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SignalProcessing_midterm2024
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Merge branch 'develop'

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Raphaelsav 2024-03-18 09:32:19 +01:00
parent 13d2c6a4e3 a552b491b2
commit a9df4f13e0
5 changed files with 77 additions and 1 deletions
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1
hammingWin.m
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@ -27,6 +27,7 @@ windowDuration = signal_duration/2;
hammingWin = zeros(1, length(t));
% change sampling_freq/4 to shift the signal
for l_sample=1:windowDuration*sampling_freq
hammingWin(l_sample+signal_duration*sampling_freq/4) = (0.54 - 0.46*cos(2*pi*(l_sample)/(signal_duration*sampling_freq/2)));
end

1
hanningWin.m
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@ -27,6 +27,7 @@ windowDuration = signal_duration/2;
hanningWin = zeros(1, length(t));
% change sampling_freq/4 to shift the signal
for l_sample=1:windowDuration*sampling_freq
hanningWin(l_sample+signal_duration*sampling_freq/4) = (0.5 - 0.5*cos(2*pi*(l_sample)/(signal_duration*sampling_freq/2)));
end

73
main.m Normal file
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@ -0,0 +1,73 @@
%%%%%%%%%%%%%%%%%%%%%%
% UNKNOWN SIGNAL
% Sampling frequency: 300 Hz
% Duration; 2 s
% First second: 0.1Hz, 30 Hz, 30.5 Hz, 60 Hz, 61 Hz
% Second second: 0.1Hz, 32 Hz, 36 Hz, 64 Hz, 72 Hz
%%%%%%%%%%%%%%%%%%%%%%
% loads the signal package on Octave
% pkg load signal
% loads signal and its characteristics
signal = csvread('unknownsignal.csv');
%%%%%SIGNAL CHARACTERISTICS%%%%%
% sets sampling frequency
fps = 300; % -> freqMax of the signal should be < 150 Hz (Shannon-Nyquisit theorem), in practice freqMax < 60 Hz would be better
% computes the duration of the signal
duration = length(signal) / fps; % in s
% estimates its original frequency resolution
resolution = fps / length(signal); % in Hz
%%%%%STATIONARITY%%%%%
% temporal plot
figure;
plot(signal);
xticks(0:0.2*fps:length(signal)*fps);
xticklabels(0:0.2:length(signal)/fps);
xlabel('Time (s)');
ylabel('Amplitude (a.u.)');
% spectrogram
step_size = 50; %ms
window_size = 100; %ms
spectrogram(signal, fps, step_size, window_size);
% ccl: signal is not stationary, it is composed of 2 parts
%%%%%SPLIT SIGNAL INTO 2 PARTS%%%%%
% First part: [0 1s]
signal_1 = signal(1:end/2);
% Second part: [1s 2s]
signal_2 = signal(end/2+1:end);
%%%%%SPECTRAL ANALYSIS (RECTANGULAR WINDOW)%%%%%
%plots power spectrum with rectangular window
% 1st part of the signal with 1 Hz resolution
frequencySpectrum(signal_1, fps, 1);
% 1st part of the signal with 0.5 Hz resolution
frequencySpectrum(signal_1, fps, 0.5);
% 2nd part of the signal with 1 Hz resolution
frequencySpectrum(signal_2, fps, 1);
% 2nd part of the signal with 0.5 Hz resolution
frequencySpectrum(signal_2, fps, 0.5);
%%%%%SPECTRAL ANALYSIS (BLACKMAN WINDOW)%%%%%
%plots power spectrum with blackman window
signal_1_win = blackmanWin(signal_1);
% 1st part of the signal with 1 Hz resolution
frequencySpectrum(signal_1_win, fps, 1);
% 1st part of the signal with 0.5 Hz resolution
frequencySpectrum(signal_1_win, fps, 0.5);
signal_2_win = blackmanWin(signal_2);
% 2nd part of the signal with 1 Hz resolution
frequencySpectrum(signal_2_win, fps, 1);
% 2nd part of the signal with 0.5 Hz resolution
frequencySpectrum(signal_2_win, fps, 0.5);

2
rectWin.m
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@ -28,7 +28,7 @@ windowDuration = signal_duration/2;
rectangularWin = zeros(1, length(t));
for l_sample=1:windowDuration*sampling_freq
rectangularWin(l_sample + signal_duration) = 1;
rectangularWin(l_sample + signal_duration*sampling_freq/4) = 1;
end
figure;

1
unknownsignal.csv Normal file
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