{"id":4591,"date":"2025-03-28T12:00:57","date_gmt":"2025-03-28T06:30:57","guid":{"rendered":"https:\/\/study.madeeasy.in\/?p=4591"},"modified":"2025-07-29T17:27:18","modified_gmt":"2025-07-29T11:57:18","slug":"fourier-transform","status":"publish","type":"post","link":"https:\/\/www.madeeasy.in\/study\/ee\/lti-systems\/fourier-transform","title":{"rendered":"Fourier Transform"},"content":{"rendered":"

INTRODUCTION<\/h2>\n

In the previous chapter, we saw that Fourier series can be generalized only for periodic signals not for aperiodic one i.e. any periodic signal is represented as linear combination of hormonally related complex exponentials.<\/p>\n

This limitation is overcome through Fourier transform which is applicable for both aperiodic and periodic signals. The Fourier transform gives a frequency domain description of time domain signal. An aperiodic signal is one which is periodic with an infinite period. As the period increases the fundamental frequency decreases which makes aperiodic signal infinitesimally close in frequency and the representation in terms of linear combination takes the form of an integral rather than sum. The resulting spectrum is called Fourier transform. Thus Fourier transform is extension of Fourier series for aperiodic signals.<\/p>\n

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Table of Contents<\/p>\nToggle<\/span><\/path><\/svg><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n