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The only difficulty in calculating the Fourier transform of any signal occurs when we have periodic signals (in eitherdomain). Realizing that the Fourier series is a special case of the Fourier transform, we simply calculate the Fourier seriescoefficients instead, and plot them along with the spectra of nonperiodic signals on the same frequency axis.

Short table of fourier transform pairs
s t S f
a t u t 1 2 f a
a t 2 a 4 2 f 2 a 2
p t 1 t Δ 2 0 t Δ 2 f Δ f
2 W t t S f 1 f W 0 f W
Fourier transform properties
Time-Domain Frequency Domain
Linearity a 1 s 1 t a 2 s 2 t a 1 S 1 f a 2 S 2 f
Conjugate Symmetry s t S f S f
Even Symmetry s t s t S f S f
Odd Symmetry s t s t S f S f
Scale Change s a t 1 a S f a
Time Delay s t τ 2 f τ S f
Complex Modulation 2 f 0 t s t S f f 0
Amplitude Modulation by Cosine s t 2 f 0 t S f f 0 S f f 0 2
Amplitude Modulation by Sine s t 2 f 0 t S f f 0 S f f 0 2
Differentiation t s t 2 f S f
Integration α t s α 1 2 f S f if S 0 0
Multiplication by t t s t 1 2 f S f
Area t s t S 0
Value at Origin s 0 f S f
Parseval's Theorem t s t 2 f S f 2

In communications, a very important operation on a signal s t is to amplitude modulate it. Using this operation more as an example rather than elaborating the communicationsaspects here, we want to compute the Fourier transform — the spectrum — of 1 s t 2 f c t Thus, 1 s t 2 f c t 2 f c t s t 2 f c t For the spectrum of 2 f c t , we use the Fourier series. Its period is 1 f c , and its only nonzero Fourier coefficients are c ± 1 1 2 . The second term is not periodic unless s t has the same period as the sinusoid. Using Euler's relation, the spectrum of the second term can be derived as s t 2 f c t f S f 2 f t 2 f c t Using Euler's relation for the cosine, s t 2 f c t 1 2 f S f 2 f f c t 1 2 f S f 2 f f c t s t 2 f c t 1 2 f S f f c 2 f t 1 2 f S f f c 2 f t s t 2 f c t f S f f c S f f c 2 2 f t Exploiting the uniqueness property of the Fourier transform,we have

s t 2 f c t S f f c S f f c 2
This component of the spectrum consists of the original signal's spectrum delayed and advanced in frequency . The spectrum of the amplitude modulated signal is shown in [link] .

A signal which has a triangular shaped spectrum is shown inthe top plot. Its highest frequency — the largest frequency containing power — is W Hz. Once amplitude modulated, the resulting spectrum has "lines" corresponding to the Fourier series componentsat ± f c and the original triangular spectrum shifted to componentsat ± f c and scaled by 1 2 .

Note how in this figure the signal s t is defined in the frequency domain. To find its time domain representation, we simply use the inverse Fourier transform.

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What is the signal s t that corresponds to the spectrum shown in the upper panel of [link] ?

The signal is the inverse Fourier transform of thetriangularly shaped spectrum, and equals s t W W t W t 2

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What is the power in x t , the amplitude-modulated signal? Try the calculation inboth the time and frequency domains.

The result is most easily found in the spectrum's formula: the power in the signal-related part of x t is half the power of the signal s t .

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In this example, we call the signal s t a baseband signal because its power is contained at low frequencies. Signals such as speech and the Dow Jonesaverages are baseband signals. The baseband signal's bandwidth equals W , the highest frequency at which it has power. Since x t 's spectrum is confined to a frequency band not close to the origin(we assume f c W ), we have a bandpass signal . The bandwidth of a bandpass signal is not its highest frequency, but the range of positive frequencies wherethe signal has power. Thus, in this example, the bandwidth is 2 W Hz . Why a signal's bandwidth should depend on its spectral shape will become clear once we developcommunications systems.

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Source:  OpenStax, Fundamentals of electrical engineering i. OpenStax CNX. Aug 06, 2008 Download for free at http://legacy.cnx.org/content/col10040/1.9
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