What is the Sampling Period Range for Recoverable Signals?

In summary, the conversation discusses the signal y(t) generated by convolving two band limited signals x1(t) and x2(t), and the impulse train sampling of y(t) to obtain yp(t). The problem at hand is determining the range of values for the sampling period T that ensures y(t) can be recovered from yp(t). The hint given is to use the convolution theorem and the Nyquist rate, which would be equal to 2fmax in order to avoid aliasing and oversampling. The key to solving this problem is understanding where Y(jω) is zero and deducing the maximum frequency (fmax) from that information.
  • #1
satchmo05
114
0

Homework Statement



The signal y(t) is generated by convolving a band limited signal x1(t) with another band limited signal x2(t) that is y(t)=x1(t)*x2(t) where:

--> X1(jω)=0 for|ω| > 1000Π
--> X2(jω)=0 for|ω| >2000Π

Impulse train sampling is performed on y(t) to obtain:
--> yp(t)= [summation from n = (−∞,∞)] y(nT)δ(t− nT)

Specify the range of values for sampling period T which ensures that y(t) is recoverable from yp(t).

Homework Equations


All of the equations that I would are most likely showing.

The Attempt at a Solution


My thoughts were to plug in (nT) for every t in both x1(t) and x2(t) and then take the Fourier transform of that, cut of the edges where the transforms are equal to zero and then that is where I go blank...

I imagine that is the right implementation to start the problem with, but please correct me if I am wrong. Thank you in advance to all who may be able to help - it is much appreciated!
 
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  • #2
Hint: convolution theorem.
 
  • #3
I can see where convolution comes into play, but how can I implement the CTF transforms that are given?
 
  • #4
What sets the lower limit on the sampling rate if you want to be able to recover the original signal?
 
  • #5
The Nyquist rate, to sample at the perfect rate (without aliasing/oversampling to occur) - it would be = 2fmax
 
  • #6
I am pretty confused at what you're trying to hint at here. I appreciate the help, but my mind is still blank.
 
  • #7
It's kind of hard to say anything without giving away the answer. Think about Y(jω). Where is it zero? Can you deduce fmax from that information?
 

Related to What is the Sampling Period Range for Recoverable Signals?

1. What is Fourier analysis?

Fourier analysis is a mathematical technique used to decompose a complex signal into its component frequencies. It allows us to understand the different frequencies present in a signal and how they contribute to its overall shape or behavior.

2. Why is Fourier analysis important?

Fourier analysis is important because it has a wide range of applications in various fields, such as engineering, physics, and mathematics. It allows us to analyze signals and data in the frequency domain, which can provide valuable insights and help solve problems that cannot be easily solved in the time domain.

3. How does Fourier analysis work?

Fourier analysis works by representing a signal as a sum of sinusoidal functions with different frequencies, amplitudes, and phases. This representation allows us to see how each frequency contributes to the overall signal and can help us identify patterns and anomalies in the data.

4. What is the difference between continuous and discrete Fourier analysis?

Continuous Fourier analysis is used for signals that are continuous and have no gaps or breaks, while discrete Fourier analysis is used for signals that are discrete and have a finite number of data points. Discrete Fourier analysis is often used for digital signals, while continuous Fourier analysis is used for analog signals.

5. Can Fourier analysis be used for non-periodic signals?

Yes, Fourier analysis can be used for non-periodic signals. However, it requires a different approach called the Fourier transform, which is used for signals that do not repeat periodically. The Fourier transform is a generalization of Fourier analysis and is commonly used in fields such as image processing and data compression.

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