Aliasing and discrete sinusoids

In summary, a continuous time, continuous amplitude sinusoid is 2pi periodic and can be represented by sin(2pi*f*t)=sin(2pi*f*t+m*2pi), where m is any positive or negative integer. When sampling this sinusoid at a frequency of fs, the discrete signal x[n] can be written as sin{2pi(f+m/(n*ts))*n*ts}. If m is an integer multiple of n, it can be replaced with k so that x[n]=sin{2pi(f+kf*s)n*ts}. However, if m is not an integer multiple of n, x[n] will not be defined for non-integer arguments.
  • #1
fisico30
374
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Hello Forum,

a continuous time, continuous amplitude sinusoid like sin(2pi*f*t) is 2pi periodic:

sin(2pi*f*t)=sin(2pi*f*t+m*2pi)

where m can be any positive or negative integer. Let's sample the sinusoid at a sampling frequency fs (sample interval is ts=1/fs) and get the discrete signal

x[n]=sin(2pi*f*n*ts)=sin(2pi*f*n*ts+2pi*m)=sin{2pi(f+m/(n*ts))*n*ts}

This book says: if we let m be an integer multiple of n, m=k*n, we can replace the ration m/n with k so that

x[n]=sin(2pi*f*n*ts)=sin{2pi(f+kf*s)n*ts}

What happens if m is not an integer multiple of n? Both m and n are integers (I get that), but I don't understand the constraint m=k*n...That does not seem general enough...

thanks
fisico30
 
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  • #2
well in discrete time, x[n] will not be defined for non integer arguments
 

FAQ: Aliasing and discrete sinusoids

What is Aliasing?

Aliasing is a phenomenon that occurs when a signal is sampled at a rate that is too low to accurately represent its frequency content. This results in the loss of high-frequency information and can cause distortions in the reconstructed signal.

How does Aliasing affect discrete sinusoids?

Discrete sinusoids are prone to aliasing because they have a finite number of samples and a discrete frequency spectrum. If the sampling rate is too low, frequencies that are higher than the Nyquist frequency will be folded back into the spectrum, resulting in aliasing.

What is the Nyquist-Shannon sampling theorem?

The Nyquist-Shannon sampling theorem states that in order to accurately represent a continuous signal, it must be sampled at a rate that is at least twice the highest frequency present in the signal. This ensures that there is no overlap or aliasing in the frequency spectrum.

How can we avoid aliasing in discrete sinusoids?

To avoid aliasing in discrete sinusoids, the sampling rate must be chosen carefully according to the Nyquist-Shannon sampling theorem. Additionally, anti-aliasing filters can be used to remove high-frequency components before sampling.

Can aliasing be beneficial in any way?

In some cases, aliasing can be used intentionally to achieve certain effects, such as in audio signal processing or computer graphics. However, in most cases, aliasing is undesirable and steps should be taken to avoid it in order to accurately represent signals.

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