Estimate noise improvement as a funtion of sampling rate

In summary: Thanks for all the help!In summary, this individual is asking if it is possible to increase the sampling rate without causing more noise. They are also asking for advice on how to quantise the signal at a higher sampling rate.
  • #1
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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  • #2
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.
 
  • #3
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.
 
  • #4
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.
 
  • #5
Yes, I integrate before sampling, why?
 
  • #6
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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  • #7
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?
 
  • #8
I think you got it. Good luck.
 
  • #9
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.
 

FAQ: Estimate noise improvement as a funtion of sampling rate

1. What is meant by "noise improvement"?

"Noise improvement" refers to the reduction of unwanted or random fluctuations in a signal, resulting in a clearer and more accurate representation of the underlying data.

2. How is noise improvement quantified?

Noise improvement is typically quantified using a metric called the signal-to-noise ratio (SNR), which compares the strength of the signal to the level of background noise present in the data. A higher SNR indicates better noise reduction and therefore a more accurate signal.

3. What is the relationship between sampling rate and noise improvement?

The sampling rate, or the rate at which data points are collected, has a direct impact on noise improvement. As the sampling rate increases, the amount of data being collected also increases, allowing for a more detailed and accurate representation of the signal and resulting in better noise reduction.

4. Is there a specific sampling rate that guarantees a certain level of noise improvement?

No, there is no specific sampling rate that guarantees a certain level of noise improvement. The amount of noise reduction achieved depends on a variety of factors, including the characteristics of the signal and the noise, as well as the sampling rate.

5. Are there any limitations to using sampling rate as a means of improving noise?

While increasing the sampling rate can improve noise reduction, there are practical limitations to consider. Higher sampling rates require more storage space and processing power, and there may be diminishing returns as the rate increases beyond a certain point. Additionally, other methods such as signal filtering may also be necessary to achieve optimal noise reduction.

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