Connexions

You are here: Home » Content » Properties of the Continuous-Time Fourier Transform
Content Actions
Lenses

What is a lens?

Lenses

A lens is a custom view of Connexions content. You can think of it as a fancy kind of list that will let you see Connexions through the eyes of organizations and people you trust.

What is in a lens?

Lens makers point to Connexions materials (modules and collections), creating a guide that includes their own comments and descriptive tags about the content.

Who can create a lens?

Any individual Connexions member, a community, or a respected organization.

This content is ...
Affiliated with (?)
This content is either by members of the organizations listed or about topics related to the organizations listed. Click each link to see a list of all content affiliated with the organization.

  • This module is included inLens: Rice University OpenCourseWare
    By: OpenCourseWare ConsortiumAs a part of collection:"Signals and Systems"

    Click the "Rice University OCW" link to see all content affiliated with them.

    Rice University OCW
Also in these lenses

  • This module is included inLens: richb's DSP resources
    By: Richard BaraniukAs a part of collection:"Signals and Systems"

    Comments:

    "My introduction to signal processing course at Rice University."

    Click the "richb's DSP" link to see all content selected in this lens.

    richb's DSP
Tags

(?)

These tags come from the endorsement, affiliation, and other lenses that include this content.

Properties of the Continuous-Time Fourier Transform

Module by: Melissa Selik, Richard Baraniuk

Summary: Lists some properties of the Continuous Time Fourier Transform.

This module will look at some of the basic properties of the Continuous-Time Fourier Transform (CTFT). The first section contains a table that illustrates the properties, and the sections following it discuss a few of the more interesting properties in more depth. In the table, click on the operation name to be taken to the properties explanation found later on this page. Look at this module for an expanded table of more Fourier transform properties.
note: We will be discussing these properties for aperiodic, continuous-time signals but understand that very similar properties hold for discrete-time signals and periodic signals as well.

Table of CTFT Properties

Operation Name Signal ( ft f t ) Transform ( Fω F ω )
Addition f1t+f2t f1 t f2 t F1ω+F2ω F1 ω F2 ω
Scalar Multiplication αft α f t αFt α F t
Symmetry Ft F t 2πf-ω 2 f ω
Time Scaling fαt f α t 1|α|Fωα 1 α F ω α
Time Shift ft-τ f t τ Fωⅇ-ⅈωτ F ω ω τ
Modulation (Frequency Shift) ftⅇⅈφt f t φ t Fω-φ F ω φ
Convolution in Time f1tf2t f1 t f2 t F1tF2t F1 t F2 t
Convolution in Frequency f1tf2t f1 t f2 t 12πF1tF2t 1 2 F1 t F2 t
Differentiation dndtnft t n f t ⅈωnFω ω n F ω

Discussion of Fourier Transform Properties

After glancing at the above table and getting a feel for the properties of the CTFT, we will now take a little more time to discuss some of the more interesting, and more useful, properties.

Linearity

The combined addition and scalar multiplication properties in the table above demonstrate the basic property of linearity. What you should see is that if one takes the Fourier transform of a linear combination of signals then it will be the same as the linear combination of the Fourier transforms of each of the individual signals. This is crucial when using a table of transforms to find the transform of a more complicated signal.
Example 1 
We will begin with the following signal:
zt=αf1t+αf2t z t α f1 t α f2 t (1)
Now, after we take the Fourier transform, shown in the equation below, notice that the linear combination of the terms is unaffected by the transform.
Zω=αF1ω+αF2ω Z ω α F1 ω α F2 ω (2)

Symmetry

Symmetry is a property that can make life quite easy when solving problems involving Fourier transforms. Basically what this property says is that since a rectangular function in time is a sinc function in frequency, then a sinc function in time will be a rectangular function in frequency. This is a direct result of the similarity between the forward CTFT and the inverse CTFT. The only difference is the scaling by 2π2 and a frequency reversal.

Time Scaling

This property deals with the effect on the frequency-domain representation of a signal if the time variable is altered. The most important concept to understand for the time scaling property is that signals that are narrow in time will be broad in frequency and vice versa. The simplest example of this is a delta function, a unit pulse with a very small duration, in time that becomes an infinite-length constant function in frequency.
The table above shows this idea for the general transformation from the time-domain to the frequency-domain of a signal. You should be able to easily notice that these equations show the relationship mentioned previously: if the time variable is increased then the frequency range will be decreased.

Time Shifting

Time shifting shows that a shift in time is equivalent to a linear phase shift in frequency. Since the frequency content depends only on the shape of a signal, which is unchanged in a time shift, then only the phase spectrum will be altered. This property can be easily proved using the Fourier Transform, so we will show the basic steps below:
Example 2 
We will begin by letting zt=ft-τ z t f t τ . Now let us take the Fourier transform with the previous expression substituted in for zt z t .
Zω=∫-∞∞ft-τⅇ-ⅈωtdt Z ω t f t τ ω t (3)
Now let us make a simple change of variables, where σ=t-τ σ t τ . Through the calculations below, you can see that only the variable in the exponential are altered thus only changing the phase in the frequency domain.
Zω=∫-∞∞fσⅇ-ⅈωσ+τtdτ=ⅇ-ⅈωτ∫-∞∞fσⅇ-ⅈωσdσ=ⅇ-ⅈωτFω Z ω τ f σ ω σ τ t ω τ σ f σ ω σ ω τ F ω (4)

Modulation (Frequency Shift)

Modulation is absolutely imperative to communications applications. Being able to shift a signal to a different frequency, allows us to take advantage of different parts of the electromagnetic spectrum is what allows us to transmit television, radio and other applications through the same space without significant interference.
The proof of the frequency shift property is very similar to that of the time shift; however, here we would use the inverse Fourier transform in place of the Fourier transform. Since we went through the steps in the previous, time-shift proof, below we will just show the initial and final step to this proof:
zt=12π∫-∞∞Fω-φⅇⅈωtdω z t 1 2 ω F ω φ ω t (5)
Now we would simply reduce this equation through another change of variables and simplify the terms. Then we will prove the property expressed in the table above:
zt=ftⅇⅈφt z t f t φ t (6)

Convolution

Convolution is one of the big reasons for converting signals to the frequency domain, since convolution in time becomes multiplication in frequency. This property is also another excellent example of symmetry between time and frequency. It also shows that there may be little to gain by changing to the frequency domain when multiplication in time is involved.
We will introduce the convolution integral here, but if you have not seen this before or need to refresh your memory, then look at the continuous-time convolution module for a more in depth explanation and derivation.
yt=f1tf2t=∫-∞∞f1τf2t-τdτ y t f1 t f2 t τ f1 τ f2 t τ (7)

Time Differentiation

Since LTI systems can be represented in terms of differential equations, it is apparent with this property that converting to the frequency domain may allow us to convert these complicated differential equations to simpler equations involving multiplication and addition. This is often looked at in more detail during the study of the Laplace Transform.

Comments, questions, feedback, criticisms?

Send feedback