I just need an example:
%% =========================
% [2.1] FFT calculation & plotting
% [2.5] Band power computation
% [1.1] Numerical integration (trapz)
% Custom PSD function (FFT-based periodogram)
% =========================
function [f, PSD] = myPSD(x, Fs)
x = x(:); % force column vector
x = x – mean(x); % remove DC offset

N = length(x);
X = fft(x);

K = floor(N/2); % one-sided spectrum length index
X = X(1:K+1);

% One-sided PSD estimate
PSD = (1/(Fs*N)) * abs(X).^2;

% Double interior bins (not DC / Nyquist)
if K > 1
PSD(2:end-1) = 2*PSD(2:end-1);
end

% Frequency vector (matches PSD length exactly)
f = (0:K)’ * (Fs/N);
end

%% Compute PSDs for original signals
[f1, PSD1] = myPSD(sig1, Fs);
[f2, PSD2] = myPSD(sig2, Fs);

%% Plot PSDs (same axes is what the lab asks)
% NOTE: If plotting linear PSD, ylabel should NOT be dB/Hz.
figure;
plot(f1, PSD1, ‘LineWidth’, 1.1); hold on;
plot(f2, PSD2, ‘LineWidth’, 1.1);
grid on;
xlim([0 200]);
xlabel("Frequency (Hz)");
ylabel("Power/Frequency (linear)");
title("PSD of Signal 1 and Signal 2");
legend("sig1","sig2");

% Optional dB version (more readable visually)
figure;
plot(f1, pow2db(PSD1), ‘LineWidth’, 1.1); hold on;
plot(f2, pow2db(PSD2), ‘LineWidth’, 1.1);
grid on;
xlim([0 200]);
xlabel("Frequency (Hz)");
ylabel("Power/Frequency (dB/Hz)");
title("PSD of Signal 1 and Signal 2 (dB)");

%% 20-100 Hz Bandpower using trapz
% [2.5] Band power computation
% [1.1] Numerical integration (trapz)
idx1 = (f1 >= 20) & (f1 <= 100);
idx2 = (f2 >= 20) & (f2 <= 100);

bandpower1 = trapz(f1(idx1), PSD1(idx1));
bandpower2 = trapz(f2(idx2), PSD2(idx2));

fprintf(‘Original bandpower (20-100 Hz): sig1 = %.6g, sig2 = %.6gn’, bandpower1, bandpower2);

%% =========================
% [1.5] Down-sampling
% Anti-aliasing before downsampling (Butterworth lowpass)
% =========================
Fs_down = 200; % new sampling rate (Nyquist = 100 Hz)
downFactor = Fs / Fs_down; % should be integer for downsample()

% Anti-alias lowpass filter (remove content above new Nyquist)
fc = 90; % use < 100 Hz for safety
order = 4;
Wn = fc / (Fs/2); % normalized cutoff

[b, a] = butter(order, Wn, ‘low’);

sig1_filt = filtfilt(b, a, sig1); % zero-phase filtering
sig2_filt = filtfilt(b, a, sig2);

% Downsample filtered signals
sig1_down = downsample(sig1_filt, downFactor);
sig2_down = downsample(sig2_filt, downFactor);

%% PSDs of anti-aliased + downsampled signals
[f1_down, PSD1_down] = myPSD(sig1_down, Fs_down);
[f2_down, PSD2_down] = myPSD(sig2_down, Fs_down);

figure;
plot(f1_down, PSD1_down, ‘LineWidth’, 1.1); hold on;
plot(f2_down, PSD2_down, ‘LineWidth’, 1.1);
grid on;
xlim([0 100]);
xlabel("Frequency (Hz)");
ylabel("Power/Frequency (linear)");
title("PSD of Downsampled Signals");
legend("sig1 down","sig2 down");

%% Bandpower (20-100 Hz) after anti-aliasing + downsampling
idx1_down = (f1_down >= 20) & (f1_down <= 100);
idx2_down = (f2_down >= 20) & (f2_down <= 100);

bandpower1_down = trapz(f1_down(idx1_down), PSD1_down(idx1_down));
bandpower2_down = trapz(f2_down(idx2_down), PSD2_down(idx2_down));

fprintf(‘Downsampled+AA bandpower (20-100 Hz): sig1 = %.6g, sig2 = %.6gn’, bandpower1_down, bandpower2_down);

%% =========================
% [1.4] Signal rectification & enveloping
% =========================
% Lab asks: rectify, then lowpass at 2 Hz to create heartbeat envelope
sig1_rect = abs(sig1_down);
sig2_rect = abs(sig2_down);

fc_env = 2; % envelope lowpass cutoff
order_env = 4;
Wn_env = fc_env / (Fs_down/2);

[b_env, a_env] = butter(order_env, Wn_env, ‘low’);

env1 = filtfilt(b_env, a_env, sig1_rect);
env2 = filtfilt(b_env, a_env, sig2_rect);

t1 = (0:length(env1)-1)’/Fs_down;
t2 = (0:length(env2)-1)’/Fs_down;

figure;
plot(t1, env1); grid on;
xlabel("Time (s)"); ylabel("Envelope amplitude");
title("Envelope of Signal 1");

figure;
plot(t2, env2); grid on;
xlabel("Time (s)"); ylabel("Envelope amplitude");
title("Envelope of Signal 2");

%% Rescale envelopes to [0,1] (helps peak detection)
env1_scaled = rescale(env1);
env2_scaled = rescale(env2);

figure;
plot(t1, env1_scaled); grid on;
xlabel("Time (s)"); ylabel("Scaled envelope");
title("Scaled Envelope of Signal 1");

figure;
plot(t2, env2_scaled); grid on;
xlabel("Time (s)"); ylabel("Scaled envelope");
title("Scaled Envelope of Signal 2");

%% =========================
% [1.2] Peak identification
% =========================
[pks1, locs1] = findpeaks(env1_scaled, ‘MinPeakProminence’, 0.1);
[pks2, locs2] = findpeaks(env2_scaled, ‘MinPeakProminence’, 0.1);

% Optional visualization of peaks
figure;
plot(t1, env1_scaled); hold on; grid on;
plot(locs1/Fs_down, pks1, ‘o’);
xlabel("Time (s)"); ylabel("Scaled envelope");
title("Signal 1 Peaks");

figure;
plot(t2, env2_scaled); hold on; grid on;
plot(locs2/Fs_down, pks2, ‘o’);
xlabel("Time (s)"); ylabel("Scaled envelope");
title("Signal 2 Peaks");

%% =========================
% [1.2] Peak timing -> Heart rate + beat-to-beat timing
% =========================
tpeaks1 = locs1 / Fs_down; % seconds
tpeaks2 = locs2 / Fs_down;

IBI1 = diff(tpeaks1); % inter-beat intervals (s)
IBI2 = diff(tpeaks2);

HR1_bpm = 60 / mean(IBI1); % average heart rate
HR2_bpm = 60 / mean(IBI2);

% Lab also asks for beat-to-beat timing variance
IBIvar1 = var(IBI1);
IBIvar2 = var(IBI2);

fprintf(‘HR sig1 = %.2f bpm, HR sig2 = %.2f bpmn’, HR1_bpm, HR2_bpm);
fprintf(‘Beat-to-beat variance: sig1 = %.6g s^2, sig2 = %.6g s^2n’, IBIvar1, IBIvar2);

%% =========================
% [1.5] Interpolation (recreate original sig1 from downsampled)
% Compare methods using RMSE
% =========================
t_down = (0:length(sig1_down)-1)’ / Fs_down;
t_orig = (0:length(sig1)-1)’ / Fs;

% Try a few methods (lab says experiment with interp1 methods)
methods = ["nearest","linear","spline","pchip","cubic"];
rmseVals = zeros(size(methods));

for k = 1:length(methods)
sig1_int = interp1(t_down, sig1_down, t_orig, methods(k))’;
rmseVals(k) = sqrt(mean((sig1 – sig1_int).^2, ‘omitnan’));
end

% Show RMSEs
disp(table(methods’, rmseVals’, ‘VariableNames’, {‘Method’,’RMSE’}));

% Pick best method (lowest RMSE)
[bestRMSE, idxBest] = min(rmseVals);
bestMethod = methods(idxBest);

% Recompute best interpolation for plotting
sig1_best = interp1(t_down, sig1_down, t_orig, bestMethod)’;

fprintf(‘Best interpolation method: %s (RMSE = %.6g)n’, bestMethod, bestRMSE);

% Plot original vs best interpolated (short window for visibility)
figure;
plot(t_orig, sig1, ‘LineWidth’, 1); hold on; grid on;
plot(t_orig, sig1_best, ‘–‘, ‘LineWidth’, 1);
xlim([0 2]); % zoom into first 2 s so differences are visible
xlabel("Time (s)"); ylabel("Amplitude");
title("Original sig1 vs Interpolated sig1");
legend("Original","Interpolated");I just need an example:
%% =========================
% [2.1] FFT calculation & plotting
% [2.5] Band power computation
% [1.1] Numerical integration (trapz)
% Custom PSD function (FFT-based periodogram)
% =========================
function [f, PSD] = myPSD(x, Fs)
x = x(:); % force column vector
x = x – mean(x); % remove DC offset

N = length(x);
X = fft(x);

K = floor(N/2); % one-sided spectrum length index
X = X(1:K+1);

% One-sided PSD estimate
PSD = (1/(Fs*N)) * abs(X).^2;

% Double interior bins (not DC / Nyquist)
if K > 1
PSD(2:end-1) = 2*PSD(2:end-1);
end

% Frequency vector (matches PSD length exactly)
f = (0:K)’ * (Fs/N);
end

%% Compute PSDs for original signals
[f1, PSD1] = myPSD(sig1, Fs);
[f2, PSD2] = myPSD(sig2, Fs);

%% Plot PSDs (same axes is what the lab asks)
% NOTE: If plotting linear PSD, ylabel should NOT be dB/Hz.
figure;
plot(f1, PSD1, ‘LineWidth’, 1.1); hold on;
plot(f2, PSD2, ‘LineWidth’, 1.1);
grid on;
xlim([0 200]);
xlabel("Frequency (Hz)");
ylabel("Power/Frequency (linear)");
title("PSD of Signal 1 and Signal 2");
legend("sig1","sig2");

% Optional dB version (more readable visually)
figure;
plot(f1, pow2db(PSD1), ‘LineWidth’, 1.1); hold on;
plot(f2, pow2db(PSD2), ‘LineWidth’, 1.1);
grid on;
xlim([0 200]);
xlabel("Frequency (Hz)");
ylabel("Power/Frequency (dB/Hz)");
title("PSD of Signal 1 and Signal 2 (dB)");

%% 20-100 Hz Bandpower using trapz
% [2.5] Band power computation
% [1.1] Numerical integration (trapz)
idx1 = (f1 >= 20) & (f1 <= 100);
idx2 = (f2 >= 20) & (f2 <= 100);

bandpower1 = trapz(f1(idx1), PSD1(idx1));
bandpower2 = trapz(f2(idx2), PSD2(idx2));

fprintf(‘Original bandpower (20-100 Hz): sig1 = %.6g, sig2 = %.6gn’, bandpower1, bandpower2);

%% =========================
% [1.5] Down-sampling
% Anti-aliasing before downsampling (Butterworth lowpass)
% =========================
Fs_down = 200; % new sampling rate (Nyquist = 100 Hz)
downFactor = Fs / Fs_down; % should be integer for downsample()

% Anti-alias lowpass filter (remove content above new Nyquist)
fc = 90; % use < 100 Hz for safety
order = 4;
Wn = fc / (Fs/2); % normalized cutoff

[b, a] = butter(order, Wn, ‘low’);

sig1_filt = filtfilt(b, a, sig1); % zero-phase filtering
sig2_filt = filtfilt(b, a, sig2);

% Downsample filtered signals
sig1_down = downsample(sig1_filt, downFactor);
sig2_down = downsample(sig2_filt, downFactor);

%% PSDs of anti-aliased + downsampled signals
[f1_down, PSD1_down] = myPSD(sig1_down, Fs_down);
[f2_down, PSD2_down] = myPSD(sig2_down, Fs_down);

figure;
plot(f1_down, PSD1_down, ‘LineWidth’, 1.1); hold on;
plot(f2_down, PSD2_down, ‘LineWidth’, 1.1);
grid on;
xlim([0 100]);
xlabel("Frequency (Hz)");
ylabel("Power/Frequency (linear)");
title("PSD of Downsampled Signals");
legend("sig1 down","sig2 down");

%% Bandpower (20-100 Hz) after anti-aliasing + downsampling
idx1_down = (f1_down >= 20) & (f1_down <= 100);
idx2_down = (f2_down >= 20) & (f2_down <= 100);

bandpower1_down = trapz(f1_down(idx1_down), PSD1_down(idx1_down));
bandpower2_down = trapz(f2_down(idx2_down), PSD2_down(idx2_down));

fprintf(‘Downsampled+AA bandpower (20-100 Hz): sig1 = %.6g, sig2 = %.6gn’, bandpower1_down, bandpower2_down);

%% =========================
% [1.4] Signal rectification & enveloping
% =========================
% Lab asks: rectify, then lowpass at 2 Hz to create heartbeat envelope
sig1_rect = abs(sig1_down);
sig2_rect = abs(sig2_down);

fc_env = 2; % envelope lowpass cutoff
order_env = 4;
Wn_env = fc_env / (Fs_down/2);

[b_env, a_env] = butter(order_env, Wn_env, ‘low’);

env1 = filtfilt(b_env, a_env, sig1_rect);
env2 = filtfilt(b_env, a_env, sig2_rect);

t1 = (0:length(env1)-1)’/Fs_down;
t2 = (0:length(env2)-1)’/Fs_down;

figure;
plot(t1, env1); grid on;
xlabel("Time (s)"); ylabel("Envelope amplitude");
title("Envelope of Signal 1");

figure;
plot(t2, env2); grid on;
xlabel("Time (s)"); ylabel("Envelope amplitude");
title("Envelope of Signal 2");

%% Rescale envelopes to [0,1] (helps peak detection)
env1_scaled = rescale(env1);
env2_scaled = rescale(env2);

figure;
plot(t1, env1_scaled); grid on;
xlabel("Time (s)"); ylabel("Scaled envelope");
title("Scaled Envelope of Signal 1");

figure;
plot(t2, env2_scaled); grid on;
xlabel("Time (s)"); ylabel("Scaled envelope");
title("Scaled Envelope of Signal 2");

%% =========================
% [1.2] Peak identification
% =========================
[pks1, locs1] = findpeaks(env1_scaled, ‘MinPeakProminence’, 0.1);
[pks2, locs2] = findpeaks(env2_scaled, ‘MinPeakProminence’, 0.1);

% Optional visualization of peaks
figure;
plot(t1, env1_scaled); hold on; grid on;
plot(locs1/Fs_down, pks1, ‘o’);
xlabel("Time (s)"); ylabel("Scaled envelope");
title("Signal 1 Peaks");

figure;
plot(t2, env2_scaled); hold on; grid on;
plot(locs2/Fs_down, pks2, ‘o’);
xlabel("Time (s)"); ylabel("Scaled envelope");
title("Signal 2 Peaks");

%% =========================
% [1.2] Peak timing -> Heart rate + beat-to-beat timing
% =========================
tpeaks1 = locs1 / Fs_down; % seconds
tpeaks2 = locs2 / Fs_down;

IBI1 = diff(tpeaks1); % inter-beat intervals (s)
IBI2 = diff(tpeaks2);

HR1_bpm = 60 / mean(IBI1); % average heart rate
HR2_bpm = 60 / mean(IBI2);

% Lab also asks for beat-to-beat timing variance
IBIvar1 = var(IBI1);
IBIvar2 = var(IBI2);

fprintf(‘HR sig1 = %.2f bpm, HR sig2 = %.2f bpmn’, HR1_bpm, HR2_bpm);
fprintf(‘Beat-to-beat variance: sig1 = %.6g s^2, sig2 = %.6g s^2n’, IBIvar1, IBIvar2);

%% =========================
% [1.5] Interpolation (recreate original sig1 from downsampled)
% Compare methods using RMSE
% =========================
t_down = (0:length(sig1_down)-1)’ / Fs_down;
t_orig = (0:length(sig1)-1)’ / Fs;

% Try a few methods (lab says experiment with interp1 methods)
methods = ["nearest","linear","spline","pchip","cubic"];
rmseVals = zeros(size(methods));

for k = 1:length(methods)
sig1_int = interp1(t_down, sig1_down, t_orig, methods(k))’;
rmseVals(k) = sqrt(mean((sig1 – sig1_int).^2, ‘omitnan’));
end

% Show RMSEs
disp(table(methods’, rmseVals’, ‘VariableNames’, {‘Method’,’RMSE’}));

% Pick best method (lowest RMSE)
[bestRMSE, idxBest] = min(rmseVals);
bestMethod = methods(idxBest);

% Recompute best interpolation for plotting
sig1_best = interp1(t_down, sig1_down, t_orig, bestMethod)’;

fprintf(‘Best interpolation method: %s (RMSE = %.6g)n’, bestMethod, bestRMSE);

% Plot original vs best interpolated (short window for visibility)
figure;
plot(t_orig, sig1, ‘LineWidth’, 1); hold on; grid on;
plot(t_orig, sig1_best, ‘–‘, ‘LineWidth’, 1);
xlim([0 2]); % zoom into first 2 s so differences are visible
xlabel("Time (s)"); ylabel("Amplitude");
title("Original sig1 vs Interpolated sig1");
legend("Original","Interpolated"); I just need an example:
%% =========================
% [2.1] FFT calculation & plotting
% [2.5] Band power computation
% [1.1] Numerical integration (trapz)
% Custom PSD function (FFT-based periodogram)
% =========================
function [f, PSD] = myPSD(x, Fs)
x = x(:); % force column vector
x = x – mean(x); % remove DC offset

N = length(x);
X = fft(x);

K = floor(N/2); % one-sided spectrum length index
X = X(1:K+1);

% One-sided PSD estimate
PSD = (1/(Fs*N)) * abs(X).^2;

% Double interior bins (not DC / Nyquist)
if K > 1
PSD(2:end-1) = 2*PSD(2:end-1);
end

% Frequency vector (matches PSD length exactly)
f = (0:K)’ * (Fs/N);
end

%% Compute PSDs for original signals
[f1, PSD1] = myPSD(sig1, Fs);
[f2, PSD2] = myPSD(sig2, Fs);

%% Plot PSDs (same axes is what the lab asks)
% NOTE: If plotting linear PSD, ylabel should NOT be dB/Hz.
figure;
plot(f1, PSD1, ‘LineWidth’, 1.1); hold on;
plot(f2, PSD2, ‘LineWidth’, 1.1);
grid on;
xlim([0 200]);
xlabel("Frequency (Hz)");
ylabel("Power/Frequency (linear)");
title("PSD of Signal 1 and Signal 2");
legend("sig1","sig2");

% Optional dB version (more readable visually)
figure;
plot(f1, pow2db(PSD1), ‘LineWidth’, 1.1); hold on;
plot(f2, pow2db(PSD2), ‘LineWidth’, 1.1);
grid on;
xlim([0 200]);
xlabel("Frequency (Hz)");
ylabel("Power/Frequency (dB/Hz)");
title("PSD of Signal 1 and Signal 2 (dB)");

%% 20-100 Hz Bandpower using trapz
% [2.5] Band power computation
% [1.1] Numerical integration (trapz)
idx1 = (f1 >= 20) & (f1 <= 100);
idx2 = (f2 >= 20) & (f2 <= 100);

bandpower1 = trapz(f1(idx1), PSD1(idx1));
bandpower2 = trapz(f2(idx2), PSD2(idx2));

fprintf(‘Original bandpower (20-100 Hz): sig1 = %.6g, sig2 = %.6gn’, bandpower1, bandpower2);

%% =========================
% [1.5] Down-sampling
% Anti-aliasing before downsampling (Butterworth lowpass)
% =========================
Fs_down = 200; % new sampling rate (Nyquist = 100 Hz)
downFactor = Fs / Fs_down; % should be integer for downsample()

% Anti-alias lowpass filter (remove content above new Nyquist)
fc = 90; % use < 100 Hz for safety
order = 4;
Wn = fc / (Fs/2); % normalized cutoff

[b, a] = butter(order, Wn, ‘low’);

sig1_filt = filtfilt(b, a, sig1); % zero-phase filtering
sig2_filt = filtfilt(b, a, sig2);

% Downsample filtered signals
sig1_down = downsample(sig1_filt, downFactor);
sig2_down = downsample(sig2_filt, downFactor);

%% PSDs of anti-aliased + downsampled signals
[f1_down, PSD1_down] = myPSD(sig1_down, Fs_down);
[f2_down, PSD2_down] = myPSD(sig2_down, Fs_down);

figure;
plot(f1_down, PSD1_down, ‘LineWidth’, 1.1); hold on;
plot(f2_down, PSD2_down, ‘LineWidth’, 1.1);
grid on;
xlim([0 100]);
xlabel("Frequency (Hz)");
ylabel("Power/Frequency (linear)");
title("PSD of Downsampled Signals");
legend("sig1 down","sig2 down");

%% Bandpower (20-100 Hz) after anti-aliasing + downsampling
idx1_down = (f1_down >= 20) & (f1_down <= 100);
idx2_down = (f2_down >= 20) & (f2_down <= 100);

bandpower1_down = trapz(f1_down(idx1_down), PSD1_down(idx1_down));
bandpower2_down = trapz(f2_down(idx2_down), PSD2_down(idx2_down));

fprintf(‘Downsampled+AA bandpower (20-100 Hz): sig1 = %.6g, sig2 = %.6gn’, bandpower1_down, bandpower2_down);

%% =========================
% [1.4] Signal rectification & enveloping
% =========================
% Lab asks: rectify, then lowpass at 2 Hz to create heartbeat envelope
sig1_rect = abs(sig1_down);
sig2_rect = abs(sig2_down);

fc_env = 2; % envelope lowpass cutoff
order_env = 4;
Wn_env = fc_env / (Fs_down/2);

[b_env, a_env] = butter(order_env, Wn_env, ‘low’);

env1 = filtfilt(b_env, a_env, sig1_rect);
env2 = filtfilt(b_env, a_env, sig2_rect);

t1 = (0:length(env1)-1)’/Fs_down;
t2 = (0:length(env2)-1)’/Fs_down;

figure;
plot(t1, env1); grid on;
xlabel("Time (s)"); ylabel("Envelope amplitude");
title("Envelope of Signal 1");

figure;
plot(t2, env2); grid on;
xlabel("Time (s)"); ylabel("Envelope amplitude");
title("Envelope of Signal 2");

%% Rescale envelopes to [0,1] (helps peak detection)
env1_scaled = rescale(env1);
env2_scaled = rescale(env2);

figure;
plot(t1, env1_scaled); grid on;
xlabel("Time (s)"); ylabel("Scaled envelope");
title("Scaled Envelope of Signal 1");

figure;
plot(t2, env2_scaled); grid on;
xlabel("Time (s)"); ylabel("Scaled envelope");
title("Scaled Envelope of Signal 2");

%% =========================
% [1.2] Peak identification
% =========================
[pks1, locs1] = findpeaks(env1_scaled, ‘MinPeakProminence’, 0.1);
[pks2, locs2] = findpeaks(env2_scaled, ‘MinPeakProminence’, 0.1);

% Optional visualization of peaks
figure;
plot(t1, env1_scaled); hold on; grid on;
plot(locs1/Fs_down, pks1, ‘o’);
xlabel("Time (s)"); ylabel("Scaled envelope");
title("Signal 1 Peaks");

figure;
plot(t2, env2_scaled); hold on; grid on;
plot(locs2/Fs_down, pks2, ‘o’);
xlabel("Time (s)"); ylabel("Scaled envelope");
title("Signal 2 Peaks");

%% =========================
% [1.2] Peak timing -> Heart rate + beat-to-beat timing
% =========================
tpeaks1 = locs1 / Fs_down; % seconds
tpeaks2 = locs2 / Fs_down;

IBI1 = diff(tpeaks1); % inter-beat intervals (s)
IBI2 = diff(tpeaks2);

HR1_bpm = 60 / mean(IBI1); % average heart rate
HR2_bpm = 60 / mean(IBI2);

% Lab also asks for beat-to-beat timing variance
IBIvar1 = var(IBI1);
IBIvar2 = var(IBI2);

fprintf(‘HR sig1 = %.2f bpm, HR sig2 = %.2f bpmn’, HR1_bpm, HR2_bpm);
fprintf(‘Beat-to-beat variance: sig1 = %.6g s^2, sig2 = %.6g s^2n’, IBIvar1, IBIvar2);

%% =========================
% [1.5] Interpolation (recreate original sig1 from downsampled)
% Compare methods using RMSE
% =========================
t_down = (0:length(sig1_down)-1)’ / Fs_down;
t_orig = (0:length(sig1)-1)’ / Fs;

% Try a few methods (lab says experiment with interp1 methods)
methods = ["nearest","linear","spline","pchip","cubic"];
rmseVals = zeros(size(methods));

for k = 1:length(methods)
sig1_int = interp1(t_down, sig1_down, t_orig, methods(k))’;
rmseVals(k) = sqrt(mean((sig1 – sig1_int).^2, ‘omitnan’));
end

% Show RMSEs
disp(table(methods’, rmseVals’, ‘VariableNames’, {‘Method’,’RMSE’}));

% Pick best method (lowest RMSE)
[bestRMSE, idxBest] = min(rmseVals);
bestMethod = methods(idxBest);

% Recompute best interpolation for plotting
sig1_best = interp1(t_down, sig1_down, t_orig, bestMethod)’;

fprintf(‘Best interpolation method: %s (RMSE = %.6g)n’, bestMethod, bestRMSE);

% Plot original vs best interpolated (short window for visibility)
figure;
plot(t_orig, sig1, ‘LineWidth’, 1); hold on; grid on;
plot(t_orig, sig1_best, ‘–‘, ‘LineWidth’, 1);
xlim([0 2]); % zoom into first 2 s so differences are visible
xlabel("Time (s)"); ylabel("Amplitude");
title("Original sig1 vs Interpolated sig1");
legend("Original","Interpolated"); signal processing MATLAB Answers — New Questions

​

Hi – how do I get the line intersection between two polyshapes that overlap along a line? A super-simplified example is below. For the real case, the shapes are more complicated, but I think the simplified case covers it. I’m maybe not understanding how the intersection function works! Thank you!

function main()

%clear all; % completely superfluous in function workspace
x1 = [1 4 4 1];
y1 = [1 1 4 4];
x2 = [4 6 6 4];
y2 = [2 2 3 3];
poly1 = polyshape(x1,y1);
% the poly2 ‘grows’ from poly1
poly2 = polyshape(x2,y2);
poly2 = union(poly1,poly2);
growthPoly = subtract(poly2,poly1,"KeepCollinearPoints",true);
close all;
hold on;
plot(poly1,’FaceColor’,’none’,’EdgeColor’,’k’,’LineWidth’,12);
plot(poly2,’FaceColor’,’none’,’EdgeColor’,’g’,’LineWidth’,8);
plot(growthPoly,’FaceColor’,’none’,’EdgeColor’,’r’,’LineWidth’,4);
% this comes back empty and nothing gets plotted
growthLine = intersect(poly1,growthPoly,"KeepCollinearPoints",true);
plot(growthLine,’FaceColor’,’none’,’EdgeColor’,’b’,’LineWidth’,2);
legend({‘The initial poly1’,…
‘poly2 grew from poly1’,…
‘The growth area is the difference between poly2 and poly1′},’Box’,’off’,’Location’,’south’,’FontSize’,18);
title({‘How do I get the ”growthLine” part that is red-on-black?’;’I thought it would be the intersection of the black and red, but no dice’;’I thought it would show up as a blue line’},’FontSize’,22);
endHi – how do I get the line intersection between two polyshapes that overlap along a line? A super-simplified example is below. For the real case, the shapes are more complicated, but I think the simplified case covers it. I’m maybe not understanding how the intersection function works! Thank you!

function main()

%clear all; % completely superfluous in function workspace
x1 = [1 4 4 1];
y1 = [1 1 4 4];
x2 = [4 6 6 4];
y2 = [2 2 3 3];
poly1 = polyshape(x1,y1);
% the poly2 ‘grows’ from poly1
poly2 = polyshape(x2,y2);
poly2 = union(poly1,poly2);
growthPoly = subtract(poly2,poly1,"KeepCollinearPoints",true);
close all;
hold on;
plot(poly1,’FaceColor’,’none’,’EdgeColor’,’k’,’LineWidth’,12);
plot(poly2,’FaceColor’,’none’,’EdgeColor’,’g’,’LineWidth’,8);
plot(growthPoly,’FaceColor’,’none’,’EdgeColor’,’r’,’LineWidth’,4);
% this comes back empty and nothing gets plotted
growthLine = intersect(poly1,growthPoly,"KeepCollinearPoints",true);
plot(growthLine,’FaceColor’,’none’,’EdgeColor’,’b’,’LineWidth’,2);
legend({‘The initial poly1’,…
‘poly2 grew from poly1’,…
‘The growth area is the difference between poly2 and poly1′},’Box’,’off’,’Location’,’south’,’FontSize’,18);
title({‘How do I get the ”growthLine” part that is red-on-black?’;’I thought it would be the intersection of the black and red, but no dice’;’I thought it would show up as a blue line’},’FontSize’,22);
end Hi – how do I get the line intersection between two polyshapes that overlap along a line? A super-simplified example is below. For the real case, the shapes are more complicated, but I think the simplified case covers it. I’m maybe not understanding how the intersection function works! Thank you!

function main()

%clear all; % completely superfluous in function workspace
x1 = [1 4 4 1];
y1 = [1 1 4 4];
x2 = [4 6 6 4];
y2 = [2 2 3 3];
poly1 = polyshape(x1,y1);
% the poly2 ‘grows’ from poly1
poly2 = polyshape(x2,y2);
poly2 = union(poly1,poly2);
growthPoly = subtract(poly2,poly1,"KeepCollinearPoints",true);
close all;
hold on;
plot(poly1,’FaceColor’,’none’,’EdgeColor’,’k’,’LineWidth’,12);
plot(poly2,’FaceColor’,’none’,’EdgeColor’,’g’,’LineWidth’,8);
plot(growthPoly,’FaceColor’,’none’,’EdgeColor’,’r’,’LineWidth’,4);
% this comes back empty and nothing gets plotted
growthLine = intersect(poly1,growthPoly,"KeepCollinearPoints",true);
plot(growthLine,’FaceColor’,’none’,’EdgeColor’,’b’,’LineWidth’,2);
legend({‘The initial poly1’,…
‘poly2 grew from poly1’,…
‘The growth area is the difference between poly2 and poly1′},’Box’,’off’,’Location’,’south’,’FontSize’,18);
title({‘How do I get the ”growthLine” part that is red-on-black?’;’I thought it would be the intersection of the black and red, but no dice’;’I thought it would show up as a blue line’},’FontSize’,22);
end polyshape, intersection, union, subtract MATLAB Answers — New Questions

​

In Matlab I have generated C code (.c/.h files) from a Matlab function using codegen from Matlab Coder.
I call this function in a while loop on an ARM quad-core Cortex A53 using AMD Vitis.
Before calling this function I use scanf/printf to get user input and to print to the serial monitor. However, scanf/printf only work once in the 1st iteration (before calling the function) and not in the 2nd iteration or later (after calling the function).
Is this a common problem and has anyone encountered this problem before?
I think, but am not sure, that the problem is in the generated C code and not in Vitis itself.
Thank you in advance!In Matlab I have generated C code (.c/.h files) from a Matlab function using codegen from Matlab Coder.
I call this function in a while loop on an ARM quad-core Cortex A53 using AMD Vitis.
Before calling this function I use scanf/printf to get user input and to print to the serial monitor. However, scanf/printf only work once in the 1st iteration (before calling the function) and not in the 2nd iteration or later (after calling the function).
Is this a common problem and has anyone encountered this problem before?
I think, but am not sure, that the problem is in the generated C code and not in Vitis itself.
Thank you in advance! In Matlab I have generated C code (.c/.h files) from a Matlab function using codegen from Matlab Coder.
I call this function in a while loop on an ARM quad-core Cortex A53 using AMD Vitis.
Before calling this function I use scanf/printf to get user input and to print to the serial monitor. However, scanf/printf only work once in the 1st iteration (before calling the function) and not in the 2nd iteration or later (after calling the function).
Is this a common problem and has anyone encountered this problem before?
I think, but am not sure, that the problem is in the generated C code and not in Vitis itself.
Thank you in advance! matlab coder, code generation MATLAB Answers — New Questions

​

Hello. I am working on a Jukebox to play a song that corrisponds with set number (this part is not the issue I am just trying to provide some context).
When the code is run I would like the Opening menu to appear in the command window so the user can see the song choices before being prompted to enter an input. The problem is that when I run the code it does not print any of my text and just waits for an input. I think it has something to do with the fact that other than declaring some variables, there is nothing happening before the first fprintf. I am not getting any error or warning messages on my end.
Is this a quirk of using the online version? I am using the online verison of MATlab because I am having techinal issues with installing the desktop version.
(This is not all of my code, I chose to only share what is relevent to my issue)
clear
clc
close all
%% Declarations
rate = 32768; % sampling rate
songs = 1:9;
octave = 0;
speed = 0;
% Opening menu. Give list of options
fprintf([‘Welcome to Jukebox.n——– SONG MENU ——–n 1 |’…
‘ Bohemian Rhapsody n 2 | ‘ …
‘Dies Irae n 3 | Entry Into Valhalla n 4 | Fur Elise n 5 | ‘ …
‘Game of Thrones Theme n 6 | He”s a Pirate n 7 | Megalovania ‘ …
‘Long n 8 | Paint it Black n’])
% Prompt user for selection, and check validity of choice
songNum = input([‘Enter the number of the song you wish to play –> n’]);
while songNum < 0 || songNum > 9 || mod(songNum,1) ~= 0
songNum = input(‘Try again –> ‘);
endHello. I am working on a Jukebox to play a song that corrisponds with set number (this part is not the issue I am just trying to provide some context).
When the code is run I would like the Opening menu to appear in the command window so the user can see the song choices before being prompted to enter an input. The problem is that when I run the code it does not print any of my text and just waits for an input. I think it has something to do with the fact that other than declaring some variables, there is nothing happening before the first fprintf. I am not getting any error or warning messages on my end.
Is this a quirk of using the online version? I am using the online verison of MATlab because I am having techinal issues with installing the desktop version.
(This is not all of my code, I chose to only share what is relevent to my issue)
clear
clc
close all
%% Declarations
rate = 32768; % sampling rate
songs = 1:9;
octave = 0;
speed = 0;
% Opening menu. Give list of options
fprintf([‘Welcome to Jukebox.n——– SONG MENU ——–n 1 |’…
‘ Bohemian Rhapsody n 2 | ‘ …
‘Dies Irae n 3 | Entry Into Valhalla n 4 | Fur Elise n 5 | ‘ …
‘Game of Thrones Theme n 6 | He”s a Pirate n 7 | Megalovania ‘ …
‘Long n 8 | Paint it Black n’])
% Prompt user for selection, and check validity of choice
songNum = input([‘Enter the number of the song you wish to play –> n’]);
while songNum < 0 || songNum > 9 || mod(songNum,1) ~= 0
songNum = input(‘Try again –> ‘);
end Hello. I am working on a Jukebox to play a song that corrisponds with set number (this part is not the issue I am just trying to provide some context).
When the code is run I would like the Opening menu to appear in the command window so the user can see the song choices before being prompted to enter an input. The problem is that when I run the code it does not print any of my text and just waits for an input. I think it has something to do with the fact that other than declaring some variables, there is nothing happening before the first fprintf. I am not getting any error or warning messages on my end.
Is this a quirk of using the online version? I am using the online verison of MATlab because I am having techinal issues with installing the desktop version.
(This is not all of my code, I chose to only share what is relevent to my issue)
clear
clc
close all
%% Declarations
rate = 32768; % sampling rate
songs = 1:9;
octave = 0;
speed = 0;
% Opening menu. Give list of options
fprintf([‘Welcome to Jukebox.n——– SONG MENU ——–n 1 |’…
‘ Bohemian Rhapsody n 2 | ‘ …
‘Dies Irae n 3 | Entry Into Valhalla n 4 | Fur Elise n 5 | ‘ …
‘Game of Thrones Theme n 6 | He”s a Pirate n 7 | Megalovania ‘ …
‘Long n 8 | Paint it Black n’])
% Prompt user for selection, and check validity of choice
songNum = input([‘Enter the number of the song you wish to play –> n’]);
while songNum < 0 || songNum > 9 || mod(songNum,1) ~= 0
songNum = input(‘Try again –> ‘);
end fprintf, input, matlab MATLAB Answers — New Questions

​

I have a .M file from Octave that I want to run in MATLAB, but I obtain an error. Here is the content of .M file (it’s just one line):
load "Slovenia_centered.mat"
And here is the error:
Error using load
Unable to read file ‘"Slovenia_centered.mat"’: Invalid argument.

Error in
Slovenia_centered (line 1)
load "Slovenia_centered.mat"
^^^^I have a .M file from Octave that I want to run in MATLAB, but I obtain an error. Here is the content of .M file (it’s just one line):
load "Slovenia_centered.mat"
And here is the error:
Error using load
Unable to read file ‘"Slovenia_centered.mat"’: Invalid argument.

Error in
Slovenia_centered (line 1)
load "Slovenia_centered.mat"
^^^^ I have a .M file from Octave that I want to run in MATLAB, but I obtain an error. Here is the content of .M file (it’s just one line):
load "Slovenia_centered.mat"
And here is the error:
Error using load
Unable to read file ‘"Slovenia_centered.mat"’: Invalid argument.

Error in
Slovenia_centered (line 1)
load "Slovenia_centered.mat"
^^^^ load MATLAB Answers — New Questions

​

Hello everyone,
I am a graduate student and a relatively new user of MATLAB. I am currently working on a research project involving the analysis of ARK (Auto-Refractor Keratometer) retroillumination images to study cataracts.
I have successfully completed the first step, which is segmenting the pupil ROI (mask) from the anterior segment images using the Segment Anything Model (SAM).
Now, I am facing a challenge: I need to precisely mask the opacities (cloudy areas) within the identified pupil region. While I can perform this task for a single image using SAM, I have a dataset of approximately 500 images, making individual manual processing impractical.
I am looking for guidance on how to automate this process in MATLAB. Specifically:
Are there recommended image processing techniques (e.g., adaptive thresholding, texture analysis, or morphological operations) that work well for identifying cataract opacities within a pupil mask?
Is there a way to batch-process these 500 images efficiently while maintaining high precision?
Are there any existing File Exchange entries or toolboxes you would recommend for this type of medical image segmentation?
I would greatly appreciate any advice, code snippets, or references to help me move forward with my research.
Thank you in advance for your help!
Best regards,Hello everyone,
I am a graduate student and a relatively new user of MATLAB. I am currently working on a research project involving the analysis of ARK (Auto-Refractor Keratometer) retroillumination images to study cataracts.
I have successfully completed the first step, which is segmenting the pupil ROI (mask) from the anterior segment images using the Segment Anything Model (SAM).
Now, I am facing a challenge: I need to precisely mask the opacities (cloudy areas) within the identified pupil region. While I can perform this task for a single image using SAM, I have a dataset of approximately 500 images, making individual manual processing impractical.
I am looking for guidance on how to automate this process in MATLAB. Specifically:
Are there recommended image processing techniques (e.g., adaptive thresholding, texture analysis, or morphological operations) that work well for identifying cataract opacities within a pupil mask?
Is there a way to batch-process these 500 images efficiently while maintaining high precision?
Are there any existing File Exchange entries or toolboxes you would recommend for this type of medical image segmentation?
I would greatly appreciate any advice, code snippets, or references to help me move forward with my research.
Thank you in advance for your help!
Best regards, Hello everyone,
I am a graduate student and a relatively new user of MATLAB. I am currently working on a research project involving the analysis of ARK (Auto-Refractor Keratometer) retroillumination images to study cataracts.
I have successfully completed the first step, which is segmenting the pupil ROI (mask) from the anterior segment images using the Segment Anything Model (SAM).
Now, I am facing a challenge: I need to precisely mask the opacities (cloudy areas) within the identified pupil region. While I can perform this task for a single image using SAM, I have a dataset of approximately 500 images, making individual manual processing impractical.
I am looking for guidance on how to automate this process in MATLAB. Specifically:
Are there recommended image processing techniques (e.g., adaptive thresholding, texture analysis, or morphological operations) that work well for identifying cataract opacities within a pupil mask?
Is there a way to batch-process these 500 images efficiently while maintaining high precision?
Are there any existing File Exchange entries or toolboxes you would recommend for this type of medical image segmentation?
I would greatly appreciate any advice, code snippets, or references to help me move forward with my research.
Thank you in advance for your help!
Best regards, image processing, masking, image processing toolbox MATLAB Answers — New Questions

​

I just need an example:
%% Load data
% Lab says use readtable, then convert to array
T = readtable("Section2.csv");
data = table2array(T);

% Assumption: column 1 is time/index, columns 2:11 are the 10 subjects
X = data(:,2:11); % Nx10 matrix (each column = one subject)

%% =========================
% [3.2] Central tendency + dispersion for each subject
% mean, median, range, std (one value per subject/column)
% =========================
mean_data = mean(X, ‘omitnan’); % 1×10
median_data = median(X, ‘omitnan’); % 1×10

% Use max-min for range (robust if MATLAB "range" gives issues)
range_data = max(X, [], 1) – min(X, [], 1); % 1×10

std_data = std(X, 0, ‘omitnan’); % 1×10 (0 = sample std)

%% =========================
% [3.2] Find which subject has max/min for each metric
% Lab asks to do this by code (not by looking)
% =========================
[max_mean, subj_max_mean] = max(mean_data);
[min_mean, subj_min_mean] = min(mean_data);

[max_median, subj_max_median] = max(median_data);
[min_median, subj_min_median] = min(median_data);

[max_range, subj_max_range] = max(range_data);
[min_range, subj_min_range] = min(range_data);

[max_std, subj_max_std] = max(std_data);
[min_std, subj_min_std] = min(std_data);

% Display summary
fprintf(‘n=== Subject Extremes (Subjects 1-10) ===n’);
fprintf(‘Mean : max = %.2f (Subject %d), min = %.2f (Subject %d)n’, …
max_mean, subj_max_mean, min_mean, subj_min_mean);

fprintf(‘Median : max = %.2f (Subject %d), min = %.2f (Subject %d)n’, …
max_median, subj_max_median, min_median, subj_min_median);

fprintf(‘Range : max = %.2f (Subject %d), min = %.2f (Subject %d)n’, …
max_range, subj_max_range, min_range, subj_min_range);

fprintf(‘Std Dev: max = %.2f (Subject %d), min = %.2f (Subject %d)n’, …
max_std, subj_max_std, min_std, subj_min_std);

%% =========================
% [3.2] Time spent > (mean + 20 bpm), in HOURS
% Lab says each row = 5 seconds
% =========================
function hours = time_over(colVec, meanVal)
% colVec = one subject’s HR time series (Nx1)
% meanVal = mean HR for that subject
count = sum(colVec > (meanVal + 20)); % number of samples above threshold
hours = (count * 5) / 3600; % 5 s/sample -> hours
end

% Compute for all 10 subjects
hours_over = zeros(1,10);
for s = 1:10
hours_over(s) = time_over(X(:,s), mean_data(s));
end

% Optional: display results neatly
fprintf(‘n=== Time above (mean + 20 bpm) ===n’);
for s = 1:10
fprintf(‘Subject %d: %.3f hoursn’, s, hours_over(s));
end

%% Optional: put everything into a table (nice for report/checking)
subject_id = (1:10)’;
resultsTbl = table(subject_id, mean_data’, median_data’, range_data’, std_data’, hours_over’, …
‘VariableNames’, {‘Subject’,’MeanHR’,’MedianHR’,’RangeHR’,’StdHR’,’HoursAboveMeanPlus20′});

disp(resultsTbl);I just need an example:
%% Load data
% Lab says use readtable, then convert to array
T = readtable("Section2.csv");
data = table2array(T);

% Assumption: column 1 is time/index, columns 2:11 are the 10 subjects
X = data(:,2:11); % Nx10 matrix (each column = one subject)

%% =========================
% [3.2] Central tendency + dispersion for each subject
% mean, median, range, std (one value per subject/column)
% =========================
mean_data = mean(X, ‘omitnan’); % 1×10
median_data = median(X, ‘omitnan’); % 1×10

% Use max-min for range (robust if MATLAB "range" gives issues)
range_data = max(X, [], 1) – min(X, [], 1); % 1×10

std_data = std(X, 0, ‘omitnan’); % 1×10 (0 = sample std)

%% =========================
% [3.2] Find which subject has max/min for each metric
% Lab asks to do this by code (not by looking)
% =========================
[max_mean, subj_max_mean] = max(mean_data);
[min_mean, subj_min_mean] = min(mean_data);

[max_median, subj_max_median] = max(median_data);
[min_median, subj_min_median] = min(median_data);

[max_range, subj_max_range] = max(range_data);
[min_range, subj_min_range] = min(range_data);

[max_std, subj_max_std] = max(std_data);
[min_std, subj_min_std] = min(std_data);

% Display summary
fprintf(‘n=== Subject Extremes (Subjects 1-10) ===n’);
fprintf(‘Mean : max = %.2f (Subject %d), min = %.2f (Subject %d)n’, …
max_mean, subj_max_mean, min_mean, subj_min_mean);

fprintf(‘Median : max = %.2f (Subject %d), min = %.2f (Subject %d)n’, …
max_median, subj_max_median, min_median, subj_min_median);

fprintf(‘Range : max = %.2f (Subject %d), min = %.2f (Subject %d)n’, …
max_range, subj_max_range, min_range, subj_min_range);

fprintf(‘Std Dev: max = %.2f (Subject %d), min = %.2f (Subject %d)n’, …
max_std, subj_max_std, min_std, subj_min_std);

%% =========================
% [3.2] Time spent > (mean + 20 bpm), in HOURS
% Lab says each row = 5 seconds
% =========================
function hours = time_over(colVec, meanVal)
% colVec = one subject’s HR time series (Nx1)
% meanVal = mean HR for that subject
count = sum(colVec > (meanVal + 20)); % number of samples above threshold
hours = (count * 5) / 3600; % 5 s/sample -> hours
end

% Compute for all 10 subjects
hours_over = zeros(1,10);
for s = 1:10
hours_over(s) = time_over(X(:,s), mean_data(s));
end

% Optional: display results neatly
fprintf(‘n=== Time above (mean + 20 bpm) ===n’);
for s = 1:10
fprintf(‘Subject %d: %.3f hoursn’, s, hours_over(s));
end

%% Optional: put everything into a table (nice for report/checking)
subject_id = (1:10)’;
resultsTbl = table(subject_id, mean_data’, median_data’, range_data’, std_data’, hours_over’, …
‘VariableNames’, {‘Subject’,’MeanHR’,’MedianHR’,’RangeHR’,’StdHR’,’HoursAboveMeanPlus20′});

disp(resultsTbl); I just need an example:
%% Load data
% Lab says use readtable, then convert to array
T = readtable("Section2.csv");
data = table2array(T);

% Assumption: column 1 is time/index, columns 2:11 are the 10 subjects
X = data(:,2:11); % Nx10 matrix (each column = one subject)

%% =========================
% [3.2] Central tendency + dispersion for each subject
% mean, median, range, std (one value per subject/column)
% =========================
mean_data = mean(X, ‘omitnan’); % 1×10
median_data = median(X, ‘omitnan’); % 1×10

% Use max-min for range (robust if MATLAB "range" gives issues)
range_data = max(X, [], 1) – min(X, [], 1); % 1×10

std_data = std(X, 0, ‘omitnan’); % 1×10 (0 = sample std)

%% =========================
% [3.2] Find which subject has max/min for each metric
% Lab asks to do this by code (not by looking)
% =========================
[max_mean, subj_max_mean] = max(mean_data);
[min_mean, subj_min_mean] = min(mean_data);

[max_median, subj_max_median] = max(median_data);
[min_median, subj_min_median] = min(median_data);

[max_range, subj_max_range] = max(range_data);
[min_range, subj_min_range] = min(range_data);

[max_std, subj_max_std] = max(std_data);
[min_std, subj_min_std] = min(std_data);

% Display summary
fprintf(‘n=== Subject Extremes (Subjects 1-10) ===n’);
fprintf(‘Mean : max = %.2f (Subject %d), min = %.2f (Subject %d)n’, …
max_mean, subj_max_mean, min_mean, subj_min_mean);

fprintf(‘Median : max = %.2f (Subject %d), min = %.2f (Subject %d)n’, …
max_median, subj_max_median, min_median, subj_min_median);

fprintf(‘Range : max = %.2f (Subject %d), min = %.2f (Subject %d)n’, …
max_range, subj_max_range, min_range, subj_min_range);

fprintf(‘Std Dev: max = %.2f (Subject %d), min = %.2f (Subject %d)n’, …
max_std, subj_max_std, min_std, subj_min_std);

%% =========================
% [3.2] Time spent > (mean + 20 bpm), in HOURS
% Lab says each row = 5 seconds
% =========================
function hours = time_over(colVec, meanVal)
% colVec = one subject’s HR time series (Nx1)
% meanVal = mean HR for that subject
count = sum(colVec > (meanVal + 20)); % number of samples above threshold
hours = (count * 5) / 3600; % 5 s/sample -> hours
end

% Compute for all 10 subjects
hours_over = zeros(1,10);
for s = 1:10
hours_over(s) = time_over(X(:,s), mean_data(s));
end

% Optional: display results neatly
fprintf(‘n=== Time above (mean + 20 bpm) ===n’);
for s = 1:10
fprintf(‘Subject %d: %.3f hoursn’, s, hours_over(s));
end

%% Optional: put everything into a table (nice for report/checking)
subject_id = (1:10)’;
resultsTbl = table(subject_id, mean_data’, median_data’, range_data’, std_data’, hours_over’, …
‘VariableNames’, {‘Subject’,’MeanHR’,’MedianHR’,’RangeHR’,’StdHR’,’HoursAboveMeanPlus20′});

disp(resultsTbl); multichannel statistical analysis MATLAB Answers — New Questions

​

I am experiencing a persistent assessment failure in the "Solar Energy" section, Task 1, of the Power Systems Simulation Onramp course.
The Problem: I have connected the signal labeled P_AC to the Signal Assessment block as instructed. The connection in the model is a solid black line, yet the assessment returns "Incorrect" with a hint suggesting an unconnected signal.
Symptoms:
The Assessment graph shows red dots (incorrect data) specifically at the end of the simulation period (approx. seconds 23-25).
I have confirmed that the Enable MPPT block is set to 0.
Steps already taken:
Deleted and re-connected the P_AC signal multiple times to ensure a solid connection.
Ran the simulation until completion (100% ready).
Used the "Reset" button for the task and re-attempted.
Could you please check if this is a known issue with the auto-grader for this specific task?I am experiencing a persistent assessment failure in the "Solar Energy" section, Task 1, of the Power Systems Simulation Onramp course.
The Problem: I have connected the signal labeled P_AC to the Signal Assessment block as instructed. The connection in the model is a solid black line, yet the assessment returns "Incorrect" with a hint suggesting an unconnected signal.
Symptoms:
The Assessment graph shows red dots (incorrect data) specifically at the end of the simulation period (approx. seconds 23-25).
I have confirmed that the Enable MPPT block is set to 0.
Steps already taken:
Deleted and re-connected the P_AC signal multiple times to ensure a solid connection.
Ran the simulation until completion (100% ready).
Used the "Reset" button for the task and re-attempted.
Could you please check if this is a known issue with the auto-grader for this specific task? I am experiencing a persistent assessment failure in the "Solar Energy" section, Task 1, of the Power Systems Simulation Onramp course.
The Problem: I have connected the signal labeled P_AC to the Signal Assessment block as instructed. The connection in the model is a solid black line, yet the assessment returns "Incorrect" with a hint suggesting an unconnected signal.
Symptoms:
The Assessment graph shows red dots (incorrect data) specifically at the end of the simulation period (approx. seconds 23-25).
I have confirmed that the Enable MPPT block is set to 0.
Steps already taken:
Deleted and re-connected the P_AC signal multiple times to ensure a solid connection.
Ran the simulation until completion (100% ready).
Used the "Reset" button for the task and re-attempted.
Could you please check if this is a known issue with the auto-grader for this specific task? power systems simulation onramp, mppt, signal asse MATLAB Answers — New Questions

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