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− | + | [[Category:problem solving]] | |
− | On Computing the DFT of a discrete-time periodic signal | + | [[Category:ECE438]] |
+ | [[Category:discrete Fourier transform]] | ||
+ | |||
+ | <center><font size= 4> | ||
+ | '''[[Digital_signal_processing_practice_problems_list|Practice Question on "Digital Signal Processing"]]''' | ||
+ | </font size> | ||
+ | |||
+ | Topic: Discrete Fourier Transform | ||
+ | |||
+ | (On Computing the DFT of a discrete-time periodic signal.) | ||
+ | </center> | ||
---- | ---- | ||
+ | ==Question== | ||
Compute the discrete Fourier transform of the discrete-time signal | Compute the discrete Fourier transform of the discrete-time signal | ||
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---- | ---- | ||
Post Your answer/questions below. | Post Your answer/questions below. | ||
− | + | ---- | |
+ | ==Answer 1== | ||
<math>X [k] = \sum_{k=0}^{N-1} x[n].e^{-j.2\pi k n/N}</math> | <math>X [k] = \sum_{k=0}^{N-1} x[n].e^{-j.2\pi k n/N}</math> | ||
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<math> X [k] = 1+ e^{-j(1)(\frac{2}{3}\pi)(1+k)} +e^{-j\frac{4}{3}\pi(1+k)}</math> <span style="color:green"> This gives you a very complicated answer. -pm </span> | <math> X [k] = 1+ e^{-j(1)(\frac{2}{3}\pi)(1+k)} +e^{-j\frac{4}{3}\pi(1+k)}</math> <span style="color:green"> This gives you a very complicated answer. -pm </span> | ||
+ | |||
+ | <math> X [0] = 1+ e^{-j(\frac{2}{3}\pi)(1+0)} +e^{-j\frac{4}{3}\pi(1+0)}</math> | ||
+ | |||
+ | <math> X [0] = 1+ e^{-j(\frac{2}{3}\pi)} +e^{-j(\frac{4}{3}\pi)}</math> | ||
+ | |||
+ | complex result: Noting <math>-2/3\pi</math> and <math>-4/3\pi</math> are conjugates cancel the imaginary component. | ||
+ | |||
+ | <math> 1+cos(-2/3\pi) +cos(-4/3\pi) = X[0] = 0 </math> | ||
+ | |||
+ | <math> X [1] = 1+ e^{-j(\frac{2}{3}\pi)(1+1)} +e^{-j\frac{4}{3}\pi(1+1)}</math> | ||
+ | |||
+ | <math> X [1] = 1+ e^{-j(\frac{4}{3}\pi)} +e^{-j\frac{8}{3}\pi}</math> | ||
+ | |||
+ | complex result: Noting <math>-4/3\pi</math> and <math>-8/3\pi</math> are conjugates cancel the imaginary component. | ||
+ | |||
+ | <math> 1+cos(-4/3\pi) +cos(-8/3\pi) = X[1] = 0</math> | ||
+ | |||
+ | <math> X [2] = 1+ e^{-j(\frac{2}{3}\pi)(1+2)} +e^{-j\frac{4}{3}\pi(1+2)}</math> | ||
+ | |||
+ | <math> X [2] = 1+ e^{-j(2\pi)} +e^{-j(4\pi)}</math> | ||
+ | |||
+ | <math> X [2] = 1+ 1 + 1 = 3</math> | ||
+ | |||
- AJFunche <span style="color:green"> Nice effort! -pm---- | - AJFunche <span style="color:green"> Nice effort! -pm---- | ||
---- | ---- | ||
+ | ==Answer 2== | ||
<math>x[n] = \frac{1}{N}\sum_{k=0}^{N-1} X[k]e^{j2\pi k\frac{n}{N}}</math> | <math>x[n] = \frac{1}{N}\sum_{k=0}^{N-1} X[k]e^{j2\pi k\frac{n}{N}}</math> | ||
<math>x[n] = \frac{1}{3}\sum_{k=0}^{2} X[k]e^{j\frac{2\pi}{3}kn}</math> | <math>x[n] = \frac{1}{3}\sum_{k=0}^{2} X[k]e^{j\frac{2\pi}{3}kn}</math> | ||
− | <math>x[n] = \frac{1}{3} \cdot (X[0] + X[1]e^{j(\frac{2\pi}{3}n | + | <math>x[n] = \frac{1}{3} \cdot (X[0] + X[1]e^{j(\frac{2\pi}{3}(1)n )} + X[2]e^{j(\frac{2\pi}{3}(2)n)})</math> |
:<span style="color:green"> Notice that all the powers of e in this expression are positive, but the signal x[n] is expressed as a negative power of e, so you cannot compare just yet. -pm </span> | :<span style="color:green"> Notice that all the powers of e in this expression are positive, but the signal x[n] is expressed as a negative power of e, so you cannot compare just yet. -pm </span> | ||
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X[1] = 0 | X[1] = 0 | ||
− | X[2] = 3 | + | X[2] = 3 |
− | + | ||
− | + | ||
---- | ---- | ||
+ | ==Answer 3== | ||
+ | This could easily be shown that due to the modulation property that this is a shifted delta in the DFT world. | ||
+ | But to do a little mathematical proof.... | ||
+ | <math> | ||
+ | \begin{align} | ||
+ | X [k] &= \sum_{n=0}^{N-1} x[n]e^{\frac{-j2\pi k n}{N}} \\ | ||
+ | &= \sum_{n=0}^{N-1} e^{-j\frac{2}{3}\pi n}e^{\frac{-j2\pi k n}{N}} \text{ where N=3 because of periodicity of the signal} \\ | ||
+ | &= \sum_{n=0}^{2} e^{j\frac{4}{3}\pi n}e^{-j \frac{2}{3} \pi n k} \\ | ||
+ | &= \sum_{n=0}^{2} e^{-j\frac{2}{3}\pi n (k-2)} | ||
+ | \end{align} | ||
+ | </math> | ||
− | + | Now to get the final result you must compare this equation to the IDFT formula and you get that | |
+ | |||
+ | <math> | ||
+ | \begin{align} | ||
+ | X [k] &= \sum_{n=0}^{N-1} x[n]e^{\frac{-j2\pi k n}{N}} &= 3 \delta (k-2) | ||
+ | \end{align} | ||
+ | </math> | ||
+ | |||
+ | In class we compared the IDFT and the Fourier Series expansion and the Fourier coefficients can be expressed (if I remember correctly) as | ||
+ | |||
+ | <math > | ||
+ | A_{k}= \frac{X[k]}{N} | ||
+ | </math> | ||
+ | |||
+ | :<span style="color:purple"> This is a different and interesting way of looking at the problem. -pm---- | ||
+ | |||
+ | :<span style="color:green"> But does that mean my solution is wrong or just unique...? - my | ||
+ | ---- | ||
*Answer/question | *Answer/question | ||
---- | ---- |
Latest revision as of 13:22, 21 April 2013
Practice Question on "Digital Signal Processing"
Topic: Discrete Fourier Transform
(On Computing the DFT of a discrete-time periodic signal.)
Contents
Question
Compute the discrete Fourier transform of the discrete-time signal
$ x[n]= e^{-j \frac{2}{3} \pi n} $.
How does your answer related to the Fourier series coefficients of x[n]?
Post Your answer/questions below.
Answer 1
$ X [k] = \sum_{k=0}^{N-1} x[n].e^{-j.2\pi k n/N} $
$ N=3 $ That's correct! -pm
$ x[n]= e^{-j \frac{2}{3} \pi n} $
$ X [k] = \sum_{k=0}^{2}e^{-j(n)(\frac{2}{3}\pi)(1+k)} $ You are using the long route, instead of the short route. -pm
$ X [k] = 1+ e^{-j(1)(\frac{2}{3}\pi)(1+k)} +e^{-j\frac{4}{3}\pi(1+k)} $ This gives you a very complicated answer. -pm
$ X [0] = 1+ e^{-j(\frac{2}{3}\pi)(1+0)} +e^{-j\frac{4}{3}\pi(1+0)} $
$ X [0] = 1+ e^{-j(\frac{2}{3}\pi)} +e^{-j(\frac{4}{3}\pi)} $
complex result: Noting $ -2/3\pi $ and $ -4/3\pi $ are conjugates cancel the imaginary component.
$ 1+cos(-2/3\pi) +cos(-4/3\pi) = X[0] = 0 $
$ X [1] = 1+ e^{-j(\frac{2}{3}\pi)(1+1)} +e^{-j\frac{4}{3}\pi(1+1)} $
$ X [1] = 1+ e^{-j(\frac{4}{3}\pi)} +e^{-j\frac{8}{3}\pi} $
complex result: Noting $ -4/3\pi $ and $ -8/3\pi $ are conjugates cancel the imaginary component.
$ 1+cos(-4/3\pi) +cos(-8/3\pi) = X[1] = 0 $
$ X [2] = 1+ e^{-j(\frac{2}{3}\pi)(1+2)} +e^{-j\frac{4}{3}\pi(1+2)} $
$ X [2] = 1+ e^{-j(2\pi)} +e^{-j(4\pi)} $
$ X [2] = 1+ 1 + 1 = 3 $
- AJFunche Nice effort! -pm----
Answer 2
$ x[n] = \frac{1}{N}\sum_{k=0}^{N-1} X[k]e^{j2\pi k\frac{n}{N}} $
$ x[n] = \frac{1}{3}\sum_{k=0}^{2} X[k]e^{j\frac{2\pi}{3}kn} $
$ x[n] = \frac{1}{3} \cdot (X[0] + X[1]e^{j(\frac{2\pi}{3}(1)n )} + X[2]e^{j(\frac{2\pi}{3}(2)n)}) $
- Notice that all the powers of e in this expression are positive, but the signal x[n] is expressed as a negative power of e, so you cannot compare just yet. -pm
whoops, I was doing the homework. is that correct? - ksoong
- Tecnically yes, but not realy useful for computing the DFT. Instead, use the fact that $ e^{ 2 \pi n j}=1 $ to rewrite x[n] as a positive power of e. (Just add $ 2 \pi n j $ to the exponent of e). -pm
$ \begin{align} x[n]&= e^{-j \frac{2}{3} \pi n} \\ &= e^{-j \frac{2}{3} \pi n} e^{j 2 \pi n} \text{ (since this is the same as multiplying by one, for any integer n)}\\ &= e^{-j \frac{2}{3} \pi n +j 2 \pi n } \\ & = e^{j \frac{4}{3} \pi n} \\ & = e^{j 2 \frac{2\pi n }{3} } \end{align} $
Now compare with the inverse DFT formula.
$ e^{j 2 \frac{2\pi n }{3} } \ \ compare \ with \ \ \frac{1}{3} \cdot (X[0] + X[1]e^{j(\frac{2\pi}{3}n)} + X[2]e^{j(\frac{2\pi}{3}(2)n)}) $
X[0] = 0
X[1] = 0
X[2] = 3
Answer 3
This could easily be shown that due to the modulation property that this is a shifted delta in the DFT world. But to do a little mathematical proof.... $ \begin{align} X [k] &= \sum_{n=0}^{N-1} x[n]e^{\frac{-j2\pi k n}{N}} \\ &= \sum_{n=0}^{N-1} e^{-j\frac{2}{3}\pi n}e^{\frac{-j2\pi k n}{N}} \text{ where N=3 because of periodicity of the signal} \\ &= \sum_{n=0}^{2} e^{j\frac{4}{3}\pi n}e^{-j \frac{2}{3} \pi n k} \\ &= \sum_{n=0}^{2} e^{-j\frac{2}{3}\pi n (k-2)} \end{align} $
Now to get the final result you must compare this equation to the IDFT formula and you get that
$ \begin{align} X [k] &= \sum_{n=0}^{N-1} x[n]e^{\frac{-j2\pi k n}{N}} &= 3 \delta (k-2) \end{align} $
In class we compared the IDFT and the Fourier Series expansion and the Fourier coefficients can be expressed (if I remember correctly) as
$ A_{k}= \frac{X[k]}{N} $
- This is a different and interesting way of looking at the problem. -pm----
- But does that mean my solution is wrong or just unique...? - my
- Answer/question