The proposed model handles both detection scenarios, single and multiple.
Specifically, LP solves linear models; OA, SLP, SLA, SQP, and GRG serve to deal with mildly non-linear models; BB, GARS, and MS―in proper combination with SLA, SQP, and GRG―serve to handle highly nonlinear models.
On the other hand, Bayesian analysis is able to handle highly complex models efficiently when frequentist approaches to estimation (i.e., ML) often fail (e.g., McArdle, Grimm, Hamagami, Bowles, & Meredith, 2009).
Each of the stages requires specialized software facilitating model handling.
The proposed methodology is suitable to handle highly non-linear and large scale systems since it is based on structural models.
Not only is the office seasoned in handling highly publicized cases, it has become adept at handling multiple trials.
Parallel finite element nonlinear Stokes models have been successfully used for three-dimensional ice-sheet and glacier simulations due to their accuracy and efficiency, and their capability for easily handling highly irregular domains and different types of boundary conditions.
To handle highly distorted and damaged elements the approach based on the element deletion is employed.
In this paper, only preliminary results were presented regarding soft gripper handling highly deformable objects.
Tao and Matuszewski [17] also employed manifold learning-based diffusion maps to handle highly deformable objects.
