Proof of Parseval's Identity for a Fourier Sine/Cosine transform

In summary, the conversation is about the Proof of Parseval Identity for Fourier Sine/Cosine transform. The individual is having difficulty in proving the identity and is seeking help. They have already successfully proved the Parseval Identity for Complex Fourier Transform and are now trying to figure out where the term '2/π' comes in the formula for Fourier Sine and Cosine Transform. The conversation includes a link to a document that may help with the proof and suggests separating the real and imaginary parts of the complex identity.
  • #1
tanaygupta2000
208
14
Can anyone help me with the Proof of Parseval Identity for Fourier Sine/Cosine transform :

2/π [integration 0 to ∞] Fs(s)•Gs(s) ds = [integration 0 to ∞] f(x)•g(x) dx

I've successfully proved the Parseval Identity for Complex Fourier Transform, but I'm unable to figure out from where does the term '2/π' comes in the Parseval formula for Fourier Sine and Cosine Transform.
Any help will be appreciated.
Thank You.
 
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  • #2
It would help if you defined your terms and variables.
 
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  • #3
I'm having difficulty in proving this. According to me, the term '2/π' is not coming.
Parseval%20identity.jpeg
 

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  • #4
It would help if the definitions were given for ##F_C(s),G_C(s)##, etc.
 
  • #5
Refer this picture
Screenshot_2019-03-06-08-13-58-035_com.adobe.reader.jpeg
 

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  • #6
I haven't tried to work it through, but it looks like the factor ##\sqrt{\frac{2}{\pi}}## appearing twice in setting up the integral leads to ##\frac{2}{\pi}##.
 
  • #7
I've proved it the same way as Complex transform identity.
IMG_20190307_063526.jpeg
 

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  • #8
tanaygupta2000 said:
I've proved it the same way as Complex transform identity.View attachment 239848
Please post attachment horizontally.
 
  • #9
Help proving this :
IMG_20190308_100451.jpeg
 

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  • #10

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  • #12
  • #13
Can't you simply separate the real and imaginary parts of the complex identity?
 

Related to Proof of Parseval's Identity for a Fourier Sine/Cosine transform

1. What is Parseval's Identity for a Fourier Sine/Cosine transform?

Parseval's Identity is a fundamental theorem in Fourier analysis that relates the energy or power of a signal in the time domain to the energy or power of its Fourier transform in the frequency domain. It states that the sum of the squared magnitudes of the Fourier coefficients for a signal is equal to the integral of the squared magnitude of the signal itself.

2. How is Parseval's Identity derived for a Fourier Sine/Cosine transform?

The derivation of Parseval's Identity for a Fourier Sine/Cosine transform involves using the orthogonality properties of sine and cosine functions and the properties of the Fourier transform. By expressing the signal as a sum of sine and cosine functions and applying the Fourier transform, the integral of the squared magnitude of the signal can be simplified to the sum of squared Fourier coefficients, resulting in the identity.

3. What is the significance of Parseval's Identity in signal processing?

Parseval's Identity is a powerful tool in signal processing as it allows for the analysis of signals in both the time and frequency domains. It also provides a way to quantify the energy or power of a signal, which is useful in applications such as signal filtering and noise reduction.

4. Can Parseval's Identity be extended to other types of Fourier transforms?

Yes, Parseval's Identity can be extended to other types of Fourier transforms, such as the discrete Fourier transform (DFT) and the continuous-time Fourier transform (CTFT). However, the specific form of the identity may vary depending on the type of transform and the properties of the signal being analyzed.

5. How is Parseval's Identity used in practical applications?

In practical applications, Parseval's Identity is used to analyze signals and determine their energy or power in both the time and frequency domains. It is also used in the design and evaluation of signal processing algorithms, such as filter design and spectral analysis. Additionally, it is an important concept in fields such as telecommunications, audio signal processing, and image processing.

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