Understanding Quantization Effects

In summary, the speaker is obtaining data with a sample rate of 6250 samples/second on a digital oscilloscope, and is expecting a sinusoid output through a system from a sinusoid input. However, the actual results are lower in amplitude than expected. The speaker suspects that this is due to the system itself, but has also been asked to consider the effect of 8-bit quantization on the outcome. They are unsure of how this could be affecting the results and are seeking advice or information on the matter. The conversation also mentions the input impedance of scopes and the potential impact of load impedance on voltage. Additionally, the speaker mentions an equation from a communications book that shows how bit rate affects the signal to noise ratio and how
  • #1
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I am obtaining data with a sample rate of 6250 samples/second on a digital oscilloscope. Ideally, I am obtaining a sinusoid output through a system from a sinusoid input. My expected results through the system are lower in amplitude than I am actually recording. I have some reasons for why this is occurring pertaining to the system itself, but I have been asked to also describe how the 8-bit quantization of the data can be affecting the outcome. I don't really know why this is. I would appreciate any advice or information anyone can give on the matter.

Thanks

Brent
 
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  • #2
Scopes have input impedance 1 to 10 Mega Ohms so they affect the measurment so little they can't even measure that.

The problem might be the load impedance of your circuit. Could be low enough to lower the voltage.
 
  • #3
How much lower is the amplitude than what you expect? For 8 bits it shouldn't be by much. I think the key here is how bit rate affects the signal to noise ratio. Pulling up an equation from my old communications book, this error ratio that is *inherent to A/D conversion* can be expressed in terms of bits as 10*log(3*2^(2*bits)*Sx) where Sx is the original signal power (just set Sx equal to 1 if it disturbs you). This equation isn't all-encompassing and actually makes some assumptions like your quantization levels are equally spaced and that there is an equal probability that the signal gets rounded down by the same amount as up somewhere else in the sample, but the general concept is there.

Quantization error causes some power of the original signal to be moved to other garbage frequencies, and therefore your sinusoidal signal's output would appear slightly smaller than expected on the o-scope.
 
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FAQ: Understanding Quantization Effects

What is quantization and how does it affect data?

Quantization is the process of converting continuous values into discrete values. In data, this means rounding off numbers to a specific precision. This can lead to a loss of information and accuracy in the data.

How does quantization affect digital images and videos?

Quantization can significantly impact the quality of digital images and videos. In image and video compression, quantization is used to reduce the file size by rounding off pixel values. However, this can result in a loss of detail and visual quality in the final image or video.

Can quantization be reversed or undone?

No, once data has been quantized, it cannot be reversed or undone. This is because the original continuous values have been rounded off and lost in the process.

How can we minimize the effects of quantization?

One way to minimize the effects of quantization is by using a smaller quantization interval. This means using a finer precision in rounding off values, which can result in less data loss. Additionally, using more advanced quantization techniques such as dithering can also help minimize the effects of quantization.

What are the real-world applications of understanding quantization effects?

Understanding quantization effects is crucial in digital signal processing, data compression, and image and video processing. It is also important in fields such as finance and economics, where data is often quantized for analysis and decision-making.

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