When to use fourier integral instead of fourier series expansion

AI Thread Summary
Fourier integrals are used for non-periodic signals to obtain their amplitude spectrum, while Fourier series are applicable only to periodic functions. The Fourier integral can handle square integrable functions and can also represent periodic functions using the Dirac delta function. In contrast, Fourier series expansion is specifically designed for periodic functions. The key distinction lies in the nature of the signal: use Fourier series for periodic signals and Fourier integrals for non-periodic ones. Understanding when to apply each method is crucial for accurate signal analysis.
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Suppose, I have a non-periodic signal for which amplitude spectrum is to be obtained . For this why should Fourier integral be used instead of Fourier series expansion . I want to know when to use Fourier inerals and when to use Fourier series . Please let me know all the details .
 
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If memory serves Fourier integral applies to square integrable functions and, with the use of Dirac delta function, to periodic functions like sine and cosine. The Fourier series expansion applies to periodic functions.
 
You use Fourier series if the function is periodic, transforms if it's not.
 
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