Edge-Machine-Learning-Assisted Robust Magnetometer Based on Randomly Oriented NV-Ensembles in Diamond
At a Glance
Section titled āAt a Glanceā| Metadata | Details |
|---|---|
| Publication Date | 2023-01-18 |
| Journal | Sensors |
| Authors | Jonas Homrighausen, Ludwig Horsthemke, Jens Pogorzelski, Sarah Trinschek, Peter Glƶsekƶtter |
| Institutions | FH MĆ¼nster |
| Citations | 15 |
| Analysis | Full AI Review Included |
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View Original Abstract
Quantum magnetometry based on optically detected magnetic resonance (ODMR) of nitrogen vacancy centers in nano- or micro-diamonds is a promising technology for precise magnetic-field sensors. Here, we propose a new, low-cost and stand-alone sensor setup that employs machine learning on an embedded device, so-called edge machine learning. We train an artificial neural network with data acquired from a continuous-wave ODMR setup and subsequently use this pre-trained network on the sensor device to deduce the magnitude of the magnetic field from recorded ODMR spectra. In our proposed sensor setup, a low-cost and low-power ESP32 microcontroller development board is employed to control data recording and perform inference of the network. In a proof-of-concept study, we show that the setup is capable of measuring magnetic fields with high precision and has the potential to enable robust and accessible sensor applications with a wide measuring range.
Tech Support
Section titled āTech SupportāOriginal Source
Section titled āOriginal SourceāReferences
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