Recovering a Signal Under Nyquist Sampling Constraints

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In summary, the signal x(t) can be recovered even with the given constraints of X(jw) = 0 for |w|>5000pi, as long as the sampling frequency is greater than 2x10^4pi. This is due to the Nyquist-Shannon sampling theorem which states that the sampling frequency must be greater than 2 times the highest frequency component present in the signal in order to accurately reconstruct the continuous signal from its discrete samples.
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Homework Statement


A signal x(t) with Fourier transform X(jw) undergoes impulse-train sampling to generate

xp(t) = sum[ x(nT)deltafnc(t-nT)

Where T = 10^(-4), Determine with these constraints whether the signal can be recovered.
a) X(jw) = 0 for |w|> 5000pi


Homework Equations



ws>2wm
ws = 2pi/T


The Attempt at a Solution



So the sampling period is T = 10^(-4). And the sampling frequency ws = 2pi/10^(-4)

we know that ws must be > 10000pi, therefore the signal is not recoverable given the constraints.


SOLVED**********************
 
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Hello there! Thank you for your post. I would like to provide some additional insight and clarification on your solution.

Firstly, let's define some important terms. The sampling frequency, also known as the sampling rate, is the number of samples per second taken from a continuous signal to create a discrete signal. In this case, the sampling frequency is ws = 2pi/10^(-4) = 2x10^4pi. This means that for every second, 2x10^4pi samples are taken from the continuous signal x(t).

Next, we have the Nyquist-Shannon sampling theorem, which states that in order to accurately reconstruct a continuous signal from its discrete samples, the sampling frequency must be greater than twice the highest frequency component present in the signal. In other words, we need to sample at a rate greater than 2 times the highest frequency present in the signal to avoid aliasing (the distortion or loss of information in the reconstructed signal).

In this problem, we are given that X(jw) = 0 for |w|>5000pi. This means that the highest frequency component present in the signal is 5000pi. Therefore, according to the Nyquist-Shannon sampling theorem, the sampling frequency should be greater than 2x5000pi = 10000pi.

Now, let's compare this to our sampling frequency of 2x10^4pi. We can see that it meets the requirement of being greater than 10000pi, therefore the signal can be recovered with these constraints.

In conclusion, while your solution was on the right track, it is important to fully understand the concepts and equations involved to ensure an accurate solution. I hope this helps! Keep up the good work.
 

1. What is the Nyquist-Shannon sampling theorem?

The Nyquist-Shannon sampling theorem is a fundamental concept in signal processing that states a continuous signal can be accurately represented by a discrete sequence of samples as long as the sampling rate is greater than twice the maximum frequency of the signal.

2. What are Nyquist sampling constraints?

Nyquist sampling constraints refer to the conditions that must be met in order to accurately reconstruct a continuous signal from discrete samples, as stated by the Nyquist-Shannon sampling theorem. These constraints include a sampling rate greater than twice the maximum frequency of the signal and a sufficient number of samples to capture the full range of the signal.

3. Why is the Nyquist sampling theorem important?

The Nyquist sampling theorem is important because it provides a mathematical explanation for how to accurately sample a continuous signal without losing information. This is crucial in many applications, such as digital signal processing and data compression, where it is necessary to accurately represent a signal with a limited number of samples.

4. What is the relationship between the sampling rate and the maximum frequency of a signal?

The sampling rate must be at least twice the maximum frequency of a signal in order to accurately represent it. This means that for signals with higher frequencies, a higher sampling rate is required to avoid aliasing and accurately reconstruct the original signal.

5. How can Nyquist sampling constraints be applied in real-world scenarios?

Nyquist sampling constraints can be applied in real-world scenarios by carefully choosing the sampling rate and number of samples when collecting data from a continuous signal. This ensures that the signal can be accurately represented and analyzed in digital form, without losing important information. In applications such as audio and video recording, the Nyquist sampling theorem is used to determine the necessary sampling rate to capture the full range of frequencies present in the signal.

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