Picture of boy being examining by doctor at a tuberculosis sanatorium

Understanding our future through Open Access research about our past...

Strathprints makes available scholarly Open Access content by researchers in the Centre for the Social History of Health & Healthcare (CSHHH), based within the School of Humanities, and considered Scotland's leading centre for the history of health and medicine.

Research at CSHHH explores the modern world since 1800 in locations as diverse as the UK, Asia, Africa, North America, and Europe. Areas of specialism include contraception and sexuality; family health and medical services; occupational health and medicine; disability; the history of psychiatry; conflict and warfare; and, drugs, pharmaceuticals and intoxicants.

Explore the Open Access research of the Centre for the Social History of Health and Healthcare. Or explore all of Strathclyde's Open Access research...

Image: Heart of England NHS Foundation Trust. Wellcome Collection - CC-BY.

Parallel preconditioners and high order elements for microwave imaging

Bonazzoli, M. and Dolean, V. and Rapetti, F. and Tournier, P. -H. (2017) Parallel preconditioners and high order elements for microwave imaging. International Journal of Numerical Modelling: Electronic Networks, Devices and Fields.

[img]
Preview
Text (Bonazzoli-etal-IJNM-2017-Parallel-preconditioners-and-high-order-discretizations-arising-from-full-system-modeling)
Bonazzoli_etal_IJNM_2017_Parallel_preconditioners_and_high_order_discretizations_arising_from_full_system_modeling.pdf
Accepted Author Manuscript

Download (1MB) | Preview

Abstract

This paper combines the use of high order finite element methods with parallel preconditioners of domain decomposition type for solving electromagnetic problems arising from brain microwave imaging. The numerical algorithms involved in such complex imaging systems are computationally expensive since they require solving the direct problem of Maxwell's equations several times. Moreover, wave propagation problems in the high frequency regime are challenging because a sufficiently high number of unknowns is required to accurately represent the solution. In order to use these algorithms in practice for brain stroke diagnosis, running time should be reasonable. The method presented in this paper, coupling high order finite elements and parallel preconditioners, makes it possible to reduce the overall computational cost and simulation time while maintaining accuracy.