arXiv subjects

Bao Chen

Publications and source records attributed to Bao Chen.

6 recordsLinked to original sources

arXiv2025

A Fully-integrated Diamond Nitrogen-Vacancy Magnetometer with Nanotesla Sensitivity

Ensemble diamond nitrogen-vacancy (DNV) centers have emerged as a promising platform for precise earth-field vector magnetic sensing, particularly in applications that require high mobility. Nevertheless, integrating all control utilities into a compact form has proven challenging, thus far limiting the sensitivity of mobile DNV magnetometers to the uT-level. This study introduces a fully integrated DNV magnetometer that encompasses all the essential components typically found in traditional platforms, while maintaining compact dimensions of approximately 13 cm * 26 cm. In contrast to previous efforts, we successfully address these challenges by integrating a high-power laser, a lock-in amplifier, and a digitally-modulated microwave source. These home-made components show comparable performance with commercial devices under our circumstance, resulting in an optimal sensitivity of 2.14 nT/sqrt{Hz}. The limitations in this system as well as possible future improvements are discussed. This work paves the way for the use of DNV magnetometry in cost-effective, mobile unmanned aerial vehicles, facilitating a wide range of practical applications.

quant-ph↗

arXiv2023

Hierarchical Topological States in Thermal Diffusive Networks

The integration of topological concepts into electronic energy band theory has been a transformative development in condensed matter physics. Since then, this paradigm has broadened its reach, extending to a variety of physical systems, including open ones. In this study, we employ analogues of the generalized $n$-dimensional Su-Schrieffer-Heeger model, a cornerstone in understanding topological insulators and higher-order topological states, to unveil a dimensional hierarchy of topological states within thermal diffusive networks. Unlike their electronic counterparts, the topological states in these networks are characterized by confined temperature profiles of dimension $(n-d)$ with constant diffusive rates, where $n$ represents the system's dimension and $d$ is the order of the topological state. Our findings demonstrate the existence of topological corner states in thermal diffusive systems up to $n=3$, along with surface and hinge states. We also identify and discuss an intermediate-order topological phase in the case $n=3$, characterized by the presence of hinge states but the absence of corner states. Furthermore, our work delves into the influence of chiral symmetry in these thermal networks, particularly focusing on topological thermal states with a near-zero diffusion rate. This research lays the foundation for advanced thermal management strategies that utilize topological states in multiple dimensions.

cond-mat.supr-con↗

arXiv2023

Noise prediction and reduction of single electron spin by deep-learning-enhanced feedforward control

Noise-induced control imperfection is an important problem in applications of diamond-based nano-scale sensing, where measurement-based strategies are generally utilized to correct low-frequency noises in realtime. However, the spin-state readout requires a long time due to the low photon-detection efficiency. This inevitably introduces a delay in noise-reduction process and limits its performance. Here we introduce the deep learning approach to relax this restriction by predicting the trend of noise and compensating the delay. We experimentally implement feedforward quantum control of nitrogen-vacancy center in diamond to protect its spin coherence and improve the sensing performance against noise. The new approach effectively enhances the decoherence time of the electron spin, which enables exploring more physics from its resonant spectroscopy. A theoretical model is provided to explain the improvement. This scheme could be applied in general sensing schemes and extended to other quantum systems.

quant-ph↗

arXiv2022

Sensitivity-enhanced magnetometry using nitrogen-vacancy ensembles via adaptively complete transitions overlapping

Nitrogen-vacancy (NV) centers in diamond are suitable sensors of high-sensitivity magnetometry which have attracted much interest in recent years. Here, we demonstrate sensitivity-enhanced ensembles magnetometry via adaptively complete transitions overlapping with a bias magnetic field equally projecting onto all existing NV orientations. Under such conditions, the spin transitions corresponding to different NV orientations are completely overlapped which will bring about an obviously improved photoluminescence contrast. And we further introduce particle swarm optimization into the calibration process to generate this bias magnetic field automatically and adaptively using computer-controlled Helmholtz coils. By applying this technique, we realize an approximate 1.5 times enhancement and reach the magnetic field sensitivity of $\rm855\ pT/\sqrt{Hz}$ for a completely overlapped transitions compared to $\rm 1.33\ nT/\sqrt{\rm Hz}$ for a separate transition on continuous-wave magnetometry. Our approach can be conveniently applied to direction-fixed magnetic sensing and obtain the potentially maximum sensitivity of ensemble-NV magnetometry.

quant-ph↗

arXiv2022

Pursuing 3D Scene Structures with Optical Satellite Images from Affine Reconstruction to Euclidean Reconstruction

How to use multiple optical satellite images to recover the 3D scene structure is a challenging and important problem in the remote sensing field. Most existing methods in literature have been explored based on the classical RPC (rational polynomial camera) model which requires at least 39 GCPs (ground control points), however, it is not trivial to obtain such a large number of GCPs in many real scenes. Addressing this problem, we propose a hierarchical reconstruction framework based on multiple optical satellite images, which needs only 4 GCPs. The proposed framework is composed of an affine dense reconstruction stage and a followed affine-to-Euclidean upgrading stage: At the affine dense reconstruction stage, an affine dense reconstruction approach is explored for pursuing the 3D affine scene structure without any GCP from input satellite images. Then at the affine-to-Euclidean upgrading stage, the obtained 3D affine structure is upgraded to a Euclidean one with 4 GCPs. Experimental results on two public datasets demonstrate that the proposed method significantly outperforms three state-of-the-art methods in most cases.

cs.CV↗

arXiv2012

Airfoil Roll Control by Bang-Bang Optimal Control Method with Plasma Actuators

The bang-bang optimal control method was proposed for glow discharge plasma actuators, taking account of practical issues, such as limited actuation states with instantaneously varied aerodynamic control performance. Hence, the main contribution of this Note is to integrate flight control with active flow control in particular for plasma actuators. Flow control effects were examined in wind tunnel experiments, which show that the plasma authority for flow control is limited. Flow control effects are only obvious at pitch angles near stall. However, flight control simulations suggest that even those small plasma-induced roll moments can satisfactorily fulfill the maneuver tasks and meet flight quality specifications. In addition, the disturbance from volatile plasma-induced roll moments can be rejected. Hence, the proposed bang-bang control method is a promising candidate of control design methodology for plasma actuators.

physics.plasm-ph↗