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In this paper, we derive, based on the analytical model developed by Cali et al., a multi-class model to study how to adaptively tune parameters in IEEE 802.11e EDCA and support service differentiation in WLANs.
By constructing a facet grid around the nose tip in each of the two facial surfaces, we can derive, based on its ordered structure, m×n valid pairs of corresponding facets, where (m n) represent the size of the grid.
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Corrections to the one-sample log-rank test are also derived based on the Edgeworth expansion.
Genes were derived based on the genome coordinates.
The relative CML content was derived based on previous published protocol14,15.
A consistent solution is then derived based on the guidance from a domain expert.
The controller is derived based on the backstepping method.
Sufficient conditions are derived, based on the Lyapunov theory.
New CFs are derived based on time-propagated measurement concept.
Their parameters are derived based on evaluated data.
Governing equations are derived based on the Hamiltonian principle.
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