Power law noise identification using the LAG 1 autocorrelation by overlapping samples
There are various random errors in the fiber optical gyroscope (FOG) output signal. At the aim of improving its accuracy, it is need to identify the kinds of errors. The most common method for power law noise identification is simply to observe the slope of a log-log plot of the Allan or modified Allan deviation versus averaging time, either manually or by fitting a line to it. The lag 1 autocorrelation method is a new method for power law noise identification that can determine the dominant noise type for all common noise processes, from phase or frequency data, for all averaging factors, in a consistent and analytic manner. This paper describes an improvement of it by overlapping samples, which improves the confidence of the resulting stability estimate at the expense of greater computational time.
Handbook of Frequency Stability Analysis | NIST
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Frontal cognitive dysfunction in juvenile myoclonic epilepsy
Purpose: The aim of the present study was to investigate the possible frontal cognitive dysfunction in patients with juvenile myoclonic epilepsy (JME) and to compare the results with those of patients with frontal lobe epilepsy (FLE) and temporal lobe epilepsy (TLE), as well as with controls.