The uniqueness of our inverse problem is shown by using a maximum principle related to an integro-differential operator.
In this section, we combine the received signals in all phases in order to detect S1 and S2 by using a maximum ratio combining (MRC) technique and a cooperative maximum ratio combining (C-MRC) technique.
In this paper, a robust nonlinear controller for sensorless interior permanent magnet synchronous motors (IPMSM) is designed to track a desired reference which is computed by using a maximum-torque-per-ampere strategy (MTPA).
Once the signal combining, i.e. (23) and (24), has been done at the destination node, the destination node's receiver will perform the symbol detection, i.e. S1 and S2, by using a maximum-likelihood (ML) receiver.
In maximum likelihood template selection, each frame of a cluster center s* as generated by the MDTS method is relabeled by a set of GMMs that are selected by using a maximum likelihood criterion, so as to make the representative better characterize the templates in each cluster.
Phylogenetic trees were constructed by maximum parsimony method from aligned sequences or by using a maximum likelihood method from the best model identified by Modeltest 3.7.
Phylogenetic tree construction was performed with PAUP* version 4.0b10 [34] by using a maximum likelihood (ML) approach, under an optimum model of evolution as determined by MODELTEST v3.7 [35].
An NJ tree was constructed in MEGA4 with complete deletion of gap positions and by using a maximum composite likelihood substitution model with heterogeneous pattern among lineages with a gamma distribution of 1.0.
The opening and closing rate constants were estimated from the interval durations by using a maximum likelihood algorithm [36] after imposing a dead time correction of, typically, 25 µs.
However, the accuracy of the approach is criticized [3][35], therefore we reconfirm the power law statistic over exponential by using a maximum likelihood method explained in [35], and calculates the value of μ using a more recent, accurate, method explained therein, shown in (9), where L is the whole data set of the flight lengths.
