Discrete Fourier Transform (DFT) Matching

In summary, the conversation discusses matching discrete-time signals with their DFTs. The DFTs of Signals 1 and 6 correspond to DFTs 3 and 5, respectively, while Signals 2, 4, and 5 correspond to DFTs 8, 2, and 7, respectively. Signals 3 and 7 are undersampled cosines and Signal 8 is a pure cosine sampled twice per oscillation period. Their corresponding DFTs are 4, 6, and 1, respectively. The scale of the DFT represents the spectrum of an FFT which is not fftshifted. The zero-frequency component (DC) is the first element, followed by the positive and negative
  • #1
roam
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Homework Statement


Match each discrete-time signal with its DFT:

dfts.png


Homework Equations

The Attempt at a Solution



I am mainly confused about Signal 7 and Signal 8.

Signal 1 is the discrete equivalent to a constant function, therefore its DFT is an impulse (Dirac ##\delta##), so it corresponds to DFT 3.

DFT of an impulse is a constant. Therefore Signal 6 corresponds to DFT 5.

Signal 2 is a sampled version of a full period of a cosine. So we expect ##X(1)## and ##X(-1)## to be nonzero (##X(-1)## really is ##X(N-1)=X(25)##). Therefore Signal 2 corresponds to DFT8.

By similar arguments, Signal 4 has exactly 2 cycles of a cosine and corresponds to DFT2. And Signal 3 has one and a half periods of a cosine, as we do not have complete periods we should expect spectral leakage, so signal 3 corresponds to DFT4 (the main peaks are around 1 & 2 plus negative frequencies). Likewise, Signal 5 corresponds to DFT7.

Here is a summary of the results so far:

table.jpg


Only Signals 7 & 8 and DFT 6 & 1 are left:

dftset.png
What do signals 7 and 8 represent? Is Signal 7 an undersampled cosine? How do we go about matching them with their corresponding DFTs?

Any explanation would be greatly appreciated.
 
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  • #2
Could you please clarify what the scale of the DFT represents? Is it the the same order as the output of an FFT (from 0 to highest positive frequency, followed by lowest negative frequency back to 0)?

roam said:
What do signals 7 and 8 represent? Is Signal 7 an undersampled cosine? How do we go about matching them with their corresponding DFTs?
Both can be see as undersampled cosines, with Signal 7 being a cosine with a varying amplitude envelope.
 
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  • #3
DrClaude said:
Could you please clarify what the scale of the DFT represents? Is it the the same order as the output of an FFT (from 0 to highest positive frequency, followed by lowest negative frequency back to 0)?

It is similar to the spectrum of an FFT which is not fftshifted. The zero-frequency component (DC) is the first element (##r=0##). Then it is the positive frequencies, but I think it is lowest to largest, followed by negative frequencies.

Both can be see as undersampled cosines, with Signal 7 being a cosine with a varying amplitude envelope.

Yes, this is right. Any ideas how to identify the DFT for each signal?
 
  • #4
roam said:
Any ideas how to identify the DFT for each signal?
What case would correspond to a single frequency?
 
  • #5
DrClaude said:
What case would correspond to a single frequency?

Is it Signal 8?

So, the spectrum of Signal 8 is DFT6? What would be a good explanation? Signal 7 looks like at least two cosine waves being heterodyned (i.e. a cosine wave contained in a lower frequency cosine envelope).
 
  • #6
roam said:
So, the spectrum of Signal 8 is DFT6? What would be a good explanation? Signal 7 looks like at least two cosine waves being heterodyned (i.e. a cosine wave contained in a lower frequency cosine envelope).
That's basically it. Signal 8 corresponds to a pure cosine sampled twice per oscillation period, and therefore has a single frequency. The effect of changing the envelop (modulating the amplitude) is a spreading out of the frequency, as given by DFT1 (you can see it as an "uncertainty" in the frequency due to the limited time, or pulse nature, of the signal, or due to the fact that the signal is similar to a beat coming from overlapping signals of similar frequencies).
 
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1. What is Discrete Fourier Transform (DFT) Matching?

Discrete Fourier Transform (DFT) Matching is a technique used in signal processing to compare two signals and determine their degree of similarity. It involves transforming the signals into the frequency domain using the DFT algorithm and then comparing their corresponding frequency components.

2. How does DFT Matching work?

DFT Matching works by first converting the signals into the frequency domain using the DFT algorithm. Then, the frequency components of the two signals are compared using a matching metric, such as cross-correlation or Euclidean distance. The result is a value that represents the degree of similarity between the two signals.

3. What are the applications of DFT Matching?

DFT Matching has various applications in signal and image processing, such as pattern recognition, speech recognition, and image matching. It is also used in fields like astronomy, physics, and engineering for analyzing data and identifying patterns.

4. What are the advantages of using DFT Matching?

One of the main advantages of DFT Matching is its ability to accurately compare signals in the presence of noise or distortion. It also has a relatively fast computational speed and can handle signals of any length, making it widely applicable in different fields.

5. Are there any limitations of DFT Matching?

While DFT Matching is a powerful tool, it has some limitations. It assumes that the signals being compared are time-invariant, which may not always be the case. It also does not take into account the phase information of the signals, which can sometimes be important in certain applications.

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