Therefore, we concentrate on finding a lower bound on the capacity.
In root theory we are interested in finding a lower bound for the number of roots of at.
First, the problem of finding a lower bound on the maximum sampling period that preserves the stability is formulated.
If we follow the method employed for Lemma 4.4 (which is based on the change of variable ψ = ϕ − z ), we end up with the problem of finding a uniform bound for { ∫ t 0 t | z ( s ) | 1 2 d s, t 0 ∈ R } (cf. equations (4.11 - 4.12)).
To select critical maximal clique κ c, the proposed algorithm starts by finding a lower bound on the potential new affected receivers, for the first ℓ video layers from time slot t to time slot t+1, that may result from selecting each vertex from critical IDNC graph (mathcal G_{c}^{1:ell }).
For a transgene with high CAI, the probability of finding a ribosome bound at a given codon is negatively correlated with the abundance of corresponding iso-accepting tRNAs (Pearson correlation, R = −0.802), but this correlation is much weaker for a transgene with low CAI (R = 0.042 and p > 0.05).
For each sensor (or actuator), the problem of finding an upper bound on the lowest sampling frequency (or refresh rate) that guarantees exponential stability is cast as an optimization problem in terms of linear matrix inequalities (LMIs).
The result of Theorem 2 is obtained by finding an upper bound on the worst-case pFDR given by pFDR max = max j ∈ { 1, …, m } pFDR ( j ) Γ ≤ max j ∈ { 1, …, m } ( P e ) max 1 − p ( j ) − ( P e ) max = ( P e ) max 1 − p max − ( P e ) max.
We are now interested in finding an upper bound on the value of o p t∗ by making use of the possibility given by EUCALYPT to limit the distance of switches.
In this paper, an alternative approach of finding a sharper upper bound for classical optimality criteria is proposed.
