Using our simulation framework, we were able to rapidly find a configuration that exactly matches the data-rate constraint.
The big issue, as Android Police points out, is that you'd be hard-pressed to find a configuration that works well while bouncing between your favorite apps.
In line 2 to 10, in order to find a configuration with maximal simulated SNR (phase I), calculated SINR is utilized.
Finding a configuration of a distributed system satisfying performance goals is a complex search problem that involves many design parameters, like hardware selection, job distribution and process configuration.
It is hard, however, to find a configuration of an SPL that meets an arbitrary requirement set and does not violate any configuration constraints in the SPL.
This process performs local relaxation optimization in two separate phases: In the first phase, the optimization finds a configuration of the highest SNR; in the second phase, starting from the found configuration, the optimization searches an optimal point using the relaxation optimization.
We can then run different simulations increasing both the battery size and the solar panel 〈β〉 parameter in order to find a configuration that satisfies a 100 bits/s requirement.
The proposed method uses the least design variable, i.e. only the number of nodes needed to find a configuration of an irregular tensegrity structure, without any information on the symmetrical geometry or other predefined initial structural conditions.
The aim is to find a configuration of the fresh fuel assemblies and remnants in order to keep the power plant running at full power by the largest time as possible.
The optimal configuration is thus defined as follows: Given a set of transactions (mathcal {T}) and the fee (f_t) associated with each transaction (t in mathcal {T}), find a configuration c with size of T with respect to (mathcal {T}) such that the aggregated fees are maximized: begin{aligned} argmax_{c} sum _t f_t end{aligned}.
