arXiv subjects

Bing Zhu

Publications and source records attributed to Bing Zhu.

At least 55 records · Page 3Linked to original sources

arXiv2023

Control Lyapunov-Barrier Function Based Model Predictive Control for Stochastic Nonlinear Affine Systems

A stochastic model predictive control (MPC) framework is presented in this paper for nonlinear affine systems with stability and feasibility guarantee. We first introduce the concept of stochastic control Lyapunov-barrier function (CLBF) and provide a method to construct CLBF by combining an unconstrained control Lyapunov function (CLF) and control barrier functions. The unconstrained CLF is obtained from its corresponding semi-linear system through dynamic feedback linearization. Based on the constructed CLBF, we utilize sampled-data MPC framework to deal with states and inputs constraints, and to analyze stability of closed-loop systems. Moreover, event-triggering mechanisms are integrated into MPC framework to improve performance during sampling intervals. The proposed CLBF based stochastic MPC is validated via an obstacle avoidance example.

eess.SY↗

arXiv2023

A Fast and Robust Camera-IMU Online Calibration Method For Localization System

Autonomous driving has spurred the development of sensor fusion techniques, which combine data from multiple sensors to improve system performance. In particular, localization system based on sensor fusion , such as Visual Simultaneous Localization and Mapping (VSLAM), is an important component in environment perception, and is the basis of decision-making and motion control for intelligent vehicles. The accuracy of extrinsic calibration parameters between camera and IMU has significant effect on the positioning precision when performing VSLAM system. Currently, existing methods are time-consuming using complex optimization methods and sensitive to noise and outliers due to off-calibration, which can negatively impact system performance. To address these problems, this paper presents a fast and robust camera-IMU online calibration method based space coordinate transformation constraints and SVD (singular Value Decomposition) tricks. First, constraint equations are constructed based on equality of rotation and transformation matrices between camera frames and IMU coordinates at different moments. Secondly, the external parameters of the camera-IMU are solved using quaternion transformation and SVD techniques. Finally, the proposed method is validated using ROS platform, where images from the camera and velocity, acceleration, and angular velocity data from the IMU are recorded in a ROS bag file. The results showed that the proposed method can achieve robust and reliable camera-IMU online calibration parameters results with less tune consuming and less uncertainty.

eess.SY↗

arXiv2023

Configured Quantum Reservoir Computing for Multi-Task Machine Learning

Amidst the rapid advancements in experimental technology, noise-intermediate-scale quantum (NISQ) devices have become increasingly programmable, offering versatile opportunities to leverage quantum computational advantage. Here we explore the intricate dynamics of programmable NISQ devices for quantum reservoir computing. Using a genetic algorithm to configure the quantum reservoir dynamics, we systematically enhance the learning performance. Remarkably, a single configured quantum reservoir can simultaneously learn multiple tasks, including a synthetic oscillatory network of transcriptional regulators, chaotic motifs in gene regulatory networks, and the fractional-order Chua's circuit. Our configured quantum reservoir computing yields highly precise predictions for these learning tasks, outperforming classical reservoir computing. We also test the configured quantum reservoir computing in foreign exchange (FX) market applications and demonstrate its capability to capture the stochastic evolution of the exchange rates with significantly greater accuracy than classical reservoir computing approaches. Through comparison with classical reservoir computing, we highlight the unique role of quantum coherence in the quantum reservoir, which underpins its exceptional learning performance. Our findings suggest the exciting potential of configured quantum reservoir computing for exploiting the quantum computation power of NISQ devices in developing artificial general intelligence.

quant-ph↗

arXiv2023

GNN-PMB: A Simple but Effective Online 3D Multi-Object Tracker without Bells and Whistles

Multi-object tracking (MOT) is among crucial applications in modern advanced driver assistance systems (ADAS) and autonomous driving (AD) systems. The global nearest neighbor (GNN) filter, as the earliest random vector-based Bayesian tracking framework, has been adopted in most of state-of-the-arts trackers in the automotive industry. The development of random finite set (RFS) theory facilitates a mathematically rigorous treatment of the MOT problem, and different variants of RFS-based Bayesian filters have then been proposed. However, their effectiveness in the real ADAS and AD application is still an open problem. In this paper, it is demonstrated that the latest RFS-based Bayesian tracking framework could be superior to typical random vector-based Bayesian tracking framework via a systematic comparative study of both traditional random vector-based Bayesian filters with rule-based heuristic track maintenance and RFS-based Bayesian filters on the nuScenes validation dataset. An RFS-based tracker, namely Poisson multi-Bernoulli filter using the global nearest neighbor (GNN-PMB), is proposed to LiDAR-based MOT tasks. This GNN-PMB tracker is simple to use, and it achieves competitive results on the nuScenes dataset. Specifically, the proposed GNN-PMB tracker outperforms most state-of-the-art LiDAR-only trackers and LiDAR and camera fusion-based trackers, ranking the $3^{rd}$ among all LiDAR-only trackers on nuScenes 3D tracking challenge leader board at the time of submission.

eess.SY↗

arXiv2023

Contrastive Learning for Automotive mmWave Radar Detection Points Based Instance Segmentation

The automotive mmWave radar plays a key role in advanced driver assistance systems (ADAS) and autonomous driving. Deep learning-based instance segmentation enables real-time object identification from the radar detection points. In the conventional training process, accurate annotation is the key. However, high-quality annotations of radar detection points are challenging to achieve due to their ambiguity and sparsity. To address this issue, we propose a contrastive learning approach for implementing radar detection points-based instance segmentation. We define the positive and negative samples according to the ground-truth label, apply the contrastive loss to train the model first, and then perform fine-tuning for the following downstream task. In addition, these two steps can be merged into one, and pseudo labels can be generated for the unlabeled data to improve the performance further. Thus, there are four different training settings for our method. Experiments show that when the ground-truth information is only available for a small proportion of the training data, our method still achieves a comparable performance to the approach trained in a supervised manner with 100% ground-truth information.

cs.CV↗

arXiv2023

Deep Instance Segmentation with Automotive Radar Detection Points

Automotive radar provides reliable environmental perception in all-weather conditions with affordable cost, but it hardly supplies semantic and geometry information due to the sparsity of radar detection points. With the development of automotive radar technologies in recent years, instance segmentation becomes possible by using automotive radar. Its data contain contexts such as radar cross section and micro-Doppler effects, and sometimes can provide detection when the field of view is obscured. The outcome from instance segmentation could be potentially used as the input of trackers for tracking targets. The existing methods often utilize a clustering-based classification framework, which fits the need of real-time processing but has limited performance due to minimum information provided by sparse radar detection points. In this paper, we propose an efficient method based on clustering of estimated semantic information to achieve instance segmentation for the sparse radar detection points. In addition, we show that the performance of the proposed approach can be further enhanced by incorporating the visual multi-layer perceptron. The effectiveness of the proposed method is verified by experimental results on the popular RadarScenes dataset, achieving 89.53% mean coverage and 86.97% mean average precision with the IoU threshold of 0.5, which is superior to other approaches in the literature. More significantly, the consumed memory is around 1MB, and the inference time is less than 40ms, indicating that our proposed algorithm is storage and time efficient. These two criteria ensure the practicality of the proposed method in real-world systems.

cs.CV↗

arXiv2022

Dead-beat model predictive control for discrete-time linear systems

In this paper, model predictive control (MPC) strategies are proposed for dead-beat control of linear systems with and without state and control constraints. In unconstrained MPC, deadbeat performance can be guaranteed by setting the control horizon to the system dimension, and adding an terminal equality constraint. It is proved that the unconstrained deadbeat MPC is equivalent to linear deadbeat control. The proposed constrained deadbeat MPC is designed by setting the control horizon equal to the system dimension and penalizing only the terminal cost. The recursive feasibility and deadbeat performance are proved theoretically.

math.OC↗

arXiv2022

Improved characterization of Feshbach resonances and interaction potentials between $^{23}$Na and $^{87}$Rb atoms

The ultracold mixture of \Na and \Rb atoms has become an important system for investigating physics in Bose-Bose atomic mixtures and for forming ultracold ground-state polar molecules. In this work, we provide an improved characterization of the most commonly used Feshbach resonance near 347.64 G between \Na and \Rb in their absolute ground states. We form Feshbach molecules using this resonance and measure their binding energies by dissociating them via magnetic field modulation. We use the binding energies to refine the singlet and triplet potential energy curves, using coupled-channel bound-state calculations. We then use coupled-channel scattering calculations on the resulting potentials to produce a high-precision mapping between magnetic field and scattering length. We also observe 10 additional $s$-wave Feshbach resonances for \Na and \Rb in different combinations of Zeeman sublevels of the $F = 1$ hyperfine states. Some of the resonances show 2-body inelastic decay due to spin exchange. We compare the resonance properties with coupled-channel scattering calculations that full take account of inelastic properties.

cond-mat.quant-gas↗

arXiv2022

Observation of photon recoil effects in single-beam absorption spectroscopy with an ultracold strontium gas

We report on observing photon recoil effects in the absorption of a single monochromatic light at 689~nm through an ultracold $^{88}$Sr gas, where the recoil frequency is comparable to natural linewidth of the narrow-line transition $5\mathrm{s^2}\, ^1\textrm{S}_0 - 5\mathrm{s}5\mathrm{p}\, ^3\textrm{P}_1$ in strontium. In the regime of high-saturation, the absorption profile becomes asymmetric due to the photon-recoil shift, which is of the same order as the natural linewidth. The lineshape is described by an extension of the optical Bloch equations including the momentum transfers to atoms during emission and absorption of photons. Our work reveals the photon recoil effects in a simplest single-beam absorption setting, which is of significant relevance to other applications such as saturation spectroscopy, Ramsey interferometry, and absorption imaging.

physics.atom-ph↗

arXiv2021

Dynamics of position disordered Ising spins with a soft-core potential

We theoretically study magnetization relaxation of Ising spins distributed randomly in a $d$-dimension homogeneous and Gaussian profile under a soft-core two-body interaction potential $\propto1/[1+(r/R_c)^α]$ ($α\ge d$), where $r$ is the inter-spin distance and $R_c$ is the soft-core radius. The dynamics starts with all spins polarized in the transverse direction. In the homogeneous case, an analytic expression is derived at the thermodynamic limit, which starts as $\propto\exp(-t^2)$ and follows a stretched-exponential law asymptotically at long time with an exponent $β=d/α$. In between an oscillating behaviour is observed with a damping amplitude. For Gaussian samples, the degree of disorder in the system can be controlled by the ratio $l_ρ/R_c$ with $l_ρ$ the mean inter-spin distance and the magnetization dynamics is investigated numerically. In the limit of $l_ρ/R_c\ll1$, a coherent many-body dynamics is recovered for the total magnetization despite of the position disorder of spins. In the opposite limit of $l_ρ/R_c\gg1$, a similar dynamics as that in the homogeneous case emerges at later time after a initial fast decay of the magnetization. We obtain a stretched exponent of $β\approx0.18$ for the asymptotic evolution with $d=3, α=6$, which is different from that in the homogeneous case ($β=0.5$).

cond-mat.stat-mech↗

arXiv2021

Rydberg blockade in an ultracold strontium gas revealed by two-photon excitation dynamics

We demonstrate the interaction-induced blockade effect in an ultracold $^{88}$Sr gas via studying the time dynamics of a two-photon excitation to the triplet Rydberg series $5\mathrm{s}n\mathrm{s}\, ^3\textrm{S}_1$ for five different principle quantum numbers $n$ ranging from 19 to 37. By using a multi-pulse excitation sequence to increase the detection sensitivity we could identify Rydberg-excitation-induced atom losses as low as $<1\%$. Based on an optical Bloch equation formalism, treating the Rydberg-Rydberg interaction on a mean-field level, the van der Waals coefficients are extracted from the observed dynamics, which agree fairly well with \emph{ab initio} calculations.

physics.atom-ph↗

arXiv2020

Scattering of two heavy Fermi polarons: resonances and quasibound states

Impurities in a Fermi sea, or Fermi polarons, experience a Casimir interaction induced by quantum fluctuations of the medium. When there is short-range attraction between impurities and fermions, also the induced interaction between two impurities is strongly attractive at short distance and oscillates in space for larger distances. We theoretically investigate the scattering properties and compute the scattering phase shifts and scattering lengths between two heavy impurities in an ideal Fermi gas at zero temperature. While the induced interaction between impurities is weakly attractive for weak impurity-medium interactions, we find that impurities strongly and attractively interacting with the medium exhibit resonances in the induced scattering with a sign change of the induced scattering length and even strong repulsion. These resonances occur whenever a three-body Efimov bound state appears at the continuum threshold. At energies above the continuum threshold, we find that the Efimov state in medium can turn into a quasibound state with a finite decay width.

cond-mat.quant-gas↗

arXiv2020

Fermions meet two bosons -- the heteronuclear Efimov effect revisited

In this article, we revisit the heteronuclear Efimov effect in a Bose-Fermi mixture with large mass difference in the Born-Oppenheimer picture. As a specific example, we consider the combination of bosonic $^{133}\mathrm{Cs}$ and fermionic $^6\mathrm{Li}$. In a system consisting of two heavy bosons and one light fermion, the fermion-mediated potential between the two heavy bosons gives rise to an infinite series of three-body bound states. The intraspecies scattering length determines the three-body parameter and the scaling factor between consecutive Efimov states. In a second scenario, we replace the single fermion by an entire Fermi Sea at zero temperature. The emerging interaction potential for the two bosons exhibits long-range oscillations leading to a weakening of the binding and a breakup of the infinite series of Efimov states. In this scenario, the binding energies follow a modified Efimov scaling law incorporating the Fermi momentum. The scaling factor between deeply bound states is governed by the intraspecies interaction, analogous to the Efimov states in vacuum.

cond-mat.quant-gas↗

arXiv2020

On-chip Erbium-doped lithium niobate microcavity laser

The commercialization of lithium niobate on insulator (LNOI) wafer has sparked significant on-chip photonic integration application due to its remarkable photonic, photoacoustic, electro-optic and piezoelectric nature. A variety of on-chip LNOI-based optical devices with high performance has been realized in recent years. Here we developed 1 mol\% erbium-doped LN crystal and its LNOI wafer, and fabricated an erbium-doped LNOI microdisk with high quality ($ \sim $ 1.05$\times 10^{^5}$ ). C-band laser emission with $ \sim $1530 nm and $ \sim $1560 nm from the high-Q erbium-doped LNOI microdisk was demonstrated both with 974 nm and 1460 nm pumping, and the latter has better thermal stability. This microlaser would play an important role in the photonic integrated circuits of lithium niobate platform.

physics.optics↗

arXiv2020

On the Optimal Minimum Distance of Fractional Repetition Codes

Fractional repetition (FR) codes are a class of repair efficient erasure codes that can recover a failed storage node with both optimal repair bandwidth and complexity. In this paper, we study the minimum distance of FR codes, which is the smallest number of nodes whose failure leads to the unrecoverable loss of the stored file. We consider upper bounds on the minimum distance and present several families of explicit FR codes attaining these bounds. The optimal constructions are derived from regular graphs and combinatorial designs, respectively.

cs.IT↗

arXiv2020

Manipulation of heteronuclear spin dynamics with microwave and vector light shift

We report the observation and manipulation of heteronuclear spin dynamics in a spin-1 mixture of ultracold $^{87}$Rb and $^{23}$Na atoms. The dynamics is driven by the interspecies spin-dependent interaction and shows a pronounced dependence on magnetic fields with influences from both linear and quadratic Zeeman shifts. Similar to the well-studied homonuclear cases, the interspecies spin dynamics can be controlled by tuning the quadratic Zeeman shift with far-detuned microwave fields. In addition, we successfully realize spin dynamics control with vector light shifts which act as a species-selective effective magnetic field on $^{87}$Rb atoms. Both methods show negligible loss of atoms thus will be powerful techniques for investigating spin dynamics with fast temporal and high spatial resolutions.

cond-mat.quant-gas↗

arXiv2019

Spin-rotation coupling in p-wave Feshbach resonances

We report evidence for spin-rotation coupling in $p$-wave Feshbach resonances in an ultracold mixture of fermionic $^6$Li and bosonic $^{133}$Cs lifting the commonly observed degeneracy of states with equal absolute value of orbital-angular-momentum projection on the external magnetic field. By employing magnetic field dependent atom-loss spectroscopy we find triplet structures in $p$-wave resonances. Comparison with coupled-channel calculations, including contributions from both spin-spin and spin-rotation interactions, yields a spin-rotation coupling parameter $|γ|=0.566(50)\times10^{-3}$. Our findings highlight the potential of Feshbach resonances in revealing subtle molecular couplings and providing precise information on electronic and nuclear wavefunctions, especially at short internuclear distance. The existence of a non-negligible spin-rotation splitting may have consequences for future classifications of $p$-wave superfluid phases in spin-polarized fermions.

cond-mat.quant-gas↗

arXiv2019

High partial-wave Feshbach resonances in an ultracold $^6$Li-$^{133}$Cs mixture

We measure higher partial wave Feshbach resonances in an ultracold mixture of fermionic $^6$Li and bosonic $^{133}$Cs by magnetic field dependent atom-loss spectroscopy. For the $p$-wave Feshbach resonances we observe triplet structures corresponding to different projections of the pair rotation angular momentum onto the external magnetic field axis. We attribute the splittings to the spin-spin and spin-rotation couplings by modelling the observation using a full coupled-channel calculation. Comparison with an oversimplified model, estimating the spin-rotation coupling by describing the weakly bound close-channel molecular state with the perturbative multipole expansion, reveals the significant contribution of the molecular wavefunction at short internuclear distances. Our findings highlight the potential of Feshbach resonances in providing precise information on short- and intermediate-range molecular couplings and wavefunctions. The observed $d$-wave Feshbach resonances allow us to refine the LiCs singlet and triplet ground-state molecular potential curves at large internuclear separations.

cond-mat.quant-gas↗