I really appreciated your explanation of quantization errors and possible solutions, as well as the trade-offs that are involved. It seems to me from reading your blog that an important first step of any project would be to have a very good understanding of the precision required so that one knows what effect quantization errors would have and how far one should go in attempting to reduce or eliminate them. Thanks for the great information, Jon!
Thanks, Nancy. Yes, before you think about digitizing analog signals you must know much about them. Unfortunately, some engineers jump in and specify data-acquisition equipment they later find doesn't give them the results they expect. Early in my career I made similar mistakes.
Great article! Sometimes you have a fast ADC and a lot of time for a precision measurement. In 1989 we scrapped a whole board of high precision analog components on a Shuttle experiment which took 5 seconds to produce a single 12 bit digitization by supeimposing a precision sine wave on the DC raw data and summing 2048 samples from the 12bit ADC. We achieved 18 bit precision in one second. The accuracy was improved by intermingling precision references and board temperature measurements, and applying post processing corrections. (US Patent 4973914).
In practice, oversampling is implemented in order to achieve cheaper higher-resolution A/D and D/A conversion. For instance, to implement a 24-bit converter, it is sufficient to use a 20-bit converter that can run at 256 times the target sampling rate.
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For industrial control applications, or even a simple assembly line, that machine can go almost 24/7 without a break. But what happens when the task is a little more complex? That’s where the “smart” machine would come in. The smart machine is one that has some simple (or complex in some cases) processing capability to be able to adapt to changing conditions. Such machines are suited for a host of applications, including automotive, aerospace, defense, medical, computers and electronics, telecommunications, consumer goods, and so on. This discussion will examine what’s possible with smart machines, and what tradeoffs need to be made to implement such a solution.