Classification is the last, and usually most time-consuming step in recognition. Most recently proposed classification algorithms have adopted machine learning (ML) as main approach, regardless of time consumption. This study proposes a statistical feature cubic spline interpolation (FC-CSI) algorithm to classify emotions speech using curve fitting technique. FC-CSI utilized emotion recognition...

This paper presented the G1 cubic trigonometric spline function with three shape parameters that generate a constrained curve interpolates 2D data. The purpose of this research is to ensure generated passes through all data point yet satisfied cases line constraints given. are: must lie above Li below and lastly, between two lines Li,1 Li,2. A simpler theorem implemented involving roles paramet...

BACKGROUND Manual segmentations of the whole intracranial vault in high-resolution magnetic resonance images are often regarded as very time-consuming. Therefore it is common to only segment a few linearly spaced intracranial areas to estimate the whole volume. The purpose of the present study was to evaluate how the validity of intracranial volume estimates is affected by the chosen interpolat...

In this paper we present a novel interpolation technique for Vlasov simulations of intense space charge dominated beams. This new technique enables to localize the cubic spline interpolation generally performed in semi-Lagrangian Vlasov codes and thus to improve the scalability of the parallel version. This new method is applied to the propagation of a potassium beam in a periodic focusing chan...

A trivariate Lagrange interpolation method based on C cubic splines is described. The splines are defined over a special refinement of the Freudenthal partition of a cube partition. The interpolating splines are uniquely determined by data values, but no derivatives are needed. The interpolation method is local and stable, provides optimal order approximation, and has linear complexity.