Mach Field Sensor / Detector and Results

2019 ◽  
Author(s):  
P. M. Jansson ◽  
W. S. Zanardi ◽  
P. Kaladius ◽  
E. L. Jansson ◽  
S. Sedig ◽  
...  
Keyword(s):  
Field Sensor

Transient Response of Electrostatic Field due to Collision ESD between Spherical Electrodes by using Bare-chip Optical Electric Field Sensor

2020 ◽  
Vol 140 (12) ◽  
pp. 599-600
Author(s):  
Kento Kato ◽  
Ken Kawamata ◽  
Shinobu Ishigami ◽  
Ryuji Osawa ◽  
Takeshi Ishida ◽  
...  
Keyword(s):  
Electric Field ◽  
Transient Response ◽  
Electrostatic Field ◽  
Electric Field Sensor ◽  
Field Sensor

Frequency Discriminating EM Field Sensor Having All Optical Interconnects.

1996 ◽  
Author(s):  
John Rohrbaugh
Keyword(s):  
Optical Interconnects ◽  
All Optical ◽  
Field Sensor ◽  
Em Field

scholarly journals Magnetoelastic Coupling and Delta-E Effect in Magnetoelectric Torsion Mode Resonators

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2022
Author(s):  
Benjamin Spetzler ◽  
Elizaveta V. Golubeva ◽  
Ron-Marco Friedrich ◽  
Sebastian Zabel ◽  
Christine Kirchhof ◽  
...  
Keyword(s):  
Low Frequency ◽  
High Sensitivity ◽  
Element Model ◽  
Mode Number ◽  
Frequency Change ◽  
General Description ◽  
Resonance Modes ◽  
Field Sensor ◽  
Magnetic Sensitivity ◽  
Bending Modes

Magnetoelectric resonators have been studied for the detection of small amplitude and low frequency magnetic fields via the delta-E effect, mainly in fundamental bending or bulk resonance modes. Here, we present an experimental and theoretical investigation of magnetoelectric thin-film cantilevers that can be operated in bending modes (BMs) and torsion modes (TMs) as a magnetic field sensor. A magnetoelastic macrospin model is combined with an electromechanical finite element model and a general description of the delta-E effect of all stiffness tensor components Cij is derived. Simulations confirm quantitatively that the delta-E effect of the C66 component has the promising potential of significantly increasing the magnetic sensitivity and the maximum normalized frequency change ∆fr. However, the electrical excitation of TMs remains challenging and is found to significantly diminish the gain in sensitivity. Experiments reveal the dependency of the sensitivity and ∆fr of TMs on the mode number, which differs fundamentally from BMs and is well explained by our model. Because the contribution of C11 to the TMs increases with the mode number, the first-order TM yields the highest magnetic sensitivity. Overall, general insights are gained for the design of high-sensitivity delta-E effect sensors, as well as for frequency tunable devices based on the delta-E effect.

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

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

A Single Chip LiNbO₃ Photonic 2D Electric Field Sensor Using Two Perpendicular Electrodes

2020 ◽  
Vol 32 (23) ◽  
pp. 1501-1504
Author(s):  
Jiahong Zhang ◽  
Dubing Yang ◽  
Changsheng Zhang ◽  
Zhengang Zhao
Keyword(s):  
Electric Field ◽  
Single Chip ◽  
Electric Field Sensor ◽  
Field Sensor

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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