Question about Fourier transformation

In summary, the conversation discusses Fourier's transformation and two models that seek to emulate it. The power, which is proportional to the number of times the phenomenon occurs at different frequencies, is used to predict and analyze the differences between periods in which the phenomenon can happen. The power spectral density is a measure of energy in a specific frequency range and is represented by the magnitude-squared of the Fourier coefficient.
  • #1
Frank Einstein
170
1
Hello everybody.

I am currently comparing fourier's transformation of one physical phenomena and a two models which seek to emulate it.

One of the models nails the frecuency and the other one even though it's displaced to higher frequencies the power (defined as 2* absolute value of fourier's coefficient) looks like the original.

If I understand correctly, the power is proportional to the amount of times the phenomenon has happened for these frequencies, so the first one is good to predict that something is happening at some frequencies, but the second is better for knowing the differences between the periods in which the phenomenon can happen.

If someone could tell me about the meaning of the power of fourier's transform so I could provide a good analysis of both models I wolud be exrtremley thankful.

Thanks for reading.
 
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  • #2
I can't follow quite what you are saying, so I will reply with just a general explanation. The power spectral density (PSD) is given by the magnitude-squared of the Fourier coefficient. It has units of W/Hz or J, and is a measure of the energy contained in the frequency slice represented by that coefficient.
 

FAQ: Question about Fourier transformation

1. What is Fourier transformation?

Fourier transformation is a mathematical technique used to decompose a complex signal into its individual frequency components. It is commonly used in signal processing and data analysis.

2. How does Fourier transformation work?

Fourier transformation works by converting a signal from its original domain (e.g. time domain) to its frequency domain. This is achieved by representing the signal as a sum of sine and cosine waves of different frequencies.

3. What is the importance of Fourier transformation?

Fourier transformation is important because it allows us to analyze complex signals and extract useful information. It is used in a variety of fields such as engineering, physics, and mathematics.

4. What are some applications of Fourier transformation?

Fourier transformation has many applications, including signal processing, image analysis, audio and video compression, and solving differential equations. It is also used in fields such as astronomy, geology, and medicine.

5. Are there any limitations to Fourier transformation?

While Fourier transformation is a powerful tool, it does have some limitations. For example, it assumes that the signal is periodic and that it contains a finite number of frequency components. It also requires a large amount of data to accurately represent a signal.

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