Parallel algorithms are used, which can be used to simultaneous computation in multiprocessor computers.
Parallelizing optimization requires many licenses when commercial FEM codes are used, which can be very expensive.
For such an environmental dosimeter, BeO is used, which can be read out using optically stimulated luminescence (OSL).
In our experiment the following algorithms were used which can be formulated in terms of machine learning methods.
For parallelization issues the OpenMP programming model has been used which can be efficiently applied on C-level.
For this purpose, a PEM fuel cell behaviour model is used, which can be related to various fuel cell losses.
Thus, in this paper, the generalized sidelobe canceler (GSC) structure is used, which can be thought as an updating rule of the equivalent problem without constraints.
Alternatively, clustering approaches could be used, which can be optimised to give better results, albeit on the expense of the computation cost.
Among the several formulations of the generalized derivative, the Riemann-Liouville and Caputo definition are most commonly used, which can be described as follows [3].
We can also see that the relay transmit power of the proposed method is 2 dB higher than the case that the perfect CSI is used, which can be viewed as the price for the robustness.
In addition, this includes amplified stocks of libraries from each of the tissues used, which can be used for further studies.
