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Adrien COMBE 2023-04-20 10:39:14 +02:00
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#Date: 20/04/2023
#Author: Adrien COMBE
#Description: Midterm Signal processing
%The signal
signal = csvread("unknownsignal.csv");
%Sampling Frequency
fs = 300; %Hz
%Power spectrum
frequencySpectrum(signal,fs)

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function power = frequencySpectrum(signal, fs)
%%%%%%%%%%%%%%%%%%
%function frequencySpectrum(signal, fs)
%
% Task: Display the power spectrum of a given signal
%
% Input:
% - signal: the input signal to process
% - fs: the sampling rate
%
% Output:
% - power: power spectrum of the signal
%
%
% Guillaume Gibert, guillaume.gibert@ecam.fr
% 25/04/2022
%%%%%%%%%%%%%%%%%%
n = length(signal); % number of samples
y = fft(signal, n);% compute DFT of input signal
power = abs(y).^2/n; % power of the DFT
[val, ind] = max(power); % find the mx value of DFT and its index
% plots
figure;
subplot(1,3,1) % time plot
t=0:1/fs:(n-1)/fs; % time range
plot(t, signal)
xticks(0:0.1*fs:n*fs);
xticklabels(0:0.1:n/fs);
xlabel('Time (s)');
ylabel('Amplitude (a.u.)');
subplot(1,3,2) % linear frequency plot
f = (0:n-1)*(fs/n); % frequency range
plot(f,power, 'b*'); hold on;
plot(f,power, 'r');
xlabel('Frequency (Hz)')
ylabel('Power (a.u.)')
subplot(1,3,3) % log frequency plot
plot(f,10*log10(power/power(ind)));
xlabel('Frequency (Hz)')
ylabel('Power (dB)')

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