Search Results (424)

Location-based services (LBSs) and positioning systems have spread worldwide due to the emergence of Internet of Things (IoT) and other application domains that require real-time estimation of the position of a person, tag, or asset in general in order to provide users with services and apps with added value. Whereas Global Navigation Satellite Systems (GNSSs) are well-established solutions outdoors, positioning is still an open challenge indoors, where different sensory technologies may be considered for that purpose, such as radio frequency, infrared, or ultrasounds, among others. With regard to ultrasonic systems, previous works have already developed indoor positioning systems capable of achieving accuracies in the range of centimeters but limited to a few square meters of coverage and severely affected by the Doppler effect coming from moving targets, which significantly degrades the overall positioning performance. Furthermore, the actual bandwidth available in commercial transducers often constrains the ultrasonic transmission, thus reducing the position accuracy as well. In this context, this work proposes a novel excitation and processing method for an ultrasonic positioning system, which significantly improves the transmission capabilities between an emitter and a receiver. The proposal employs a superheterodyne approach, enabling simultaneous transmission and reception of signals across multiple channels. It also adapts the bandwidths and central frequencies of the transmitted signals to the specific bandwidth characteristics of available transducers, thus optimizing the system performance. Binary spread spectrum sequences are utilized within a multicarrier modulation framework to ensure robust signal transmission. The ultrasonic signals received are then processed using filter banks and matched filtering techniques to determine the Time Differences of Arrival (TDoA) for every transmission, which are subsequently used to estimate the target position. The proposal has been modeled and successfully validated using a digital twin. Furthermore, experimental tests on the prototype have also been conducted to evaluate the system’s performance in real scenarios, comparing it against classical approaches in terms of ranging distance, signal-to-noise ratio (SNR), or multipath effects. Experimental validation demonstrates that the proposed narrowband scheme reliably operates at distances up to 40 m, compared to the 34 m limit of conventional wideband approaches. Ranging errors remain below 3 cm at 40 m, whereas the wideband scheme exhibits errors exceeding 8 cm. Furthermore, simulation results show that the narrowband scheme maintains stable operation at SNR as low as $-32$ dB, whereas the wideband one only achieves up to $-17$ dB, highlighting the significant performance advantages of the proposed approach in both experimental and simulated scenarios. Full article

To address the problem of degraded positioning accuracy in traditional visual–inertial navigation systems (VINS) due to interference from moving objects in dynamic scenarios, this paper proposes an improved algorithm based on the VINS-Fusion framework, which resolves this issue through a synergistic combination of multi-scale feature optimization and real-time dynamic feature elimination. First, at the feature extraction front-end, the SuperPoint encoder structure is reconstructed. By integrating dual-branch multi-scale feature fusion and 1 × 1 convolutional channel compression, it simultaneously captures shallow texture details and deep semantic information, enhances the discriminative ability of static background features, and reduces mis-elimination near dynamic–static boundaries. Second, in the dynamic processing module, the ASORT (Adaptive Simple Online and Realtime Tracking) algorithm is designed. This algorithm combines an object detection network, adaptive Kalman filter-based trajectory prediction, and a Hungarian algorithm-based matching mechanism to identify moving objects in images in real time, filter out their associated dynamic feature points from the optimized feature point set, and ensure that only reliable static features are input to the backend optimization, thereby minimizing pose estimation errors caused by dynamic interference. Experiments on the KITTI dataset demonstrate that, compared with the original VINS-Fusion algorithm, the proposed method achieves an average improvement of approximately 14.8% in absolute trajectory accuracy, with an average single-frame processing time of 23.9 milliseconds. This validates that the proposed approach provides an efficient and robust solution for visual–inertial navigation in highly dynamic environments. Full article

Unmanned Aerial Vehicles (UAVs) are promising nodes for Integrated Sensing and Communication (ISAC), but accurate Direction-of-Arrival (DoA) estimation on a small airframe is challenged by platform loading, motion, attitude, and multipath. Traditionally, DoA algorithms have been developed and evaluated for stationary, ground-based (or otherwise mechanically stable) antenna arrays. Extending them to UAVs violates these assumptions. This work designs a six-element Uniform Circular Array (UCA) at 2.4 GHz (radius $\approx 0.5\lambda$) for a quadrotor and introduces a Pose-Aware MUSIC (MUltiple SIgnal Classification) estimator for DoA. The novelty is a MUSIC formulation that (i) applies pose correction using the drone’s instantaneous roll–pitch–yaw (pose correction) and (ii) applies a Doppler correction that accounts for platform velocity. Performance is assessed using data synthesized from embedded-element patterns obtained by electromagnetic characterization of the installed array, with additional channel/hardware effects modeled in post-processing (Rician LOS/NLOS mixing, mutual coupling, per-element gain/phase errors, and element–position jitter). Results with the six-element UCA show that pose and Doppler compensation preserve high-resolution DoA estimates and reduce bias under realistic flight and platform conditions while also revealing how coupling and jitter set practical error floors. The contribution is a practical PA-MUSIC approach for UAV ISAC, combining UCA design with motion-aware signal processing, and an evaluation that quantifies accuracy and offers clear guidance for calibration and field deployment in GNSS-denied scenarios. The results show that, across 0–25 dB SNR, the proposed hybrid DoA estimator achieves <$0.5^{\circ }$ RMSE in azimuth and elevation for ideal conditions and ≈$5^{\circ }$–$6^{\circ }$ RMSE when full platform coupling is considered, demonstrating robust performance for UAV ISAC tracking. Full article

Accurate co-registration between on-scalp Optically Pumped Magnetometer (OPM)–Magnetoencephalography (MEG) sensors and anatomical Magnetic Resonance Imaging (MRI) remains a critical bottleneck restricting the spatial fidelity of source localization. Optical Scanning Image (OSI) methods can provide high spatial accuracy but depend on surface visibility and cannot directly determine the internal sensitive point of each OPM sensor. Coil-based magnetic dipole localization, in contrast, targets the sensor’s internal sensitive volume and is robust to occlusion, yet its accuracy is affected by coil fabrication imperfections and the validity of the dipole approximation. To integrate the complementary advantages of both approaches, we propose a hybrid co-registration framework that combines Rigid Coil Structures (RCS), magnetic dipole-based sensor localization, and optical orientation constraints. A complete multi-stage co-registration pipeline is established through a unified mathematical formulation, including MRI–OSI alignment, OSI–RCS transformation, and final RCS–sensor localization. Systematic simulations are conducted to evaluate the accuracy of the magnetic dipole approximation for both cylindrical helical coils and planar single-turn coils. The results quantify how wire diameter, coil radius, and turn number influence dipole model fidelity and offer practical guidelines for coil design. Experiments using 18 coils and 11 single-axis OPMs demonstrate positional accuracy of a few millimeters, and optical orientation priors suppress dipole-only orientation ambiguity in unstable channels. To improve the stability of sensor orientation estimation, optical scanning of surface markers is incorporated as a soft constraint, yielding substantial improvements for channels that exhibit unstable results under dipole-only optimization. Overall, the proposed hybrid framework demonstrates the feasibility of combining magnetic and optical information for robust OPM–MEG co-registration. Full article

The integration of the digital economy and higher education is a core driver of sustainable development, yet the mechanisms and heterogeneous effects of this interaction remain underexplored. Using balanced panel data from 30 Chinese provinces over 2011–2020, this study empirically investigates the impact of the digital economy on higher education development (scale, structure, quality) and its transmission channels. The digital economy development index (DEDI) is constructed via the entropy-weighted method, and a comprehensive empirical strategy is adopted, including baseline regression, instrumental variable (IV) estimation, difference-in-differences (DID), mediation analysis, and regional heterogeneity tests. The results reveal three key findings: (1) The digital economy exerts a significantly positive causal effect on higher education scale and structure optimization, with robustness confirmed by multiple tests. (2) It has no direct impact on higher education quality, but indirectly promotes quality through regional income levels, while institutional quality partially mediates the effect on structure. (3) Significant regional heterogeneity exists: the impact is strongest in eastern provinces, moderate in central provinces, and insignificant in western provinces, constrained by weak digital infrastructure. This study enriches the theoretical framework of digital economy–education interaction and provides actionable policy implications for promoting sustainable, balanced higher education development: strengthening digital infrastructure in underdeveloped regions, aligning educational structure with digital industrial demand, linking digital economic growth to educational investment, and implementing region-specific policies. These findings contribute to advancing the synergy between digital transformation and high-quality higher education, supporting long-term sustainable economic and social development. Full article

Rapid advances in digital technologies are reshaping value creation and the trade landscape of global financial services, yet the channels through which they influence the spatial evolution of financial services global value chains (GVCs) remain insufficiently identified. Using a global panel of 52 countries over 2013–2021, we estimate a dynamic Spatial Durbin Model (SDM) to identify overall effects and quantify spatial spillovers and temporal dynamics. We then combine Geographically and Temporally Weighted Regression (GTWR) with spatial mediation models to examine heterogeneity and underlying mechanisms. Our findings show that digital technology significantly drives the spatial evolution of financial services GVCs. Its influence is dominated by spatial diffusion, exhibiting a dynamic pattern of a strong short-run boost followed by long-run reallocation. This dynamic effect is not homogeneous; rather, it reflects a pronounced dual-driver structure: the momentum is more robust when human capital and R&D output reinforce each other, whereas increases in innovation level alone are unlikely to translate into sustained impetus for spatial restructuring. Crucially, digital technologies reshape GVC geography through three core channels: attenuating distance decay, strengthening spatial proximity, and amplifying spatial heterogeneity. These forces deepen the domestic diffusion of knowledge, capital, and technology and extend their spillovers to neighboring and connected economies. The results provide robust empirical evidence on financial geography in the digital era and have clear implications for policies that facilitate cross-border financial services and strengthen regional coordination in support of the 2030 Agenda for Sustainable Development, particularly SDG 8 (financial inclusion) and SDG 10 (global financial governance). Full article

Based on panel data from 274 prefecture-level cities in China (2011–2022), this study employs a peer effects model to examine three questions: whether peer effects exist in sustainable rural development, what mechanisms underlie them, and which regions are imitated. It thereby offers a new perspective on the endogenous drivers of rural development. The main findings are as follows. Baseline regression results confirm a significant positive peer effect on rural sustainable development. This result remains robust after a series of tests addressing endogeneity and robustness, including the replacement of explanatory variables, data indentation, exclusion of provincial capitals, placebo tests, and instrumental variable estimation. Heterogeneity analysis reveals that central and western regions are more inclined to learn from other cities in the process of sustainable rural development, whereas the eastern region leans more toward innovation. After the Rural Revitalization Strategy was introduced in 2017, regions have actively explored new rural development models, leading to a decline in the peer effects coefficient compared to the pre-2017 period. Mechanism analysis indicates that both learning-based imitation and competitive imitation serve as channels for peer effects in rural sustainable development. A region’s own development experience does not suppress peer effects. Economically more developed regions are more likely to become imitation targets. Moreover, performance pressure on local officials and the degree of competition among prefecture-level cities strengthen the peer effects. After reclassifying peer groups based on economic structure and geographical location, results show that in the process of rural sustainable development, local governments primarily learn from other regions within the same province that share similar economic structures and are geographically proximate. Based on these findings, this paper proposes differentiated policy recommendations to support sustainable rural development. Full article

This study optimizes the placement of μ-PMUs using the BPSO and BGWO algorithms for the IEEE 33-bus and 69-bus systems, with a focus on minimizing deployment costs while ensuring robust system observability. Three case studies are analysed: Case 1 (normal conditions), Case 2 (single μ-PMU outage), and Case 3 (Zero Injection Buses, ZIBs). In Case 1, both algorithms identified 24 μ-PMUs as the optimal placement for the IEEE 69-bus system, achieving the minimum PMUs required for full observability. For Case 2, redundancy requirements increased the μ-PMU count to 24 μ-PMUs for the IEEE 33-bus system and 51 μ-PMUs for the IEEE 69-bus system, ensuring full observability even under a single μ-PMU failure. Case 3, leveraging Zero Injection Buses (ZIBs), reduced the μ-PMU count to 20 μ-PMUs for both BPSO and BGWO, optimizing the system configuration while maintaining observability. A trade-off analysis was performed to examine the trade-off between redundancy and PMU count, showing that increasing the number of μ-PMUs improves system resilience. Voltage and current channels were measured from the optimized placements to ensure accurate voltage measurement in all case studies. Subsequently, the Weighted Least Squares algorithm was applied for voltage estimation, serving as a peripheral to the main objective of the optimal μ-PMU placement. Voltage estimation was conducted under three noise levels: 0.01 STD for basic analysis and 0.02 and 0.04 STD to observe the impact of varying measurement noise. The results highlight that higher μ-PMU placements improve voltage estimation accuracy, particularly under higher noise levels. Statistical analysis confirms that BGWO outperforms BPSO in terms of computational efficiency, stability, and convergence, especially in large-scale systems. By enhancing grid monitoring and state estimation, this research directly contributes to the development of more resilient and efficient power networks, which is a fundamental prerequisite for integrating renewable energy sources and advancing overall power system sustainability. This research emphasizes the balance between cost and reliability in μ-PMU placement and provides a comprehensive methodology for state estimation in modern power systems. Full article

Remote photoplethysmography (rPPG) enables non-contact heart rate estimation but remains highly sensitive to illumination variation and dataset-dependent factors. This study proposes CHROM-Y, a robust 2D feature representation that combines chrominance (Ω, Φ) with luminance (Y) to improve physiological signal extraction under varying lighting conditions. The proposed features were evaluated using U-Net, ResNet-18, and VGG16 for heart rate estimation and waveform reconstruction on the UBFC-rPPG and BhRPPG datasets. On UBFC-rPPG, U-Net with CHROM-Y achieved the best performance with a Peak MAE of 3.62 bpm and RMSE of 6.67 bpm, while ablation experiments confirmed the importance of the Y-channel, showing degradation of up to 41.14% in MAE when removed. Although waveform reconstruction demonstrated low Pearson correlation, dominant frequency preservation enabled reliable frequency-based HR estimation. Cross-dataset evaluation revealed reduced generalization (MAE up to 13.33 bpm and RMSE up to 22.80 bpm), highlighting sensitivity to domain shifts. However, fine-tuning U-Net on BhRPPG produced consistent improvements across low, medium, and high illumination levels, with performance gains of 11.18–29.47% in MAE and 12.48–27.94% in RMSE, indicating improved adaptability to illumination variations. Full article

Conventional tourism planning in ecologically fragile regions often adopts a reductionist perspective, failing to address the synergistic spatial interactions between ecological conservation, resource utilization, and infrastructure. To bridge this gap, this study develops a multi-constraint synergistic assessment framework for the dry-hot valley of Lujiang Dam (LJD) in China. Grounded in the understanding of rural tourism as a complex adaptive system, the framework innovatively integrates the InVEST model, kernel density estimation, and cumulative cost-distance algorithms to identify Natural Spatial Suitability for Tourism Development (NSSTD). Key findings include (1) pronounced spatial heterogeneity in habitat quality, with high-quality zones in the west/southeast requiring strict conservation; (2) a “barbell-shaped” clustering of natural/cultural resources at the valley’s northern and southern extremities, highly congruent with ethnic settlements; and (3) a “concentric layered” accessibility pattern where 88.08% of resources are within a 90 min drive. Crucially, the spatial overlay analysis revealed that NSSTD (54.74 km²) emerges not from single high-value zones but from areas of synergy, such as those with medium habitat quality coupled with high resource endowment and accessibility. These results provide a scientifically robust, spatially explicit layer for China’s “Multi-plan Integration” territorial spatial planning. They enable differentiated strategies—channeling development to southern corridors, implementing niche tourism in northern “structural hole” villages, and enforcing conservation in western habitats—thereby offering a replicable methodology to balance ecological integrity with sustainable rural development. Full article

This study examines how digital transformation enhances supply chain resilience among Chinese firms, with a focus on the underlying mechanisms and contextual conditions. Grounded in dynamic capabilities theory, we conceptualize supply chain resilience along two dimensions: proactive capability and reactive capability. Using data from A-share listed companies between 2007 and 2022, we construct firm-level resilience measures through entropy weighting. Digital transformation is measured by textual analysis of corporate annual reports, supplemented with policy documents and academic literature to enrich the keyword dictionary. Empirical results, validated through instrumental variable estimation, Heckman two-stage models, and multiple robustness checks, show that digital transformation significantly improves overall supply chain resilience, with a stronger effect on reactive capability. Further analysis identifies three mediating channels: improved information sharing across the supply chain, enhanced firm-level innovation, and reduced exposure to environmental uncertainty. Heterogeneity tests reveal that the positive impact of digital transformation is more pronounced in non-state-owned enterprises, high-tech firms, and firms in technology-intensive or labor-intensive industries. The effect is also stronger for firms operating under high environmental uncertainty or located in regions with lower levels of marketization. These findings offer practical guidance for managers and policymakers aiming to strengthen supply chains through digitalization, particularly in an era marked by growing global disruptions and sustainability challenges. Full article

Accurate estimation of flow discharge, Q, through hydraulic structures such as spillways and gates is of great importance in water resources engineering. Each hydraulic structure, due to its unique characteristics, requires a specific and comprehensive study. In this regard, the present study innovatively focuses on predicting Q through Rectangular Top-Hinged Gates (RTHGs) using advanced Gradient Boosting (GB) models. The GB models evaluated in this study include Categorical Boosting (CatBoost), Histogram-based Gradient Boosting (HistGBoost), Light Gradient Boosting Machine (LightGBoost), Natural Gradient Boosting (NGBoost), and Extreme Gradient Boosting (XGBoost). One of the essential factors in developing artificial intelligence models is the accurate and proper tuning of their hyperparameters. Therefore, four powerful metaheuristic algorithms—Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Sparrow Search Algorithm (SSA), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA)—were evaluated and compared for hyperparameter tuning, using LightGBoost as the baseline model. An assessment of error metrics, convergence speed, stability, and computational cost revealed that SSA achieved the best performance for the hyperparameter optimization of GB models. Consequently, hybrid models combining GB algorithms with SSA were developed to predict Q through RTHGs. Random split was used to divide the dataset into two sets, with 70% for training and 30% for testing. Prediction uncertainty was quantified via Confidence Intervals (CI) and the R-Factor index. CatBoost-SSA produced the most accurate prediction performance among the models (R² = 0.999 training, 0.984 testing), and NGBoost-SSA provided the lowest uncertainty (CI = 0.616, R-Factor = 3.596). The SHapley Additive exPlanations (SHAP) method identified h/B (upstream water depth to channel width ratio) and channel slope, S, as the most influential predictors. Overall, this study confirms the effectiveness of SSA-optimized boosting models for reliable and interpretable hydraulic modeling, offering a robust tool for the design and operation of gated flow control systems. Full article

Accurate estimation of battery state of health is essential for ensuring safety, supporting fault diagnosis, and optimizing the lifetime of electric vehicles. This study proposes a compact dual-path architecture that combines Convolutional Neural Networks with Convolutional Long Short-Term Memory (ConvLSTM) units to jointly extract spatial and temporal degradation features from charge-cycle voltage and current measurements. Residual and inter-path connections enhance gradient flow and feature fusion, while a three-channel preprocessing strategy aligns cycle lengths and isolates padded regions, improving learning stability. Operating end-to-end, the model eliminates the need for handcrafted features and does not rely on discharge data or temperature measurements, enabling practical deployment in minimally instrumented environments. The model is evaluated on the NASA battery aging dataset under two scenarios: Same-Battery Evaluation and Leave-One-Battery-Out Cross-Battery Generalization. It achieves average RMSE values of 1.26% and 2.14%, converging within 816 and 395 epochs, respectively. An ablation study demonstrates that the dual-path design, ConvLSTM units, residual shortcuts, inter-path exchange, and preprocessing pipeline each contribute to accuracy, stability, and reduced training cost. With only 4913 parameters, the architecture remains robust to variations in initial capacity, cutoff voltage, and degradation behavior. Edge deployment on an NVIDIA Jetson AGX Orin confirms real-time feasibility, achieving 2.24 ms latency, 8.24 MB memory usage, and 12.9 W active power, supporting use in resource-constrained battery management systems. Full article

This paper presents DeepMask-GPR, a novel deep learning framework for automatic detection and geometric estimation of buried cylindrical objects in ground-penetrating radar (GPR) B-scan images. Built upon Mask R-CNN, the proposed method integrates hyperbola detection, apex localization, and real-world coordinate mapping in an end-to-end architecture. A curvature-enhanced dual-channel input improves the visibility of weak hyperbolic patterns, while a quadratic regression loss guides the network to recover precise geometric parameters. DeepMask-GPR eliminates the need for raw signal data or manual post-processing, enabling robust and scalable deployment in field scenarios. On two public datasets, DeepMask-GPR achieves consistently higher TPR/IoU for spatial localization than baselines. On an in-house B-scan set, it attains low MAE/RMSE for radius estimation. Full article

Accurate estimation of lithium-ion battery (LIB) state of health (SOH) is critical for prolonging battery life and ensuring safe operation. To address the limitations of existing data-driven models in robustness and feature coupling, this paper presents a new Bagging-PiFormer framework for SOH estimation. The framework integrates ensemble learning with an improved Transformer architecture to achieve accurate and stable performance across various degradation conditions. Specifically, multiple PiFormer base models are trained independently under the Bagging strategy to enhance generalization. Each PiFormer consists of a stack of PiFormer layers, which combines a cross-channel attention mechanism to model voltage–current interactions and a local convolutional feed-forward network (LocalConvFFN) to extract local degradation patterns from charging curves. Residual connections and layer normalization stabilize gradient propagation in deep layers, while a purely linear output head enables precise regression of the continuous SOH values. Experimental results on three datasets demonstrate that the proposed method achieves the lowest MAE, RMSE, and MAXE values among all compared models, reducing overall error by 10–33% relative to mainstream deep-learning methods such as Transformer, CNN-LSTM, and GCN-BiLSTM. These results confirm that the Bagging-PiFormer framework significantly improves both the accuracy and robustness of battery SOH estimation. Full article