(14) The voxel re-classification is formulated as a maximization problem: (15) is the best estimation for the true class label vector.
We formulate this problem as a maximization of the degree of decentralization, subject to a given H∞-performance.
We formulate this problem as a maximization of the degree of decentralization, subject to a given error performance in terms of the H∞-norm between the centralized and the decentralized closed loop.
The problem is formulated as the maximization of the posterior density as follows: begin{aligned} varvec{x}^ = mathop {text{arg max}}limits _{varvec{x}} p varvec{x} | { varvec{c}_{ij} }_{langle i,j rangle in {C}}).
Given an initial value for the sub-carrier and power allocations (P 0),ρ(0)), the optimization problem to determine SDTs is formulated as the maximization of the average total SU capacity while keeping the PUs' rate loss upper bounded.
The model is formulated as a profit maximization problem and solved using Genetic Algorithm.
Similarly, CCO can be formulated as the maximization of CC fitness, ΨCC (see Eqn. 26), subject to the constraint that the codon pair sequence can be translated into the target protein (see Eqns. 7, 8 and 12).
Channel state, physical-layer characteristics, queueing delay and/or QoS requirements are projected into utility functions and the multi-objective optimization problem is then formulated as a constrained utility maximization problem, where the objective function is the maximization of the user services' utility functions.
The clustering problem is re-formulated as the maximization of elements in the matrix, and we transform the exhaustive search of the optimal clustering pattern to the sequential selection of small cells.
Taking the maximization of the energy utility as the design objective, the optimization problem is formulated as a function of three variables, which are sensing time, sensing threshold, and the number of cooperative SUs, subject to the target detection probability.
