@article{HomrighausenHorsthemkePogorzelskietal.2023,
  author    = {Homrighausen, Jonas and Horsthemke, Ludwig and Pogorzelski, Jens and Trinschek, Sarah and Gl{\"o}sek{\"o}tter, Peter and Gregor, Markus},
  title     = {Edge-Machine-Learning-Assisted Robust Magnetometer Based on Randomly Oriented NV-Ensembles in Diamond},
  series = {Sensors},
  volume    = {23},
  journal   = {Sensors},
  number    = {3},
  doi       = {10.3390/s23031119},
  year      = {2023},
  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.},
  language  = {en}
}