How Oscilloscope Acquisition Modes Work - Workbench Wednesdays

[@mariusbogdan9036]

Thank you! Congratulations! Although I have known the usefulness of the peak detect sampling method for a long time, I had to explain it to a colleague in an understandable way. (He's a mechanical engineer, but he's interested in electronics) I couldn't articulate it so clearly and succinctly. I am reminded of my late electrical engineering - physics teacher, to whom I have a lot to thank. I believe that we must be born for this, that we pass on knowledge in an understandable way. Like an artist.

[@baghdadiabdellatif1581]

I have now found the best content about oscilloscope settings.Thank you. Greetings from north Africa (Algeria )

[@AjinkyaMahajan]

I didn't know about these features. It was a great add on to my knowledge base. Thanks Cheers

[@johnnguyen8508]

thank you for the energy and pacing in your instructional videos! I don't feel like going to sleep while listening to you :)

[@FEPLabsRadio]

James, your videos are excellent. Every time I watch your vids, I pick up something new. Thanks!👍

[@MAYERMAKES]

Ah finally all the modes in one place

[@dimitartodorov3111]

Excellent video with clear explanations. Nice work!

[@castlecodersltd]

Thank you for this. A great explanation

[@7alfatech860]

Gotta love digital magic! Thanks for this excellent video.

[@waelsadek81]

Thanks for the video, so average and envelope works on multiple acquisitions while sample, peak and high res works on a single acquisition ?

[@Bob-tu9jq]

Thank you James!!!!!!

[@geoffwade8144]

Thank you.

[@aronhighgrove4100]

2:48 "Accuracy is the representation, precision is the display" is a nice mnemonic. However difficult to apply in a device that has several levels of data processing, at each level precision and accuracy come into play again, so both get affected, and you have to chose the level that gets affected most. Representation and display is essentially the same in a digital system, since at each step you have a representation/data: what is your acquired data, and how much of it do you show to the next stage (what portion of that data do you show on a screen). But at the final step, representation and display should be identical (data you write into graphics/display memory). To simplify, easiest would be to think of it as an image viewer, you have the raw data from the image file, and then the interpolation and rounding or data reduction due to displaying the image on screen.

[@aronhighgrove4100]

2:45 It affects both accuracy and precision. It could also affect resolution if it applies oversampling. See below for more. What it really does, though, is partially remove overlaid signals you want to filter out, so you could say it more accurately represents the "original" signal you are *interested in*. Your interest defines here what is accurate, but usually you think of accuracy and precision in an acquisition context, not in a filtering context, and even less in a much broader context, like here, where many data processing steps are involved. So the confusion comes from thinking of accurate signal = signal you are interested in/cleaned up signal, which you would usually express as: averaging out random noise. However accuracy vs. precision is meant in an acquisition context: an accurate and precise acquisition captures the signal as is, including all the random noise. Resolution (so essentially step size), can affect both accuracy and precision. Accuracy is a systematic error (like an offset), and precision is repeatability (same measurement conditions, same result). Sampling noise due to resolution limitations can affect precision (as rounding errors can amplify tiny variations). But step size (depends on resolution) can also introduce systematic errors, like adding a small offset when rounding a sample always in one direction while digitizing it (one step up or one step down). Averaging will mostly improve precision as it takes more samples over time to reduce their variation over time (assumes the signal is only unsteady over time due to noise), but it shouldn't affect accuracy (unless the variations include a big glitch or offset). That's however assuming that the averaging doesn't remove actual signal noise (vs. just sampling noise introduced by the oscilloscope). If it averages out signal noise, and you assume the true signal is without the noise, you could say it improves accuracy. So what is the correct term depends on what kind of noise you want to remove (what level in the signal acquisition or signal creation you look at, and where the signal to average out is introduced), and what you assume your true signal to be and where the noise comes from.

[@bald_engineer]

Sure.

[@aronhighgrove4100]

4:58 Does averaging really increase the effective number of bits? It averages out random noise, but if the trigger point is exactly at the same period it should not affect resolution/number of effective bits. You probably would need at least some slight variation of the trigger period (to see new data) or some oversampling method to increase the effective number of bits. Varying the trigger period might also affect sampling noise, so that averaging could have a benefit.

[@bald_engineer]

Yes, waveform averaing does increase the SNR, which is part of improving ENOB. It is primarily meant to remove quantiziation error, but waveform averaging also reduces a signal's random noise as well. There's no way for an ADC to know the difference. And no trigger circuit is perfect enough to perfectly overlap waveforms and samples. So there is always slight variation when using waveform averaging.

[@TrippLilley]

Accuracy is hitting the right spot. Precision is hitting the same spot every time.

[@jonnymakers9560]

At my lab we always use infinite persistence. There can only be one