Neural Network for Metal Detection Based on Magnetic Impedance Sensor

The efficiency of the metal detection method using deep learning with data obtained from multiple magnetic impedance (MI) sensors was investigated. The MI sensor is a passive sensor that detects metal objects and magnetic field changes. However, when detecting a metal object, the amount of change in the magnetic field caused by the metal is small and unstable with noise. Consequently, there is a limit to the detectable distance. To effectively detect and analyze this distance, a method using deep learning was applied. The detection performances of a convolutional neural network (CNN) and a recurrent neural network (RNN) were compared from the data extracted from a self-impedance sensor. The RNN model showed better performance than the CNN model. However, in the shallow stage, the CNN model was superior compared to the RNN model. The performance of a deep-learning-based (DLB) metal detection network using multiple MI sensors was compared and analyzed. The network was detected using long short-term memory and CNN. The performance was compared according to the number of layers and the size of the metal sheet. The results are expected to contribute to sensor-based DLB detection technology.

Theoretical Investigation of the Shubnikov-de Haas Magnetoresistance Oscillations in a Quantum well under the Influence of Confined Acoustic Phonons

The Shubnikov – de Haas magnetoresistance oscillations in the Quantum well (QW) under the influence of confined acoustic phonons, The theoretical results show that the conductivity tensor, the complex magnetic impedance of the magnetic field, the frequency, the amplitude of the laser radiation, the QW width, the temperature of the system and especially the quantum index m characterizes the confinement of the phonon. The amplitude of the oscillations of the Shubnikov-de Haas impedance decreases with the increase of the influence of the confined acoustic phonons. The results for bulk phonons in a QW could be achieved, when m goes to zero. We has been compared with other studies when perform the numerical calculations are also achieved for the GaAs/AlGaAs in the QW. Results show that The Shubnikov-de Haas magnetoresistance oscillations amplitude decrease when phonon confinement effect increasing and when width L of the QW increases to a certain value, The Shubnikov – de Haas magnetoresistance oscillations amplitude completely disappears can not be observed.