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Effective number of bits (ENOB) is a measure of the real dynamic range of an analog-to-digital converter (ADC), digital-to-analog converter (DAC), or associated circuitry. . Although the resolution of a converter may be specified by the number of bits used to represent the analog value, real circuits however are imperfect and introduce additional noise and distor
Differential nonlinearity (acronym DNL) is a commonly used measure of performance in digital-to-analog (DAC) and analog-to-digital (ADC) converters. It is a term describing the deviation between two analog values corresponding to adjacent input digital values.
The notable unsolved problems in statistics are generally of a different flavor; according to John Tukey, [1] "difficulties in identifying problems have delayed statistics far more than difficulties in solving problems." A list of "one or two open problems" (in fact 22 of them) was given by David Cox. [2]
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The MSE either assesses the quality of a predictor (i.e., a function mapping arbitrary inputs to a sample of values of some random variable), or of an estimator (i.e., a mathematical function mapping a sample of data to an estimate of a parameter of the population from which the data is sampled).
In ADCs, it is the deviation between the ideal input threshold value and the measured threshold level of a certain output code. This measurement is performed after offset and gain errors have been compensated. [1] The ideal transfer function of a DAC or ADC is a straight line. The INL measurement depends on what line is chosen as ideal.
Any non-linear differentiable function, (,), of two variables, and , can be expanded as + +. If we take the variance on both sides and use the formula [11] for the variance of a linear combination of variables (+) = + + (,), then we obtain | | + | | +, where is the standard deviation of the function , is the standard deviation of , is the standard deviation of and = is the ...
It is remarkable that the sum of squares of the residuals and the sample mean can be shown to be independent of each other, using, e.g. Basu's theorem.That fact, and the normal and chi-squared distributions given above form the basis of calculations involving the t-statistic: