The result reveals that the variance of timing drift is in inverse proportion to SNR and grows linearly by square of the number of total subcarrier, i.e., the timing drift will be obvious in the condition of either bad channel quality or large subcarrier number.
More than 85% of the variance in timing of the maximum growth is explained by the models and similar for the seasonal growth of mesic and wet vegetation types.
In this way, the limiting variances for timing can be used to get an insight about the positioning accuracy.
Previous findings have demonstrated that the relationship between timing variance and timed duration is different in different temporal tasks [7], [8], [26], [36] and is influenced by interval structure (auditory/visual, filled/empty) [54], [55] and sensorimotor context (perception/production) [24], [25].
Finally, an important aspect of the investigation of the seasonal parameters is that the main period of analysis must be reasonably 'stationary' (i.e. no overall trends in the mean, the variance or the timing or peaks with time).
When variability in temporal estimates was modeled, the rate of increase in variability (the slope in the linear regression between timing variance and the standard duration) was larger in the implicit task and ascribable to the temporal, rather than non-temporal, mechanism engaged.
The additive genetic variance in the timing of metamorphoses could then be estimated as four times the sire variance [25].
Fig. 12 Minimum variance spectrum for timing estimation.
Using the twin design, this study investigates whether genetic variability explains some of the variance in coincidence timing and whether common genetic factors account for the association with intellectual functioning.
The study is based on the analysis of the S-curve and loop noise variance of the timing error detector, from which an approximation of the asymptotic performance of the joint estimation algorithm is derived.
