Estimate noise improvement as a funtion of sampling rate

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Discussion Overview

The discussion revolves around estimating the noise of a signal as a function of sampling rate, particularly in the context of experimental data involving multiple electrodes. Participants explore the implications of sampling frequency on noise and signal-to-noise ratio, referencing theoretical concepts and practical applications.

Discussion Character

  • Exploratory
  • Technical explanation
  • Conceptual clarification
  • Debate/contested

Main Points Raised

  • One participant inquires about estimating noise at higher sampling rates and seeks references for further reading.
  • Another participant mentions the Nyquist Sampling Theorem, suggesting that sampling noise increases as sampling frequency decreases.
  • A participant describes their experimental setup involving AC voltage biasing of electrodes and the integration of current over cycles, explaining the impact of scanning multiple electrodes on sampling rate.
  • There is a suggestion that using an integrating ADC could be beneficial for the participant's application.
  • A later reply discusses the relationship between the integrator as a low pass filter and the anti-aliasing math, indicating tools available for calculating signal-to-noise ratio based on sampling frequency.
  • One participant reflects on the Whittaker–Shannon interpolation formula as a means to understand the effects of sampling frequency on noise.
  • Another participant mentions quantization noise being consistent across sample rates and discusses the potential for noise improvement when oversampling, suggesting a possible 3dB improvement for doubling the sampling frequency.

Areas of Agreement / Disagreement

Participants express varying levels of understanding and approaches to the problem, with some agreeing on the relevance of specific formulas and concepts, while others raise questions or propose alternative views. The discussion does not reach a consensus on the best method for estimating noise improvement.

Contextual Notes

Participants reference theoretical concepts such as the Nyquist Sampling Theorem and the Whittaker–Shannon interpolation formula, but there are unresolved details regarding the specific application of these theories to the participant's experimental setup. The discussion includes assumptions about the behavior of quantization noise and its relationship to sampling frequency.

TryingTo
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Hi all,

I would like to estimate from my experimental data what would be the noise of the signal if I could sample at a higher rate. It is possible to do that? Do you have any references to good books that explain this topic?

Thank you!
 
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I assume that you are familiar with the Nyquist Sampling Theorum. There, sampling noise is increased as sampling frequency decreases.

If that's not what you mean, then please explain your question better.
 
Hi, thanks for your reply. I'll explain better.

I bias several electrodes with an AC voltage and measure the current at each of them. I don't monitor the current in real time, I integrate the current of 1 electrode during several cycles N and later measure the average current dividing by N. I can't do this for all the electrodes simultaneously so after I've done this for electrode 1 I move to electrode 2, and so on. Because I have to scan all the electrodes the sampling rate for 1 electrode is affected, and is lower than if I could scan only one electrode. I could implement experimentally that only a single electrode is measured but this is quite complicated so I want to study if it is worth it. Basically I want to do some analysis to the current experimental trace of electrode 1 and estimate (if it is possible): if my sampling rate would have been X times faster than the original value, what would be the new trace noise or signal to noise ratio?

Thanks again.
 
It sounds like a perfect application for an integrating DAC, where the signal is integrated on the analog side before sampling. Is that what you are using?

Edit: I meant to say ADC, not DAC. Analog-digital-converter.
 
Yes, I integrate before sampling, why?
 
OK, your integrator is a low pass filter, with a known slope. Given that, the anti-ailiasing math in the Wikipedia Nyquist Sampling article linked gives you the tools you need to calculate signal-to-noise ratio as a function of sampling frequency. I must be missing something, what more do you need?
 
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I think I was mising something..What I understand now is that the Whittaker–Shannon interpolation formula will give me the expression I need, changing T I can see the effect of the frequency sample. Am I right?
 
I think you got it. Good luck.
 
Thanks a lot! You've been very helpful!
 
  • #10
I seem to remember that the quantisation noise is the same power for all sample rates (defined by quantising size) When you sample at a higher frequency, this power is spread over a bigger frequency range and, because you are oversampling, you can safely LP filter to get rid of the HF components, so that the only quantising noise that will emerge will be that which is in the baseband bandwidth. Iirc, it means a possible 3dB improvement for doubling the sampling frequency.
I'm sure that a Google search with these terms in it will produce something that works for your particular level. I know that single bit ADCs (bit slice) can have excellent noise performance (once you've dealt with some very high speed circuitry problems.
 

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