What is the purpose/significance of doing a FFT on a signal?

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The Fast Fourier Transform (FFT) is a crucial computational method for analyzing signals by decomposing them into sinusoidal components of varying amplitudes and frequencies. It efficiently computes the discrete Fourier transformation, allowing for the representation of complex signals as sums of simpler sinusoidal waves. This technique is particularly valuable in linear electronic systems, where the response to a sum of signals can be analyzed individually and then recombined. FFT is widely applicable across various fields, including mechanical vibrations and acoustics, and is essential for identifying weak periodic signals amidst noise. Additionally, it plays a significant role in data compression, making it integral to many image and sound compression algorithms.
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it seems like a pretty commonly used computational/mathematical method in analyzing experimental data, such as voltage signals
 
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it is used to figure out what a signal consists of...you know, by decomposing it into a bunch of sinusoidal of various amplitudes and frequencies.
 
FFT is just a computer algorithm to efficiently compute discrete Fourier transformation - represent given signal of weird shape as a sum of sinusoidal ones.
Most of electronic systems are:
1. linear - they response to sum of signals is equal to sum of responses to individual ones;
2. they response to sinusoidal stimulation is also a sinusoid of the same frequency (just amplitude is changed and phase shifted) - easy to analyse theoretically

So if you want to analyse its behaviour, you may first decompose your input signal as a sum of sinusoidal signals, compute the system response to every one of them, then sum the resulting sinusoidal signals again.

The same approach may apply not only to electronics, but also to many other fields - e.g. vibrations of mechanical systems, acoustics, etc.
 
zheng89120 said:
it seems like a pretty commonly used computational/mathematical method in analyzing experimental data, such as voltage signals
1. It shows you the frequency spectrum. Like the one on Cisco logo :)
2. If you are looking for weak periodic signals among the noise, they will show up as spikes on frequency plot. Also it's easy to suppress unwanted periodic signals interfering with data.
3. Many common operations are much easier in frequency domain, correlation being prime example.
etc. etc.

DK
 
It's also very useful in data compression, particularly if your analogue signal is very nearly a sinusoid anyway. A whole time series (lots of data) can be compressed to a very small number of amplitudes in the frequency domain. I'm pretty sure the FFT is used in most image/sound compression algorithms.
 
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