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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.
Cross-saturation of the gain media in intra-cavity pumped lasers leads to complex dynamics of the laser power. We present experimental results and a detailed theoretical analysis of this nonlinear dynamics for an intra-cavity pumped Yb:YAG thin-disk laser in the framework of a rate-equation model. The gain medium of this laser is residing in the resonator of a conventional, diode-pumped Yb:YAG thin-disk laser. Continuous-wave operation, periodic pulse trains, and chaotic fluctuations of the optical power of both lasers were observed. The dynamics is not driven by external perturbations but arises naturally in this laser system. Further examination revealed that these modes of operation can be controlled by the resonator length of the diode-pumped laser but that the system can also show hysteresis and multi-stability.
Continuous versus arrested spreading of biofilms at solid-gas interfaces: The role of surface forces
(2017)