Search Results (488)

With the increasing deployment of unmanned aerial vehicles (UAVs) in indoor environments, the demand for high-precision six-degrees-of-freedom (6-DoF) localization has grown significantly. Ultra-wideband (UWB) technology has emerged as a key enabler for indoor UAV navigation due to its robustness against multipath effects and high-accuracy ranging capabilities. However, conventional UWB-based systems primarily rely on range measurements, operate at low measurement frequencies, and are incapable of providing attitude information. This paper proposes a tightly coupled error-state extended Kalman filter (TC–ESKF)-based UWB/inertial measurement unit (IMU) fusion framework. To address the challenge of initial state acquisition, a weighted nonlinear least squares (WNLS)-based initialization algorithm is proposed to rapidly estimate the UAV’s initial position and attitude under static conditions. During dynamic navigation, the system integrates time-difference-of-arrival (TDOA) and angle-of-arrival (AOA) measurements obtained from the UWB module to refine the state estimates, thereby enhancing both positioning accuracy and attitude stability. The proposed system is evaluated through simulations and real-world indoor flight experiments. Experimental results show that the proposed algorithm outperforms representative fusion algorithms in 3D positioning and yaw estimation accuracy. Full article

Solid-state LiDAR systems are widely recognized for their high reliability, low cost, and lightweight design, but they encounter significant challenges in SLAM tasks due to their limited field of view and uneven horizontal scanning patterns, especially in indoor environments with geometric constraints. To address these challenges, this paper proposes SS-LIO, a precise, robust, and real-time LiDAR–Inertial odometry solution designed for solid-state LiDAR systems. SS-LIO uses uncertainty propagation in LiDAR point-cloud modeling and a tightly coupled iterative extended Kalman filter to fuse LiDAR feature points with IMU data for reliable localization. It also employs voxels to encapsulate planar features for accurate map construction. Experimental results from open-source datasets and self-collected data demonstrate that SS-LIO achieves superior accuracy and robustness compared to state-of-the-art methods, with an end-to-end drift of only 0.2 m in indoor degraded scenarios. The detailed and accurate point-cloud maps generated by SS-LIO reflect the smoothness and precision of trajectory estimation, with significantly reduced drift and deviation. These outcomes highlight the effectiveness of SS-LIO in addressing the SLAM challenges posed by solid-state LiDAR systems and its capability to produce reliable maps in complex indoor settings. Full article

Continuous learning is fundamental to professional and personal growth. Therefore, robust digital solutions are required to support adaptable and scalable educational demands. The Qualifications-Based Learning Model (QBLM) is a framework for qualifications-based learning, defining a software architecture and data models. However, the existing implementations of QBLM lack horizontal scalability and flexibility, which results in tightly coupled components that limit the adaptability of the model to the evolving needs of learners and institutions. Therefore, a new Qualifications-Based Learning Platform (QBLM Platform) is planned, which extends the QBLM approach by utilizing a modular software architecture that enables flexible service integration, scalability, and operational resilience. However, to design such a QBLM Platform, a requirements analysis is necessary. By employing both quantitative and qualitative research methods, which include a survey and expert interviews, the requirements for a QBLM Platform are identified. The result of the research is used to define not only the essential features and characteristics of the QBLM Platform but also learning platforms in general. Full article

The demand for versatility and finer resolution drives phased array radars to develop towards multi-band operating. However, the bandwidth limitations of conventional electronic devices make multi-band manipulation of frequency and phase rather challenging. This paper introduces a joint phase–frequency distribution manipulation method. By introducing a time delay line after optical pulses, the frequency conversion and phase shift are tightly coupled. Then, the phase–frequency–time mapping for multi-band signals in a single phased array system is established. The generation, transmission, and reception of multi-band signals are simultaneously achieved. Our approach enables multi-band frequency conversion and phase shifting in a single hardware framework, ensuring synchronization and coherence across multiple bands. We experimentally demonstrate the generation, frequency conversion, and phase control of signals across four bands (S, X, Ku, and K). Beamforming and data fusion of four-band linear frequency-modulated signals with a total bandwidth of 4 GHz are achieved, resulting in a four-fold improvement in range resolution. It is also verified that the number of bands and total bandwidth can be further expanded through channel interleaving. Full article

Single-frequency global navigation satellite system/inertial navigation system (GNSS/INS) integration has wide application prospects in urban environments; however, correct integer ambiguity is the major challenge because of GNSS-blocked environments. In this paper, a sequential strategy of partial ambiguity resolution (PAR) of GNSS/INS for tightly coupled integration based on the robust posteriori residual, elevation angle, and azimuth in the body frame using INS aids is presented. First, the satellite is eliminated if the maximum absolute value of the robust posteriori residuals exceeds the set threshold. Otherwise, the satellites with a minimum elevation angle of less than or equal to 35° are successively eliminated. If satellites have elevation angles greater than 35°, these satellites are divided into different quadrants based on their azimuths calculated in body frame. The satellite with the maximum azimuth in each quadrant is selected as the candidate satellite, the candidate satellites are eliminated one by one, and the remaining satellites are used to calculate the position dilution of the precision (PDOP). Finally, the candidate satellite with the lowest PDOP is eliminated. Two sets of vehicle-borne data with a low-cost GNSS/INS integrated system are used to analyze the performance of the proposed algorithm. These experiments demonstrate that the proposed algorithm has the highest ambiguity fixing rates among all the designed PAR methods, and the fixing rates for these two sets of data are 99.40% and 98.74%, respectively. Additionally, among all the methods compared in this paper, the proposed algorithm demonstrates the best positioning performance in GNSS-blocked environments. Full article

Simultaneous Localization and Mapping (SLAM) remains challenging in dynamic environments. Recent approaches combining deep learning with algorithms for dynamic scenes comprise two types: faster, less accurate object detection-based methods and highly accurate, computationally costly instance segmentation-based methods. In addition, maps lacking semantic information hinder robots from understanding their environment and performing complex tasks. This paper presents a lightweight visual-inertial SLAM system. The system is based on the classic ORB-SLAM3 framework, which starts a new thread for object detection and tightly couples the semantic information of object detection with geometric information to remove feature points from dynamic objects. In addition, Inertial Measurement Unit (IMU) data are employed to assist in feature point extraction, thereby compensating for visual pose tracking loss. Finally, a dense octree-based semantic map is constructed by fusing semantic information and visualized using ROS. LVID-SLAM demonstrates excellent pose accuracy and robustness in highly dynamic scenes on the public TUM dataset, with an average ATE reduction of more than 80% compared to ORB-SLAM3. The experimental results demonstrate that LVID-SLAM outperforms other methods in dynamic conditions, offering both real-time capability and robustness. Full article

The global burden of diabetes mellitus (DM) continues to escalate, posing significant challenges to healthcare systems worldwide. This study compares machine learning (ML) and deep learning (DL) methods, their hybrids, and ensemble strategies for predicting the health outcomes of diabetic patients. This work aims to find the best solutions that strike a balance between computational efficiency and accurate prediction. The study systematically assessed a range of predictive models, including sophisticated DL techniques and conventional ML algorithms, based on computational efficiency and performance indicators. The study assessed prediction accuracy, processing speed, scalability, resource consumption, and interpretability using publicly accessible diabetes datasets. It methodically evaluates the selected models using key performance indicators (KPIs), training times, and memory usage. AdaBoost had the highest F1-score (0.74) on PIMA-768, while RF excelled on PIMA-2000 (~0.73). An RNN led the 3-class BRFSS survey (0.44), and a feed-forward DNN excelled on the binary BRFSS subset (0.45), while RF also achieved perfect accuracy on the EMR dataset (1.00) confirming that model performance is tightly coupled to each dataset’s scale, feature mix and label structure. The results highlight how lightweight, interpretable ML and DL models work in resource-constrained environments and for real-time health analytics. The study also compares its results with existing prediction models, confirming the benefits of selected ML approaches in enhancing diabetes-related medical outcomes that are substantial for practical implementation, providing a reliable and efficient framework for automated diabetes prediction to support initiative-taking disease management techniques and tailored treatment. The study concludes the essentiality of conducting a thorough assessment and validation of the model using current institutional datasets as this enhances accuracy, security, and confidence in AI-assisted healthcare decision-making. Full article

A tightly coupled hybrid monocular visual SLAM system for unmanned underwater vehicles (UUVs) is introduced in this paper. Specifically, we propose a robust three-step hybrid tracking strategy. The feature-based method initially provides a rough pose estimate, then the direct method refines it, and finally, the refined results are used to reproject map points to improve the number of features tracked and stability. Furthermore, a tightly coupled visual hybrid optimization method is presented to address the inaccuracy of the back-end pose optimization. The selection of features for stable tracking is achieved through the integration of two distinct residuals: geometric reprojection error and photometric error. The efficacy of the proposed system is demonstrated through quantitative and qualitative analyses in both artificial and natural underwater environments, demonstrating excellent stable tracking and accurate localization results. Full article

With the development of urban construction and the improvement of residents’ quality of life, the focus of governance has shifted to a people-centered approach. In the core area of Beijing, there is a mismatch between the existing large comprehensive hospitals (LCHs) and the current demand for medical technology and services. Therefore, this study focuses on tertiary LCHs in the core area of Beijing from the perspective of supply and demand (SD) and explores in depth the problem of matching the crowd concentration and the SD of the surrounding public service facilities in the process of seeking medical treatment. By comprehensively analyzing the public service facilities within a 15-min walking distance around the hospital and considering demand, supply, and transport factors, this study identifies 15 key indicators, constructs an SD coupling model (SDCM) evaluation system, and systematically evaluates the space of public service facilities around an LCH. The results show that the higher the spatial coupling and coordination of these facilities around the LCH, the more tightly the system is connected, and the more pronounced the coupling effect is in the vicinity of the hospital, which suggests that the hospital has a clustering effect on its radiating area. Full article

Accurate indoor positioning services have become increasingly important in modern applications. Various new indoor positioning methods have been developed. Among them, visual–inertial odometry (VIO)-based techniques are notably limited by lighting conditions, while ultrawideband (UWB)-based algorithms are highly susceptible to environmental interference. To address these limitations, this study proposes a hybrid indoor positioning algorithm that combines UWB and VIO. The method first utilizes a tightly coupled UWB/inertial measurement unit (IMU) fusion algorithm based on a sliding-window factor graph to obtain initial position estimates. These estimates are then combined with VIO outputs to formulate the system’s motion and observation models. Finally, an extended Kalman filter (EKF) is applied for data fusion to achieve optimal state estimation. The proposed hybrid positioning algorithm is validated on a self-developed mobile platform in an indoor environment. Experimental results show that, in indoor environments, the proposed method reduces the root mean square error (RMSE) by 67.6% and the maximum error by approximately 67.9% compared with the standalone UWB method. Compared with the stereo VIO model, the RMSE and maximum error are reduced by 55.4% and 60.4%, respectively. Furthermore, compared with the UWB/IMU fusion model, the proposed method achieves a 50.0% reduction in RMSE and a 59.1% reduction in maximum error. Full article

Epithelial differentiation and function are tightly coupled to the keratin intermediate filament cytoskeleton. Keratin filaments are unique among the cytoskeletal filament systems in terms of biochemical properties, diversity and turnover mechanisms supporting epithelial plasticity in response to a multitude of environmental cues. Epidermal growth factor (EGF) is such a cue. It is not only intricately intertwined with epithelial physiology but also modulates keratin filament network organization by increasing keratin filament turnover. The involved EGF receptor (EGFR)-dependent intracellular signaling cascades, however, have not been identified to date. We therefore tested the effect of selective inhibitors of downstream effectors of the EGFR on keratin filament turnover using quantitative fluorescence recovery after photobleaching experiments as readouts. We find that SRC and ERK kinases are involved in the regulation of keratin filament turnover, whereas PI3K/AKT and FAK have little or no effect. The identification of SRC and ERK as major keratin filament regulators extends beyond EGF signaling since they are also activated by other signals and stresses. Our data unveil a mechanism that allows modification of the properties of keratin filaments at very high temporal and spatial acuity. Full article

In the practical application of Real-Time Precise Point Positioning (RT-PPP), the outages in receiving spatial state representation (SSR) information due to communication anomalies can result in a decrease or even divergence of the positioning accuracy of RT-PPP. To mitigate the decline in positioning accuracy, we propose a method of INS aiding RT-PPP based on an optimized stochastic model. First, the correlation between SISRE and SSR age was analyzed by using a dataset of 1800 continuous time series. A new stochastic model called clock–orbit degradation (COD) stochastic model was established to match clock–orbit time-varying statistical characteristics. Second, we introduced Inertial Navigation System (INS) enhancement information to optimize the functional model, leveraging its autonomy and high-precision short-term position constraints. Finally, the real-world static and kinematic experiments were designed to verify the proposed method. The static results showed that the RT-PPP positioning accuracy with COD stochastic model is always higher than the traditional fixed equivalent-weight stochastic model at different level SSR outages. Even with SSR interruptions, the positioning accuracy can reach 0.131 m in the horizontal direction and 0.269 m in the 3D direction, representing improvements of 23.2% and 19.0%, respectively. Furthermore, the kinematic results showed that the positioning accuracy of PPP/INS with COD stochastic model had improved by 38.7% in the horizontal direction and 69.9% in the 3D direction at half an hour of SSR age. Full article

This article presents a novel tightly coupled dipole array (TCDA) with a bandwidth of 26.2:1 (VSWR < 3) across 0.20–5.23 GHz. By adding a new dual-stopband resistive frequency selective surface (RFSS) between the dipole and the floor, the short-circuit points formed by the floor at the frequency points corresponding to λ = 2 h and h are both eliminated (h is the height from the antenna to the floor). A specially integrated feed network is also applied to significantly reduce the complexity and profile height to 0.05 λ_low. The simulation and experimental results show that the designed TCDA has extremely wide bandwidth, good directivity and beam scanning potential. Compared with previous designs, it greatly extends the bandwidth, improves the gain, reduces the profile height, and simplifies the feeding method. Full article

Semi-annual climate oscillations in the Western Hemisphere (20 S–35 N, 150 W–20 E) were studied via empirical orthogonal function (EOF) eigenvector loading patterns and principal component time scores from 1980 to 2023. The spatial loading maximum for 850 hPa zonal wind extended from the north Atlantic to the east Pacific; channeling was evident over the southwestern Caribbean. The eigenvector loading maximum for precipitation reflected an equatorial trough, while the semi-annual SST formed a dipole with loading maxima in upwelling zones off Angola (10 E) and Peru (80 W). Weakened Caribbean trade winds and strengthened tropical convection correlated with a warm Atlantic/cool Pacific pattern (R = 0.46). Wavelet spectral analysis of principal component time scores found a persistent 6-month rhythm disrupted only by major El Nino Southern Oscillation events and anomalous mid-latitude conditions associated with negative-phase Arctic Oscillation. Historical climatologies revealed that 6-month cycles of wind, precipitation, and sea temperature were tightly coupled in the Western Hemisphere by heat surplus in the equatorial ocean diffused by meridional overturning Hadley cells. External forcing emerged in early 2010 when warm anomalies over Canada diverted the subtropical jet, suppressing subtropical trade winds and evaporative cooling and intensifying the equatorial trough across the Western Hemisphere. Climatic trends of increased jet-stream instability suggest that the semi-annual amplitude may grow over time. Full article

The combination of GNSS RTK and INS offers complementary advantages but faces significant challenges in urban canyons. Frequent cycle slips in carrier phase measurements and ambiguity resolution algorithms increase computational burden without improving positioning accuracy. Additionally, environmental interference introduces noise into observations, potentially leading to complete signal loss. To address these issues, this paper proposes a factor graph optimization (FGO) positioning algorithm incorporating predictive observation factors. First, a penalized spline (P-spline) is constructed to predict and smooth Doppler measurements. The predicted Doppler is then fused with the dynamics model predictions to enhance robustness. Using predictive Doppler, carrier phase and pseudorange observations are reconstructed, generating predictive constraint factors to improve positioning accuracy. Real-world tests conducted in urban canyons, including Shanghai, demonstrate that the proposed method maintains stable positioning performance even under short-term signal outages, effectively mitigating cumulative positioning errors caused by data loss. Compared to traditional methods that rely solely on available observations, the proposed algorithm improves northward and dynamic positioning accuracy by 35% and 29%, respectively, providing a highly robust navigation solution for complex urban environments. Full article