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The main purpose of Step1 is to compute an upper bound for the heartbeat interval η max, given the input values provided by the application: the upper bound on the detection time (({T_{D}^{U}})), upper bound on the average mistake duration (({T_{M}^{U}})), and lower bound on the average mistake recurrence time ((T_{MR}^{L})).
The parameters include the upper bounds on the detection time and mistake duration, and the lower bound on the average mistake recurrence time.
({T_{M}^{U}}): an upper bound on the average mistake duration; (T_{MR}^{L}): a lower bound on the average mistake recurrence time.
The PEP is utilized to determine the upper bound on the average bit error probability (ABEP).
Introduce d k as a lower bound on the average rate Rk,F/T.
First, we derive the bound on the average number of hops.
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Lower bound on the percentage of average total number of products dispatched to customers.
Upper bound on the percentage of average total number of products dispatched to customers.
The shapes of a subcollection of only D summand-sets determine the bound on the distance between the average set and its convex hull; thus, as the number of summands increases to infinity, the bound decreases to zero (for summand-sets of uniformly bounded size).
Thus, Starr's bound on the non-convexity of the average : ∑ Qn decreases as the number of summands N increases.
In fact, Starr's bound on the non-convexity of this average set decreases to zero as the number of summands N increases to infinity (when the inner radii of all the summands are bounded by the same number).
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limitations on the average
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