Search Results (1,690)

Accurate estimation of sparse underwater acoustic channels is challenging because of multipath delay spread, correlated inputs, and impulsive non-Gaussian noise. Existing KRSL-based algorithms still suffer from limited convergence speed and tracking capability in time-varying sparse scenarios. This paper proposes a VFF-PRKRSL algorithm, which jointly introduces an error-driven variable forgetting factor and a proportionate gain matrix into the recursive KRSL framework to achieve adaptive historical-information weighting and enhanced updating of dominant taps. Simulation results show that for Bellhop-generated underwater acoustic channels with sparsity levels of 0.125 and 0.4688, the proposed algorithm achieves NMSD values of $-38.4763$ dB and $-37.9417$ dB at the 2000th iteration, improving upon PRKRSL by approximately 5.31 dB and 5.29 dB, respectively.Under Cauchy noise, it reaches an NMSD of $-46.3042$ dB, about 5.95 dB better than PRKRSL. Ablation results indicate that the variable forgetting factor is the main source of the performance gain and is complementary to the proportionate update mechanism. These results demonstrate that VFF-PRKRSL outperforms existing methods in convergence speed, steady-state accuracy, and robustness against impulsive noise. Full article

In urban environments with signal blockage and multipath effects, GNSS observation errors often exhibit temporal correlation. The Gaussian white noise assumption adopted in conventional tightly coupled Kalman filtering is prone to model mismatch under such conditions, which may lead to an underestimation of state uncertainty and consequently cause the protection level (PL) to fail to reliably bound the true positioning error. To address this issue, this paper proposes a tightly coupled GNSS/INS integrity monitoring method based on state augmentation and frequency-domain constrained parameter tuning. The method introduces first-order Gauss-Markov processes (GMP) to model major time-correlated error sources, including residual ephemeris and clock errors, residual tropospheric delay, and code multipath, by augmenting them into the filter state for joint estimation. The model parameters are further conservatively tuned based on power spectral density (PSD) envelope constraints to obtain more consistent covariance estimates. Based on this, the covariance output from the augmented filter is incorporated into the multiple hypothesis solution separation (MHSS) framework, enabling the protection level computation to better match the actual error statistics. Experimental results using vehicular field test data show that the proposed method effectively improves estimation consistency and significantly reduces the risk of PL underestimation in degraded environments. Furthermore, it achieves reliable bounding of horizontal positioning errors without noticeable degradation in positioning accuracy, while maintaining good system availability. These results demonstrate the effectiveness of covariance construction based on physical error modeling and PSD envelope constraints for integrity monitoring in complex environments. Full article

Human performance in critical environments is frequently degraded by mistimed communication delivered during periods of visual–cognitive saturation. In such settings, failures arise not only from individual limitations but also from poor coordination between operators under rapidly changing workload conditions. We present a dual-loop neuro-adaptive simulation framework based on real-time spectral–topographic EEG representations, in which multichannel cortical activity is transformed into dynamic spatial maps and decoded to regulate both operator assistance and team communication. The system integrates 14-channel wireless EEG (Emotiv EPOC X, 256 Hz), gaze tracking, telemetry, and communication events through an LSL-based multimodal synchronization pipeline. A hybrid CNN–LSTM model processes sequences of spectral-topographic EEG maps to classify three operationally actionable neurocognitive states—Channelized Attention, Diverted Attention, and Surprise/Startle—while also estimating a continuous Cognitive Load Index (CLI). These representation-derived features are then used by a multi-agent proximal policy optimization (MAPPO) controller to generate two coordinated outputs: (i) adaptive haptic guidance for the pilot, designed to reduce reliance on overloaded visual and auditory channels, and (ii) a traffic-light communication gate for the telemetry engineer, regulating whether radio intervention should proceed, be delayed, or be withheld. In a high-fidelity dual-station simulation with 25 pilot–engineer pairs, the proposed framework was associated with a reduction of more than 30% in communication breakdown errors relative to open-loop telemetry, with the strongest effects observed during peak-load windows, while preserving realistic task progression. It also improved pilot reaction time to time-critical warnings and reduced engineer decision load under the tested conditions. These findings support the use of spectral-topographic EEG representations as a practical basis for combining multimodal neurophysiological sensing, spatiotemporal pattern decoding, and adaptive coordination in high-pressure human–machine teams. At the same time, the study should be interpreted as evidence of controlled feasibility in a simulated setting rather than as definitive proof of field-level generalization. We further discuss deployment constraints and propose privacy-by-design safeguards to ensure that neurocognitive signals are used exclusively for operational adaptation rather than employability assessment or performance scoring. Full article

Rockfall events are significant natural hazards on fractured rock cliffs, often driven by environmental forcing, including thermal variations that induce stress and fatigue in rocks. This study presents the first application of Ground-Based Interferometric Synthetic Aperture Radar (GB-InSAR) for high-resolution monitoring of sub-millimeter thermally induced displacements on a rock slope. An eight-day pilot experiment conducted at the La Cornalle molasse cliff (Vaud, Switzerland) revealed cyclic displacement signals with a clear 24 h periodicity, identified through Fourier and wavelet analyses, with a mean amplitude of 5 × 10⁻⁴ m. Simultaneously, infrared thermography (IRT) and a weather station recorded rock surface and air temperature variations, allowing a first estimation of the time lag between thermal forcing and mechanical response, with delays of 1–8 h relative to air temperature and 1–6 h relative to solar radiation. An analytical deformation model based on thermal diffusion predicts a daily displacement amplitude of 4.2 × 10⁻⁵ m, highlighting a significant difference with GB-InSAR observations and emphasizing the influence of structural complexity and thermo-hydro-mechanical processes in rock slopes. These results demonstrate the capability of combined high-resolution remote sensing techniques to quantify thermo-mechanical behavior in rock masses and provide a methodological framework for future investigations of rockfall-prone slopes. Full article

Tendon-driven manipulators possess obvious advantages compared to rigid-link manipulators, such as lighter weight, greater flexibility, and adaptability to confined spaces. To solve the problems of backlash and improve the accuracy of motion in specific application environments, this paper proposes a novel single-tendon-driven design for each joint of the manipulator. Kinematic modeling of the manipulator is systematically derived. Then, a decoupling algorithm is designed to mitigate motion coupling effects and enable accurate mapping between motor inputs and joint motions. Moreover, to improve the accuracy of trajectory tracking control for the tendon-driven manipulator, this paper proposes a nonsingular terminal sliding mode (NTSM) control scheme based on time-delay estimation (TDE). TDE is used to estimate unknown disturbances. An adjustable parameter was introduced based on TDE technology, which can enhance the system’s robustness against uncertainties and external disturbances. The stability of the closed-loop control system is verified through Lyapunov stability theory. Finally, decoupling experiments are conducted to validate the kinematic model and the feasibility of the proposed design. And comparative experiments are performed to prove the advantages of the proposed control scheme. Full article

We analyze parameter identifiability in a Marchuk-type immune-response model using longitudinal whole-blood transcriptomic signatures from the influenza challenge. Latent states are extracted from curated gene signatures derived from nine symptomatic and eight asymptomatic subjects. The governing delay differential equations are cast in a linear-in-parameters form; derivatives are estimated by smoothing splines, coefficients are fit by ridge regression, and the delay $\tau$ is selected by grid search. We find that the parameters governing viral and innate dynamics are consistently identifiable, with low relative error, and are highly determined, whereas adaptive-immunity and tissue-damage parameters are poorly constrained by transcriptomics alone. Introducing a small additive background term and tissue dependence markedly reduces residual variance and stabilizes estimates. Symptomatic patients exhibit a characteristic regulatory delay near 21 h. These results show that aggregated transcriptomic time series can reliably identify some subsystems of classical immune models, but that adaptive immunity and damage dynamics require explicit structural extensions or additional data modalities. The study provides a practical identification pipeline and concrete guidance on model extensions needed for transcriptomic-driven mechanistic inference. Full article

Various global precipitation datasets have been used in precipitation-related fields such as hydrology, meteorology, climatology, and ecology to achieve different research objectives. Error analysis is an integral part before applying them to operational fields. However, the growing number of precipitation products and the absence of comprehensive error comparison research jointly impede users in distinguishing product-specific error patterns and constrain developers from enhancing precipitation estimation accuracy. To address this issue, we performed error analysis and comparison of thirteen global precipitation products—categorized as delayed time (DT), near real-time (NRT), and real-time (RT) types—across mainland China. Results revealed that GSMaP-Gauge (Gauge-adjusted Global Satellite Mapping of Precipitation) performed best in terms of detection indicators, while MGP (Multi-source merged global precipitation product) performed best in estimating precipitation accuracy. However, IMERG-Final (Integrated Multisatellite Retrievals for Global Precipitation Measurement Final Run) proved ineffective in reducing the overestimations of both storm and light precipitation events in regions of complex topography. Furthermore, two DT products (i.e., ERA5 (Fifth generation of ECMWF atmospheric reanalyses of the global climate) and MGP) overestimated the frequency of light precipitation events, with relative rainfall occurrence biases exceeding 80%. This bias is attributable to both false detections and the misclassification of high intensity rainfall as light precipitation. Although GSMaP-NOW (based exclusively on passive microwave data) detected precipitation more effectively than the infrared-only PDIRNow (Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)—Dynamic Infrared Rain Rate (Now)), it achieved lower accuracy. This discrepancy reflects the tradeoff between the higher precipitation sensitivity of passive microwave observations and their sparse temporal sampling, compared with the continuous coverage provided by infrared data. Finally, our findings indicated that current evaluation approaches do not reliably determine the optimal precipitation product, since product superiority is contingent upon the selected error metric. This underscores the urgent need to develop theoretically grounded and operationally reliable methods for selecting optimal precipitation products to support data users in deriving robust and reliable conclusions in hydrology, meteorology, and ecology. Full article

This paper investigates the leader–follower consensus problem for fractional-order nonlinear multi-agent systems operating under simultaneous Sybil and Denial-of-Service (DoS) attacks. The communication topology is modeled as a time-varying directed graph with intermittent link failures due to DoS disruptions, while malicious data injection induced by Sybil attacks is incorporated into the agent dynamics. In addition, bounded disturbances and time-varying input delays are explicitly considered. To counter these challenges, an event-triggered distributed control framework was developed to reduce communication load while preserving agents’ tracking performance. Furthermore, an adaptive compensation mechanism is introduced to estimate and attenuate the combined effects of cyber attacks and external disturbances. A novel Wirtinger-type fractional integral inequality is established, providing a less conservative tool for constructing Lyapunov–Krasovskii functionals in fractional-order systems. Sufficient conditions for asymptotic leader–follower consensus are obtained in terms of linear matrix inequalities using fractional Lyapunov stability theory. The proposed scheme guarantees the convergence of tracking errors, excludes Zeno behavior through a decaying triggering threshold, and ensures robustness against malicious signal injection and communication interruptions. The results demonstrate that the developed event-triggered adaptive strategy achieves resilient consensus in fractional-order multi-agent systems despite simultaneous cyber attacks at both the network and information layers. Full article

Background/Objectives: Central retinal artery occlusion (CRAO) is a vision-threatening condition with limited evidence-based treatment options. Hyperbaric oxygen therapy (HBOT) has emerged as a potential intervention, but its efficacy remains debated. This systematic review and meta-analysis evaluated the therapeutic efficacy and safety of HBOT in CRAO. Methods: Relevant studies were identified across seven databases using optimized Boolean and MeSH-based strategies. Eligible studies evaluated HBOT in CRAO and reported visual or safety outcomes. Extracted data included demographics, intervention details, treatment timing, visual acuity outcomes, and adverse events. Risk of bias was assessed using ROBINS-I. Visual acuity outcomes were standardized to logMAR whenever directly reported or convertible, and subgroup analyses were stratified by HBOT initiation time (<12 h vs. >12 h), study type, and baseline visual severity when reported. A random-effects model was used, and pooled estimates were expressed as odds ratios (ORs) with 95% confidence intervals (CIs). Results: Twelve studies were included. The pooled efficacy analysis favored HBOT (OR = 0.47; 95% CI: 0.26–0.87; p = 0.02), although heterogeneity was substantial (Tau² = 0.64; I² = 78%). Stratified synthesis showed that studies in which HBOT was initiated within 12 h consistently reported greater visual improvement, whereas delayed or variably timed treatment showed attenuated and inconsistent benefits. After outcome harmonization, studies reporting logMAR-compatible data generally demonstrated clinically relevant visual improvement, while adverse-event rates did not differ significantly between HBOT and non-HBOT groups (OR = 0.70; 95% CI: 0.43–1.16; p = 0.17; I² = 0%). Conclusions: HBOT appears most beneficial when initiated early, particularly within the first 12 h. However, heterogeneity in treatment timing, study design, and baseline severity reporting limits the certainty of these results and supports the need for standardized outcome reporting and protocol-driven prospective studies. Full article

Overhead cranes are underactuated systems with significant model uncertainties that pose major challenges for precise anti-swing control. These uncertainties, including unknown parameters and varying dynamics, severely limit the performance of conventional controllers. To address the control challenge of 7-degree-of-freedom (7-DOF) overhead cranes with variable cable length and double spherical pendulum dynamics, this paper proposes an adaptive sliding mode control method integrated with time-delay estimation. First, a comprehensive dynamic model that accounts for bridge movement, trolley travel, hoisting motion, and spherical swings of both the hook and the payload is established. Then, a sliding surface is constructed based on the coupling analysis between actuated and unactuated dynamics. The core innovation lies in the integration of time-delay estimation with adaptive sliding mode control, where the time-delay estimator provides accurate approximation of unknown system dynamics, while the adaptive mechanism compensates for estimation errors and parameter variations. This dual approach ensures robust performance despite model inaccuracies. Lyapunov stability analysis rigorously confirms the uniform ultimate boundedness of all closed-loop signals under model uncertainties. Experimental tests further show that the designed controller achieves accurate positioning and robust swing suppression, outperforming conventional controllers in challenging working conditions. Full article

The suspension system of high-speed maglev trains has composite time-delay factors, such as inductance delay and control circuit latency, which lead to a decrease in the tracking and robustness of the current control loop. Based on the study of electromagnetic characteristics of suspension systems, this paper proposes a composite anti-time-delay current loop based on adaptive parameter optimization. First, a finite element analysis model of the suspension electromagnet is constructed to analyze the changes in suspension force and inductance of the suspension electromagnet. A self-tuning PI current loop is constructed to achieve time-varying parameter matching. Second, to tackle the inherent time delays and disturbances in the control loop, a predictive PI control algorithm combined with an extended state observer (ESO) is introduced, which effectively estimates and compensates for disturbances and phase lags. Furthermore, a parameter optimization strategy based on the adaptive differential evolution (ADE) algorithm is proposed to address the difficulties in current loop tuning. The results demonstrate that compared to traditional current loop strategies, the dynamic performance of the designed composite anti-time-delay current loop is significantly improved, enhancing the current following control capability of the suspension system under complex operating conditions. Full article

Multidimensional line spectral estimation plays a fundamental role in communication and sensing systems, where it is often used for estimating channel parameters such as angles of arrival and time delays. Existing channel parameter estimation methods often suffer from limited resolution, high computational complexity, or strong sensitivity to noise, and the multidimensional variational line spectral estimation (MDVALSE) algorithm, although effective in off-grid estimation, degrades significantly under low signal-to-noise ratio (SNR) conditions. Recently, generative models, especially diffusion models, have demonstrated strong capabilities in prior-guided denoising and reconstruction of noise-contaminated signals by effectively learning the underlying data structure. Motivated by this, we propose a diffusion-enhanced multidimensional variational line spectral estimation algorithm for channel parameter extraction. Specifically, a diffusion model is first employed to denoise the estimated channel response and improve the observation quality. Then, considering that the residual error after diffusion-based denoising is generally colored rather than white, a colored-noise extension of MDVALSE, termed C-MDVALSE, is derived to better match the statistical structure of the denoised observations. Simulation results in various scenarios show that the proposed algorithm achieves more accurate channel reconstruction and channel parameter estimation than MDVALSE and other existing methods, with particularly significant improvements in low-SNR regimes. Full article

The rapid proliferation of Internet of Things (IoT) devices increases the demand for task offloading mechanisms that satisfy strict latency constraints while limiting security exposure in edge computing environments. This paper proposes a security-aware task offloading framework for IoT edge networks, using Software-Defined Networking (SDN) as a centralized control plane. The SDN controller combines real-time monitoring, threat-aware risk estimation, and a lightweight heuristic decision engine to assign tasks to heterogeneous edge nodes according to latency constraints, resource availability, and task security sensitivity. To avoid optimistic scalability assumptions, the evaluation explicitly models contention through load-dependent queueing delay at edge nodes and reduced effective bandwidth on shared links. Simulation results with realistic IoT task parameters and heterogeneous edge capacities show that the proposed framework achieves an average latency of approximately $125\pm 5$ ms, a task completion ratio (TCR) of about $92\pm 2$%, and a security success rate (SSR) near $95\pm 1.5$%, compared to the considered baselines. These results indicate that incorporating risk assessment into SDN-based offloading decisions can improve security-related outcomes while maintaining practical performance under contention. Limitations include the use of an analytical risk model and a single-controller SDN setting; future work will investigate multi-controller deployments, attack-trace-driven evaluation, and energy-aware extensions. Full article

Real-time traffic signal control represents a key challenge in modern intelligent transportation systems, particularly under highly variable traffic flows and the presence of priority vehicles. This study proposes a hybrid framework for adaptive signal plan control at a signalized intersection. The framework integrates deep learning-based traffic prediction, surrogate-based performance evaluation, and reinforcement learning-based adaptive control. Short-term traffic flow is predicted using recurrent neural networks, providing anticipatory information for traffic control decisions. Based on predicted flows and generated candidate signal plans, a machine learning surrogate model enables fast estimation of key performance indicators, including average vehicle delay and queue length. Adaptive control is implemented using the Proximal Policy Optimization algorithm within the SUMO environment via TraCI, which enables real-time fine-tuning of signal phases. A dedicated priority and stability module ensures effective emergency vehicle preemption and adaptive public transport priority while preserving intersection stability. Simulation results show that the proposed framework reduces average vehicle delay by up to 35% compared with FT and by up to 15% compared with standalone RL, while also improving traffic flow efficiency and priority vehicle performance. Full article

The rising market penetration of new energy vehicles (NEVs) is transforming urban traffic into a heterogeneous mix of battery electric (BEVs), hybrid electric (HEVs), and conventional fuel vehicles (FVs). For analytical brevity, traditional internal combustion engine vehicles (ICEVs) are hereafter referred to as ‘fuel vehicles (FVs)’ in the discussion of New Energy Vehicle (NEV) networks. This research investigates the efficacy of centralized coordination for NEVs within a localized region, as opposed to individualized speed control, in enhancing the mitigation of traffic congestion. Evaluating traffic efficiency and decarbonization strategies in such settings often requires extensive random sampling and Monte Carlo simulations over a large set of parameter combinations. However, conventional microscopic traffic simulators (e.g., SUMO), which rely on fine-grained modeling of vehicle dynamics and signal control, incur prohibitive computational time when scaled to large networks and numerous experimental scenarios. In this study, battery electric vehicles and hybrid electric vehicles are designed as density-aware vehicles, whose movement speed is adaptively adjusted according to the regional traffic density in their vicinity and the control parameter β. In contrast, fuel vehicles adopt a stochastic movement speed and, together with other vehicle types, exhibit either movement or stoppage in the lattice environment. This density-driven speed-adaptive control and lattice arbitration mechanism is intended to reproduce, in a simplified yet extensible manner, changes in mobility and traffic-flow stability under high-density traffic conditions. The simulation results indicate that, under the same Manhattan road network and vehicle-density conditions, tuning the β parameter of new energy vehicles to reduce their movement speed in high-density areas and to mitigate abrupt position changes can suppress traffic-flow oscillations, delay the onset of the congestion phase transition, and promote spatial equilibrium of traffic flow. Meanwhile, this study develops simplified energy-consumption and carbon emission models for battery electric vehicles, hybrid electric vehicles, and fuel vehicles, demonstrating that incorporating a speed-adaptive density strategy into mixed traffic flow not only helps alleviate abnormal congestion but also reduces potential energy use and carbon emissions caused by congestion and stop-and-go behavior. From a sensing and practical perspective, the proposed framework assumes that future connected and autonomous vehicles (CAVs) can estimate vehicle states and local traffic density through GNSS–IMU multi-sensor fusion and V2X communications, indicating methodological consistency between the proposed model and real-world CAV sensing capabilities and making it a suitable and effective experimental platform for investigating the relationships among new energy vehicle penetration, density-control strategies, and carbon footprint. Full article