Representing Fourier Transform of Periodic Signal in LTI System

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SUMMARY

The discussion focuses on representing the Fourier transform of a periodic signal, specifically a sequence of triangle pulses symmetric to the origin, as input in a Linear Time-Invariant (LTI) system. The Fourier transform is expressed as 2π (sum (Xn delta(omega - (n)omega0)). The user seeks to simplify the Xn to constants but encounters difficulty with the term n omega0. It is noted that the Fourier transform of a triangle pulse can be derived from the transform of a rectangular pulse using the convolution theorem.

PREREQUISITES
  • Understanding of Fourier Transform principles
  • Familiarity with Linear Time-Invariant (LTI) systems
  • Knowledge of convolution theorem applications
  • Basic concepts of triangular and rectangular pulse functions
NEXT STEPS
  • Study the derivation of the Fourier transform for triangular pulses
  • Learn about the convolution theorem in signal processing
  • Explore the properties of Linear Time-Invariant (LTI) systems
  • Investigate the relationship between rectangular and triangular pulses in Fourier analysis
USEFUL FOR

Signal processing engineers, electrical engineers, and students studying Fourier analysis and LTI systems will benefit from this discussion.

SrEstroncio
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How do you represent the Fourier transform of a periodic signal as the input in a LTI system?
More precisely, a sequence of triangle pulses symmetric to the origin.


I know what the Fourier transform is 2\pi (sum (Xn delta(omega - (n)omega0)))

I was told to reduce the Xn to constants, but can't figure how to reduce the n omega0.
 
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This may not help you, but you may obtain the Fourier transform of a triangle pulse using the transform of a rectangular pulse and the convolution theorem. The convolution of two rectangular pulses is a triangular pulse.
 
X(n)=\frac{1}{T}\int_{t_0}^{t_0+T} f(n)e^{-i2\pi nt/T}
 

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