61 lines
1.6 KiB
Matlab
61 lines
1.6 KiB
Matlab
function main()
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%{
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function main()
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Main function to analyze the unknown signal
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Tasks:
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- Load signal
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- Plot raw signal
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- Perform and plot power spectrum
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Author: Tikea TE
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Date: 16/04/2025
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%}
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% ==== Load signal ====
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load unknownsignal.mat
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Fs = 200; % given sampling frequency (Hz)
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% ==== Plot the raw signal ====
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plotRawSignal(X, Fs);
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% ==== Analyze frequency spectrum ====
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[f, power] = frequencySpectrum(X, Fs, 1); % set 1 to plot
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% ======== Apply a bandpass filter ==============
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% [filteredSignal, Z, P] = iirFilter(10, [5 20], X, Fs, 1);
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% ======== Apply FirFilter ===============
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filteredSignal = firFilter(30, [5 20], X, 200); % for a 30th-order bandpass FIR filter
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% ============ plot the filtered signal ============
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plotFilteredSignal(filteredSignal, Fs);
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plotRawVsFiltered(X, filteredSignal, Fs);
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% ==== Power spectrum of the filtered signal ====
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[f_filtered, power_filtered] = frequencySpectrum(filteredSignal, Fs, 1);
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% === Create time vector for windowing ===
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t = (0:length(filteredSignal)-1) / Fs;
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% === Choose window duration ===
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duration_signal = length(filteredSignal) / Fs;
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win_duration = 2; % seconds
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% === Create and apply Hanning window ===
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win_hanning = createtemporalWindow(t, duration_signal, win_duration, Fs, 2);
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x_hanning = applyTemporalwindow(filteredSignal, t, win_hanning);
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% === Plot raw vs windowed ===
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figure;
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plot(t, filteredSignal, 'r'); hold on;
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plot(t, x_hanning, 'b');
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legend('Filtered signal', 'Hanning-windowed');
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xlabel('Time (s)');
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ylabel('Amplitude');
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title('Windowing Effect on Filtered Signal');
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grid on;
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end
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