A question about fourier analysis

In summary, the conversation discusses the use of integration notation in a mathematical context. It is mentioned that sometimes the dx, dy, dz notation is omitted, and while this may make sense in certain situations, it is not always correct. The conversation also brings up the idea that the variables used in integration may be constants, depending on the context. It is suggested to check the book for any errors or to use Fubini's Theorem to determine the correct order of integration.
  • #1
rar0308
56
0
Hi.
see the image.
In the integral there's no dx,dy,dz thing. Does this make any sense?
Isn't it errata?
 

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  • #2
It is wrong without the dx, dy, dz thing. If this comes from some hand written note, it's usually implied. If this comes from a book, you need to kick the editor in the nut...
 
  • #3
Thanks.
It's from page 11 <Quantum Theory> David Bohm, Dover.
 
  • #4
It makes sense if the order does not matter for integration. If it is obvious in context that the variables here are x,y,z (L is clearly a constant as integrals contain it) and Fubini's Theorem is satisfied then you are free to choose.
 
  • #5
daveyp225 said:
It makes sense if the order does not matter for integration. If it is obvious in context that the variables here are x,y,z (L is clearly a constant as integrals contain it) and Fubini's Theorem is satisfied then you are free to choose.

How do you know that l, m, n, l', m', n' are constant?
 
  • #6
pwsnafu said:
How do you know that l, m, n, l', m', n' are constant?

I said "if it was obvious in context the variables are x, y and z". Otherwise, call it a good hunch, as there are only three integration variables present and the ' notation often signifies a relationship between, for example, m and m'. All six cannot be integration variables, and if the first three were then the second sine function is only a constant with respect to integration and would probably not be part of the integrand.
 

1. What is Fourier analysis?

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

2. Why is Fourier analysis important?

Fourier analysis allows us to understand the frequency content of a signal, which can help us identify patterns and trends in data. It also has many practical applications, such as in audio and image compression, filtering, and noise reduction.

3. How does Fourier analysis work?

Fourier analysis works by representing a signal as a sum of sine and cosine waves with different frequencies and amplitudes. By using mathematical transformations, we can determine the frequency components of a signal and their relative strengths.

4. What are the limitations of Fourier analysis?

Fourier analysis assumes that a signal is periodic, meaning it repeats itself over time. This may not always be the case in real-world data, which can lead to inaccuracies. Additionally, Fourier analysis may not be suitable for non-linear or non-stationary signals.

5. How is Fourier analysis used in different fields?

Fourier analysis has a wide range of applications in various fields. In physics, it is used to analyze and understand wave phenomena. In engineering, it is used for signal processing and control systems. In mathematics, it has applications in number theory and differential equations. It is also commonly used in fields such as economics, biology, and chemistry.

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