The initial digital model for each patient was then virtually modified by two different modelling techniques to achieve the corresponding target model: parametric and freeform modelling with the software Geomagic®.
We combined a task factor (prosaccades: look towards target; antisaccades: look away from target) with a parametric factor of transformation demand (single vs. multiple peripheral targets) in a two-factorial block design.
Instead of constraining realizations to match a parametric target covariance model over a wide range of spatial lags, we constrain the realizations only at smaller lags where the available geophysical data cannot provide enough information.
In this paper, we propose a method for approximating target surfaces, which are parametric surfaces of varying or constant curvatures, using generalized waterbomb tessellations.
In our previous work [5, 20], the two heuristic detectors, exploiting the features of targets HRRPs rather than rigorous parametric models of target returns, were developed in white Gaussian noise.
When the number of targets M is known, simple parametric clustering methods such as the k - means can be used [31], pp. 424 429.
Since the Kolmogorov-Smirnov test results showed a normal distribution of the target variables, we performed a parametric analysis.
Therefore, refined parametric compensation for each target can be carried out to obtain a well-focused image.
Conditions are established for the convergence of statistics of sequences of parametric translation models to target statistics.
Sensitivity analysis of mathematical models describing complex networks could allow for linking robustness properties to network structure by measuring the degree to which parametric perturbations change various target dynamics [7], [8].
