arXiv subjects

Xiaoping Hu

Publications and source records attributed to Xiaoping Hu.

9 recordsLinked to original sources

arXiv2026

Dynamics of a microroller under confinement

Rotating particles can translate when placed near a surface, forming microrollers with a wide range of biomedical and microfluidic applications. In this work, we investigate the dynamics of microrollers in confined microchannels with different geometries by combining experiments, numerical simulations, and scaling analysis. In constricted channels, we find that the translational velocity of a microroller decreases as it approaches the constricted region. In both rectangular and cylindrical channels, velocity reversal occurs as the characteristic channel width decreases. Using the force-free condition for free translation, we develop a systematic scaling framework that can be generalized to different channel geometries. The scaling analysis yields functional dependences of the translational velocity on the degree of confinement, which agree well with both experiments and simulations. Importantly, we demonstrate that the viscous stress generated by the far-field rotlet flow governs the observed velocity reduction and reversal, while the translational resistance resulting from the near-field shear flow suppresses translation under tight confinement. The distinct roles of these flow components revealed by our analysis may provide practical guidance for controlling microroller dynamics in confined fluid environments.

physics.flu-dyn↗

arXiv2025

Passive cell body plays active roles in microalgal swimming via nonreciprocal interactions

The cell body of flagellated microalgae is commonly considered to act merely as a passive load during swimming, and a larger body size would simply reduce the speed. In this work, we use numerical simulations based on a boundary element method to investigate the effect of body-flagella hydrodynamic interactions (HIs) on the swimming performance of the biflagellate, \textit{C. reinhardtii}. We find that body-flagella HIs significantly enhance the swimming speed and efficiency. As the body size increases, the competition between the enhanced HIs and the increased viscous drag leads to an optimal body size for swimming. Based on the simplified three-sphere model, we further demonstrate that the enhancement by body-flagella HIs arises from an effective non-reciprocity: the body affects the flagella more strongly during the power stroke, while the flagella affect the body more strongly during the recovery stroke. Our results have implications for both microalgal swimming and laboratory designs of biohybrid microrobots.

physics.flu-dyn↗

arXiv2024

DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking and Loop-Closing

The performance of visual SLAM in complex, real-world scenarios is often compromised by unreliable feature extraction and matching when using handcrafted features. Although deep learning-based local features excel at capturing high-level information and perform well on matching benchmarks, they struggle with generalization in continuous motion scenes, adversely affecting loop detection accuracy. Our system employs a Model-Agnostic Meta-Learning (MAML) strategy to optimize the training of keypoint extraction networks, enhancing their adaptability to diverse environments. Additionally, we introduce a coarse-to-fine feature tracking mechanism for learned keypoints. It begins with a direct method to approximate the relative pose between consecutive frames, followed by a feature matching method for refined pose estimation. To mitigate cumulative positioning errors, DK-SLAM incorporates a novel online learning module that utilizes binary features for loop closure detection. This module dynamically identifies loop nodes within a sequence, ensuring accurate and efficient localization. Experimental evaluations on publicly available datasets demonstrate that DK-SLAM outperforms leading traditional and learning based SLAM systems, such as ORB-SLAM3 and LIFT-SLAM. These results underscore the efficacy and robustness of our DK-SLAM in varied and challenging real-world environments.

cs.RO↗

arXiv2022

SelfOdom: Self-supervised Egomotion and Depth Learning via Bi-directional Coarse-to-Fine Scale Recovery

Accurately perceiving location and scene is crucial for autonomous driving and mobile robots. Recent advances in deep learning have made it possible to learn egomotion and depth from monocular images in a self-supervised manner, without requiring highly precise labels to train the networks. However, monocular vision methods suffer from a limitation known as scale-ambiguity, which restricts their application when absolute-scale is necessary. To address this, we propose SelfOdom, a self-supervised dual-network framework that can robustly and consistently learn and generate pose and depth estimates in global scale from monocular images. In particular, we introduce a novel coarse-to-fine training strategy that enables the metric scale to be recovered in a two-stage process. Furthermore, SelfOdom is flexible and can incorporate inertial data with images, which improves its robustness in challenging scenarios, using an attention-based fusion module. Our model excels in both normal and challenging lighting conditions, including difficult night scenes. Extensive experiments on public datasets have demonstrated that SelfOdom outperforms representative traditional and learning-based VO and VIO models.

cs.CV↗

arXiv2022

A Predictor-Informed Multi-Subject Bayesian Approach for Dynamic Functional Connectivity

Time Varying Functional Connectivity (TVFC) investigates how the interactions among brain regions vary over the course of an fMRI experiment. The transitions between different individual connectivity states can be modulated by changes in underlying physiological mechanisms that drive functional network dynamics, e.g., changes in attention or cognitive effort as measured by pupil dilation. In this paper, we develop a multi-subject Bayesian framework for estimating dynamic functional networks as a function of time-varying exogenous physiological covariates that are simultaneously recorded in each subject during the fMRI experiment. More specifically, we consider a dynamic Gaussian graphical model approach, where a non-homogeneous hidden Markov model is employed to classify the fMRI time series into latent neurological states, borrowing strength over the entire time course of the experiment. The state-transition probabilities are assumed to vary over time and across subjects, as a function of the underlying covariates, allowing for the estimation of recurrent connectivity patterns and the sharing of networks among the subjects. Our modeling approach further assumes sparsity in the network structures, via shrinkage priors. We achieve edge selection in the estimated graph structures, by introducing a multi-comparison procedure for shrinkage-based inferences with Bayesian false discovery rate control. We apply our modeling framework on a resting-state experiment where fMRI data have been collected concurrently with pupillometry measurements, leading us to assess the heterogeneity of the effects of changes in pupil dilation, previously linked to changes in norepinephrine-containing locus coeruleus, on the subjects' propensity to change connectivity states.

stat.ME↗

arXiv2021

A 2D Georeferenced Map Aided Visual-Inertial System for Precise UAV Localization

Precise geolocalization is crucial for unmanned aerial vehicles (UAVs). However, most current deployed UAVs rely on the global navigation satellite systems (GNSS) or high precision inertial navigation systems (INS) for geolocalization. In this paper, we propose to use a lightweight visual-inertial system with a 2D georeference map to obtain accurate and consecutive geodetic positions for UAVs. The proposed system firstly integrates a micro inertial measurement unit (MIMU) and a monocular camera as odometry to consecutively estimate the navigation states and reconstruct the 3D position of the observed visual features in the local world frame. To obtain the geolocation, the visual features tracked by the odometry are further registered to the 2D georeferenced map. While most conventional methods perform image-level aerial image registration, we propose to align the reconstructed points to the map points in the geodetic frame; this helps to filter out the large portion of outliers and decouples the negative effects from the horizontal angles. The registered points are then used to relocalize the vehicle in the geodetic frame. Finally, a pose graph is deployed to fuse the geolocation from the aerial image registration and the local navigation result from the visual-inertial odometry (VIO) to achieve consecutive and drift-free geolocalization performance. We have validated the proposed method by installing the sensors to a UAV body rigidly and have conducted two flights in different environments with unknown initials. The results show that the proposed method can achieve less than 4m position error in flight at 100m high and less than 9m position error in flight about 300m high.

cs.RO↗

arXiv2020

Multimodal assessment of nigrosomal degeneration in Parkinson's disease

Background: Approximately forty percent of all dopaminergic neurons in SNpc are located in five dense neuronal clusters, named nigrosomes. T2- or T2*-weighted images are used to delineate the largest nigrosome, named nigrosome-1. In these images, nigrosome-1 is a hyperintense region in the caudal and dorsal portion of the T2- or T2*-weighted substantia nigra. In PD, nigrosome-1 experiences iron accumulation, which leads to a reduction in T2-weighted hyperintensity. Here, we examine neuromelanin-depletion and iron deposition in regions of interest (ROIs) derived from quantitative-voxel based morphometry (qVBM) on neuromelanin-sensitive images and compare the ROIs with nigrosome-1 identified in T2*-weighted images. Methods: Neuromelanin-sensitive and multi-echo gradient echo imaging data were obtained. R2* was calculated from multi-echo gradient echo imaging data. qVBM analysis was performed on neuromelanin-sensitive images and restricted to SNpc. Mean neuromelanin-sensitive contrast and R2* was measured from the resulting qVBM clusters. Nigrosome-1 was segmented in T2*-weighted images of control subjects and its location was compared to the spatial location of the qVBM clusters. Results: Two bilateral clusters emerged from the qVBM analysis. These clusters showed reduced neuromelanin-sensitive contrast and increased mean R2* in PD as compared to controls. Cluster-1 from the qVBM analysis was in a similar spatial location as nigrosome-1, as seen in T2*-weighted images. Conclusion: qVBM cluster-1 shows reduced neuromelanin-sensitive contrast and is in a similar spatial position as nigrosome-1. This region likely corresponds to nigrosome-1 while the second cluster may correspond to nigrosome-2.

physics.med-ph↗

arXiv2019

Characterization of Age-related Microstructural Changes in Locus Coeruleus and Substantia Nigra Pars Compacta

Locus coeruleus (LC) and substantia nigra pars compacta (SNpc) degrade with normal aging, but not much is known regarding how these changes manifest in MRI images, or whether these markers predict aspects of cognition. Here, we use high-resolution diffusion-weighted MRI to investigate microstructural and compositional changes in LC and SNpc in young and aged cohorts, as well as their relationship with cognition. In LC, the older cohort exhibited a significant reduction in mean and radial diffusivity, but a significant increase in fractional anisotropy compared to the young cohort. We observed a significant correlation between the decrease in LC mean and radial diffusivities with delayed recall. This observation suggests that LC is involved in retaining cognitive abilities. In addition, we observed that iron deposition in SNpc occurs early in life and continues during normal aging. Since neuronal loss occurs in both LC and SNpc in Parkinson's disease, but occurs only in the LC in Alzheimer's disease, our results may lead to early stage imaging biomarkers for these diseases.

physics.med-ph↗

arXiv2011

Observability of Strapdown INS Alignment: A Global Perspective

Alignment of the strapdown inertial navigation system (INS) has strong nonlinearity, even worse when maneuvers, e.g., tumbling techniques, are employed to improve the alignment. There is no general rule to attack the observability of a nonlinear system, so most previous works addressed the observability of the corresponding linearized system by implicitly assuming that the original nonlinear system and the linearized one have identical observability characteristics. Strapdown INS alignment is a nonlinear system that has its own characteristics. Using the inherent properties of strapdown INS, e.g., the attitude evolution on the SO(3) manifold, we start from the basic definition and develop a global and constructive approach to investigate the observability of strapdown INS static and tumbling alignment, highlighting the effects of the attitude maneuver on observability. We prove that strapdown INS alignment, considering the unknown constant sensor biases, will be completely observable if the strapdown INS is rotated successively about two different axes and will be nearly observable for finite known unobservable states (no more than two) if it is rotated about a single axis. Observability from a global perspective provides us with insights into and a clearer picture of the problem, shedding light on previous theoretical results on strapdown INS alignment that were not comprehensive or consistent.. The reporting of inconsistencies calls for a review of all linearization-based observability studies in the vast literature. Extensive simulations with constructed ideal observers and an extended Kalman filter are carried out, and the numerical results accord with the analysis. The conclusions can also assist in designing the optimal tumbling strategy and the appropriate state observer in practice to maximize the alignment performance.

cs.RO↗