Global Estimation Research Articles

MVINS: Tightly Coupled Mocap–Visual–Inertial Fusion for Global and Drift-Free Pose Estimation

MVINS: Tightly Coupled Mocap–Visual–Inertial Fusion for Global and Drift-Free Pose Estimation

Distributed Cooperative Quantum Learning for Discrete-Time Multiagent Source Exploration With Information Prompts.

In light of optimization theory and swarm evolutionary schemes, under multiple single-integrator mobile agents equipped with sensors and prompters, this article addresses a discrete-time multiagent source exploration problem with information prompts. Regarding information prompts as constraints on the unknown target, by virtue of penalty function skills (PFSs) and sequential unconstrained minimization techniques (SUMTs), the agents are driven toward the source under the guidance of the control strategy. In two cases of available and unavailable gradient information, a quantum potential well, an average optimal position estimator (AOPE), and a global optimal position estimator (GOPE) are introduced into swarm evolutionary schemes with a periodically oscillating weight, such that distributed cooperative quantum learning (DCQL) policy is proposed as a control strategy under communication restrictions, where AOPE and GOPE are developed relying on distributed consensus theory. In particular, when the gradient is unavailable, we put forth an adaptive generalized Bernstein neural network (AGBNN) to replace it based on excellent properties of Bernstein polynomials and adaptive approaches. Further, a performance analysis for the proposed policy is executed on the convergence and computational complexity, which ensures the accuracy and efficiency of the source exploration in theory. Ultimately, a simulation test is carried out, and the results validate the practicability and effectiveness of the offered method.

Rates of bronchopulmonary dysplasia in very low birth weight neonates: a systematic review and meta-analysis

ImportanceLarge-scale estimates of bronchopulmonary dysplasia (BPD) are warranted for adequate prevention and treatment. However, systematic approaches to ascertain rates of BPD are lacking.ObjectiveTo conduct a systematic review and meta-analysis to assess the prevalence of BPD in very low birth weight (≤ 1,500 g) or very low gestational age (< 32 weeks) neonates.Data sourcesA search of MEDLINE from January 1990 until September 2019 using search terms related to BPD and prevalence was performed.Study selectionRandomized controlled trials and observational studies evaluating rates of BPD in very low birth weight or very low gestational age infants were eligible. Included studies defined BPD as positive pressure ventilation or oxygen requirement at 28 days (BPD28) or at 36 weeks postmenstrual age (BPD36).Data extraction and synthesisTwo reviewers independently conducted all stages of the review. Random-effects meta-analysis was used to calculate the pooled prevalence. Subgroup analyses included gestational age group, birth weight group, setting, study period, continent, and gross domestic product. Sensitivity analyses were performed to reduce study heterogeneity.Main outcomes and measuresPrevalence of BPD defined as BPD28, BPD36, and by subgroups.ResultsA total of 105 articles or databases and 780,936 patients were included in this review. The pooled prevalence was 35% (95% CI, 28-42%) for BPD28 (n = 26 datasets, 132,247 neonates), and 21% (95% CI, 19-24%) for BPD36 (n = 70 studies, 672,769 neonates). In subgroup meta-analyses, birth weight category, gestational age category, and continent were strong drivers of the pooled prevalence of BPD.Conclusions and relevanceThis study provides a global estimation of BPD prevalence in very low birth weight/low gestation neonates.

A Vision/Inertial Navigation/Global Navigation Satellite Integrated System for Relative and Absolute Localization in Land Vehicles.

This paper presents an enhanced ground vehicle localization method designed to address the challenges associated with state estimation for autonomous vehicles operating in diverse environments. The focus is specifically on the precise localization of position and orientation in both local and global coordinate systems. The proposed approach integrates local estimates generated by existing visual-inertial odometry (VIO) methods into global position information obtained from the Global Navigation Satellite System (GNSS). This integration is achieved through optimizing fusion in a pose graph, ensuring precise local estimation and drift-free global position estimation. Considering the inherent complexities in autonomous driving scenarios, such as the potential failures of a visual-inertial navigation system (VINS) and restrictions on GNSS signals in urban canyons, leading to disruptions in localization outcomes, we introduce an adaptive fusion mechanism. This mechanism allows seamless switching between three modes: utilizing only VINS, using only GNSS, and normal fusion. The effectiveness of the proposed algorithm is demonstrated through rigorous testing in the Carla simulation environment and challenging UrbanNav scenarios. The evaluation includes both qualitative and quantitative analyses, revealing that the method exhibits robustness and accuracy.

Global food security estimation: Refining the IFSA caloric threshold using population estimates and projections by age and sex

Global food security estimation: Refining the IFSA caloric threshold using population estimates and projections by age and sex

Heterogeneity constrained color ellipsoid prior image dehazing algorithm

To address the issue of inaccurate transmission estimation in areas with sudden depth changes in hazy images, the image dehazing algorithm with Heterogeneity Constrained Color Ellipsoid Prior (HC-CEP) is proposed. Firstly, a local threshold optimization quadtree search method is designed, in order to solve the problem of inaccurate global atmospheric light estimation and avoid the overall darkening of the image caused by white objects.Then, different regions are selected by a local block heterogeneity window to construct diversity prior vectors to estimate the initial transmission. Finally, by constructing neighborhood heterogeneity weighted constraints on each pixel of the initial transmission and improving the high-order differential filtering of the Scharr operator, the optimization of the transmission is achieved, eliminating halo artifacts in the depth mutation area of the image. Both qualitative and quantitative experimental results show that the proposed algorithm comprehensively considers the heterogeneity of local pixel characteristics, and the dehazing image obtained has better color fidelity and edge detail preservation ability, which can effectively eliminate halo artifacts on the edges of hazy images.The proposed method has better dehazing performance and has a 8% improvement in terms of the FADE metric compared to state-of-the-art dehazing methods.

HIDE: Hierarchical iterative decoding enhancement for multi‐view 3D human parameter regression

AbstractParametric human modeling are limited to either single‐view frameworks or simple multi‐view frameworks, failing to fully leverage the advantages of easily trainable single‐view networks and the occlusion‐resistant capabilities of multi‐view images. The prevalent presence of object occlusion and self‐occlusion in real‐world scenarios leads to issues of robustness and accuracy in predicting human body parameters. Additionally, many methods overlook the spatial connectivity of human joints in the global estimation of model pose parameters, resulting in cumulative errors in continuous joint parameters.To address these challenges, we propose a flexible and efficient iterative decoding strategy. By extending from single‐view images to multi‐view video inputs, we achieve local‐to‐global optimization. We utilize attention mechanisms to capture the rotational dependencies between any node in the human body and all its ancestor nodes, thereby enhancing pose decoding capability. We employ a parameter‐level iterative fusion of multi‐view image data to achieve flexible integration of global pose information, rapidly obtaining appropriate projection features from different viewpoints, ultimately resulting in precise parameter estimation. Through experiments, we validate the effectiveness of the HIDE method on the Human3.6M and 3DPW datasets, demonstrating significantly improved visualization results compared to previous methods.

Contribution of PRIDE VLBI products to the joint JUICE-Europa Clipper moons’ ephemerides solution

In the coming decade, JUICE and Europa Clipper radio-science will yield the most accurate estimation to date of the Galilean moons’ physical parameters and ephemerides. JUICE’s PRIDE (Planetary Radio Interferometry and Doppler Experiment) will help achieve such a solution by providing VLBI (Very Long Baseline Interferometry) observations of the spacecraft’s lateral position, complementing nominal radio-science measurements. In this paper, we quantify how PRIDE VLBI can contribute to the moons’ ephemerides determination, in terms of attainable solution improvement and validation opportunities. To this end, we simulated VLBI data for JUICE, but also investigated the possibility to perform simultaneous tracking of JUICE and Europa Clipper, thus ultimately generating both single- and dual-spacecraft VLBI. We considered various tracking and data quality scenarios for both VLBI types, and compared the formal uncertainties provided by covariance analyses with and without VLBI. These analyses were performed for both global and local (i.e. per-flyby) estimations of the moons’ states, as eventually achieving a global solution first requires proceeding arc-per-arc. We showed that both single- and multi-spacecraft VLBI measurements only bring limited improvement to the global state estimation, but significantly contribute to the moons’ normal points (i.e. local states at flyby times), most notably in the out-of-plane direction. Additionally, we designed a validation plan exploiting PRIDE VLBI to progressively validate the classical radio-science solution, whose robustness and statistical realism is sensitive to modelling inconsistencies. By improving the local state estimations and offering various validation opportunities, PRIDE will be invaluable in overcoming possible dynamical challenges. It can therefore play a key role in reconstructing a global solution for the Galilean moons’ dynamics with the uncertainty levels promised by JUICE-Europa Clipper analyses. This, in turn, is critical to the accurate characterisation of tidal dissipation in the Jovian system, holding the key to the long-term evolution of the Galilean moons.

Deep Learning-Based Indoor Localization Using Wireless Sensor Network: An Efficient Approach for Livestock Monitoring

Indoor localization for livestock is important as it facilitates effective monitoring and management of animals within confined spaces, such as barns or stables. By accurately tracking the position of individual animals, farmers and livestock managers can gain valuable insights into their behavior, health, and welfare. This information enables the early detection of potential issues, such as diseases or injuries, allowing for prompt intervention and treatment. While GPS sensors offer global position estimation, they are limited to outdoor environments and inherently exhibit inaccuracies of several meters. In indoor spaces, alternative sensors like lasers and cameras can estimate positions, but they necessitate maps and substantial computational resources to process complex algorithms. Presently, Wireless Networks (WN) are extensively accessible in indoor environments, providing efficient global localization with relatively low cost and computing demands. This paper presents a novel approach to estimate the location of cows in a given area using Deep Neural Networks (DNNs) applied to LQI data. This method aims to improve the efficiency of livestock management, particularly in large-scale farming operations, by enabling precise tracking and monitoring of individual animals. Our proposed model leverages data from wireless sensor networks (WSNs) and demonstrates promising results in terms of accuracy and computational efficiency. This study contributes to the ongoing research in smart agriculture and the application of advanced technologies in the livestock industry.

A distributed particle filter with sampling-based consensus density fusion for speaker tracking in distributed microphone networks

This paper proposes a novel distributed particle filter with sampling-based consensus density fusion for speaker tracking in distributed microphone networks (DMNs). Firstly, to combat the reverberation and ambient noise, multi-hypothesis likelihood model is applied to describe the likelihood distribution in the state-space model of speaker tracking problem. Then, the sequential importance resampling (SIR) is performed on each node to obtain the local posteriors of the state vector of the speaker. Since the non-Gaussian noise and the reverberation may make local posteriors presenting non-Gaussian and multi-modal characters, in this paper, the local posterior densities are approximated by Gaussian mixtures whose parameters are further transmitted to neighboring nodes to complete the distributed fusion based on the consensus method. We investigate a novel adaptive multiple importance sampling (AMIS) algorithm combining parallel tempering Monte Carlo (PTMC) samplers to achieve the sampling-based consensus density fusion of the non-Gaussian local posteriors and estimate the global posterior locally on each node. At last, the optimal global estimation of the speaker state vector could be calculated by using the estimated global posterior on each node in DMNs. Simulation and real-world recording experiment results show that the proposed speaker tracking method exhibits satisfactory tracking performance under reverberant and noisy environments. Moreover, it is easy and feasible to extend the proposed tracking algorithm to solve the multi-speaker tracking problem.

Whole-heart electromechanical simulations using Latent Neural Ordinary Differential Equations

Cardiac digital twins provide a physics and physiology informed framework to deliver personalized medicine. However, high-fidelity multi-scale cardiac models remain a barrier to adoption due to their extensive computational costs. Artificial Intelligence-based methods can make the creation of fast and accurate whole-heart digital twins feasible. We use Latent Neural Ordinary Differential Equations (LNODEs) to learn the pressure-volume dynamics of a heart failure patient. Our surrogate model is trained from 400 simulations while accounting for 43 parameters describing cell-to-organ cardiac electromechanics and cardiovascular hemodynamics. LNODEs provide a compact representation of the 3D-0D model in a latent space by means of an Artificial Neural Network that retains only 3 hidden layers with 13 neurons per layer and allows for numerical simulations of cardiac function on a single processor. We employ LNODEs to perform global sensitivity analysis and parameter estimation with uncertainty quantification in 3 hours of computations, still on a single processor.

Estimates of global and regional prevalence of Helicobacter pylori infection among individuals with obesity: a systematic review and meta-analysis.

The prevalence of obesity is an escalating concern in modern populations, predominantly attributed to the widespread adoption of sedentary lifestyles observed globally. Extensive research has established a significant association between obesity and Helicobacter pylori (H. pylori). Nonetheless, a comprehensive assessment of the global prevalence of H. pylori among individuals with obesity remains undetermined. A systematic search strategy was applied to PubMed, Scopus, and Web of Science. The resulting records were screened using the Rayyan online tool for the management of systematic reviews. Freeman-Tukey double arcsine transformation was used. Subgroup analyses (continent, regional classifications, developmental status, religion, global hemisphere, income, access to international waters, and H. pylori eradication) and multivariate meta-regression (latitude, longitude, male-to-all ratio, mean age, and body mass index) were done to estimate the effects of the moderators. Risk of bias assessment was done using JBI checklist for prevalence studies. A total of 472,511 individuals with obesity from 208 studies were included. The global estimation of H. pylori prevalence among individuals with obesity was 32.3% (95% CI 26.9%, 38.0%). South America had the highest prevalence. Based on the different classifications of countries, resource-rich, low-/middle-income, developing, and Islamic countries had the highest prevalence. Lower pooled prevalence was observed in the studies with adequate sample sizes (n ≥ 270). The findings have the potential to influence future health policies for preventing and treating H. pylori infection. However, there is variability among the included studies, indicating the need for more population-based research.

Global estimation of range resolved thermodynamic profiles from micropulse differential absorption lidar

We demonstrate thermodynamic profile estimation with data obtained using the MicroPulse DIAL such that the retrieval is entirely self contained. The only external input is surface meteorological variables obtained from a weather station installed on the instrument. The estimator provides products of temperature, absolute humidity and backscatter ratio such that cross dependencies between the lidar data products and raw observations are accounted for and the final products are self consistent. The method described here is applied to a combined oxygen DIAL, potassium HSRL, water vapor DIAL system operating at two pairs of wavelengths (nominally centered at 770 and 828 nm). We perform regularized maximum likelihood estimation through the Poisson Total Variation technique to suppress noise and improve the range of the observations. A comparison to 119 radiosondes indicates that this new processing method produces improved temperature retrievals, reducing total errors to less than 2 K below 3 km altitude and extending the maximum altitude of temperature retrievals to 5 km with less than 3 K error. The results of this work definitively demonstrates the potential for measuring temperature through the oxygen DIAL technique and furthermore that this can be accomplished with low-power semiconductor-based lidar sensors.

Federal Algorithm Design and Numerical Experiments of Ensemble Kalman Filter Data Assimilation Analysis Process

Based on the federated Kalman filter proposed by Carlson with linear systems, we propose a federated computing scheme for the ensemble Kalman filter (EnKF) assimilation analysis process for nonlinear systems, and give an optimal information fusion estimation algorithm weighted by diagonal matrix under the linear minimum variance criterion, that is, the assimilation analysis values of each variable in the global estimation are linear combinations of the assimilation analysis values of the corresponding variables in the local estimation of sub-filters, and the calculation of the combination coefficients is given. The federated algorithm of the EnKF assimilation analysis process for nonlinear systems is verified by the Lorenz (1963) system.

Improved estimation of drift coefficients using optimal local bandwidths

Stochastic differential equations (SDEs) are commonly used to model various systems. Data-driven methods have been widely used to estimate the drift and diffusion terms of a Langevin equation. Among the most commonly used estimation methods is the Nadaraya–Watson estimator, which is a non-parametric data-driven approach. In this study, we propose a method to improve the estimation of the drift coefficient of a stochastic process using optimal local bandwidths that minimize the error of the approximation of the first conditional moments of a univariate system. This approach is compared to a global bandwidth estimation and an estimation based on a fixed number of nearest neighbors. The proposed method has the potential to reduce the error of the drift estimation, thereby improving the accuracy of the model.

A Global Assessment of Eddy‐Induced Salinity Anomalies and Salt Transport by Eddy Movement

AbstractEddy‐induced salt transport is essential in maintaining the oceanic salinity balance and global climate. However, the paucity of in situ measurements poses challenges in obtaining the spatial structure of the eddy salt transport globally. Here, we conduct a global estimation of eddy‐induced salinity anomalies and salt transport by eddy movement using 2 million historical hydrographic profile measurements and satellite‐based eddy observations spanning from 1993 to 2019. The results demonstrate rich geographic and vertical variability in the salinity anomalies induced by eddies across the global ocean. Vertical sign switches of salinity anomalies within eddies are observed in the tropical and subtropical oceans, as well as in the Kuroshio Extension region. Additionally, meridional sign switches of eddy‐induced salinity anomalies are observed in the Antarctic Circumpolar Current region. By integrating eddy occurrence probability, we refine the methodology for estimating eddy‐induced salt transport, and provide global spatial patterns of both meridional and zonal salt transport induced by eddy movement at each 2° × 2° grid. Significant salt transport by meridional eddy movement is observed in the mid‐latitude oceans, with the peak zonal‐depth integrated salt transport reaching 106 kg·s−1 in the mid‐latitudes of the southern hemisphere. Specifically, the Brazil‐Malvinas Confluence region is identified as a highly efficient pathway for meridional salt transport, with a magnitude of −1.28 × 106 kg·s−1, significantly surpassing other regions in the global ocean. These findings may provide important references for the understanding and simulation of global oceanic salinity transport.

Assimilating Space‐Based Thermospheric Neutral Density (TND) Data Into the TIE‐GCM Coupled Model During Periods With Low and High Solar Activity

AbstractThe global estimation of Thermospheric Neutral Density (TND) and electron density (Ne) on various altitudes are provided by upper atmosphere models, however, the quality of their forecasts needs to be improved. In this study, we present the impact of assimilating space‐based TNDs, measured along Low Earth Orbit (LEO) mission, into the NCAR Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model (TIE‐GCM). In these experiments, the Ensemble Kalman Filter (EnKF) merger of the Data Assimilation Research Testbed (DART) community software is applied. To cover various space‐based TND data and both low and high solar activity periods, we used the measurements of CHAMP (Challenging Minisatellite Payload) and Swarm‐C as assimilated observations. The TND forecasts are then validated against independent TNDs of GRACE (Gravity Recovery and Climate Experiment mission) and Swarm‐B, respectively. To introduce the impact of the thermosphere on estimating ionospheric parameters, the outputs of Ne are validated against the radio occultation data. The Data Assimilation (DA) results indicate that TIE‐GCM overestimates (underestimates) TND and Ne during low (high) solar activity. Considerable improvements are found in forecasting TNDs after DA, that is, the Root Mean Squared Error (RMSE) is reduced by 79% and 51% during low and high solar activity periods, respectively. The reduction values for Ne are found to be 52.3% and 40.4%, respectively.

On the Use of Inverse Exponentiation to Improve the Efficiency of Calibration Estimators in Stratified Double Sampling

This study introduces the concept of inverse exponentiation in formulating calibration weights in stratified double sampling and proposes a more improved calibration estimator based on Koyuncu and Kadilar (2014) calibration estimator. The variance of the proposed logarithmic calibration estimator has been derived under large sample approximation. Calibration asymptotic optimum estimator and its approximate variance estimator are derived for the proposed logarithmic calibration estimator. Results of empirical study showed that the proposed logarithmic calibration estimator performs better than the Koyuncu and Kadilar (2014) calibration estimator with appreciable gains in efficiency. Also, simulation study for the comparison of the proposed logarithmic estimator with a Global estimator [Generalized Regression (GREG) estimator ] proved the robustness of the proposed logarithmic calibration estimator and by extension the efficacy of inverse exponentiation in calibration weightings. Analysis and evaluation are presented. International Journal of Statistical Sciences, Vol.24(1), March, 2024, pp 91-102

Quantum Parameter Estimation With Graph States In SU(N) Dynamics

AbstractIn quantum metrology, achieving optimal simultaneous multiparameter estimation is of great significance but remains highly challenging. The research approach involving evolution on dynamics provides a framework to investigate simultaneous multiparameter estimation within graph states. For single‐parameter estimation, it is observed that the precision limit exceeds the Heisenberg limit in higher‐dimensional spin systems. For multiparameter estimation, two scenarios are considered: one with commutative Hamiltonian operators and another with non‐commutative Hamiltonian operators. The results demonstrate that the global estimation precision exceeds the local estimation precision. Under the conditions of parameter limit, the precision of parameter estimation for simultaneously estimating each parameter is equal to that of single‐parameter estimation. Furthermore, a precision‐enhancement scheme has been identified that depends on the dynamics of . The smaller the value of in the dynamic evolution, the higher the precision of the parameter estimation. Finally, it is demonstrated that graph states serve as optimal states in quantum metrology. A set of optimal measurement bases is also identified, and it is illustrated that the precision limit of multiparameter estimation can attain the quantum Cramér‐Rao bound.

Higher-order multi-scale computational approach and its convergence for nonlocal gradient elasticity problems of composite materials

A novel higher-order multi-scale computational approach for efficient nonlocal gradient elasticity simulation of composite materials is proposed in this paper. Based on the use of decoupling technique, raw fourth-order nonlocal gradient equations with periodically oscillatory coefficients are decoupled as the new second-order multi-scale equations for reducing the continuity requirements when implementing multi-scale simulation. Next, a novel macro-micro coupled multi-scale computational model with higher-order corrected terms is rigorously devised for high-resolution multi-scale and nonlocal analysis of composite materials via multi-scale asymptotic analysis. Then, the local error analysis of higher-order multi-scale solutions in the point-wise sense illustrates that establishing higher-order asymptotic corrected terms plays a vital role in investigating the micromechanical mechanisms. Moreover, a rigorous global error estimation with an explicit rate of higher-order multi-scale solutions is first derived in the energy norm sense. In addition, an efficient multi-scale numerical algorithm is presented to effectively simulate nonlocal gradient elasticity problems of composite materials based on finite element method (FEM). And we also derive the convergence estimation of the proposed numerical algorithm. Finally, numerical experiments are conducted to gauge the efficiency and accuracy of the proposed higher-order multi-scale method. This paper offers a unified higher-order two-scale computational framework for enabling nonlocal gradient elasticity behavior simulation of composite materials.