Sampling a continuous-time signal, aliasing/Nyquist

In summary, the analog signal x(t) = cos(2pi f t) is sampled at 1 kHz using ideal impulse sampling. The sampled signal is then passed through an ideal lowpass filter with transfer function H(2pi f ) = 0.001 rect (0.001 f ). When f = 1.01 kHz, aliasing occurs as the sampling rate is not greater than twice the signal frequency. The output frequency from the filter is 0.01 kHz. Similarly, when f = 0.99 kHz, aliasing occurs and the output frequency is -0.01 kHz. However, when f = 0.49 kHz, the signal is not aliased as the sampling rate is greater than twice
  • #1
alc95
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Homework Statement


The analog signal x(t) = cos(2pi f t) is sampled at a rate of 1 kHz, using ideal
impulse sampling, to obtain the sampled signal x^(s)(t). The sampled signal is then sent through an ideal
lowpass filter with transfer function H(2pi f ) = 0.001 rect (0.001 f ).
(a) If f =1.01kHz, what is the output frequency from the filter? Is there aliasing or not?
(b) If f = 0.99kHz, what is the output frequency from the filter? Is there aliasing or not?
(c) If f = 0.49kHz, what is the output frequency from the filter? Is there aliasing or not?

Homework Equations


To perfectly reconstruct a signal, we need: sampling rate > 2*signal frequency

The Attempt at a Solution


I think these are correct, not 100% sure though:
(a)
aliasing occurs, as 1.00 kHz !> 2 × 1.01 kHz
fout = 1.01 kHz – n × 1.00 kHz = 1.01 kHz – 1 × 1.00 kHz = 0.01 kHz

(b)
aliasing occurs, as 1.00 kHz !> 2 × 0.99 kHz
fout = 0.99 kHz – n × 1.00 kHz = 0.99 kHz – 1.00 kHz = - 0.01 kHz

(c)
aliasing does not occur, as 1.00 kHz > 2 × 0.49 kHz
fout = 0.49 kHz
 
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  • #2
alc95 said:

Homework Statement


The analog signal x(t) = cos(2pi f t) is sampled at a rate of 1 kHz, using ideal
impulse sampling, to obtain the sampled signal x^(s)(t). The sampled signal is then sent through an ideal
lowpass filter with transfer function H(2pi f ) = 0.001 rect (0.001 f ).
Not sure I understand this terminology. What really is the ideal low-pass cutoff frequency? Is it really a function of input frequency f? Do you have to divide by 2π? Etc. ?

I don't know your method, and as I say I'm not sure what your cutoff filter really looks like, but it basically seems to work.

Just FYI I first determine the cutoff frequency of the low-pass filter = f0, the I take the sampling frequency fs and my input frequency f and find spectral components:

f, |fs - f|, fs + f, |2fs - f|, 2fs + f, ...

and then compare each component against the cutoff frequency f0.
The only unaliased signal is at f so anything below f0 that is a mix of f and fs is an aliased component.
 

1. What is sampling a continuous-time signal?

Sampling a continuous-time signal refers to the process of taking discrete samples of a continuous signal at regular intervals of time. This is done in order to convert the continuous signal into a digital or discrete signal that can be processed by a computer or other digital system.

2. What is aliasing?

Aliasing is a phenomenon that occurs when a signal is sampled at a rate that is lower than the Nyquist rate. This can result in a distorted or inaccurate representation of the original signal, as high frequency components of the signal may be incorrectly represented as lower frequency components.

3. What is the Nyquist rate?

The Nyquist rate is the minimum sampling rate required to accurately represent a continuous signal without introducing distortion or aliasing. It is equal to twice the highest frequency component in the signal.

4. How can I prevent aliasing?

To prevent aliasing, the sampling rate must be at least equal to the Nyquist rate. This can be achieved by using a low-pass filter to remove high frequency components from the signal before sampling, or by increasing the sampling rate to be higher than the Nyquist rate.

5. What are some applications of sampling and Nyquist theory?

Sampling and Nyquist theory are used in a variety of applications, including digital signal processing, audio and video encoding, and telecommunications. They are essential in converting analog signals into digital signals that can be processed and transmitted efficiently and accurately.

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