Related papers: Magnetic field regression using artificial neural …
Ray tracing is widely employed to model the propagation of radio-frequency (RF) signal in complex environment. The modelling performance greatly depends on how accurately the target scene can be depicted, including the scene geometry and…
We demonstrate real-time magnetometry by detecting fluorescence from a nitrogen vacancy center in the setting of coherent population trapping and by estimating magnetic field from the time series of the observed photon counts, which are…
Computing pseudospectra of non-normal matrices is essential for understanding the stability and transient behavior of dynamical systems. Such analysis is critical in applications including fluid dynamics, control systems, and differential…
We present a novel approach to detect millicharged dark matter (mDM) by using a high-sensitivity magnetometer coupled with the resonant and broadband readout circuits. In the external magnetic field, the interaction between mDM and the…
With the second quantum revolution underway, quantum-enhanced sensors are moving from laboratory demonstrations to field deployments, providing enhanced and even new capabilities. Signal processing and operational software is becoming…
In this paper we investigate the usage of machine learning for interpreting measured sensor values in sensor modules. In particular we analyze the potential of artificial neural networks (ANNs) on low-cost micro-controllers with a few…
We apply a machine learning algorithm, the artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts. The multi-dimensional samples consisting of data corresponding to the statistical and…
Measuring spins is the corner stone of a variety of analytical techniques including modern magnetic resonance imaging (MRI). The full potential of spin imaging and sensing across length scales is hindered by the achievable signal-to-noise…
We present a novel approach allowing an optically pumped magnetometer (OPM) to be operated within Earth's magnetic field as a vector magnetometer whose sensitive axis can be freely defined. This approach enables the measurement of any…
In this study, we employ Graph Neural Networks (GNNs) to accelerate the discovery of novel 2D magnetic materials which have transformative potential in spintronics applications. Using data from the Materials Project database and the…
Detection of vector magnetic fields at nanoscale dimensions is critical in applications ranging from basic material science, to medical diagnostic. Meanwhile, an all-electric operation is of great significance for achieving a simple and…
The mean field algorithm is a widely used approximate inference algorithm for graphical models whose exact inference is intractable. In each iteration of mean field, the approximate marginals for each variable are updated by getting…
In the present paper a newer application of Artificial Neural Network (ANN) has been developed i.e., predicting response-function results of electrical-mechanical system through ANN. This method is specially useful to complex systems for…
The feasibility of using ultra-low-field magnetic resonance (ULF MR) for direct neuronal current imaging (NCI) is investigated by phantom measurements. The aim of NCI is to improve the current localization accuracy for neuronal activity of…
Accurate estimation of the Cosmic Microwave Background (CMB) angular power spectrum is enticing due to the prospect for precision cosmology it presents. Galactic foreground emissions, however, contaminate the CMB signal and need to be…
We demonstrate a method of imaging spatially varying magnetic fields using a thin layer of nitrogen-vacancy (NV) centers at the surface of a diamond chip. Fluorescence emitted by the two-dimensional NV ensemble is detected by a CCD array,…
Atomic magnetometry was performed at Earth's magnetic field over a free-space distance of ten meters. Two laser beams aimed at a distant alkali-vapor cell excited and detected the $^{87}$Rb magnetic resonance, allowing the magnetic field…
Accelerator magnets steer particle beams according to the field integrated along the trajectory over the magnet length. Purpose-wound coils measure these relevant parameters with high precision and complement efficiently point-like…
This paper proposes a pipeline to automatically track and measure displacement and vibration of structural specimens during laboratory experiments. The latest Mask Regional Convolutional Neural Network (Mask R-CNN) can locate the targets…
This study is devoted to the inference problem of extracting the nuclear matter properties directly from a set of mass-radius observations. We employ Bayesian neural networks (BNNs), which is a probabilistic model capable of estimating the…