1start2 = round(f3);
N1end2 = round(f4);
signalphase(1:NFFT) = 0;

for I = 1:NFFT
if (I <= N1end1 && I >= N1start1) || (I <= N1end2 && I >= N1start2)
signalphase(I) = 0;
else
signalphase(I) = pi / 2 * (I – Istart) + 1 * pi / Itotal / 2 * (I – Istart)^2;
end
end

signalF = 1/2 * signalFFTShow .* exp(1i * (signalphase + signalFFT_phase));
signalT = ifft(NFFT * signalF);
t1 = (0:NFFT-1) * T;

%% Phase spectrogram
x1 = 1:NFFT;
figure(3);
plot(x1, signalphase)
xlabel(‘frequency’);
ylabel(‘Phase’);
title(‘Phase spectrum’);

%% Signal waveform after modulation
signaltime = (0:NFFT-1) * T;
figure(4);
plot(signaltime, real(signalT));
xlabel(‘time’);
ylabel(‘amplitude’);
signalT_real = real(signalT);
savefile = ‘signalT_real.txt’;
x = Stime’;
y = signalT_real’;
save(savefile, ‘x’, ‘y’, ‘-ASCII’)

%% STFT Calculation
NN = 10;
win_size = round(datalength / NN);
overlap = 0;
fft_size = win_size * 2 + 1;
stft_result = [];

for i = 1:win_size*(1-overlap):length(signalT)-win_size
window = signalT(i:i+win_size-1);
fft_result = fft(window, fft_size+1);
stft_result = [stft_result; abs(fft_result(1:fft_size))];
end

time_axis = linspace(0, Gridlength, size(stft_result, 1));
freq_axis = linspace(fstart, fend, fft_size);

% Extract intensity for notch frequencies (116 to 119 kHz)
notch_indices = find(freq_axis >= 116000 & freq_axis <= 119000);
notch_intensity = mean(stft_result(:, notch_indices), 2);

% Plot the STFT spectrogram
figure(5);
imagesc(time_axis * 1000, freq_axis / 1000, stft_result.’);
axis([Gridlength * 950 Gridlength * 1050 110e3 / 1000 125e3 / 1000]);
colorbar;
ylabel(‘Frequency (kHz)’);
xlabel(‘Time (ms)’);

% % Plot the intensity over time for the notch frequencies
% figure(6);
% plot(time_axis * 1000, notch_intensity, ‘LineWidth’, 2);
% xlabel(‘Time (ms)’);
% ylabel(‘Intensity’);
% title(‘Intensity at Notch Frequencies (116 to 119 kHz)’);
% grid on;
end matlab code MATLAB Answers — New Questions

​

Implement the Gauss-Seidel method for solving a system of linear equations from scratch in MATLAB and walk me through your thought process in constructing the code. Additionally, demonstrate that your implementation works by applying it to the following system. 2x +3y −z =7 , x −2y+4z =1 , 3x +y+2z =8
%Gauss seidel
A=input(‘Enter a co-efficient matrix A: ‘);
B=input(‘Enter source vector B: ‘);
P=input(‘Enter initial guess vector: ‘);
n=input(‘Enter number of iterations: ‘);
e=input(‘Enter tolerance: ‘);
N=length(B);
X=zeros(N,1);
Y=zeros(N,1); %for stopping criteria
for j=1:n
for i=1:N
x(i)=(B(i)/A(i,i))-(A(i,[1:i-1,i+1:N])*P([1:i-1,i+1:N]))/A(i,i);
P(i)=X(i);
end
fprintf(‘Iterations no %d n’ ,j)
X
if abs (Y-X)<e
break
end
Y=X;
end
This is my code is this correct? please help meImplement the Gauss-Seidel method for solving a system of linear equations from scratch in MATLAB and walk me through your thought process in constructing the code. Additionally, demonstrate that your implementation works by applying it to the following system. 2x +3y −z =7 , x −2y+4z =1 , 3x +y+2z =8
%Gauss seidel
A=input(‘Enter a co-efficient matrix A: ‘);
B=input(‘Enter source vector B: ‘);
P=input(‘Enter initial guess vector: ‘);
n=input(‘Enter number of iterations: ‘);
e=input(‘Enter tolerance: ‘);
N=length(B);
X=zeros(N,1);
Y=zeros(N,1); %for stopping criteria
for j=1:n
for i=1:N
x(i)=(B(i)/A(i,i))-(A(i,[1:i-1,i+1:N])*P([1:i-1,i+1:N]))/A(i,i);
P(i)=X(i);
end
fprintf(‘Iterations no %d n’ ,j)
X
if abs (Y-X)<e
break
end
Y=X;
end
This is my code is this correct? please help me Implement the Gauss-Seidel method for solving a system of linear equations from scratch in MATLAB and walk me through your thought process in constructing the code. Additionally, demonstrate that your implementation works by applying it to the following system. 2x +3y −z =7 , x −2y+4z =1 , 3x +y+2z =8
%Gauss seidel
A=input(‘Enter a co-efficient matrix A: ‘);
B=input(‘Enter source vector B: ‘);
P=input(‘Enter initial guess vector: ‘);
n=input(‘Enter number of iterations: ‘);
e=input(‘Enter tolerance: ‘);
N=length(B);
X=zeros(N,1);
Y=zeros(N,1); %for stopping criteria
for j=1:n
for i=1:N
x(i)=(B(i)/A(i,i))-(A(i,[1:i-1,i+1:N])*P([1:i-1,i+1:N]))/A(i,i);
P(i)=X(i);
end
fprintf(‘Iterations no %d n’ ,j)
X
if abs (Y-X)<e
break
end
Y=X;
end
This is my code is this correct? please help me @wantquickhelp MATLAB Answers — New Questions

​

clc
clear all
close all
image_org=imread(‘C:UserspcOneDriveDesktopmemoirechapitre 3image for test’,’JPG’);
% Gaussian white noise
X = double(image_org) / 255;
NG = imnoise(X, ‘gaussian’, 0, 0.01);%NG=gaussian noise
figure(1);subplot(231); imshow(image_org);title (‘image org’)
subplot(233); imshow(NG) ; title (‘Gaussian additive noise’)
NG_gray = rgb2gray(NG);
%averaging filter
f_m = fspecial (‘average’, 3)%f_m=averaging filter
NGmoy = imfilter (NG, f_m,’replicate’);
%gaussian filter
f_g = fspecial (‘gaussian’,3,0.8)%f_g=gaussian filter
NGg = imfilter(NG,f_g,’replicate’);
%median filter
NGmed = medfilt2( NG_gray ,[3 3]);
%the show
subplot(234); imshow(NGmoy);title(‘ avraging Filter ‘)
subplot(235); imshow(NGg);title(‘ gaussien Filtre ‘)
subplot(236); imshow(NGmed);title(‘ median Filtre’)
%PSNR
EQMb=mean2((X-NG).^2 );PSNRb=-10*log10(EQMb);
EQMmoy=mean2( (NG-NGmoy).^2 );PSNRmoy=-10*log10(EQMmoy);
EQMg=mean2( (NG-NGg).^2 );PSNRg=-10*log10(EQMg);
EQMmed=mean2( (NG_gray-NGmed).^2 );PSNRmed=-10*log10(EQMmed);

% Resize the original image to match the input size of the CNN
image_resized = imresize(image_org, [299 450]);

% Add Gaussian noise to the original image
X = double(image_resized) / 255;
NG = imnoise(X, ‘gaussian’, 0, 0.03); % Add Gaussian noise

% Display the original and noisy images
figure(1);
subplot(1, 2, 1);
imshow(image_resized);
title(‘Original Image’);
subplot(1, 2, 2);
imshow(NG);
title(‘Noisy Image’);

% Prepare data for training (noisy images as input, original images as target)
inputData = NG; % Noisy images
targetData = X; % Original clean images

% Define the CNN architecture for image denoising
layers = [
imageInputLayer([299 450 3]) % Input layer with the size of the input images
convolution2dLayer(3, 64, ‘Padding’, ‘same’) % Convolutional layer with 64 filters of size 3×3
reluLayer % ReLU activation layer
convolution2dLayer(3, 64, ‘Padding’, ‘same’) % Another convolutional layer
reluLayer % ReLU activation layer
convolution2dLayer(3, 3, ‘Padding’, ‘same’) % Output convolutional layer with 3 channels (RGB)
regressionLayer % Regression layer
];

% Define training options
options = trainingOptions(‘adam’, … % Adam optimizer
‘MaxEpochs’, 50, … % Increase the number of epochs
‘MiniBatchSize’, 16, … % Decrease the mini-batch size
‘InitialLearnRate’, 1e-4, … % Decrease the initial learning rate
‘Plots’, ‘training-progress’); % Plot training progress

% Train the network
net = trainNetwork(inputData, targetData, layers, options);

% Denoise the image using the trained CNN
denoisedImage = predict(net, inputData);

% Display the original noisy image and the denoised image
figure;
subplot(1, 2, 1);
imshow(inputData);
title(‘Noisy Image’);
subplot(1, 2, 2);
imshow(denoisedImage);
title(‘Denoised Image’);clc
clear all
close all
image_org=imread(‘C:UserspcOneDriveDesktopmemoirechapitre 3image for test’,’JPG’);
% Gaussian white noise
X = double(image_org) / 255;
NG = imnoise(X, ‘gaussian’, 0, 0.01);%NG=gaussian noise
figure(1);subplot(231); imshow(image_org);title (‘image org’)
subplot(233); imshow(NG) ; title (‘Gaussian additive noise’)
NG_gray = rgb2gray(NG);
%averaging filter
f_m = fspecial (‘average’, 3)%f_m=averaging filter
NGmoy = imfilter (NG, f_m,’replicate’);
%gaussian filter
f_g = fspecial (‘gaussian’,3,0.8)%f_g=gaussian filter
NGg = imfilter(NG,f_g,’replicate’);
%median filter
NGmed = medfilt2( NG_gray ,[3 3]);
%the show
subplot(234); imshow(NGmoy);title(‘ avraging Filter ‘)
subplot(235); imshow(NGg);title(‘ gaussien Filtre ‘)
subplot(236); imshow(NGmed);title(‘ median Filtre’)
%PSNR
EQMb=mean2((X-NG).^2 );PSNRb=-10*log10(EQMb);
EQMmoy=mean2( (NG-NGmoy).^2 );PSNRmoy=-10*log10(EQMmoy);
EQMg=mean2( (NG-NGg).^2 );PSNRg=-10*log10(EQMg);
EQMmed=mean2( (NG_gray-NGmed).^2 );PSNRmed=-10*log10(EQMmed);

% Resize the original image to match the input size of the CNN
image_resized = imresize(image_org, [299 450]);

% Add Gaussian noise to the original image
X = double(image_resized) / 255;
NG = imnoise(X, ‘gaussian’, 0, 0.03); % Add Gaussian noise

% Display the original and noisy images
figure(1);
subplot(1, 2, 1);
imshow(image_resized);
title(‘Original Image’);
subplot(1, 2, 2);
imshow(NG);
title(‘Noisy Image’);

% Prepare data for training (noisy images as input, original images as target)
inputData = NG; % Noisy images
targetData = X; % Original clean images

% Define the CNN architecture for image denoising
layers = [
imageInputLayer([299 450 3]) % Input layer with the size of the input images
convolution2dLayer(3, 64, ‘Padding’, ‘same’) % Convolutional layer with 64 filters of size 3×3
reluLayer % ReLU activation layer
convolution2dLayer(3, 64, ‘Padding’, ‘same’) % Another convolutional layer
reluLayer % ReLU activation layer
convolution2dLayer(3, 3, ‘Padding’, ‘same’) % Output convolutional layer with 3 channels (RGB)
regressionLayer % Regression layer
];

% Define training options
options = trainingOptions(‘adam’, … % Adam optimizer
‘MaxEpochs’, 50, … % Increase the number of epochs
‘MiniBatchSize’, 16, … % Decrease the mini-batch size
‘InitialLearnRate’, 1e-4, … % Decrease the initial learning rate
‘Plots’, ‘training-progress’); % Plot training progress

% Train the network
net = trainNetwork(inputData, targetData, layers, options);

% Denoise the image using the trained CNN
denoisedImage = predict(net, inputData);

% Display the original noisy image and the denoised image
figure;
subplot(1, 2, 1);
imshow(inputData);
title(‘Noisy Image’);
subplot(1, 2, 2);
imshow(denoisedImage);
title(‘Denoised Image’); clc
clear all
close all
image_org=imread(‘C:UserspcOneDriveDesktopmemoirechapitre 3image for test’,’JPG’);
% Gaussian white noise
X = double(image_org) / 255;
NG = imnoise(X, ‘gaussian’, 0, 0.01);%NG=gaussian noise
figure(1);subplot(231); imshow(image_org);title (‘image org’)
subplot(233); imshow(NG) ; title (‘Gaussian additive noise’)
NG_gray = rgb2gray(NG);
%averaging filter
f_m = fspecial (‘average’, 3)%f_m=averaging filter
NGmoy = imfilter (NG, f_m,’replicate’);
%gaussian filter
f_g = fspecial (‘gaussian’,3,0.8)%f_g=gaussian filter
NGg = imfilter(NG,f_g,’replicate’);
%median filter
NGmed = medfilt2( NG_gray ,[3 3]);
%the show
subplot(234); imshow(NGmoy);title(‘ avraging Filter ‘)
subplot(235); imshow(NGg);title(‘ gaussien Filtre ‘)
subplot(236); imshow(NGmed);title(‘ median Filtre’)
%PSNR
EQMb=mean2((X-NG).^2 );PSNRb=-10*log10(EQMb);
EQMmoy=mean2( (NG-NGmoy).^2 );PSNRmoy=-10*log10(EQMmoy);
EQMg=mean2( (NG-NGg).^2 );PSNRg=-10*log10(EQMg);
EQMmed=mean2( (NG_gray-NGmed).^2 );PSNRmed=-10*log10(EQMmed);

% Resize the original image to match the input size of the CNN
image_resized = imresize(image_org, [299 450]);

% Add Gaussian noise to the original image
X = double(image_resized) / 255;
NG = imnoise(X, ‘gaussian’, 0, 0.03); % Add Gaussian noise

% Display the original and noisy images
figure(1);
subplot(1, 2, 1);
imshow(image_resized);
title(‘Original Image’);
subplot(1, 2, 2);
imshow(NG);
title(‘Noisy Image’);

% Prepare data for training (noisy images as input, original images as target)
inputData = NG; % Noisy images
targetData = X; % Original clean images

% Define the CNN architecture for image denoising
layers = [
imageInputLayer([299 450 3]) % Input layer with the size of the input images
convolution2dLayer(3, 64, ‘Padding’, ‘same’) % Convolutional layer with 64 filters of size 3×3
reluLayer % ReLU activation layer
convolution2dLayer(3, 64, ‘Padding’, ‘same’) % Another convolutional layer
reluLayer % ReLU activation layer
convolution2dLayer(3, 3, ‘Padding’, ‘same’) % Output convolutional layer with 3 channels (RGB)
regressionLayer % Regression layer
];

% Define training options
options = trainingOptions(‘adam’, … % Adam optimizer
‘MaxEpochs’, 50, … % Increase the number of epochs
‘MiniBatchSize’, 16, … % Decrease the mini-batch size
‘InitialLearnRate’, 1e-4, … % Decrease the initial learning rate
‘Plots’, ‘training-progress’); % Plot training progress

% Train the network
net = trainNetwork(inputData, targetData, layers, options);

% Denoise the image using the trained CNN
denoisedImage = predict(net, inputData);

% Display the original noisy image and the denoised image
figure;
subplot(1, 2, 1);
imshow(inputData);
title(‘Noisy Image’);
subplot(1, 2, 2);
imshow(denoisedImage);
title(‘Denoised Image’); deep learning, neural network MATLAB Answers — New Questions

​