scholarly journals Compliant Magnetic Field Sensor Technologies

2021 ◽  
Vol 6 (1) ◽  
pp. 8
Author(s):  
Denys Makarov
Keyword(s):  
Magnetic Field ◽  
Geomagnetic Field ◽  
Game Engine ◽  
Feedback Systems ◽  
Mechanical Pressure ◽  
Interactive Electronics ◽  
Human Machine Interfaces ◽  
Small Fields ◽  
Field Sensor ◽  
Virtual And Augmented Reality

We review the recent progress in the field of shapeable magnetoelectronics allowing the realization of mechanically imperceptible electronic skins, which enable perception of the geomagnetic field (e-skin compasses), featuring sensitivities down to ultra-small fields of sub-50 nT. We demonstrate that e-skin compasses allow humans to orient with respect to Earth’s magnetic field ubiquitously. The biomagnetic orientation enables novel interactive devices for virtual and augmented reality applications, which is showcased by realizing touchless control of virtual units in a game engine using omnidirectional magnetosensitive skins. This concept is further extended by demonstrating a compliant magnetic microelectromechanical platform (m-MEMS), which is able to transduce both tactile (via mechanical pressure) and touchless (via magnetic field) stimulations simultaneously and discriminate them in real time. These devices are crucial for interactive electronics and human–machine interfaces, but also for the realization of smart soft robotics with highly compliant integrated feedback systems including in medicine for physicians and surgeons.

Magnetic Field Sensor Based on a Single Josephson Junction With a Multilayer Ferromagnet/Normal Metal Barrier

2021 ◽  
Vol 31 (5) ◽  
pp. 1-5
Author(s):  
Ivan P. Nevirkovets ◽  
Mikhail A. Belogolovskii ◽  
Oleg A. Mukhanov ◽  
John B. Ketterson
Keyword(s):  
Magnetic Field ◽  
Josephson Junction ◽  
Normal Metal ◽  
Magnetic Field Sensor ◽  
Field Sensor ◽  
Metal Barrier

Test bench for calibration of magnetic field sensor prototypes for COMPASS-U tokamak

2021 ◽  
Vol 168 ◽  
pp. 112467
Author(s):  
Andre Torres ◽  
Karel Kovarik ◽  
Tomas Markovic ◽  
Jiri Adamek ◽  
Ivan Duran ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Test Bench ◽  
Magnetic Field Sensor ◽  
Field Sensor

scholarly journals Predictions of the geomagnetic secular variation based on the ensemble sequential assimilation of geomagnetic field models by dynamo simulations

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Sabrina Sanchez ◽  
Johannes Wicht ◽  
Julien Bärenzung
Keyword(s):  
Magnetic Field ◽  
Secular Variation ◽  
Geomagnetic Field ◽  
Wave Number ◽  
Main Magnetic Field ◽  
Candidate Model ◽  
Uncertainty Estimates ◽  
Geomagnetic Field Models ◽  
Dipole Configuration

Abstract The IGRF offers an important incentive for testing algorithms predicting the Earth’s magnetic field changes, known as secular variation (SV), in a 5-year range. Here, we present a SV candidate model for the 13th IGRF that stems from a sequential ensemble data assimilation approach (EnKF). The ensemble consists of a number of parallel-running 3D-dynamo simulations. The assimilated data are geomagnetic field snapshots covering the years 1840 to 2000 from the COV-OBS.x1 model and for 2001 to 2020 from the Kalmag model. A spectral covariance localization method, considering the couplings between spherical harmonics of the same equatorial symmetry and same azimuthal wave number, allows decreasing the ensemble size to about a 100 while maintaining the stability of the assimilation. The quality of 5-year predictions is tested for the past two decades. These tests show that the assimilation scheme is able to reconstruct the overall SV evolution. They also suggest that a better 5-year forecast is obtained keeping the SV constant compared to the dynamically evolving SV. However, the quality of the dynamical forecast steadily improves over the full assimilation window (180 years). We therefore propose the instantaneous SV estimate for 2020 from our assimilation as a candidate model for the IGRF-13. The ensemble approach provides uncertainty estimates, which closely match the residual differences with respect to the IGRF-13. Longer term predictions for the evolution of the main magnetic field features over a 50-year range are also presented. We observe the further decrease of the axial dipole at a mean rate of 8 nT/year as well as a deepening and broadening of the South Atlantic Anomaly. The magnetic dip poles are seen to approach an eccentric dipole configuration.

High-sensitivity fiber optic magnetic field sensor based on lossy mode resonance and hollow core-offset structure

2021 ◽  
pp. 1-12
Author(s):  
Xue-Peng Jin ◽  
Hong-Zhi Sun ◽  
Shuo-Wei Jin ◽  
Wan-Ming Zhao ◽  
Jing-Ren Tang ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Fiber Optic ◽  
High Sensitivity ◽  
Magnetic Field Sensor ◽  
Hollow Core ◽  
Field Sensor

An integrated electro-optic magnetic field sensor based on reflected Mach-Zehnder interferometer

Optik ◽  
2018 ◽  
Vol 157 ◽  
pp. 315-318 ◽  
Author(s):  
Jiahong Zhang ◽  
Xiaorong Wan ◽  
Yingna Li ◽  
Zhengang Zhao ◽  
Chuan Li
Keyword(s):  
Magnetic Field ◽  
Magnetic Field Sensor ◽  
Zehnder Interferometer ◽  
Electro Optic ◽  
Field Sensor ◽  
Mach Zehnder Interferometer

scholarly journals Augmenting Around-Device Interaction by Geomagnetic Field Built-in Sensor Utilization

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3087
Author(s):  
Sandi Ljubic ◽  
Franko Hržić ◽  
Alen Salkanovic ◽  
Ivan Štajduhar
Keyword(s):  
Magnetic Field ◽  
Neural Networks ◽  
Curve Fitting ◽  
Absolute Error ◽  
Interaction Space ◽  
Text Entry ◽  
Proof Of Concept ◽  
Field Sensor ◽  
Device Interaction ◽  
Input Techniques

In this paper, we investigate the possibilities for augmenting interaction around the mobile device, with the aim of enabling input techniques that do not rely on typical touch-based gestures. The presented research focuses on utilizing a built-in magnetic field sensor, whose readouts are intentionally affected by moving a strong permanent magnet around a smartphone device. Different approaches for supporting magnet-based Around-Device Interaction are applied, including magnetic field fingerprinting, curve-fitting modeling, and machine learning. We implemented the corresponding proof-of-concept applications that incorporate magnet-based interaction. Namely, text entry is achieved by discrete positioning of the magnet within a keyboard mockup, and free-move pointing is enabled by monitoring the magnet’s continuous movement in real-time. The related solutions successfully expand both the interaction language and the interaction space in front of the device without altering its hardware or involving sophisticated peripherals. A controlled experiment was conducted to evaluate the provided text entry method initially. The obtained results were promising (text entry speed of nine words per minute) and served as a motivation for implementing new interaction modalities. The use of neural networks has shown to be a better approach than curve fitting to support free-move pointing. We demonstrate how neural networks with a very small number of input parameters can be used to provide highly usable pointing with an acceptable level of error (mean absolute error of 3 mm for pointer position on the smartphone display).

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