Tracking conductivity variations in the absence of accurate stat evolution models in electrical impedance tomography

Hashemzadeh, P., Sahota, V., Callaghan, M., Dib, H., Tizzard, A., Svensson, L. and Bayford, R. (2010) Tracking conductivity variations in the absence of accurate stat evolution models in electrical impedance tomography. In: UNSPECIFIED, ed. 2010 4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE) : June 18-20, 2010 Chengdu, China. IEEE. pp. 1-6 ISBN 9781424447121

Full text not available from this repository.

Abstract

We present results on both linear and non-linear approaches in tracking conductivity variations in electrical impedance tomography. Throughout this study, we use both synthetic and measured data. The true system dynamics is considered as unknown and modelled as a random walk. In the linear reconstructions, the time evolution model is augmented with a Gaussian smoothness prior and results are shown using two different models for the covariance matrix of the process noise. Furthermore, we compare the reconstructions of the one step Gauss-Newton method to the Kalman filter on measured data from an adult human subject. In the non-linear study, we compare the performance of the extended Kalman filter against the particle filter on a simple test case. It is observed that the particle filter shows superior performance in tracking nonlinear/non-Gaussian conductivity variations.

Item Type: Book Section
Subjects: Q Science > QA Mathematics > QA0075 Electronic computers. Computer science
Divisions: Faculty of Social and Applied Sciences > School of Law, Criminal Justice and Computing
Depositing User: Vijay Sahota
Date Deposited: 08 Jul 2015 17:03
Last Modified: 09 Jul 2015 13:15
URI: https://create.canterbury.ac.uk/id/eprint/13540

Actions (login required)

Update Item (CReaTE staff only) Update Item (CReaTE staff only)

Downloads

Downloads per month over past year

View more statistics

Share

Connect with us

Last edited: 29/06/2016 12:23:00