Search Results (6,460)

For sensing applications, a complementary metal oxide semiconductor (CMOS) image sensor (CIS) with a lateral overflow integration capacitor (LOFIC) is in high demand. The LOFIC CIS can achieve high-dynamic-range (HDR) imaging by combining a low-conversion-gain (LCG) signal for large maximum signal electrons and a high-conversion-gain (HCG) signal for a low electron-referred noise floor. However, the LOFIC CIS faces challenges regarding the power consumption and circuit area when reading both HCG and LCG signals. To address these issues, this study proposes a readout circuit composed of area-efficient MOS capacitors using a folding DC operating point technique and an in-column signal selector for an on-chip HDR merger of HCG and LCG signals. A 10-bit test chip was fabricated with a $0.18$ µm CMOS process with MOS capacitors. The fabricated chip maintains high linearity, achieving an integral nonlinearity (INL) of +7.17/−6.93 LSB for the HCG signal and +7.95/−7.41 LSB for the LCG signal. Furthermore, the proposed design achieves a $14.92\%$ reduction in the average power consumption of the total readout circuit and a $36.5\%$ reduction in the readout circuit area. Full article

The study focuses on the results of the analysis of data recorded during Ambient Vibration Tests (AVT) conducted on a portion of the Medieval Walls of Verona (Northern Italy). Seismometric stations were installed both at the top and at the base of the walls, recording the free vibrations of the structure. Spectral analyses provide information about the principal modal frequencies, which are compared with the results obtained through Operational Modal Analysis (OMA) techniques. Numerical models were developed to describe the elastic behavior of the walls and to support the interpretation of the experimentally identified modes. Seismic noise measurements were also performed on the ground to characterize the spectral response of the soil and to estimate the soil–structure interaction. The combined use of AVT data, OMA procedures, and numerical modeling allowed for a robust identification of the fundamental dynamic properties of the walls, highlighting the predominance of out-of-plane modes and the limited dynamic coupling with the underlying soil. The study demonstrates the effectiveness of this non-invasive approach for improving the knowledge of structural assessment, reducing uncertainties in mechanical parameter calibration, and supporting informed conservation, maintenance, and risk-mitigation strategies for historic defensive masonry structures. Full article

Spectrum Sensing (SS) is expected to play a crucial role in forthcoming 6G Cognitive Radio Networks (CRNs), where unlicensed users will be able to dynamically access the spectrum and perform opportunistic transmissions without generating interference for licensed users. In this work, we investigate multiple-antenna SS techniques by analyzing the performance of several widely used detection schemes—namely, Roy’s Largest Root Test (RLRT), the Generalized Likelihood Ratio Test (GLRT), the Eigenvalue Ratio Detector (ERD), and the Energy Detector (ED)—under varying false-alarm probabilities and signal-to-noise ratios (SNRs). We assume there are a fixed number of sensors at the secondary-user receiver, namely, four. To evaluate the behavior of these detectors in realistic conditions, we developed a software-defined radio (SDR) testbed using Universal Software Radio Peripherals (USRPs), enabling both primary-user signal transmission and secondary-user data acquisition. The experimental results, illustrated through Receiver Operating Characteristic (ROC) and performance curves, are compared with simulation outcomes. The analysis is complemented by a detailed state-of-the-art listing of the available analytical characterizations of the false-alarm probabilities for the considered SS schemes. In particular, the GLRT false-alarm probability, previously unavailable in explicit form for a four-antenna equipped receiver, is computed as well. These results validate the superior detection capability of RLRT over the other schemes tested, confirming its effectiveness not only in theoretical analysis but also in practical SDR-based implementations. Full article

Accurate state-of-charge (SoC) estimation is essential for ensuring the safety, efficiency, and longevity of lithium-ion batteries in electric vehicles and energy storage systems. However, conventional methods such as ampere-hour (AH) integration and the extended Kalman filter (EKF) often suffer from error accumulation, sensitivity to initial conditions, and inadequate handling of strong nonlinearities and time-varying noise. To overcome these limitations, this paper proposes a novel LDL-Decomposition-Based Multi-Innovation Adaptive Unscented Kalman Filter (LDL-MIAUKF) algorithm that integrates three key innovations: (1) multi-innovation theory to exploit historical measurement sequences for enhanced state correction; (2) an adaptive mechanism to dynamically adjust process and observation noise covariances in real time; and (3) LDL decomposition (instead of Cholesky) to guarantee numerical stability and positive definiteness of the covariance matrix during sigma point generation. A second-order RC equivalent circuit model is established for the lithium battery, and its parameters are identified online using the forgetting factor recursive least squares (FFRLS) method under Hybrid Pulse Power Characterization (HPPC) test conditions. The proposed LDL-MIAUKF algorithm is then applied to estimate SoC using real battery data. Experimental results demonstrate that the LDL-MIAUKF achieves a maximum SoC estimation error of less than 1% at 25 °C and effectively tracks the reference SoC with high robustness. Furthermore, the terminal voltage prediction error of the identified model remains within ±0.1 V, confirming model accuracy. These results validate that the proposed LDL-MIAUKF algorithm significantly improves estimation accuracy, stability, and adaptability, making it a promising solution for advanced battery management systems. Full article

Phase unwrapping is a critical step in interferometric imaging modalities such as holography and synthetic aperture radar, yet conventional analytical algorithms struggle in low signal-to-noise and high-speckle environments. This study presents an artificial intelligence (AI)-based phase-unwrapping framework using a Pix2Pix conditional generative adversarial network (cGAN). A model was designed for robustness under Rayleigh-distributed speckle noise and phase decorrelation, conditions representative of realistic interferometric measurements. Trained on synthetically generated wrapped–unwrapped phase pairs, the AI approach was compared against established analytical phase-unwrapping methods, a quality-guided unwrapping algorithm (Herraez)and a minimum-norm network-flow optimization method (Costantini). Quantitative evaluation using the root mean square error (RMSE), structural similarity index measure (SSIM), and a composite performance index demonstrated that the cGAN was superior under noisy conditions, successfully recovering phase information beyond its training noise range at $\sigma =10$, and accurately unwrapping phases up to $\sigma =20$. This was under a pure unwrapping performance analysis, utility performance was also tested comparing all images to clean noiseless phase. The Pix2Pix model also proved resilient to detector artifacts, despite not being explicitly trained on them, and its worst performance yielded RMSE and SSIM values of 0.089 and 0.927, respectively, with perfect values being 0 and 1. The proposed framework simultaneously unwraps and denoises the phase, offering a simple, open-source, and highly adaptable alternative for phase unwrapping in noisy interferometric systems. Future work will focus on extending the framework to experimental datasets. Full article

Expressway traffic noise poses a critical threat to public health in developed high-density cities, causing chronic environmental stress in adjacent residential areas. While physical noise barriers are commonly used, the potential of audiovisual interactions in mitigating the adverse effects of traffic noise remains under-explored. Using immersive virtual reality (VR), this study examined the efficacy of visual greenery and auditory masking (birdsong) in promoting stress recovery, and tested whether audiovisual perception mediates the environment–restoration link. Following an acute stressor, 100 participants were randomly assigned to a 2 × 2 between-subjects experiment manipulating Green View Index (high vs. low) and soundscape composition (traffic noise vs. traffic noise plus birdsong), with 25 participants in each group. Restorative outcomes were assessed using self-reported measures and continuous physiological monitoring (heart rate variability [HRV] and electrodermal activity [EDA]). Results demonstrated that high-intensity visual greenery and natural sounds effectively enhance psychological restoration in noise-affected environments. Structural equation modeling revealed that audiovisual perception fully mediated the relationship between environmental features and restorative outcomes. The physiological outcome showed a distinct tiered restoration pattern, indicating that immediate psychological buffering can be achieved through natural sounds, while consistent visual reinforcement remained essential for deep physiological recovery. Consequently, soundscape planning in expressway-adjacent zones should integrate visual greening strategies to optimize the perceptual masking of traffic noise and enhance the environmental quality. Full article

This work supports the circular economy and sustainable material by facilitating the creation of low-carbon materials with enhanced elimination of nutrients from wastewater, thereby assisting in preventing eutrophication. Porous geopolymers, owing to their distinctive pore structure and numerous superior properties, including noise reduction and thermal insulation, have a wide range of potential applications in the building sector, chemical industry, and water treatment. Developing low-carbon-footprint porous geopolymer materials is an important step toward creating multipurpose lightweight materials that can serve as structural materials and, at the same time, as adsorbents. In this study, it was revealed that the porous material created during the hydrothermal synthesis of (lime–Portland cement-based aerated composition), by replacement of sand with wood biomass bottom ash (WBA), can be used as porous aggregates (PA) for adsorbent development. PA was produced with an apparent porosity of 65%, a density of 610 kg/m³, and a compressive strength of 2.0 MPa. The effectiveness of employing an air-entraining additive (AEA) and creating PA in geopolymers was tested. A different-molarity activator was used, and wood biomass fly ash (WFA) and metakaolin (MK) waste were used as precursors for the synthesis of porous geopolymers. Using an air-entraining admixture in geopolymers allows for the production of lightweight geopolymers with densities up to 1400 kg/m³, compressive strengths up to 8.0 Mpa, and apparent porosities up to 38.4%. Such properties, together with their low cost, offer good prospects for geopolymers in the construction industry. By utilizing PA in the geopolymer composition, a lightweight geopolymer (GPA) with a density of 985 kg/m³ and a compressive strength of 3.9 Mpa, with 42.0% apparent porosity, was obtained. The materials effectively removed phosphorus from biologically treated wastewater: PA had an efficiency of up to 82.5%, the geopolymer with AEA had an efficiency of up to 88.4%, and GPA had an efficiency of up to 97%. The created GPA enhances the adsorbent’s sorption capacity, resulting in extremely high phosphorus uptake efficiency. Full article

Notably, one of the key points to address low accuracy and delayed responsiveness of dam deformation prediction models lies in the timely detection of the outliers caused by environmental disturbances, sensor failures, or operational anomalies of dam monitoring sequences. Therefore, our work offers an unambiguous method for overcoming this challenge. In this paper, a robust prediction framework that integrates Complete Ensemble Empirical Mode Decomposition (CEEMD) and Isolation Forest (iForest) for effective outlier detection, followed by a Multi-Head Attention Bidirectional Gated Recurrent Unit (MHA-BiGRU) model for dam deformation prediction, is presented. The original deformation time series is first decomposed using CEEMD into a set of intrinsic mode functions (IMFs). This decomposition separates the series into trend-related components and noise components. Subsequently, the iForest algorithm is applied in outlier detection for noise components. Then, the BiGRU model is enhanced with an MHA mechanism to give more weight to the features that affect the sequences of monitoring dam deformation. By enabling the proposed model to focus on the key factors affecting dam deformation, the accuracy of the prediction results has been enhanced. Finally, a case study introducing monitoring data from a practical project in China demonstrates the performance of the proposed method. The proposed MHA-BiGRU model demonstrates superior performance across all tested scenarios. Notably, the coefficient of determination is consistently maintained above 0.98, peaking at 0.9880. In terms of error control, the model exhibits a maximum mean absolute error of 0.1789, thereby substantiating its exceptional prediction accuracy and robustness. In comparison with classical time series forecasting models, including LSTM, GRU and BiGRU, the proposed approach demonstrates enhanced robustness and delivers greater prediction accuracy. The findings provide a promising reference framework for dam structural characteristics prediction in similar projects. Full article

To achieve autonomous and reliable all-weather cross-domain aerospace navigation, this study proposes an adaptive fault-tolerant federated Kalman filter (AFTFKF) for an INS/SRNS/CNS integrated navigation system to enhance system robustness against measurement outliers. First, a noise estimator based on maximum likelihood estimation (MLE) and aided by a sequential probability ratio test (SPRT) is introduced to handle slowly growing outliers. Second, a double residual-based Chi-square test (DCST) information factor is designed to mitigate the impact of inaccurate local state estimation in subsystems under abruptly changed outliers. Finally, the SPRT-MLE-based noise estimator and the DCST-based information factor are integrated into the federated Kalman filter framework to construct the complete AFTFKF. Simulation results demonstrate that the proposed method achieves superior accuracy and strong stability for SINS/SRNS/CNS integrated navigation in the presence of outliers. Full article

Carbon fiber-reinforced resin matrix composites (CFRC) are extensively used in aerospace, automotive manufacturing, and sports equipment. However, the brittle nature of the resin matrix causes CFRC to exhibit severe vibrations and noise under dry friction conditions. Enhancing the intrinsic damping properties of the resin matrix serves as a fundamental and effective strategy to mitigate vibration and noise radiation in composite components. This study systematically investigates high-temperature co-curing damping composites using co-curing technology, aiming to improve the mechanical performance and damping characteristics of traditional fiber-reinforced epoxy resin composites. A novel carbon fiber-reinforced terminal carboxyl nitrile epoxy pre-polymer composite material demonstrates both stable chemical properties and excellent high-temperature resistance. Through formulation adjustments, the curing temperature and time of epoxy resin are matched with those of the terminal carboxyl nitrile epoxy pre-polymer. The performance of epoxy carbon fiber composites was evaluated through tensile tests, flexural tests, impact tests, infrared spectroscopy, thermogravimetric analysis, dynamic mechanical analysis, scanning electron microscopy, and X-ray diffraction. Results show that blending epoxy resin with terminal carboxyl nitrile liquid rubber enhances energy dissipation by increasing intermolecular friction and hydrogen bonding interactions. The damping ratio of epoxy resin-based carbon fiber composites reaches as high as 1.67%. Tensile strength, flexural strength, and impact strength reach 1968 MPa, 1343 MPa, and 127 kJ/m², respectively. The addition of terminal carboxylated nitrile liquid rubber facilitates the formation of continuous friction membranes, enhancing friction stability. Tensile tests demonstrate that carbon fiber composites containing 25% terminal carboxylated nitrile liquid rubber outperforms other formulations. As evidenced by impact tests, the performance of the prepared composites is superior to that of other configurations. Dynamic mechanical analysis indicates that the 25% rubber-containing composites exhibit enhanced damping characteristics and higher loss modulus. Experimental results confirm that this study advances the development of functional composites for vibration reduction and noise control applications. Full article

In urban environments, autonomous vehicles face critical challenges in localization and perception under extreme lighting conditions, including rapid illumination changes, high contrast, and nighttime low-light scenarios. To address the performance degradation of traditional LiDAR-inertial odometry systems under such conditions, this study proposes a high-precision FAST-LIO2-EC algorithm that fuses event cameras into the FAST-LIO2 framework. Event cameras, with their microsecond temporal resolution and 140 dB dynamic range, provide asynchronous edge information that complements LiDAR point clouds and IMU measurements. We validate the proposed system through real-world road tests conducted on public roads and closed test tracks, covering three typical extreme lighting scenarios: tunnel entrance/exit transitions, high-contrast shadow boundaries, and nighttime sparse-lighting conditions. The experimental platform is equipped with a 32-beam LiDAR, a 6-axis IMU, a DVS event camera, and an RTK-GNSS system for ground truth trajectory acquisition. Real-world results demonstrate that the FAST-LIO2-EC system achieves significant improvements in localization accuracy and robustness. In illumination change scenarios, the Absolute Trajectory Error (ATE) is reduced by 32.5% compared to the baseline FAST-LIO2 system, with zero tracking loss events. The point cloud quality is substantially enhanced, with more uniform distribution and clearer obstacle boundaries. In high-contrast scenarios, both systems maintain comparable performance with ATE below 0.15 m. However, in nighttime scenarios, the fusion system shows moderate improvement (15.3% ATE reduction) but reveals sensitivity to event camera noise, indicating the need for adaptive thresholding strategies. Supplementary simulation experiments validate the system’s robustness under varying speeds and sensor noise levels. This work provides a practical solution for autonomous vehicle deployment in complex urban lighting environments, with a comprehensive analysis of real-world performance boundaries and deployment considerations. Full article

Wildfire events are increasing in frequency and severity, creating an urgent need for early, accurate, and energy-efficient forest fire prediction systems that can operate at a large scale. A fundamental challenge in edge-level forest fire prediction lies in jointly achieving high detection accuracy while minimizing wireless transmissions and communication-related energy consumption. This paper proposes a communication-aware hierarchical wireless sensor network (WSN) framework that performs fire versus normal environmental state classification directly at the network edge. Multi-modal physical and constrained virtual sensor readings are fused into short-term temporal supervectors and processed locally using lightweight random forest classifiers deployed on sensor nodes and cluster heads. A temporal 2-of-3 voting mechanism is applied at the edge to suppress transient noise and improve prediction reliability before triggering communication. The proposed design enables selective, event-driven transmission, where only temporally validated abnormal states are forwarded through the hierarchy, thereby decoupling detection accuracy from continuous data reporting. Extensive experiments using real multi-modal environmental sensor data and statistically rigorous 5-fold GroupKFold cross-validation—ensuring strict node-level separation between training and testing—demonstrate the effectiveness of the approach. The proposed framework achieves a node-level accuracy of 98.82 ± 1.75% and a scenario-level detection accuracy of 96.52 ± 0.89%. Compared to periodic reporting and the LEACH protocol, the system reduces wireless transmissions by over 66% and communication-related energy consumption by more than 66% across network sizes ranging from 100 to 1000 nodes. The main contributions of this work are summarized as follows: (1) a communication-aware hierarchical Edge-AI framework for early forest fire prediction that performs local inference and temporal validation directly at sensor nodes; (2) a constrained virtual sensing strategy integrated with temporal supervector modeling to enhance spatial coverage while preserving reliability; and (3) a statistically rigorous large-scale evaluation demonstrating joint optimization of prediction accuracy, transmission reduction, and communication energy efficiency across network sizes ranging from 100 to 1000 nodes. These results show that accurate early forest fire prediction can be achieved through edge-level inference and selective communication, substantially extending network lifetime while maintaining statistically reliable detection performance. Full article

Background: Acute high-intensity noise exposure represents a critical environmental stressor; however, its impact on brain function and the underlying mechanisms remain incompletely understood. This study aimed to investigate the effects of acute high-intensity noise exposure on cognitive function in rats, utilizing multi-omics analysis to explore potential mechanisms. Methods: Rats were exposed to acute noise at 120 dB, and brain function was evaluated using the novel object recognition (NOR) test, recordings of electroencephalographic activity, and histopathological examination. Longitudinal serum metabolomics and fecal metagenomics were performed on samples collected at 0 h, 7, 14, and 28 days post-exposure. Quantitative profiling of oxylipins and proteomics were conducted at a critical time point, followed by integrative multi-omics network analysis. Results: Acute high-intensity noise exposure significantly reduced the recognition index in the NOR test, increased theta-band power, and induced hippocampal neuronal damage. Multi-omics analyses revealed time-dependent alterations in gut microbiota and metabolic profiles, identifying day 7 as the critical response window, with arachidonic acid (AA)-derived metabolites consistently downregulated across omics layers. Integrated analysis revealed a coordinated microbiota–oxylipins–proteins network, highlighting key AA-derived oxylipins (e.g., 8-HETE, 12-HETE) that correlated with specific gut microbiota and proteins involved in lipid metabolism and inflammation. Conclusions: Acute high-intensity noise exposure induces cognitive impairment and systemic molecular disturbances. AA-centered lipid metabolism acts as a key hub linking gut microbiota dysbiosis with inflammatory and metabolic protein alterations, providing multi-omics evidence for coordinated microbiota–lipid–protein dysregulation underlying noise-induced neurobiological dysfunction. Full article

Monitoring small water body coverage spatiotemporal evolution in karst areas of complex hydrogeology is pivotal for water resource management and disaster assessment. With recent infrastructure expansion, intensive tunnel excavation has occurred in Chongqing’s Geleshan, a typical karst region with fragile aquifers. It has disrupted hydrogeological systems, triggering ground subsidence, groundwater leakage, and subsequent reservoir desiccation, as well as threatening regional water security and ecology. Thus, monitoring reservoir coverage evolution is critical to clarify dynamics and driving mechanisms. Synthetic Aperture Radar (SAR) is ideal for water body mapping, enabling data acquisition independent of illumination and weather. However, traditional SAR-based water extraction methods are hampered by low-scatter noise and poor adaptability to hydrological fluctuations. To address this, a two-stage dual-polarization SAR clustering algorithm (TSDPS-Clus) was developed using 452 time-series Sentinel-1 images (7 February 2017–24 August 2025). Specifically, the Kolmogorov–Smirnov test via pixel-wise time-series statistics screened core water areas, built candidate regions, and mitigated noise. Subsequently, dual-polarization and positional features were fused via singular value decomposition (SVD) to generate a high-discrimination low-dimensional feature set, followed by the Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA) clustering for high-precision extraction. Results demonstrate that the algorithm suits reservoir storage-desiccation dynamics; dual-polarization complementarity boosts accuracy and clarifies six reservoirs’ spatiotemporal evolution. Notably, post-2023, tunnel excavation-induced land subsidence increased drying frequency and duration, with a 24-month maximum cumulative desiccation period. Full article

Magnetic Barkhausen noise (MBN) analysis has recently emerged as a powerful nondestructive tool for probing crystallographic orientation, phase transformation, and microstructural stress distribution in ferromagnetic materials. This review aims to summarize recent advances in understanding the relationship between crystallographic texture, dislocation density, and magnetic domain dynamics across different classes of steels and surface coatings. Emphasis is placed on the influence of crystal structure symmetry, residual stress gradients, and coating–substrate interactions on the MBN response. The article also discusses recent modeling approaches and potential integration of MBN with complementary techniques such as EBSD and XRD for microstructural diagnostics and materials design. Full article