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Swamidass et al. showed in [ 6] that if | A| and | B| are known, S T (A, B) can be upper-bounded by This bound can be used to speed up the search, by storing the database of fingerprints in N + 1 buckets such that bitstring B is stored in the | B|th bucket.
The BCH codes are a class of cyclic codes whose generator polynomials are chosen so that the minimum distances are guaranteed by this bound.
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By using this bound and computer searching, some known results on almost difference families by Ding and Yin are improved.
The system model of BICM-MIMO is presented in Section 2. In Section 3 a lower bound on the MSE of the channel estimator is obtained and the training sequence is designed by minimizing this bound.
By integrating this bound with respect to all but one Q-distributions from Equation (6) and maximizing the resulting functional with respect to the remaining Q-function, we obtain for every Q-function in Equation (6) a separate update rule.
Yet, given the distribution of potential mates, is bounded from above by (since ), and this bound characterizes the situation of complete preference of type k for type l.
We show how to compute this bound by solving a simpler problem.
Simulation results indicate that we can get close to this bound by using proper sparsity-based methods.
When G is a tree, Fritscher et al. [15] improved this bound by giving (S_{2}(T) leq m(T +3-frac{2}{n(T +3-frac{2}{mplies that Haemers' bound is always noT }ttainable for trees.
When (k = 2), Haemers et al. [10] proved the conjecture by showing (S_{2}(G) leq m(G +3) for any graph G. Especially when G is a tree, Fritscher et al. [14] improve this bound by showing (S_{2}(T) leq m(T +3-frac{2}{n(T +3-frac{2}{ndicaT } that Haemers' bound is alwhichnot attaindicatesr thats.
We can bound the marginal likelihood using Jensen's inequality: and optimize this bound by maximizing with respect to each factor separately until convergence.
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