Matching Discrete Fourier Transform (DFT) Pairs

In summary, the Discrete Fourier Transform (DFT) is a mathematical technique used to convert a discrete signal into its frequency domain representation, allowing for analysis and manipulation in the frequency domain. A matching DFT pair refers to a pair of signals with the same amplitude and phase values in their respective frequency domains, and having these pairs is important for efficient and accurate signal processing. To determine if two signals form a matching DFT pair, you can compare the amplitude and phase values at each frequency bin. It is also possible for two signals to have multiple matching DFT pairs, which should be considered when analyzing and manipulating signals in the frequency domain.
  • #1
roam
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Homework Statement


[/B]
I am trying to match each of the following 28-point discrete-time signals with its DFT:

Set #1:

DFTmatching.png


Set #2:

dftset2.png


Homework Equations

The Attempt at a Solution



Set #1
We have already established (here) that:

##Signal 1 \leftrightarrow DFT3##
##Signal 4 \leftrightarrow DFT2##

Now, Signal 3 looks like Signal 4. The only difference is that the temporal sample spacing has been increased. So, instead of having a single central peak in the DFT, we now have two peaks (at 7 and 21). Why?

Likewise, Signal 2 looks like Signal 1, except it was sampled at twice the sampling interval. So, why does increasing the sample spacing create an additional frequency peak in each case? :confused:

Set #2:
These signals look like rectangular pulses (but none of them are a full period of a periodic rectangular signal). The DFT of a rectangular pulse is samples of the sinc function. DFT1 & 3 look like sincs, but I am not sure how to interpret DFT2.

Clearly, each signal has a different average value. For instance, Signal 3 should have the highest DC value because it has more samples at 1 than the other two signals. But the axes of the DFTs are not labeled. So, how else can I match these?

Any suggestions would be greatly appreciated.
 
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  • #2
roam said:
Now, Signal 3 looks like Signal 4. The only difference is that the temporal sample spacing has been increased. So, instead of having a single central peak in the DFT, we now have two peaks (at 7 and 21). Why?

Likewise, Signal 2 looks like Signal 1, except it was sampled at twice the sampling interval. So, why does increasing the sample spacing create an additional frequency peak in each case? :confused:
Is there really an additional frequency? Think about it. What is special about the sampling rate for signals 1 and 4?

roam said:
Set #2:
These signals look like rectangular pulses (but none of them are a full period of a periodic rectangular signal). The DFT of a rectangular pulse is samples of the sinc function. DFT1 & 3 look like sincs, but I am not sure how to interpret DFT2.
Going back to the previous thread, think about how these signals fit in with respect to signals 1 and 6 in that problem.
 
  • #3
DrClaude said:
Is there really an additional frequency? Think about it. What is special about the sampling rate for signals 1 and 4?

In direct space, the period is 2. For instance, we can see that in signal 4, it is the largest frequency to represent a cosine with. Signal 3 looks like another cosine oscillating at a slower frequency. Where does the additional frequency come from?

Going back to the previous thread, think about how these signals fit in with respect to signals 1 and 6 in that problem.

Could you please explain more? I don't see how it relates to this problem.

Signal 1 & 6 in that problem show that a constant function corresponds to a Dirac-##\delta## spectrum, and conversely a ##\delta## impulse corresponds to a constant.

I also know this relationship between sample spacing and span in each domain:

$$\begin{array}{c|cc}
& \text{Time} & \text{Freq}\\
\hline \text{Spacing} & \Delta T & 1/N\Delta T\\
\text{Span} & N\Delta T & 1/\Delta T
\end{array}$$
 
  • #4
roam said:
In direct space, the speriod is 2. For instance, we can see that in signal 4, it is the largest frequency to represent a cosine with. Signal 3 looks like another cosine oscillating at a slower frequency. Where does the additional frequency come from?
I'll ask again: are there really two frequencies? (Think about the ordering of the DFT, as per the previous thread.)
roam said:
Could you please explain more? I don't see how it relates to this problem.
What is the relation between the length of a signal in time and the width of its frequency spectrum?
 
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  • #5
DrClaude said:
I'll ask again: are there really two frequencies? (Think about the ordering of the DFT, as per the previous thread.)

Thank you. So the highest frequency is in the center, so in DFT2 and DFT3 the two DFT coefficients overlap.

What is the relation between the length of a signal in time and the width of its frequency spectrum?

They are inversely proportional. If the length of the temporal signal is ##\text{NT}## (where ##\text{T}## is the intersample spacing, and ##\text{N}## is the number of samples), then the length of the frequency spectrum is ##\text{1/T}##.

So basically more of the overall sinc function is contained in the DFT of the signal with a smaller period? Signals 1, 2, and 3 correspond to DFTs 3, 1, and 2 respectively?

But this can't be right because from my notes, for a full period of a rectangular pulse we have this pair:

pair.png


In our problem, Signal 3 looks most like the signal shown above. So its DFT should also look more like that (i.e. ##\text{Signal3} \leftrightarrow \text{DFT3}##).
 
  • #6
roam said:
Thank you. So the highest frequency is in the center, so in DFT2 and DFT3 the two DFT coefficients overlap.
Correct. Since signals 2 and 3 correspond to half the frequency, the positive and negative components of the same absolute frequency now appear as separated.
roam said:
So basically more of the overall sinc function is contained in the DFT of the signal with a smaller period? Signals 1, 2, and 3 correspond to DFTs 3, 1, and 2 respectively?
That's not correct, and as you indicate, is not compatible with the other signal you have in your notes. The conclusion is that the shorter the duration of the rectangular pulse, the broader its frequency range is. Using that heuristic, can you now assign the DFT for signals 1, 2 and 3?
 
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  • #7
DrClaude said:
That's not correct, and as you indicate, is not compatible with the other signal you have in your notes. The conclusion is that the shorter the duration of the rectangular pulse, the broader its frequency range is. Using that heuristic, can you now assign the DFT for signals 1, 2 and 3?

DFT2 has 1 zero, DFT1 has 3 zeros, DFT3 has 7 zeros. So, DFT2 is the broadest because there are more frequency components present in its spectrum?

In summary:
##\text{Signal 1} \leftrightarrow \text{DFT 2}##
##\text{Signal 2} \leftrightarrow \text{DFT 1}##
##\text{Signal 3} \leftrightarrow \text{DFT 3}##

Is that correct?
 
  • #8
roam said:
In summary:
##\text{Signal 1} \leftrightarrow \text{DFT 2}##
##\text{Signal 2} \leftrightarrow \text{DFT 1}##
##\text{Signal 3} \leftrightarrow \text{DFT 3}##

Is that correct?
Yes.
 
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  • #9
Thank you so much for your help. It makes perfect sense now.
 

What is the Discrete Fourier Transform (DFT)?

The Discrete Fourier Transform (DFT) is a mathematical technique used to convert a discrete signal, such as a time series or a digital image, into its frequency domain representation. It decomposes a signal into its constituent frequencies, allowing for analysis and manipulation in the frequency domain.

What is a matching DFT pair?

A matching DFT pair refers to a pair of signals that have the same amplitude and phase values in their respective frequency domains. In other words, the DFT of one signal is the exact same as the DFT of the other signal, but with a different sign. This pair is also known as a conjugate pair.

Why is it important to have matching DFT pairs?

Having matching DFT pairs is important because it allows for efficient and accurate signal processing. When using the DFT to analyze a signal, it is crucial to have a matching pair in order to properly reconstruct the original signal from its frequency domain representation.

How do you determine if two signals form a matching DFT pair?

To determine if two signals form a matching DFT pair, you can simply take the DFT of each signal and compare the amplitude and phase values at each frequency bin. If they are the same, but with opposite signs, then the signals form a matching DFT pair.

Can two signals have multiple matching DFT pairs?

Yes, it is possible for two signals to have multiple matching DFT pairs. This occurs when the signals have symmetric or periodic properties, resulting in multiple sets of amplitude and phase values that are the same but with different signs. It is important to consider these multiple pairs when analyzing and manipulating signals in the frequency domain.

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