this post was submitted on 28 Jul 2024
218 points (97.4% liked)

Photography

4545 readers
55 users here now

A community to post about photography:

We allow a wide range of topics here including; your own images, technical questions, gear talk, photography blogs etc. Please be respectful and don't spam.

founded 1 year ago
MODERATORS
 

The nyquist sampling theorem is a cornerstone of analog to digital conversion. It posits that to adequately preserve an analog signal when converting to digital, you have to use a sampling frequency twice as fast as what a human can sense. This is part of why 44.1 khz is considered high quality audio, even though the mic capturing the audio vibrates faster, sampling it at about 40k times a second produces a signal that to us is indistinguishable from one with an infinite resolution. As the bandwidth our hearing, at best peaks at about 20khz.

I’m no engineer, just a partially informed enthusiast. However, this picture of the water moving, somehow illustrates the nyquist theorem to me. How perception of speed varies with distance, and how distance somehow make things look clear. The scanner blade samples at about 30hz across the horizon.

Scanned left to righ, in about 20 seconds. The view from a floating pier across an undramatic patch of the Oslo fjord.

*edit: I swapped the direction of the scan in OP

you are viewing a single comment's thread
view the rest of the comments
[–] volodya_ilich@lemm.ee 4 points 3 months ago

Little nitpick to you:

Nyquist will ensure that you preserve artifacts that indicate primary frequency(ies) of interest, but you'll lose nuance for signal analysis.

When we're analyzing a signal more deeply we tend to use something like 40x expected max signal frequency, it'll give you a much better look at the signal of interest.

This is because your signal of interest, unless purely sinusoidal, has higher frequency features such as harmonics, so if you sample at Nyquist you'd lose all of that. Nyquist theorem still stands, it's just you wanna look at higher frequency than you realize because you wanna see higher frequency components of your signal.