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A wireless mobile station (STA) acts as a mesh client and is connected to an MAP through generic WLAN protocols.
Furthermore, in reality, the majority of critical care practitioners seemed to be targeting an MAP higher than 65 mmHg [26].
Practitioners and researchers tend to disagree even on such a fundamental practice as measuring an MAP [32].
We expected an MAP drop ≥20%% to occur within 15 min after 20%% of QC procedures [7].
Looking specifically at renal function, however, other studies found that targeting an MAP higher than 70 mmHg might be beneficial [23, 25].
In order to alleviate the over-fitting problem, a Gaussian prior is assumed and an MAP adaptation method is derived in[3].
To realize a practicable training algorithm for the new HMM, this paper first introduces an entropic definition of the a priori probability of the model and accordingly a maximum a posteriori (MAP) training strategy, and then designs an MAP training algorithm in the case of fixed state number based on the deterministic annealing (DA) technique.
This is where an MAP sequence saves the day.
Instead, our primary endpoint was an MAP drop ≥20%%.
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In Section "The proposed tag estimation mechanism", we propose an MAP-based tag estimation with low computational complexity.
To address this problem, we proposed an MAP-optimal solution approach that restores the ability to perform localization using well-known and established algorithms.
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