More on Sampling, Aliasing & Reconstruction

A long time ago, we looked at Sampling and the Nyquist Theorem. One of the questions I get a lot is, how do we "connect the dots"? Some claim that "there is no stair step", while others say the lack of information between samples means there's nothing we can do.

Today let's take a closer look at this conundrum, and explore our options!

= CONTENTS =
0:00 Introduction
0:37 Premise
1:22 Assumption 1: Low Pass Filtering
2:26 Assumption 2: Reconstruction Filter
2:52 How the Assumptions Help Us
5:26 Why these are just "Assumptions"
6:20 Example 1: Sampling & Scaling in Photography
7:43 Example 2: Rendering in 3D Graphics
9:13 Conclusion

= 0612 TV =

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= NERDfirst =
NERDfirst is a project allowing me to go above and beyond YouTube videos into areas like app and game development. It will also contain the official 0612 TV blog and other resources.

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[oscarbautistagonzalez]

So, what I understand about this video is that you can reconstruct the analog signal by using both assumptions mentioned here and that are in agreement with the Nyquist theorem. But, in some context such as image processing these assumptions cannot prevent aliasing in some cases. Thanks a lot for this and the previous video.

[henryrobinson]

i like this and the other video, i work in automotive field with a cheap oscilloscope. and it explains a lot when having multiple mixed signals.