RESUMO
Magnetic sensors are largely used in several engineering areas. Among them, magnetic sensors based on the Giant Magnetoimpedance (GMI) effect are a new family of magnetic sensing devices that have a huge potential for applications involving measurements of ultra-weak magnetic fields. The sensitivity of magnetometers is directly associated with the sensitivity of their sensing elements. The GMI effect is characterized by a large variation of the impedance (magnitude and phase) of a ferromagnetic sample, when subjected to a magnetic field. Recent studies have shown that phase-based GMI magnetometers have the potential to increase the sensitivity by about 100 times. The sensitivity of GMI samples depends on several parameters, such as sample length, external magnetic field, DC level and frequency of the excitation current. However, this dependency is yet to be sufficiently well-modeled in quantitative terms. So, the search for the set of parameters that optimizes the samples sensitivity is usually empirical and very time consuming. This paper deals with this problem by proposing a new neuro-genetic system aimed at maximizing the impedance phase sensitivity of GMI samples. A Multi-Layer Perceptron (MLP) Neural Network is used to model the impedance phase and a Genetic Algorithm uses the information provided by the neural network to determine which set of parameters maximizes the impedance phase sensitivity. The results obtained with a data set composed of four different GMI sample lengths demonstrate that the neuro-genetic system is able to correctly and automatically determine the set of conditioning parameters responsible for maximizing their phase sensitivities.
Assuntos
Fenômenos Magnéticos , Modelos Genéticos , Redes Neurais de Computação , Algoritmos , Impedância Elétrica , Magnetometria/métodosRESUMO
A technique has been developed, based on magnetic field measurements, to localize, in three dimensions, hypodermic and sewing needles lost in the human body. A theoretical model for the magnetic field generated by needles has been elaborated and experimentally validated. Using this model, the localization technique gives information about needle's centre, orientation and depth. The clinical measurements have been made using a SQUID system, with patients being moved under the sensor with the aid of an X-Y bed. The magnetic field associated with the remanent magnetization of the needle is acquired on-line and mapped over a plane. In all six cases that occurred, the technique allowed surgical localization of the needles with ease and high precision. This procedure can decrease the surgery time for extraction of foreign bodies by a large factor, and also reduce the generally high odds of failure.