Midterm

Hamming_Windower.m

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+function windowed_signal = Hamming_Windower(signal)
+    % Calculate the length of the signal
+    N = length(signal);
+
+    % Create the Hamming window
+    window_function = hamming(N);
+
+    % Apply the Hamming window to the signal
+    windowed_signal = signal .* window_function;
+end

Raw_Signal_Plotter.m

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+% Loading the signal
+pkg load signal
+data = csvread('unknownsignal.csv');
+signal=data';
+N = length(signal); % Number of samples
+
+% Plot the signal in the time domain
+t = linspace(0, (N-1)/Fs, N); % Time vector
+figure;
+plot(t, signal);
+xlabel('Time (s)');
+ylabel('Amplitude');
+title('Signal in Time Domain');
+grid on;
+
+% Compute the FFT and normalize
+fft_signal = fft(signal, N);
+fft_signal_normalized = fft_signal / N;
+
+% Compute the magnitude spectrum
+magnitude_spectrum = abs(fft_signal_normalized);
+
+% Generate the frequency vector
+frequencies = linspace(0, Fs/2, N/2);
+
+% Plot the spectrum in the frequency domain
+figure;
+plot(frequencies, magnitude_spectrum(1:N/2));
+xlabel('Frequency (Hz)');
+ylabel('Magnitude');
+title('Magnitude Spectrum of the Signal');
+grid on;

Signal_Processing_Midterm_Ciblak.m

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+% Load Signal
+pkg load signal
+data = csvread('unknownsignal.csv');
+signal=data';
+Fs=300
+N=length(signal)
+% Apply the Hamming Filter
+signal_Windowed = Hamming_Windower(signal);
+
+% Spectral Analysis (FFT)
+% Compute the FFT of the windowed signal
+signal_Windowed_fft = fft(signal_Windowed, N);
+
+% Normalize the FFT
+signal_Windowed_fft_norm = signal_Windowed_fft / N;
+
+% Compute the magnitude spectrum
+magnitude_spectrum = abs(signal_Windowed_fft_norm);
+
+% Generate the frequency vector
+frequencies = linspace(0, Fs/2, N/2);
+
+% Plot the magnitude spectrum in the frequency domain
+figure;
+plot(frequencies, magnitude_spectrum(1:N/2));
+xlabel('Frequency (Hz)');
+ylabel('Magnitude');
+title('Magnitude Spectrum of the Windowed Signal');
+grid on;
+
+
+% Define the frequency range of interest
+low_freq = 27;
+high_freq = 66;
+
+% Normalize the frequencies (Nyquist criterion)
+low_freq_norm = low_freq / (Fs/2);
+high_freq_norm = high_freq / (Fs/2);
+
+% Design a Butterworth bandpass filter
+filter_order = 4; % can be adjusted to modify rolloff
+[b, a] = butter(filter_order, [low_freq_norm, high_freq_norm], 'bandpass');
+
+% Apply the Butterworth filter to the windowed signal
+filtered_signal = filter(b, a, signal_Windowed);
+
+% Plot the filtered signal in the time domain
+t = linspace(0, (N-1)/Fs, N); % Time vector
+figure;
+plot(t, filtered_signal);
+xlabel('Time (s)');
+ylabel('Amplitude');
+title('Filtered Signal in Time Domain');
+grid on;
+
+% Compute the FFT of the filtered signal and normalize it
+fft_filtered_signal = fft(filtered_signal, N);
+fft_filtered_signal_normalized = fft_filtered_signal / N;
+
+% Compute the magnitude spectrum
+magnitude_spectrum_filtered = abs(fft_filtered_signal_normalized);
+
+% Generate the frequency vector
+frequencies = linspace(0, Fs/2, N/2);
+
+% Plot the magnitude spectrum of the filtered signal in the frequency domain
+figure;
+plot(frequencies, magnitude_spectrum_filtered(1:N/2));
+xlabel('Frequency (Hz)');
+ylabel('Magnitude');
+title('Magnitude Spectrum of the Filtered Signal');
+grid on;