Talk:Nyquist–Shannon sampling theorem/Archive 3

Archive 1

Archive 2

Archive 3

Figs 3, 4 and 8 -- very unclear

These figures are not very clear and should be tidied up by someone more knowledgeable than myself.

Problems:

1. The term "images" is introduced without explanation. From my mostly-forgotten understanding of Shannon's theorem, it appears to me that these "images" are similar to what are called "sidebands" in radio communications. Whatever "images" are, they should be explained either in the text or the figures.

2. The lettering on the frequency scale is unclear, particularly for Fig 3. For example, what is supposed to be made of "-f+ BB"? Some of the lettering should be moved above the scale to get it out of the way of the others. —Preceding unsigned comment added by Guyburns (talk • contribs)

Fig 3 was an .svg file and was reverted to the .png file it previous was. They tell us that the vector graphics versions of the same drawn image is better because they are scalable without sampling artifacts, but in fact, because of some screw up, the .svg files never appear the same uploaded as they were created as one can see from the comments of the image creator at Commons. The letters were jammed together. The .png file is better.

Images are not quite the same as "sideband" like in single sideband or double sideband in AM communications. If your reference point is that of an amateur radio operator or similar, images from sampling are like what happens with what we used to call a "crystal calibrator" that began as a 100 kHz signal, then passed through a nonlinearity to create more images of that 100 kHz at integer multiples of 100 kHz. The sampling operation is such a non-linear operator that takes the original spectrum and creates copies of that original spectrum centered at integer multiples of f_s. Those copies are the images and an ideal brickwall filter can remove the images while leaving the original spectrum unchanged. 70.109.185.199 (talk) 03:23, 27 April 2010 (UTC)

Dubious external link

'Undersampling and an application of it' looks a little dubious to me. It starts out quite interesting but further down some rambling starts. I'm not sure if an encyclopedia should link to this site. Even if it is legit, I don't think it's entirely on topic. In accordance with wiki guidelines to avoid external links I'd vote to remove it (and possibly use it as a reference rather than an external link in an article more focused on undersampling, in case it meets the quality guidelines). 91.113.115.233 (talk) 08:00, 18 August 2010 (UTC)

Angular frequency vs. Frequency

I think the article should show equivalent forms of the sampling theorem stated in terms of angular frequency, as many textbooks use this convention. I realize its simple to convert, but still... 173.206.212.10 (talk) 03:39, 23 November 2010 (UTC)

You might be right, but sometimes I wish we would stamp out nearly all of the use of angular frequency in EE lit because either the Fourier Transform is not "unitary" (a scaling difference between forward and inverse F.T.) or there is this awful $1/{\sqrt {2\pi }}$ scaling factor in both forward and inverse. Having a unitary transform with no scaling factor in front makes it easy to remember how specific transforms are scaled (like the rect() and sinc() functions) and makes theorems like Parsevals and duality much simpler. 71.169.180.100 (talk) 06:57, 23 November 2010 (UTC)

The Sampling Process Section

The article currently states: "In practice, for signals that are a function of time, the sampling interval is typically quite small, on the order of milliseconds, microseconds, or less."

This is not really true - it depends on which "practice" to which you are referring. What about long-term studies? Moreover, this sentence is not really helpful. It doesn't add any useful or insightful information to the article. — Preceding unsigned comment added by Wingnut123 (talk • contribs) 16:46, 22 March 2011 (UTC)