Nyquist sampling theorem

yecko

Gold Member
1. The problem statement, all variables and given/known data
In comparison with the sampling sine wave, in order to reconstruct a square wave, do we need to increase or decrease sampling frequency?

2. Relevant equations
Aliasing effect
Leakage effect

3. The attempt at a solution
No matter square wave or sine wave, the experimental results shown the higher sampling frequency (10kHz, 25kHz, 100kHz, 250kHz, 2.5MHz) construct a clearer waveform (signal freq = 25kHz).

Is there difference between sine and square wave for "increase or decrease sampling frequency"?
Thank you

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scottdave

Homework Helper
Gold Member
The square wave is composed of odd harmonic sine waves of the fundamental frequency. What is it you are trying to do with the square wave?

Delta2

Homework Helper
Gold Member
A square wave has theoretically infinite bandwidth, so you theoretically need infinite sampling frequency to perfectly reconstruct it. This practically means that the higher the sampling frequency, the better reconstruction and there is no upper bound to the sampling frequency.
A sine wave has finite bandwidth and you can perfectly reconstruct it with sampling frequency that is double of the sine wave frequency.

scottdave

Homework Helper
Gold Member
While I normally don't use Wikipedia as a single source for a subject, I think the Square Wave Wikipedia page does a nice job of explaining it. https://en.wikipedia.org/wiki/Square_wave

When you say the higher frequencies construct a clearer waveform for the Sine wave, are you referring to how it looks on the screen? This is different than being able to reconstruct a band limited signal from a set of samples.

While more samples may look nicer to the viewer, many of those extra samples are unnecessary to reconstruct the sine wave.

FactChecker

Gold Member
2018 Award
I want to point out that the basic Nyquist theorum applies to the ability to get the correct amplitude given an infinite sample. Infinite samples are rare. There are more complicated versions that give bounds for the possible errors given a limited sample.

"Nyquist sampling theorem"

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