#Date: 19/07/2023
#Author: Adrien COMBE
#Description: Retake exam Signal Processing

% Load the CSV file containing the signal
signal = csvread('unknownsignal.csv');

% Given sampling frequency Fs = 650 Hz
Fs = 650;

% Time vector
t = (0:length(signal)-1) / Fs;

% Plot the signal in the time domain
figure;
plot(t, signal);
xlabel('Time (s)');
ylabel('Amplitude');
title('Mysterious Signal in Time Domain');

% Compute the length of the signal
N = length(signal);

% Perform the Fast Fourier Transform (FFT)
fft_result = fft(signal);

% Compute the frequency axis for the FFT plot
f = (0:N-1) * Fs / N;

% Plot the magnitude spectrum of the signal
figure;
plot(f, abs(fft_result));
xlabel('Frequency (Hz)');
ylabel('Magnitude');
title('Magnitude Spectrum (FFT) of Mysterious Signal');

% Identify the dominant frequencies (sine wave components)
% Set a threshold to filter out noise
threshold = 50;

% Find the indices of dominant frequencies above the threshold
dominant_indices = find(abs(fft_result) > threshold);

% Extract the dominant frequencies and their magnitudes
dominant_frequencies = f(dominant_indices);
dominant_magnitudes = abs(fft_result(dominant_indices));

% Display the dominant frequencies and their magnitudes
disp('Dominant Frequencies (Hz):');
disp(dominant_frequencies);
disp('Magnitudes:');
disp(dominant_magnitudes);