Solving Sampling with Aliasing Homework

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In summary, the problem is that the signal bandwidth includes negative frequencies, which causes aliasing. To avoid aliasing, you should only include positive frequencies in the definition of signal bandwidth.
  • #1
Bromio
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Homework Statement


Let the signal [itex]x(t) = \left(\displaystyle\frac{\sin(50\pi t)}{\pi t}\right)^2[/itex], which we want to sample with sampling frequency [itex]\omega_s = 150\pi[/itex] in order to obtain a signal, [itex]g(t)[/itex] whose Fourier transform is [itex]G(\omega)[/itex]. Determine the maximun value for [itex]\omega_0[/itex] which guarantees that [itex]G(\omega) = 75X(\omega)[/itex] for [itex]\left|\omega\right| \leq{\omega_0}[/itex]



Homework Equations


Sampling Nyquist theoreme: [itex]\omega_s > 2B[/itex], where [itex]B[/itex] is the signal band-with.


The Attempt at a Solution


[itex]X(\omega) = FT\{x(t)\}[/itex] is a triangular signal with [itex]B = 100\pi[/itex] and amplitude [itex]X(0) = 25[/itex].

From sampling Nyquist theoreme, [itex]150\pi > 200\pi[/itex] is false, so there is aliasing.

I don't know how to finish the problem.

Thank you.
 
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  • #2
There is no aliasing. You should not define signal bandwidth to include both positive and negative frequencies if you plan to use a relation with 2B. (That is, 2B assumes that B was defined as one-sided, whereupon the factor of 2 accounts for the negative frequency portion.) With this definition, you should write
[tex]B=50\pi[/tex] and 2B<ws, so you are sampled properly.
 
  • #3
I've defined bandwith to include only positive frequencies, so my problem must be an error in Fourier transform.

I think [itex]X(\omega) = FT\{x(t)\}[/itex] is a triangular signal whose bandwith is [itex]B = 100\pi[/itex] and [itex]X(0) = 25[/itex]

Is this wrong?

Thank you.
 
  • #4
Hmm. Now that I work it out, I also get B=100pi but X(0)=50 (although the value of X(0) is not needed to solve the problem). Aliasing changes the shape of G so that the spectrum becomes horizontal between w=50pi and 75pi. You can compare the shape of G to that of X; I don't see that G exceeds X by 75, however.
 
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  • #5
You're right: B=100pi and X(0)=50.

Intuitively I understand that a horizontal line appears in the spectrum, but how can you know that this occurs between w=50pi and 75pi?

Regarding the problem question (which I've checked that it is correctly copied), I know [itex]G(\omega) = \displaystyle\frac{1}{T_s}\sum_{k=-\infty}^{\infty}X(\omega-k\omega_s)[/itex]. As [itex]\omega_s = 150\pi[/itex], then [itex]T_s = 2\pi/150\pi = 1/75[/itex], this expression can be written as [itex]G(\omega) = 75\displaystyle\sum_{k=-\infty}^{\infty}X(\omega-k\omega_s)[/itex], where 75 appears, but I don't know how to continue.

Thank you.
 
  • #6
Bromio said:
You're right: B=100pi and X(0)=50.

Intuitively I understand that a horizontal line appears in the spectrum, but how can you know that this occurs between w=50pi and 75pi?
You can do it graphically. I'll discuss only the positive frequencies, but everything to follow is mirrored for w<0. The spectrum X is a triangle X(0)=50 and X(100*pi)=0, and G is X replicated at every integer multiple of ws. The first folding frequency is 75*pi, so fold the bit of triangle from 75 to 100 pi back and add it to X to give G. The flat part of G extends from 50 to 75 pi.

Bromio said:
Regarding the problem question (which I've checked that it is correctly copied), I know [itex]G(\omega) = \displaystyle\frac{1}{T_s}\sum_{k=-\infty}^{\infty}X(\omega-k\omega_s)[/itex]. As [itex]\omega_s = 150\pi[/itex], then [itex]T_s = 2\pi/150\pi = 1/75[/itex], this expression can be written as [itex]G(\omega) = 75\displaystyle\sum_{k=-\infty}^{\infty}X(\omega-k\omega_s)[/itex], where 75 appears, but I don't know how to continue.

Thank you.
Excellent, you've shown that G=75X in the absence of aliasing. Aliasing raises the value of G between 50 and 75 pi, so 50pi is the maximum frequency where G=75X.
 
  • #7
Thank you.

I've just understood this problem.
 

1. What is sampling with aliasing?

Sampling with aliasing refers to the process of taking samples from a continuous signal and translating them into a discrete signal. This can result in the original signal being distorted or misrepresented due to the limited number of samples taken.

2. How can aliasing be avoided?

Aliasing can be avoided by following the Nyquist-Shannon sampling theorem, which states that the sampling frequency must be at least twice the highest frequency present in the signal. This ensures that the original signal can be accurately reconstructed from the samples.

3. What is the difference between under-sampling and over-sampling?

Under-sampling occurs when the sampling frequency is lower than the Nyquist rate, resulting in aliasing and loss of information. Over-sampling, on the other hand, refers to sampling at a frequency higher than the Nyquist rate, which can help in reducing noise and improving the accuracy of the reconstructed signal.

4. How does anti-aliasing filtering work?

Anti-aliasing filtering is used to remove any frequency components above the Nyquist rate from the signal before sampling. This helps prevent aliasing by ensuring that only the frequencies within the Nyquist rate are captured in the samples.

5. What are some real-world applications of sampling with aliasing?

Sampling with aliasing is commonly used in digital audio and image processing, as well as in telecommunications for signal transmission and processing. It is also used in scientific research, such as in the study of earthquakes and other natural phenomena.

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