A single solid-state magnetic sensor can measure a current flowing in a conductor in a noncontact way. In order to improve the accuracy of the measuring system and to reduce the crosstalk effects of other magnetic fields, circular arrays of magnetic sensors can be fabricated, still preserving low costs. In those kind of arrays, the sensing elements are assembled on a circle around the conductor. The sum of the sensor output signals is an approximation of Ampere's circulation. A quite satisfactory crosstalk effects reduction can be thus achieved. More sophisticated algorithms that combine sensor output signals are necessary in order to further reduce crosstalk effects. This paper presents an algorithm that is able to calculate the intensity of a dc current flowing in a rectangular bus-bar, in the presence of crosstalk fields.

This paper addresses the question of optimal sensor placement for magnetocardiographic field imaging. New magnetic sensor technologies allow less restrictive sensor positioning in this application. We develop a constraint framework for sensor positioning and use tabu search (TS) and particle swarm optimization (PSO) for finding an optimal set of sensors, whereby a new PSO algorithm is designed to fit the needs of our constraint framework. Numerical simulations are carried out with a three compartment boundary element torso model and a multi-dipole heart model. We find an optimal value of about 20 to 30 vectorial sensors and both TS and PSO yield similar sensor distributions. The comparison to sensors on regular grids shows that optimization of vectorial magnetic sensor setups may significantly improve reconstruction quality and that the number of sensors can be reduced.

In order to reduce the effects of external magnetic fields on the accuracy of magnetic sensor measurements used for the reconstruction of ac electric currents flowing in massive parallel conductors, we use a spatial circular harmonic expansion of the magnetic scalar potential. Thanks to the linearity of the magnetic field problem with respect to the sources, we can then apply the least squares inversion and obtain the set of currents from the knowledge of the magnetic field data collected by the sensor array in the vicinity of the current carrying conductors. Furthermore, we can optimize the positions and the orientations of the magnetic sensors using D-optimality theory and particle swarm optimization.

The present work aims at quantifying how, and how much, the uncertainties on the components and material parameters of a wireless power transfer (WPT) system for the static charge of electric vehicles affect the overall efficiency and functionality of the final produced device. With the aim of considering the perspective of a possible industrial developer, the parameters selected for the uncertainty quantification are chosen to be the capacitance values of the compensation capacitors and the electromagnetic material parameters used for the construction of the magnetic structure of a WPT system, i.e. the parameters of the elements to be purchased. The analysis is based on a standard system among the ones provided by the current SAE J2954 recommended practice.

High-order surface impedance boundary conditions (SIBCs) are coupled to the boundary element method to produce a new integral formulation technique for the computation of the impedance matrix of multiconductor transmission lines (MTLs). The method extends the use of the Leontovich SIBC into lower frequencies and does so quickly and efficiently in that the solution of the integral equations need only be computed once, whereas the solution may be obtained over the whole applicable frequency domain. The current formulation is theoretically rigorous and applies to arbitrarily smooth curved surfaces. The method is applied to the calculation of per-unit-length (p.u.l.) parameters in lossy MTLs of arbitrary cross sections, without taking into account the earth return path or the surrounding media resistivity.