Copy the MATLAB code from here  % The code is developed by SalimWireless.com clc; clear; close all; % Parameters samples_per_bit = 36; bit_duration = 1; num_bits = 20; sample_interval = bit_duration / samples_per_bit; time_vector = 0:sample_interval:(num_bits * bit_duration); time_vector(end) = []; % Generate and modulate binary data binary_data = randi([0, 1], 1, num_bits); modulated_bits = 2 * binary_data - 1; upsampled_signal = kron(modulated_bits, ones(1, samples_per_bit)); figure; plot(time_vector, upsampled_signal); title('Message Signal'); % Apply Gaussian filter filtered_signal = conv(GMSK_gaussian_filter1(bit_duration, samples_per_bit), upsampled_signal); filtered_signal = [filtered_signal, filtered_signal(end)]; figure; plot(filtered_signal); title('Filtered Signal'); % Integration & GMSK modulation integrated_signal = cumsum(filtered_signal); gmsk_signal = exp(1i * integrated_signal); % Plotting the real and imaginary parts of the GMSK signal ...

Pulse-width modulation (PWM), or pulse-duration modulation (PDM), is a method of controlling the average power delivered by an electrical signal.   Fig: An example of PWM in an idealized inductor driven by a blue line voltage source modulated as a series of sawtooth pulses, resulting in a red line current in the inductor.    Duty cycle A low duty cycle equates to low power because the power is off for most of the time; the word duty cycle reflects the ratio of "on" time to the regular interval or "period" of time. The duty cycle is measured in percent, with 100% representing full on. A digital signal has a duty cycle of 50% and looks like a "square" wave when it is on 50% of the time and off the other 50%. A digital signal has a duty cycle of greater than 50% when it spends more time in the on state than the off state. A digital signal has a duty cycle of 50% when it alternates between the on and off states more often than not.         Mathematical...

The windowed periodogram is a widely used technique for estimating the Power Spectral Density (PSD) of a signal. It enhances the classical periodogram by mitigating spectral leakage through the application of a windowing function. This technique is essential in signal processing for accurate frequency-domain analysis.   Power Spectral Density (PSD) The PSD characterizes how the power of a signal is distributed across different frequency components. For a discrete-time signal, the PSD is defined as the Fourier Transform of the signal’s autocorrelation function: S x (f) = FT{R x (τ)} Here, R x (τ)}is the autocorrelation function. FT : Fourier Transform   Classical Periodogram The periodogram is a non-parametric PSD estimation method based on the Discrete Fourier Transform (DFT): P x (f) = \(\frac{1}{N}\) X(f) 2 Here: X(f): DFT of the signal x(n) N: Signal length However, the classical periodogram suffers from spectral leakage due to abrupt truncation of the ...

MATLAB Code for Manual Eigenvalue Decomposition clc; clear; close all; A = [1,2]; R = A' * A; array_length = length(A); eigenvalues = manual_eigenvalue_decomposition(R, array_length); disp('Eigenvalues:'); disp(eigenvalues); eigenvectors = find_eigenvectors(R, eigenvalues); disp('Eigenvector Matrix:'); disp(eigenvectors); % Manual Eigenvalue Decomposition Function function eigenvalues = manual_eigenvalue_decomposition(A, n) eigenvalues = zeros(n, 1); % Initialize eigenvalues as a column vector for i = 1:n % Start with a random vector v = randn(n, 1); % Power iteration to find the eigenvector corresponding to the largest eigenvalue for j = 1:10 % Iteration count (power iteration steps) v = A * v; % Multiply by matrix A v = v / norm(v); % Normalize the vector end % Eigenvalue is the Rayleigh quotient eigenvalues(i) = (v' * A * v) / (v' * v); % Deflate the matrix to find the next eigenvector A = A - eigenvalues(i) * (v * v'); end end funct...

MATLAB Code % The code is developed by SalimWireless.com clc; clear; close all; % Input Signal and Parameters Fs = 1000; % Sampling frequency t = 0:1/Fs:0.3; % Time vector x = cos(2*pi*200*t) + randn(size(t)); % Signal: 200 Hz cosine + noise % Welch's Method Parameters segmentLength = 256; % Length of each segment overlapFraction = 0.5; % Fractional overlap (50%) overlapSamples = floor(segmentLength * overlapFraction); % Overlap in samples step = segmentLength - overlapSamples; % Step size N = length(x); % Length of the input signal % Define Hann Window hannWindow = 0.5 * (1 - cos(2 * pi * (0:segmentLength-1)' / (segmentLength - 1))); windowEnergy = sum(hannWindow.^2); % Normalization factor % Segment the Signal into Overlapping Windows numSegments = floor((N - overlapSamples) / step); % Number of segments segments = zeros(segmentLength, numSegments); for i = 1:numSegments startIdx = (i - 1) * step + 1; endIdx = star...