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These spatio-temporally complete datasets are interpolated to each sounding location, using the closest estimate in time.
For simplicity, we assume (boldsymbol {x}_{n}^{H} boldsymbol {x}_{n} =1), such that the estimate in time slot n is begin{array}{*{20}l} hat{h}_{n}=h+h_{n}', end{array} (22).
We already know that we have begin{aligned} sum _{k}tau left( frac{W_{2} varrho ^tau _k, varrho ^tau _{k-1})}{tau }right) ^2 le C, end{aligned} (4.16 which is the discrete version of an (H^1) estimate in time.
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Later, to enhance the accuracy of the noise PSD estimate in time-varying noise environments, an eigenvalue compensation scheme is presented, in which two eigenvalues obtained in noise-dominant regions are combined using a weighting parameter based on the speech presence probability (SPP).
According to the Nyquist theorem, summing N f delayed (by K N f ) replicas of the input signal is equivalent to filter the pilot positions in the frequency domain, and therefore, the LS estimate in time-domain is made-up of N f replicas of the CIR separated by K N f [26] h ̃ ^ n LS = ∑ m = 0 N f - 1 h ̃ n - m K N f + ∑ m = 0 N f - 1 w ̃ n - m K N f, for n = 0, 1, …, K N f - 1 0, remaider, (7).
Therefore, classical error is expected to attenuate the effect estimate in time-series epidemiologic studies.
Variogram analyses benefit from the high resolution of reflectivity estimates in time and space.
The team liked the idea of estimating in time, because this information is important to them.
Moreover, the blow-up rate estimates in time and space is obtained.
Conclusions (13) and (14) in Theorem 2 signify that the time semi-discretized solutions to Problem III are stable and attain the optimal error estimates in time.
However, because the MT method uses a time-averaged frequency-domain analysis, we then averaged the cross-phase estimates in time.
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