What are the limitations/ disadvantages of the Fourier Tran

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SUMMARY

The discussion focuses on the limitations and disadvantages of the Fourier Transform and Fourier Series. Key limitations include their ineffectiveness in converting time domain functions to position domain functions when not needed, and their reliance on the context of the task at hand. The Fourier Transform is particularly useful for analyzing frequency components in signals, but its application is constrained by the nature of the data, such as when dealing with discrete and finite time-series data. Understanding these limitations is essential for effectively utilizing the Fourier Transform in numerical analysis.

PREREQUISITES
  • Understanding of Fourier Series and Fourier Transform concepts
  • Familiarity with time domain and frequency domain analysis
  • Basic knowledge of discrete time-series data
  • Experience with numerical analysis techniques
NEXT STEPS
  • Research the applications of Discrete Fourier Transform (DFT) in signal processing
  • Explore the limitations of Fourier Transform in real-time inverse problems
  • Learn about alternative transforms, such as the Laplace Transform and Wavelet Transform
  • Study practical examples of Fourier Transform applications in various fields
USEFUL FOR

Signal processing engineers, data analysts, and researchers in numerical analysis who seek to understand the practical limitations of Fourier analysis in their work.

ramdas
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I am fond of Fourier series &
Fourier transform. In Fourier
domain, we can come to know
what frequency components are
present and the contribution of
each component in forming the
given signal.But every approach has some
advantages and
disadvantages.Here, I want to
know what are the limitations/
disadvantages of the Fourier
Transform and Fourier Series? It
would be better for me if you
explain them with the help of
example(or links or any relevant
information) to understand
them easily.
 
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Well, if you have a time domain function and you need a position domain function, the Fourier transform is unlikely to be a lot of help.
i.e. it is no good in any situation where you don't need the Fourier transform.

Tools are useful in their contexts - and most limitations for a tool in context are not about the context. i.e. a hammer is good for hammering nails, but it can be slow and tiring, especially if you want to attach wood to concrete, so ramset. But that is not a limit of the hammer, it is a limit of the user and the job (time constraint not inherent to hammer).

So to understand the limits of the Fourier transform - whose usefulness is not limited to just finding the frequency domain function from a time domain function - you need to understand the types of jobs you may want it to do and what sorts of constraints those jobs have to work under. i.e. numerical analysis of a discrete time-series as a real-time inverse problem.
 
If the original data is already discrete and finite, then there is no loss of information when doing a discrete Fourier transform. It is therefore just a different way to look at the same information content, and as Simon said, whether to look at something in time (or position) or in frequency it's a matter of what you are trying to accomplish.
 
Why my question is deleted.If it is duplicate ,where is the original one?
 
ramdas said:
Why my question is deleted.If it is duplicate ,where is the original one?

This is the original thread. I deleted the duplicate thread last night. If you have any questions about this, please message me.
 

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