We review developments, issues and challenges in electrical impedance tomography (EIT)
for the 4th Conference on Biomedical Applications of Electrical Impedance Tomography …

We give a short review on the Bayesian approach for neural network learning and
demonstrate the advantages of the approach in three real applications. We discuss the …

The main purpose of this chapter is to give a derivation, which is mathematically precise,
physically natural, and conceptually simple, of the quasilinear system of partial differential …

The solution of impedance distribution in electrical impedance tomography is a nonlinear
inverse problem that requires the use of a regularization method. The generalized Tikhonov …

Electrical impedance tomography (EIT) is a relatively new imaging method that has evolved
over the past 20 years. It has the potential to be of great value in clinical diagnosis; however …

In electrical impedance tomography an approximation for the internal resistivity distribution
is computed based on the knowledge of the injected currents and measured voltages on the …

This paper discusses the electrical impedance tomography (EIT) problem: electric currents
are injected into a body with unknown electromagnetic properties through a set of contact …

Skull conductivity has a substantial influence on EEG and combined EEG and MEG source
analysis as well as on optimized transcranial electric stimulation. To overcome the use of …

Inverse problems deal with the quest for unknown causes of observed consequences,
based on predictive models, known as the forward models, that associate the former …

In this paper we propose a method for the regularization of inverse problems whose
solutions are known to exhibit anisotropic characteristics. The method is based on the …