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Most accurate signal generator
ebastler:
--- Quote from: loop123 on April 04, 2024, 09:12:50 am ---In my last message to you. I answered the 1st and 2nd question.
--- End quote ---
I don't think you did; or I did not get your point. You told us what equipment you have and what its specs are. But you (still) have not told me what signals you need to measure, and where you find your current equipment to be lacking in its capabilities.
--- Quote ---About the third. It's small signal sitting on top of others.
--- End quote ---
OK. What is the level and the frequency (range) of the signal of interest, and ditto for the background?
--- Quote ---You said "Or do you need to pick out a 10 µV signal sitting on a 1 V background, as implied by your original questions in this thread -- in which case you need an ADC with correspondingly high resolution, if you want to use digital signal processing techniques to pick out the weak signal?".
But digital filters using oversampling with moving averages can only do brick wall filter above the cutoff frequency. How can digital signal processing techniques pick up the weak signal among the noises?? Is it not both the BMA-100 and USBamp outputs need to be pass through intensive Mathcad analysis? Or can digital signal processing somehow remove further noises below the cutoff? if not, how can it pick up the weak signal? What techniques is this called?
--- End quote ---
If you know the frequency (range) of interest, you can use bandpass filters to isolate the signal from the background. If there is a specific undesired background frequency, you can use a notch filter to suppress it. If you are looking for signals with characteristic frequencies, but you don't know the actual frequencies in advance, you can use FFT (Fast Fourier Transformation) to see all spectral components, of the signal and the background. Or you can use auto-correlation to find characteristic frequencies in the time domain (rather in the frequency domain as FFT does).
gf:
--- Quote from: loop123 link=topic=42, but463.msg5430356#msg5430356 date=1712221970 ---But digital filters using oversampling with moving averages can only do brick wall filter above the cutoff frequency. How can digital signal processing techniques pick up the weak signal among the noises?? Is it not both the BMA-100 and USBamp outputs need to be pass through intensive Mathcad analysis? Or can digital signal processing somehow remove further noises below the cutoff? if not, how can it pick up the weak signal? What techniques is this called?
--- End quote ---
It is not possible to separate an arbitrary, unknown signal from random noise. If a separation, or partial separation, is possible at all, then only if you have some a priori knowledge about the signal. The a priori knowledge could be as trivial as knowing that the signal is a sine wave, then you can easily dig it out from the noise floor with a narrow-band filter. But the a priori knowledge could also be a very complex statistical property of the signal, then you need a complex statistical analysis far beyond traditional DSP methods. So you should ask yourself the question: What do you already know about the signal you are looking for?
loop123:
--- Quote from: ebastler on April 04, 2024, 09:38:31 am ---
--- Quote from: loop123 on April 04, 2024, 09:12:50 am ---In my last message to you. I answered the 1st and 2nd question.
--- End quote ---
I don't think you did; or I did not get your point. You told us what equipment you have and what its specs are. But you (still) have not told me what signals you need to measure, and where you find your current equipment to be lacking in its capabilities.
--- Quote ---About the third. It's small signal sitting on top of others.
--- End quote ---
OK. What is the level and the frequency (range) of the signal of interest, and ditto for the background?
--- End quote ---
The range of frequency is 300Hz to 10000Hz, the level or amplitude I'm not sure. It's from at least 10uV upward to maybe below 0.5mV or between them. Background is human body potential and countless interferences.
I'm not even sure what it is i'm measuring. I'm just trying to repeat the experiments of some physicists/scientists. The signal is from another realm, not of this world. It needs to use mini-portal or Rosen Einstein bridge to tunnel the signal into this world. You know the brain is the most complex thing in the universe. Our body is also equally complex and tied up with new physics. Again I'm not sure whether the signal from another brane, a dark matter subsector, parallel universe or higher aspect of the universe. I'm still discussing with CERN scientists at other forums.
I know this message would be met with disbelief or I'd be ignored from now on. But I got most information I need already. I'll just study what you wrote below and discuss with digital signal processing experts as well as Mathcad experts from now on. I don't know how to use Mathcad. So it all boils down to signal analysis and I'll talk with other forums that deal with it. The days ahead would be harder I know.
--- Quote ---
--- Quote ---You said "Or do you need to pick out a 10 µV signal sitting on a 1 V background, as implied by your original questions in this thread -- in which case you need an ADC with correspondingly high resolution, if you want to use digital signal processing techniques to pick out the weak signal?".
But digital filters using oversampling with moving averages can only do brick wall filter above the cutoff frequency. How can digital signal processing techniques pick up the weak signal among the noises?? Is it not both the BMA-100 and USBamp outputs need to be pass through intensive Mathcad analysis? Or can digital signal processing somehow remove further noises below the cutoff? if not, how can it pick up the weak signal? What techniques is this called?
--- End quote ---
If you know the frequency (range) of interest, you can use bandpass filters to isolate the signal from the background. If there is a specific undesired background frequency, you can use a notch filter to suppress it. If you are looking for signals with characteristic frequencies, but you don't know the actual frequencies in advance, you can use FFT (Fast Fourier Transformation) to see all spectral components, of the signal and the background. Or you can use auto-correlation to find characteristic frequencies in the time domain (rather in the frequency domain as FFT does).
--- End quote ---
ebastler:
--- Quote from: loop123 on April 04, 2024, 10:07:30 am ---I'm not even sure what it is i'm measuring. I'm just trying to repeat the experiments of some physicists/scientists. The signal is from another realm, not of this world. It needs to use mini-portal or Rosen Einstein bridge to tunnel the signal into this world. You know the brain is the most complex thing in the universe. Our body is also equally complex and tied up with new physics. Again I'm not sure whether the signal from another brane, a dark matter subsector, parallel universe or higher aspect of the universe. I'm still discussing with CERN scientists at other forums.
I know this message would be met with disbelief or I'd be ignored from now on. But I got most information I need already. I'll just study what you wrote below and discuss with digital signal processing experts as well as Mathcad experts from now on. I don't know how to use Mathcad. So it all boils down to signal analysis and I'll talk with other forums that deal with it. The days ahead would be harder I know.
--- End quote ---
Good luck with your endeavors, but I think signal processing is the least of your worries. Better don't tell those CERN scientists and Matchcad experts about the signals which are not of this world. ???
shapirus:
This world or not, but if the signal exists and it is not a natural background noise, then you will be able to detect and measure it, given the right methodology and instruments.
The problem here is that the right methodology requires education and/or training and/or rich experience. The right instuments may require a lot of money.
If you don't know precisely what you're looking for, you may have to look for everything in many small isolated areas one by one until you spot something of interest -- see e.g. the bandpass filter suggestions above.
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