Search Results (17,157)

Accurate ocean wave sensing in polar and other low-temperature marine environments is of great significance for marine environmental observation, climate research, and navigation safety. However, conventional wave sensors rely on external power supplies and suffer from poor stability under low-temperature and high-salinity conditions, making long-term self-powered waves sensing a significant challenge. Herein, a highly stable composite eutectogel electrode is developed by integrating sodium lignosulfonate, Fe³⁺ crosslinking, Zn²⁺-carboxylate coordination interactions, and a choline chloride/urea deep eutectic solvent (DES). The DES effectively suppresses solvent crystallization and endows the gel with excellent low-temperature tolerance, while the synergistic effect of metal coordination and multiple non-covalent interactions constructs a robust ion-conducting network with enhanced structural stability. Furthermore, eutectogel-based composite electrode architecture is designed to improve electrical conductivity and charge collection efficiency, thereby enabling stable electrical output under harsh marine conditions. Based on the as-prepared eutectogel electrode, a self-powered solid–liquid triboelectric nanogenerator is fabricated for ocean wave-motion sensing. The device can detect the wave amplitude, with an accuracy of 0.2 cm, and sense the frequency of waves ranging from 0.2 Hz to 1.6 Hz. More importantly, the SL-TENG exhibits excellent environmental adaptability, operating reliably in 3.5 wt% simulated seawater and at 0 °C. The current retention ratio reaches approximately 91% at 0 °C, which is significantly higher than that of the hydrogel-based device (≈6%). The remarkably low-temperature and salt-tolerant performance originates from the stable ion-transport network and anti-freezing characteristics of the eutectogel electrode. This work provides an effective strategy for constructing environmentally resilient eutectogel-based triboelectric devices and offers a promising route toward self-powered wave sensing systems for long-term deployment in harsh marine environments. Full article

A novel microwave biosensor operating in the C-band is developed and characterised for enhanced glucose sensing applications. The sensor is based on a single metamaterial asymmetric split-ring resonator (SASR) and has been investigated in two configurations: a single semi-circular design (SASR-S) and a double semi-circular design (SASR-D). The structural modifications were introduced to enlarge the sensing area by creating two high-field hotspots, thereby increasing the interaction between the electromagnetic (EM) field and the sample, which consequently enhances the overall sensor sensitivity. The sensor is fabricated on a Rogers AD350A substrate and is optimised to detect glucose levels in a 1 µL solution applied within each semi-circle sensing region. To characterise the sensor’s enhanced sensitivity, we performed a 3D electromagnetic simulation of a small droplet positioned within a semicircular sensing region, varying the relative permittivity of the droplet from 45 to 65. The resulting shifts in resonant frequency served as a primary indicator of dielectric sensitivity. The sensor’s response was experimentally validated using a vector network analyser to measure the transmission coefficient (S₂₁) of samples with no glucose and at glucose concentrations of 97 mg/dL to 286 mg/dL. The results demonstrate that the resonator configuration strongly influences the resonance frequency shift and sensitivity, with the SASR-D configuration being the most effective design. This has also been confirmed by measurements demonstrating a sensitivity of approximately 2.4 MHz/(mg/dL), representing an approximately two-fold improvement over the SASR-S sensor (sensitivity: 1.27 MHz/(mg/dL)) and a notable enhancement over previously reported sensors. These findings demonstrate the practical potential of the proposed sensor for blood glucose monitoring applications. Full article

Edge computing plays an important role in structural health monitoring (SHM) for transportation infrastructure because it enables local data processing and low-latency decision support. However, SHM systems encounter challenges due to data anomalies caused by sensor faults, transmission errors, or irregular structural behavior. This study evaluates the deployment performance of a SHM anomaly data-processing workflow on resource-constrained edge devices. The workflow includes data cleaning, response separation, and anomaly detection. Missing data are processed using cubic spline interpolation. Jump points and drift are corrected using the Laida criterion, and noise is reduced using wavelet threshold denoising. Response separation is then performed using the detrending method based on time windows, the 3σ criterion, or wavelet packet decomposition. Anomaly detection is then performed using autoregressive integrated moving average with explanatory variables, support vector machines, and recurrent neural networks. Simulation data and field monitoring data from bridge displacement and highway pavement strain are used to evaluate the workflow. The evaluation focuses on runtime, memory usage, central processing unit usage, and a data-processing throughput proxy. This analysis helps to understand the performance and trade-offs of algorithms on edge devices under the resource constraints typical of SHM applications. Full article

Single-image dehazing aims to recover clear scenes from degraded images affected by atmospheric scattering, serving as a critical preprocessing technique for improving the imaging quality of visual sensors. Existing deep learning-based dehazing methods exhibit limited generalization ability in real-world scenarios, primarily due to the spatial non-uniformity of haze and its coupling with illumination and texture degradation, as well as the scarcity of real paired data. To address these issues, this paper proposes a haze-density conditional attention fusion network (DCAF-Net). The network employs an adaptive haze density perception module to fuse priors such as the dark channel, local contrast, and saturation, generating a spatial haze density guidance map. This map is then embedded as conditional information into the multi-scale feature modulation and attention fusion process, enabling adaptive restoration of regions with different degradation levels. Furthermore, a residual dense cascaded feature enhancement module is designed to leverage feature reuse, gated fusion, and residual learning to enhance the representational capacity of deep features. Training adopts a joint optimization objective combining Charbonnier reconstruction loss, perceptual contrast loss, and structural similarity loss. Experimental results demonstrate that DCAF-Net achieves competitive performance against representative methods on multiple synthetic and real-world hazy datasets, and shows promising restoration performance on representative real-world hazy scenes, and can provide high-quality image preprocessing support for visual-sensor-based intelligent perception systems. Full article

Road surface condition monitoring is critical for ensuring safe and efficient transportation networks. This study proposes and evaluates a framework that compares imagery-, Light Detection and Ranging (LiDAR), and accelerometer-based approaches for pavement anomaly detection. The analysis first focused on a 5-mile urban roadway segment, in which all three sensing modalities were evaluated under identical survey conditions using manually interpreted reference anomalies to compare detection accuracy, severity classification, and processing efficiency. The imagery-based Convolutional Transformer-based Crack Segmentation (CT-CrackSeg) model achieved a precision, recall, and F1-score of 88.5%, 88.5%, and 88.5%, respectively, but remained sensitive to environmental factors such as shadows, curbs, roadside features, and pavement texture variations. The LiDAR-based method achieved an F1-score of 93.0%, while the accelerometer-based Isolation Forest and Adaptive Threshold methods achieved F1-scores of 95.2% and 97.2%, respectively. These results indicate strong detection performance under the evaluated validation conditions; however, the reported precision values should be interpreted as dataset-specific rather than universal performance levels. Given the accelerometer-based approach’s strong detection performance, minimal processing time, and low deployment cost, it was further applied across a 36-mile roadway network to evaluate its scalability for network-level monitoring. Across the full route, the spatial agreement among accelerometer systems exceeded 0.91, while the agreement between the two detection methods exceeded 0.96, with 962–996 surface defects detected depending on the sensor and method. Integrating the anomaly detection results into a Potree-based web portal enabled interactive validation with geotagged imagery and point clouds, improving interpretability and diagnostic insight. Overall, the findings highlight that accelerometer-based monitoring, even with consumer-grade sensors, provides a practical, scalable, and low-cost solution for pavement evaluation, while LiDAR and imagery serve as complementary tools for detailed verification and characterization. Full article

Meat flavor significantly influences consumer preference and market value, particularly for indigenous pig breeds renowned for distinctive sensory characteristics. While traditional research has focused on genetic factors and feeding regimens, emerging evidence suggests that gut microbiota plays a crucial role in meat quality attributes. However, the specific contribution of bacterial–fungal co-occurrence to meat flavor formation remains largely unexplored. This study aimed to characterize the associations between intestinal bacterial–fungal co-occurrence networks and the muscle flavor-related metabolite profiles of CX pigs, using an integrated multi-omics approach. Twenty male pigs (10 CX and 10 LAN, 12 months old) were subjected to comprehensive analyses, including meat quality evaluation, electronic nose analysis, 16S and 18S rRNA sequencing, and untargeted metabolomics. CX pigs exhibited significantly superior meat quality characteristics, including higher moisture content (p < 0.001), fat content (p = 0.008), and meat color scores (p < 0.001). Electronic nose analysis revealed significantly higher response values across all ten aroma sensors in CX pigs (p < 0.001), with the most pronounced differences observed in sensors detecting sulfur compounds and organic compounds. Untargeted metabolomics identified 40 differential metabolites, with 27 up-regulated in CX pigs, including key flavor compounds such as glycocholic acid, isorhamnetin, and pantothenic acid. Microbiome analysis demonstrated significantly higher bacterial alpha diversity in CX pigs (p < 0.05), with enrichment of beneficial bacteria, including Rikenellaceae_RC9_gut_group, Prevotellaceae_UCG_003, and Phascolarctobacterium, while fungal communities showed enrichment of Candida_Lodderomyces_clade. Correlation network analysis revealed that Rikenellaceae_RC9_gut_group demonstrated strong positive correlations with flavor compounds (r = 0.575 for isorhamnetin, r = 0.535 for pantothenic acid, p < 0.001) and all electronic nose responses (r = 0.434–0.691, p < 0.001). Bacterial–fungal co-occurrence networks showed synergistic relationships, with Rikenellaceae_RC9_gut_group positively correlated with Candida_Lodderomyces_clade (r = 0.711, p < 0.001) while exhibiting antagonistic relationships with Piromyces (r = −0.714, p < 0.001). These findings offer novel insights for developing microbiome-targeted strategies to enhance meat quality in pig production systems. Full article

This paper proposes a reliability-optimized localization method for underground utility tunnels based on multi-source fusion and a denoising variational autoencoder model. The method acquires a multi-sensor localization dataset aligned with a unified time reference. By employing a convolutional neural network-assisted denoising variational autoencoder (DVAE-CNN), it regulates localization outcomes through three key aspects: a multi-source heterogeneous data quality assessment model, a formulation of the target state transition equation, and an environmental prior-information-aided weight update strategy. This approach overcomes the low-reliability issues caused by information loss and errors in the complex environment of underground utility tunnels. Compared to localization results without reliability regulation mechanisms, the proposed method achieves an average improvement of 73.6% in localization accuracy and 85.2% in localization reliability. Finally, localization experiments conducted in an underground utility tunnel demonstrate that the proposed method can provide highly robust, reliable and continuous positioning services, indicating significant potential for application and broader adoption. Full article

Section-load calibration is used in aircraft wing-box testing. This study evaluates a fixed-position quasi-static load-calibration and identification procedure for one 7050 aluminum wing-box test section instrumented with surface-bonded fiber Bragg grating (FBG) sensors. A multi-point FBG network was arranged on the skins and webs using finite-element-guided sensor placement to construct bending-, shear-, and torsion-related response features; strain-free reference FBGs provided temperature compensation. All experiments used the same specimen geometry, fixed-root boundary condition, sensor layout, and four actuator positions. Conditions 1–6 were used for regression calibration, whereas Conditions 7 and 8 were held out for interpolation-type validation within the same loading configuration. The maximum/average relative errors were 6.53%/1.51% for bending moment, 2.62%/0.86% for shear force, and 4.04%/1.23% for torsional moment. These results apply only to local laboratory calibration of the tested configuration and do not establish transfer to other geometries, boundary conditions, sensor layouts, loading positions, environmental conditions, or dynamic loading. Full article

Low-cost air temperature and relative humidity sensors are increasingly deployed in dense urban monitoring networks for the characterization of urban heat islands and heat exposure. However, measurement accuracy strongly depends on the performance of the radiation shield protecting the sensor from solar heating. This study evaluates five low-cost radiation shield designs, including naturally ventilated, forced-ventilated, spherical, and chimney-type configurations, under tropical outdoor conditions on Reunion Island. Five calibrated SHT31 sensors were deployed simultaneously alongside a reference meteorological station over a five-week measurement campaign. Shield performance was assessed using standard metrological indicators, daytime–nighttime analyses, error distributions, and two-dimensional irradiance–wind diagnostics. Temperature RMSE values ranged from 0.68 to 1.18 °C, while relative humidity RMSE ranged from 2.65 to 7.39%. The forced-ventilated shield provided the best overall temperature performance, whereas the chimney-type design exhibited the largest errors. Combined irradiance–wind analyses showed that measurement errors were primarily governed by the balance between radiative forcing and convective cooling, with maximum temperature biases exceeding 2.5 °C under high-irradiance and low-wind-speed conditions. Based on these findings, several correction approaches were evaluated. A physically interpretable semi-empirical model reduced RMSE by 50%, while a Random Forest model achieved reductions of up to 66%. These results suggest that low-cost meteorological measurements can be substantially improved through appropriate shield design and meteorologically informed calibration procedures, particularly under tropical conditions characterized by strong solar radiation and limited precipitation. Full article

This overview paper highlights the idea that the polytopic approach is more than a modeling technique. It proposes a unified perspective in which polytopic representations constitute a conceptual bridge between complex system dynamics and convex analysis. From modeling to control, the polytope is not only limited to a geometric interpretation but is used as a methodological principle—an intelligent sensor system for structuring uncertainty, representing a nonlinear behaviors, and enabling decision-making, even for critical safety systems. Indeed, the polytopic approach should not be viewed merely as a convexification tool, but as a way of thinking about complexity, i.e., a structured methodology linking representation, uncertainty management, and control synthesis. For this purpose, this work focuses on safety-critical systems, i.e., cyber-physical systems operating under malicious cyber-attacks, where ensuring resilience has become a major challenge. The review highlights recent developments that extend classical polytopic approaches beyond robust control and toward cyber-resilient estimation and control, including false-data injection attack modeling, simultaneous state and attack estimation, resilient observer-based control, and event-triggered control under communication constraints. The paper also discusses how these developments position the polytopic framework with respect to recent advances in cyber-physical security and resilient control. Overall, the proposed perspective illustrates how polytopic representations have evolved into a unifying paradigm for addressing nonlinear dynamics, cyber-attacks, uncertainties, and network-induced constraints while enhancing the resilience, reliability, and operational safety of safety-critical cyber-physical systems. Full article

Global Navigation Satellite System Reflectometry (GNSS-R) platforms, such as the CYGNSS constellation, provide unprecedented spatial coverage for monitoring ionospheric scintillation via the $S_4$ index. However, the operational utility of GNSS-R for space weather is substantially degraded by surface-induced signal contamination when sharp land–water boundaries (coastlines) trigger massive, false-positive $S_4$ pseudo-scintillations that imitate true ionospheric plasma irregularities. In this study, a robust machine learning (ML) methodology to autonomously distinguish surface-induced reflections from true atmospheric volumetric scattering was proposed. Using 1 Hz Level 1 continuous Signal-to-Noise Ratio (SNR) time-series data, morphologic features (e.g., maximum amplitude, peak prominence, and standard deviation) were extracted to train a Random Forest (RF) classifier. The model achieves 98% accuracy in differentiating coastal boundaries from ionospheric scintillation, evaluated on a global dataset of over ~450,000 anomalous events. Moreover, a multi-sensor case study of the historic May 2024 G5 geomagnetic storm is presented to validate the geophysical fidelity of the filtered data. The ML-isolated CYGNSS anomalies demonstrate strong spatial correlation with COSMIC-2 Radio Occultation (RO) F2-peak electron density (NmF2) variations and ground-based Rate of TEC Index (ROTI) maps. Furthermore, temporal cross-validation with 1 Hz localized ground magnetometer data in Northwest Mexico reveals positive synchronization between CYGNSS scattering events and localized electrodynamic disturbances. Finally, the results demonstrate that ML-de-aliased GNSS-R data can reliably link the oceanic observational gaps inherent to ground-based networks, offering a powerful new tool for global space weather monitoring. Full article

The integration of sensing systems, Artificial Intelligence (AI), and data analytics technologies is enabling the development of digital twins for monitoring and managing complex built environments. University campuses represent suitable scenarios for the deployment and evaluation of these technologies due to their diversity of facilities, heterogeneous operational systems, and dynamic usage patterns. This paper presents the design and deployment of a campus-scale operational digital twin developed for the University of Alicante Smart Campus. The proposed environment integrates multiple data sources including indoor environmental sensors, electricity and water consumption monitoring systems, photovoltaic generation data, irrigation networks, and WiFi connectivity information used as a proxy for occupancy estimation. Through a continuously updated and spatially synchronized digital representation of the campus, the platform supports environmental comfort monitoring, occupancy analysis, environmental quality assessment, anomaly detection, alert generation, and AI-enabled analytical services. A distinguishing characteristic of the proposed approach is its long-term real-world deployment. The system currently manages several years of historical data comprising hundreds of millions of time-series measurements collected from distributed monitoring systems across the campus. The results demonstrate the feasibility of maintaining a campus-scale digital twin operating as an institutional monitoring and decision-support environment. Full article

Multivariate structural-response classification supports bridge monitoring by integrating synchronized measurements from multiple sensors and assigning an observation window to a predefined structural or loading condition. The main technical challenge is to compress temporally and spatially distributed sensor information into a fixed-length representation without losing transient or localized condition-sensitive features. However, previous studies have mainly focused on backbone architecture design, whereas the influence of sequence-level aggregation has rarely been isolated under a controlled backbone and training configuration. In this study, xLSTM-m is proposed as a modified variant of the extended Long Short-Term Memory (xLSTM) network for multivariate bridge-response classification. The conventional last-hidden-state readout is replaced with masked mean pooling over all valid hidden states, while the hybrid sLSTM/mLSTM backbone and the remaining training configuration are kept unchanged. This controlled design isolates the sequence-level aggregation strategy as the only architectural variable. A comparative evaluation is conducted using eight neural models. These models include xLSTM-m, the baseline xLSTM, and six Transformer-based architectures. Three bridge-monitoring datasets are considered: an FBG strain-response dataset and a PCB Piezotronics acceleration-response dataset acquired from a laboratory-scale cable-stayed bridge model, together with the field-scale Z24-9Setup acceleration benchmark. A strict five-fold cross-validation protocol is adopted. The proposed xLSTM-m ranks first on all three datasets. It achieves a mean accuracy of 95.29%, a mean F1-score of 95.57%, and the lowest inter-fold standard deviation of 0.84. Relative to the strongest Transformer baseline for each dataset, the corresponding accuracy gains are 6.30 percentage points for FBG, 3.66 percentage points for PCB, and 31.76 percentage points for Z24-9Setup. These results indicate that, for the evaluated bridge strain and acceleration datasets, sequence-level aggregation substantially affects both classification accuracy and inter-fold stability. Further validation using quasi-static responses, environmental variables, other structural systems, and unseen operating conditions is required before broader applicability across the SHM domain can be established. Full article

Identifying the impact location (“sweet spot”) on a tennis racket is crucial for performance evaluation in tennis training. However, existing approaches typically rely on expensive vision-based systems or specialized sensors, limiting their applicability in real-world scenarios. We propose a sound-sensor-based multi-task framework for racket impact localization using acoustic signals, combining radial region classification with continuous position regression. To effectively model complex acoustic patterns, we design a multi-expert convolutional neural network (CNN) architecture with multi-scale feature extraction and task-specific optimization. Each expert branch operates at a different temporal receptive field and is trained with tailored loss functions, enabling complementary learning of global patterns, class imbalance characteristics, and hard samples. The shared backbone jointly supports both classification and regression tasks, allowing the model to learn more informative and structured representations. Experimental results demonstrate that the proposed framework consistently outperforms conventional methods in radial region classification while achieving accurate impact position estimation. Furthermore, additive noise augmentation significantly improves robustness, enabling stable performance under noisy and practical sensing conditions. Full article

Small and Midsized Manufacturers (SMM) face challenges as they adopt digital technologies to transform their production environment. A Manufacturing Execution System (MES) requires timely accurate data from shop floor processes to efficiently control production operations. An Industrial Internet of Things (IIoT) platform of sensors provides MES software with operational information through a communications network to enable data-driven decision-making. A midsized manufacturer in southeastern Pennsylvania provides comprehensive thermoformed and injected molded products for diverse markets. Their production equipment includes light and heavy gauge thermoforming, polymer calendaring, and Computer Numerical Control (CNC) part trimming equipment supported by air compressors, vacuum pumps, and water chillers. This project partnered a manufacturer with researchers to deploy engineering and information science students to access, catalog, and characterize shop floor Programmable Logic Controllers (PLC) inputs and outputs. Utilizing this critical PLC data, the expert lead team determined quality-critical parameters, machine counters, and fault codes essential for process optimization. Full article