Acoustic estimation of seafloor parameters: A radial basis functions approach

Acoustic estimation of seafloor parameters: A radial basis functions approach

A novel approach to the estimation of seafloor geoacoustic parameters from the measurement of the acoustic field in the water column is introduced. The approach is based on the idea of approximating the inverse function that links the geoacoustic parameters with the measured field through a series expansion of Radial Basis Functions. In particular, Gaussian basis functions are used in order to ...

Diffusion Propagator Estimation Using Radial Basis Functions

The average diffusion propagator (ADP) obtained from diffusion MRI (dMRI) data encapsulates important structural properties of the underlying tissue. Measures derived from the ADP can be potentially used as markers of tissue integrity in characterizing several mental disorders. Thus, accurate estimation of the ADP is imperative for its use in neuroimaging studies. In this work, we propose a sim...

A numerical approach on Hiemenz flow problem using radial basis functions

Radial basis functions

Radial basis function methods are modern ways to approximate multivariate functions, especially in the absence of grid data. They have been known, tested and analysed for several years now and many positive properties have been identified. This paper gives a selective but up-to-date survey of several recent developments that explains their usefulness from the theoretical point of view and contr...

Deformable Radial Basis Functions

Radial basis function networks (RBF) are efficient general function approximators. They show good generalization performance and they are easy to train. Due to theoretical considerations RBFs commonly use Gaussian activation functions. It has been shown that these tight restrictions on the choice of possible activation functions can be relaxed in practical applications. As an alternative differ...