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# Lec 9 - UCLA Spring 2009-2010 EE102: Systems and Signals...

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UCLA Spring 2009-2010 EE102: Systems and Signals Lecture 09: Fourier Transform Theorems April 27 2010 EE102:Systems and Signals; Spring 09-10, Lee 1

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Linearity Linear combination of two signals f 1 ( t ) and f 2 ( t ) is a signal of the form af 1 ( t ) + bf 2 ( t ) . Linearity Theorem: The Fourier transform is linear; that is, given two signals f 1 ( t ) and f 2 ( t ) and two complex numbers a and b , then af 1 ( t ) + bf 2 ( t ) aF 1 ( ) + bF 2 ( ) . This follows from linearity of integrals: Z -∞ ( af 1 ( t ) + bf 2 ( t )) e - jωt dt = a Z -∞ f 1 ( t ) e - dt + b Z -∞ f 2 ( t ) e - dt = aF 1 ( ) + bF 2 ( ) EE102:Systems and Signals; Spring 09-10, Lee 2
This easily extends to ﬁnite combinations. Given signals f k ( t ) with Fourier transforms F k ( ) and complex constants a k , k = 1 , 2 ,...K , then K X k =1 a k f k ( t ) K X k =1 a k F k ( ) . If you consider a system which has a signal f ( t ) as its input and the Fourier transform F ( ) as its output, the system is linear! EE102:Systems and Signals; Spring 09-10, Lee 3

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Linearity Example Find the Fourier transform of the signal f ( t ) = ± 1 2 1 2 ≤ | t | < 1 1 | t | ≤ 1 2 This signal can be recognized as f ( t ) = 1 2 rect ² t 2 ³ + 1 2 rect( t ) . From linearity and the fact that the transform of rect( t/T ) is T sinc( Tω/ (2 π )) , we have F ( ω ) = ² 1 2 ³ 2sinc(2 ω/ (2 π ))+ 1 2 sinc( (2 π )) = sinc( ω/π )+ 1 2 sinc( (2 π )) EE102:Systems and Signals; Spring 09-10, Lee 4
! 2.5 ! 2 ! 1.5 ! 1 ! 0.5 0 0.5 1 1.5 2 2.5 ! 0.2 0 0.2 0.4 0.6 0.8 1 1.2 ! 10 ! 8 ! 6 ! 4 ! 2 0 2 4 6 8 10 ! 0.5 0 0.5 1 1.5 2 0 2 - 2 - 1 0 2 π - 2 π - 4 π 4 π ω sinc ( ω / π )+ 1 2 sinc ( ω / ( 2 π )) 1 2 rect ( t / 2 1 2 rect ( t ) Linearity Example EE102:Systems and Signals; Spring 09-10, Lee 5

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Scaling Theorem Stretch (Scaling) Theorem : Given a transform pair f ( t ) F ( ) , and a real-valued nonzero constant a , f ( at ) 1 | a | F ± j ω a ² Proof: Here consider only a > 0 . Negative a left as an exercise. Change variables τ = at Z -∞ f ( at ) e - jωt dt = Z -∞ f ( τ ) e - jωτ/a a = 1 a F ± j ω a ² . If a = - 1 “time reversal theorem:” f ( - t ) F ( - ) EE102:Systems and Signals; Spring 09-10, Lee 6
Scaling Examples We have already seen that rect( t/T ) T sinc( Tω/ 2 π ) by brute force integration. The scaling theorem provides a shortcut proof given the simpler result rect( t ) sinc( ω/ 2 π ) . This is a good point to illustrate a property of transform pairs. Consider this Fourier transform pair for a small T and large T , say T = 1 and T = 5 . The resulting transform pairs are shown below to a common horizontal scale: EE102:Systems and Signals; Spring 09-10, Lee 7

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! 0 0.2 0.4 0.6 0.8 1 1.2 ! 0.2 0 0.2 0.4 0.6 0.8 1 1.2 ! ! 2 0 2 4 6 ! ! 2 ! 1 0 1 2 3 4 5 ω ω - 5 π - 10 π 0 5 π 10 π - 5 π - 10 π 0 5 π 10 π 0 - 5 5 - 10 10 0 - 5 5 - 10 10 t t sinc ( ω / 2 π ) 5sinc ( 5 ω / 2 π ) rect ( t ) rect ( t / 5 ) EE102:Systems and Signals; Spring 09-10, Lee 8
The narrow pulse yields a wide transform and a wide pulse yields a narrow spectrum! This example shows that the shorter the pulse (and hence the more pulses we could cram into a transmission channel), the greater the bandwidth required by the transform!!

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## This note was uploaded on 10/21/2010 for the course EE ee102 taught by Professor Levan during the Spring '09 term at UCLA.

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Lec 9 - UCLA Spring 2009-2010 EE102: Systems and Signals...

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