FT breaks down a cyclic signal into the frequencies present, and their intensity. The longer the sample of the signal, the more precise becomes detecting the real frequencies present in the signal.
Mainly is tool for to obtain information that will feed latter other algorithms. Also can be used as a signal filter with the Inverse FT.
If this does help to understand, it is homologous to use several band pass filters repeatedly for trying to obtain the root signals, but without adjustments requirements, highly faster to compute, simpler, and with better result.
So your question is, What are the uses of knowing the main elements that make up a signal?
That information is useful for analysis of sounds or images, for to detect the presence of elements composing the signal out of the expected range.
Also for pattern detection, as you may give the data a signal form of your own, and analyze the frequencies peaks for example.
For compression, the above two, as knowing the main cyclic elements and their intensity allows to determine if some ones may be latter omitted (for loss compression), or what elements latter should be replaced as parameters that the decompression algorithm will use for to reconstruct the signal.
Also it is important what said the other answer. It does not give information about the time/space moment in with is produced the element, it only tells the element is present (the frequency in the signal).
Mainly is tool for to obtain information that will feed latter other algorithms. Also can be used as a signal filter with the Inverse FT.
If this does help to understand, it is homologous to use several band pass filters repeatedly for trying to obtain the root signals, but without adjustments requirements, highly faster to compute, simpler, and with better result.
So your question is, What are the uses of knowing the main elements that make up a signal?
That information is useful for analysis of sounds or images, for to detect the presence of elements composing the signal out of the expected range.
Also for pattern detection, as you may give the data a signal form of your own, and analyze the frequencies peaks for example.
For compression, the above two, as knowing the main cyclic elements and their intensity allows to determine if some ones may be latter omitted (for loss compression), or what elements latter should be replaced as parameters that the decompression algorithm will use for to reconstruct the signal.
Also it is important what said the other answer. It does not give information about the time/space moment in with is produced the element, it only tells the element is present (the frequency in the signal).