Uncovering the Mystery of Using Cosine Transform in Fourier Analysis

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SUMMARY

The discussion centers on the application of cosine transforms in Fourier analysis, specifically in the context of the sine Fourier transform of a function denoted as S(u). The original problem involves the equation $$S(u) = -S(f(x)) \exp(- \omega y) / \omega$$, highlighting the challenge posed by the singularity at $$\omega = 0$$. To address this, the discussion reveals that the inverse cosine transform is utilized alongside convolution to effectively manage the singularity and facilitate the transformation process.

PREREQUISITES
  • Understanding of Fourier transforms, specifically sine and cosine transforms.
  • Familiarity with convolution operations in signal processing.
  • Knowledge of singularities in mathematical functions.
  • Basic proficiency in mathematical notation and transformations.
NEXT STEPS
  • Study the properties and applications of the Fourier sine and cosine transforms.
  • Explore convolution techniques in the context of Fourier analysis.
  • Investigate methods for handling singularities in mathematical functions.
  • Learn about the implications of using cosine transforms in various analytical scenarios.
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Mathematicians, physicists, and engineers involved in signal processing or Fourier analysis, particularly those interested in the nuances of sine and cosine transforms and their applications in solving complex problems.

member 428835
hi pf!

My book presents a problem and has it boiled down to $$S(u) = -S(f(x)) \exp(- \omega y) / \omega$$ where ##S(u)## is the sine Fourier transform of the function ##u##. However, we cannot directly take the transform back since the singularity at ##\omega = 0##. Thus the book then takes $$\frac{\partial}{\partial y} S(u) = S(f(x)) \exp(- \omega y)$$ and now performs an inverse COSINE transform on the exponential (also they use convolution). My question is, why are they using a cosine transform instead of a sine transform?

Thanks so much for your help!
 
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nevermind, i got it! when using convolution on sine/cosine we use one of each!
 

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