The Fourier series is a mathematical tool used to represent a periodic function as a sum of sinusoidal functions. It has numerous applications in engineering and physics, including signal processing, image processing, and control systems. Matlab is a popular software package that includes functions for computing and manipulating Fourier series.

To begin, let's consider a periodic function f(x) with period T. The Fourier series of this function can be written as:

f(x) = a0 + ∑[a_n * cos(n * ω * x) + b_n * sin(n * ω * x)]

where a0 is the average value of the function, a_n and b_n are the Fourier coefficients, ω is the angular frequency (2π/T), and the sum is taken over all integers n. The Fourier coefficients can be computed using the following formulas:

a_n = (1/T) * ∫_T[f(x) * cos(n * ω * x) dx] b_n = (1/T) * ∫_T[f(x) * sin(n * ω * x) dx]

In Matlab, the function `fft`

can be used to compute the Fourier coefficients of a discrete-time periodic signal. For example, suppose we have a periodic signal with a period of 10 seconds and 1000 samples per period. We can use the following Matlab code to compute the Fourier coefficients:

T = 10; % period of signal
fs = 1000; % sample rate
t = 0:1/fs:T-1/fs; % time vector
x = sin(2*pi*t); % periodic signal
X = fft(x); % Fourier coefficients

The output of the `fft`

function is a vector of complex values, which can be separated into the real (a_n) and imaginary (b_n) parts using the `real`

and `imag`

functions:

an = real(X); bn = imag(X);

Once the Fourier coefficients have been computed, we can use them to reconstruct the original signal using the inverse Fourier transform. In Matlab, this is done using the `ifft`

function:

x_reconstructed = ifft(X);

We can also plot the original and reconstructed signals to compare them:

plot(t,x,'b',t,x_reconstructed,'r') legend('Original','Reconstructed')

The Fourier series is a powerful tool for analyzing and synthesizing periodic signals, and Matlab provides a range of functions for computing and manipulating Fourier series. By using these functions, we can easily perform tasks such as filtering, modulation, and compression of periodic signals.