Nicolas Siauve

Identification of Jiles–Atherton Model Parameters Using Particle Swarm Optimization

This paper presents the use of the multiobjective particle swarm optimization (PSO) technique for the identification of Jiles-Atherton model parameters. This approach, implemented for the first time in order to solve this kind of problem, is tested for two magnetic materials: NO 3% SiFe and NiFe 20-80. The results are compared with those obtained with a direct search method and a genetic algorithm procedure. Experimental measures performed on both samples of materials allow us to complete and argue the validation for the PSO method.

Variational tetrahedral mesh generation from discrete volume data

In this paper, we propose a novel tetrahedral mesh generation algorithm, which takes volumic data (voxels) as an input. Our algorithm performs a clustering of the original voxels within a variational framework. A vertex replaces each cluster and the set of created vertices is triangulated in order to obtain a tetrahedral mesh, taking into account both the accuracy of the representation and the elements quality. The resulting meshes exhibit good elements quality with respect to minimal dihedral angle and tetrahedra form factor. Experimental results show that the generated meshes are well suited for Finite Element Simulations.

Electromagnetic characterization of biological cells

This paper presents the most commonly used method to characterize individual biological cells on a dielectric point of view. It is a force based technique which lays on dielectrophoresis and/or electrorotation. First the principle of these phenomena are described and analyzed with an extension to magnetic forces at the micrometric scale level. Secondly we present an experimental setup which permits to acquire the dielectrophoretic spectrum which is a dielectric signature of a cell. The main dielectric parameters can be deduced by fitting the theoretical response of the cell issued from a dielectric model and the experimental data. At the end we present an improved fitting method which takes advantage of a sensitivity analysis based on a probabilistic approach.

Exploiting curvature to compute the medial axis with Constrained Centroidal Voronoi Diagram on discrete data

In this paper, we present a novel method for medial axis approximation based on Constrained Centroidal Voronoi Diagram of discrete data (image, volume). The proposed approach is based on the shape boundary subsampling controled by a clustering approach which generates a Voronoi Diagram well suited for Medial Axis extraction. The resulting Voronoi Diagram is further filtered in order to capture the correct topology of the medial axis. The main contribution of this paper is the integration of both a curvature maps and a distance map for controlling the local variability of Voronoi cells densities. Examples of complex shape processing prove the effectiveness of the proposed approach.

Experimental Design to Quantify Influence of Electromagnetic and Thermal Tissues Properties on Predicted Numerical Temperature Distribution in Human Body Due to Radiofrequency Exposure

The knowledge of electromagnetic and thermal properties of human tissues is a critical point. The aim of this paper is to analyse the sensitivity of the numerical temperature distribution inside the human body to electromagnetic and thermal tissues properties. This analysis is realized with experimental design method