How to Understand Aliasing in Digital Sampling ('Best explanation ever!!!')

Explains Aliasing in digital sampling with a practical example using the wheel of a bicycle.

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[zhou]

Have to say that a new creative, successful explanation style is born. Thanks for creating the video. Cannot wait for more.

[edmundkemper]

Greatly appreciated professor, your initiative to give special importance to intuition when teaching a concept is what impresses me the most!

[KFJO]

Excellent video!

Nyquist sampling theorem says that if you want to figure out how fast the bike tire is moving (frequency) and which direction it's moving, you need to sample it at least twice as fast as it is spinning. Sample slower than that and it'll look like the wheel is spinning backwards, even when it's spinning forward. You won't be able to tell if the wheel is spinning forward at f_nyquist + delta or f_nyquist - delta. This is aliasing where the spectrum "folds back" onto itself about the Nyquist sampling rate. Some designs actually rely on sampling in higher Nyquist regions, though it requires a high pass filter or band pass filter to guarantee that there is no frequency content below that Nyquist region. Otherwise you will get an aliased spectrum.

[ArthurMack-un]

This is a great video, very creative way to explain a concept that can get confusing.

[beyzavardar]

YOU ARE PERFECT! I AM WATCHING YOUR VIDEOS FOR 3 DAYS WOW

[sagarraj]

I don't think there is a better video than this to explain aliasing.... Thanks a lot for the effort n

[BearfootBrad]

I like the bird yelling in the background towards the end

[PhG]

A remarkable approach on the theory of aliasing ! Very well and comprehensive explained !

[DoesntReadReplies]

Thank you for this. I wish my professor had explained it this way. So much more intuitive than staring at sine waves of different frequencies trying to puzzle out what the heck aliasing is from that.

[chengshen]

Very brilliant explanations! Haven't been to Manly since covid

[vtrandal]

Thank you for making so many wonderful videos. An example I like to use involves an ordinary clock with hour and minute hands. If we look at the clock every 11 hours, then the hour hand will appear to move backward 1 hour. Similarly for the minute hand if we observe it too infrequently it too will appear to move strangely. And many other false or aliased outcomes occur by sampling too infrequently as you have so nicely demonstrated.

[kates-creates]

this channel is great!
thank you for all of your effort

[datamatters]

Another great explanation. Thanks. The star jumps are a wonderful example.

[Nikhil-IITT]

Really Thanks a lot Best explanation ever!!!

[muzananadeem]

Wonderful Explanation sir. But I want to ask a question: here while the camera captured the backward motion of the wheel its sampling rate was high? and if I want to get the correct movement of the wheel in the video should I have to keep my sampling rate low?

[funnzie]

So why do we get aliasing in our synthesizers when we have 2x the samples of the frequency? (22.05kHz @44.1 sample rate).

[pitocipo]

Very clear and very good approach to move the concept to real world. Nice bicycle tough 👍🏻.

[neotodsoltani]

Hi, I really liked the environments that you recorded the videos in (next to the sea, etc); can you tell me what country/city is that? it was very beautiful

[mohamedhadjalla]

I really want to thank you, i have just understood decimation and interpolation with your videos, Good Luck from Algeria 🇩🇿 ❤️
I wish you make videos about Adaptive filters and Blind Source Separation, Thank you !

[TheRedBullHulk]

That was so helpful, thank you so much!