Search Results (65)

The multiscale interactive system composed of wind, leaves, and droplets serves as a critical dynamic unit in precision orchard spraying. Its coupling mechanisms fundamentally influence pesticide transport pathways, deposition patterns, and drift behavior within crop canopies, forming the foundational basis for achieving intelligent and site-specific spraying operations. This review systematically examines the synergistic dynamics across three hierarchical scales: Droplet–leaf surface wetting and adhesion at the microscale; leaf cluster motion responses at the mesoscale; and the modulation of airflow and spray plume diffusion by canopy architecture at the macroscale. Key variables affecting spray performance—such as wind speed and turbulence structure, leaf biomechanical properties, droplet size and electrostatic characteristics, and spatial canopy heterogeneity—are identified and analyzed. Furthermore, current advances in multiscale modeling approaches and their corresponding experimental validation techniques are critically evaluated, along with their practical boundaries of applicability. Results indicate that while substantial progress has been made at individual scales, significant bottlenecks remain in the integration of cross-scale models, real-time acquisition of critical parameters, and the establishment of high-fidelity experimental platforms. Future research should prioritize the development of unified coupling frameworks, the integration of physics-based and data-driven modeling strategies, and the deployment of multimodal sensing technologies for real-time intelligent spray decision-making. These efforts are expected to provide both theoretical foundations and technological support for advancing precision and intelligent orchard spraying systems. Full article

The classification of Unexploded Ordnance (UXO) from magnetometer data is a critical but challenging task, frequently hindered by the data scarcity required for training robust machine learning models. To address this, we leverage a high-fidelity digital twin to generate a comprehensive dataset of magnetometer signals from both UXO and non-UXO objects, incorporating complex remanent magnetization effects. In this study, we design and evaluate a custom Convolutional Neural Network (CNN) for UXO classification and compare it against classical machine learning baseline, including Random Forest and kNN. Our CNN model achieves a balanced accuracy of 84.65%, significantly outperforming traditional models that exhibit performance collapse under slight distortions such as additive noise, drift, and time-wrapping. Additionally, we present a compact two-block CNN variant that retains competitive accuracy while reducing the number of learnable parameters by approximately 33%, making it suitable for real-time onboard classification in underwater vehicle missions. Through extensive ablation studies, we confirm that architectural components, such as residual skip connections and element-wise batch normalization, are crucial for achieving model stability and performance. The results also highlight the practical implications of underwater vehicles for survey design, emphasizing the need to mitigate signal drift and maintain constant survey speeds. This work not only provides a robust deep learning model for UXO classification, but also offers actionable suggestions for improving both model deployment and data acquisition protocols in the field. Full article

Accurate monitoring of animal behaviors enables improved management in precision livestock farming (PLF), supporting critical applications including health assessment, estrus detection, parturition monitoring, and feed intake estimation. Although both contact and non-contact sensing modalities are utilized, wearable devices with embedded sensors (e.g., accelerometers, pressure sensors) offer unique advantages through continuous data streams that enhance behavioral traceability. Focusing specifically on contact sensing techniques, this review examines sensor characteristics and data acquisition challenges, methodologies for processing behavioral data and implementing identification algorithms, industrial applications enabled by recognition outcomes, and prevailing challenges with emerging research opportunities. Current behavior classification relies predominantly on traditional machine learning or deep learning approaches with high-frequency data acquisition. The fundamental limitation restricting advancement in this field is the difficulty in maintaining high-fidelity recognition performance at reduced acquisition rates, particularly for integrated multi-behavior identification. Considering that the computational demands and limited adaptability to complex field environments remain significant constraints, Tiny Machine Learning (Tiny ML) could present opportunities to guide future research toward practical, scalable behavioral monitoring solutions. In addition, algorithm development for functional applications post behavior recognition may represent a critical future research direction. Full article

This paper presents a robust hardware system designed for future detection of faults in wind turbines by analyzing audible noise signals. Predictive maintenance strategies have increasingly relied on acoustic monitoring as a non-invasive method for identifying anomalies that may indicate component wear, misalignment, or impending mechanical failures. The proposed device captures and processes sound signals in real-time using strategically positioned microphones, ensuring high-fidelity data acquisition without interfering with turbine operation. Signal processing techniques are applied to extract relevant acoustic features, facilitating future identification of abnormal sound patterns that may indicate mechanical issues. The system’s effectiveness was validated through rigorous field tests, demonstrating its capability to enhance the reliability and efficiency of wind turbine maintenance. Experimental results showed an average transmission latency of 131.8 milliseconds, validating the system’s applicability for near real-time audible noise monitoring in wind turbines operating under limited connectivity conditions. Full article

Structural Health Monitoring (SHM) is an essential technique for continuously assessing structural conditions using integrated sensor systems during operation. SHM technologies have evolved to address the increasing demand for efficient maintenance strategies in advanced engineering fields, such as civil infrastructure, aerospace, and transportation. However, developing a miniaturized, cost-effective, and multi-sensor solution based on Wireless Sensor Networks (WSNs) remains a significant challenge, particularly for SHM applications in weight-sensitive aerospace structures. To address this, the present study introduces a novel WSN-based Multi-Sensor System (MSS) that integrates multiple sensing capabilities onto a 3 × 3 cm flexible Printed Circuit Board (PCB). The proposed system combines a Piezoelectric Transducer (PZT) for impact detection; a strain gauge for mechanical deformation monitoring; an accelerometer for capturing dynamic responses; and an environmental sensor measuring temperature, pressure, and humidity. This high level of functional integration, combined with real-time Data Acquisition (DAQ) and precise time synchronization via Bluetooth Low Energy (LE), distinguishes the proposed MSS from conventional SHM systems, which are typically constrained by bulky hardware, single sensing modalities, or dependence on wired communication. Experimental evaluations on composite panels and aluminum specimens demonstrate reliable high-fidelity recording of PZT signals, strain variations, and acceleration responses, matching the performance of commercial instruments. The proposed system offers a low-power, lightweight, and scalable platform, demonstrating strong potential for on-board SHM in aircraft applications. Full article

An aircraft that has been carefully optimized for a single flight condition will tend to perform poorly at other flight conditions. For aircraft such as long-haul airliners, this is not necessarily a problem, since the cruise condition so heavily dominates a typical mission. However, other aircraft, such as Unmanned Aerial Vehicles (UAVs), may be expected to perform well at a wide range of flight conditions. Morphing systems may be a solution to this problem, as they allow the aircraft to adapt its shape to produce optimum performance at each flight condition. This study proposes an aerodynamic optimization framework for morphing airfoils by integrating Principal Component Analysis (PCA) for geometric dimensionality reduction and deep learning (DL) for surrogate modeling, alongside an optimization-guided data augmentation strategy. By employing PCA, the geometric dimensionality of airfoil surfaces is reduced from 24 to 18 design variables while preserving 100% shape fidelity, thus establishing a compressed morphing parameterization space. A Multi-Island Genetic Algorithm (MIGA) efficiently explores the reduced design space, while iterative retraining of the surrogate model enhances prediction accuracy, particularly in high-performance regions. Additionally, Shapley Additive Explanation (SHAP) analysis reveals interpretable correlations between principal component modes and aerodynamic performances. Experimental results show that the optimized airfoil achieves a 54.66% increase in low-speed cruise lift-to-drag ratio and 10.90% higher climb lift compared to the baseline. Overall, the proposed framework not only enhances the adaptability of morphing airfoils across various low-speed flight conditions but also facilitates targeted surrogate refinement and efficient data acquisition in high-performance regions. Full article

As an important part of national transportation infrastructure, the operation status of bridges is directly related to transportation safety and social stability. Structural deflection, which reflects the deformation behavior of bridge systems, serves as a key indicator for identifying stiffness degradation and the progression of localized damage. The accurate modeling and forecasting of deflection are thus essential for effective bridge health monitoring and intelligent maintenance. To address the limitations of traditional methods in handling multi-source data fusion and nonlinear temporal dependencies, this study proposes an enhanced iTransformer-based prediction model, termed LDAiT (LSTM Differential Attention iTransformer), which integrates Long Short-Term Memory (LSTM) networks and a differential attention mechanism for high-fidelity deflection prediction under complex working conditions. Firstly, a multi-source heterogeneous time series dataset is constructed based on wireless sensor network (WSN) technology, enabling the real-time acquisition and fusion of key structural response parameters such as deflection, strain, and temperature across critical bridge sections. Secondly, LDAiT enhances the modeling capability of long-term dependence through the introduction of LSTM and combines with the differential attention mechanism to improve the precision of response to the local dynamic changes in disturbance. Finally, experimental validation is carried out based on the measured data of Xintian Yellow River Bridge, and the results show that LDAiT outperforms the existing mainstream models in the indexes of R², RMSE, MAE, and MAPE and has good accuracy, stability and generalization ability. The proposed approach offers a novel and effective framework for deflection forecasting in complex bridge systems and holds significant potential for practical deployment in structural health monitoring and intelligent decision-making applications. Full article

Commercial wearable systems for surface electromyography (sEMG) acquisition often trade bandwidth, synchronization, and battery life for miniaturization, and their proprietary designs inhibit reproducibility and cost-effective customization. To address these limitations, we developed MOT, a fully wireless, multichannel platform built from commodity components that can be replicated in academic laboratories. Each sensor node integrates an AD8232 analog front-end configured for 19–690 Hz bandwidth (59 dB mid-band gain) with a 12-bit successive approximation ADC sampling at 1 kS/s. Packets of 120 samples are broadcast via the low-latency ESP-NOW 2.45 GHz protocol to a central hub, which timestamps and streams data to a host PC over USB-UART. Bench tests confirmed the analog response and showed mains interference at least 40 dB below voluntary contraction levels; the cumulative packet loss remained below 0.5% for six simultaneous channels at 100 m line-of-sight, with end-to-end latency under 3 ms. A 180 mAh Li-ion cell was used to power each node for 1.8 h of continuous operation at 100 mA average draw, and the complete sensor, including enclosure, was found to weigh 22 g. MOT reduced a 60 Hz artifact magnitude by up to 22 dB while preserving signal bandwidth. The hardware, therefore, provides a compact and economical solution for biomechanics, rehabilitation, and human–machine interface research that demands mobile, high-fidelity sEMG acquisition. Full article

The integration of physical modelling, artificial intelligence (AI), and embedded electronics represents a promising direction in the development of intelligent systems for rehabilitation monitoring. Most existing approaches, however, treat biomechanical simulation and sensor-based AI separately, without leveraging their potential synergy. This study introduces a hybrid framework for upper limb rehabilitation that combines finite element modelling (FEM), AI-based trend classification, and a custom-designed electronic system for real-time signal acquisition and wireless data transmission. A mechanical model, developed in COMSOL 6.2 Multiphysics, simulates the interaction between a robotic glove and a deformable latex sphere. The latex material is described using a two-parameter Mooney–Rivlin hyperelastic formulation to capture large nonlinear deformations under realistic contact conditions. The high-fidelity simulation data are used to validate the signal acquisition chain and to train a supervised AI algorithm capable of classifying rehabilitation progress—whether improving or worsening—based on biomechanical features. An integrated electronic prototype enables seamless data flow to a cloud-based monitoring platform, supporting real-time feedback and adaptability. The classification algorithm demonstrates robust performance across different test conditions, while the electronic system confirms its applicability in rehabilitation settings. The novelty of this paper lies in the closed-loop integration of FEM-based simulation, AI-driven analysis, and embedded electronics into a unified monitoring architecture. This intelligent and non-invasive approach provides a scalable tool for tracking motor recovery and enhancing therapy effectiveness through adaptive, feedback-driven interventions. Full article

High-resolution holographic visualization of built environments remains largely inaccessible due to the complexity and technical demands of traditional 3D data acquisition processes. This study proposes a workflow for producing high-quality full-color digital holographic stereograms of architectural landmarks using Google Earth Studio. By leveraging photogrammetrically reconstructed three-dimensional (3D) city models and a controlled camera path, we generated perspective image sequences of two iconic monuments, that is, the Basílica de la Sagrada Família (Barcelona, Spain) and the Arc de Triomphe (Paris, France). A custom pipeline was implemented to compute keyframe coordinates, extract cinematic image sequences, and convert them into histogram data suitable for CHIMERA holographic printing. The holograms were recorded on Ultimate U04 silver halide plates and illuminated with RGB light-emitting diodes, yielding visually immersive reconstructions with strong parallax effects and color fidelity. This method circumvented the requirement for physical 3D scanning, thereby enabling scalable and cost-effective holography using publicly available 3D datasets. In conclusion, the findings indicate the potential of combining Earth Studio with digital holography for urban visualization, cultural heritage preservation, and educational displays. Full article

Advanced multi-channel acoustic emission (AE) monitoring systems often rely on complex and costly architectures, especially those requiring custom FPGA-based hardware. In this work, we present a digital signal processing (DSP)-based approach to high-performance AE acquisition, implemented using a simplified analog front-end (AFE) and a commercially available synchronous data acquisition (DAQ) card (NI USB-6356). This design eliminates the need for specialized FPGA development, improving accessibility and reducing system complexity. A key feature of the system is the replacement of traditional analog filters with a software-defined digital band-pass filtering module implemented in LabVIEW. This allows for real-time or post-processing filtering with adjustable parameters, enhancing flexibility in data analysis. The system supports 8-channel synchronous sampling at 1.25 MS/s, and performance evaluations demonstrate a dynamic range of 79.22 dB and a signal-to-noise ratio (SNR) of 85.39 dB. These results confirm the system’s ability to maintain high fidelity in AE signal acquisition without the need for dedicated hardware filtering or custom DAQ hardware. The proposed method offers a practical and validated alternative for multi-channel AE monitoring, with potential applications in structural health monitoring, materials testing, and other engineering domains. Full article

Smart cities are urban areas that use contemporary technology to improve citizens’ overall quality of life. These modern digital civil hubs aim to manage environmental conditions, traffic flow, and infrastructure through interconnected and data-driven decision-making systems. Today, many applications employ intelligent sensors for real-time data acquisition, leveraging visualization to derive actionable insights. However, despite the proliferation of such platforms, challenges like high data volume, noise, and incompleteness continue to hinder practical visual analysis. As missing data is a frequent issue in visualizing those urban sensing systems, our approach prioritizes their correction as a fundamental step. We deploy a hybrid imputation strategy combining SARIMAX, k-nearest neighbors, and random forest regression to address this. Building on this foundation, we propose an interactive web-based pipeline that processes, analyzes, and presents the sensor data provided by Basel’s “Smarte Strasse”. Our platform receives and projects environmental measurements, i.e., NO₂, O₃, PM_2.5, and traffic noise, as well as mobility indicators such as vehicle speed and type, parking occupancy, and electric vehicle charging behavior. By resolving gaps in the data, we provide a solid foundation for high-fidelity and quality visual analytics. Built on the Flask web framework, the platform incorporates performance optimizations through Flask-Caching. Concerning the user’s dashboard, it supports interactive exploration via dynamic charts and spatial maps. This way, we demonstrate how future internet technologies permit the accessibility of complex urban sensor data for research, planning, and public engagement. Lastly, our open-source web-based application keeps reproducible, privacy-aware urban analytics. Full article

In the development of digital twin (DT) workshops, constructing accurate DT models has become a key step toward enabling intelligent manufacturing. To address challenges such as incomplete data acquisition, noise sensitivity, and the heavy reliance on manual annotations in traditional modeling methods, this paper proposes a self-supervised deep learning approach for point cloud completion. The proposed model integrates self-supervised learning strategies for inferring missing regions, a Feature Pyramid Network (FPN), and cross-attention mechanisms to extract critical geometric and structural features from incomplete point clouds, thereby reducing dependence on labeled data and improving robustness to noise and incompleteness. Building on this foundation, a point cloud-based DT workshop modeling framework is introduced, incorporating transfer learning techniques to enable domain adaptation from synthetic to real-world industrial datasets, which significantly reduces the reliance on high-quality industrial point cloud data. Experimental results demonstrate that the proposed method achieves superior completion and reconstruction performance on both public benchmarks and real-world workshop scenarios, achieving an average CD-${\ell }_2$ score of 15.96 on the 3D-EPN dataset. Furthermore, the method produces high-fidelity models in practical applications, providing a solid foundation for the precise construction and deployment of virtual scenes in DT workshops. Full article

This article presents an innovative integration of Glow Discharge Detector Focal Plane Arrays (GDD FPA) with Chirped Frequency Modulated Continuous Wave (FMCW) Radar, enhancing millimeter-wave (MMW) imaging. The cost-effective FPA design using GDDs as pixel elements forms the foundation of the system. We investigate MMW effects on GDD discharge currents via basic data acquisition (DAQ) and implement a scanning mechanism with a step motor for sub-pixel imaging. The setup integrates an MMW source, optical components, a timer/counter, and an 8 × 8 FPA with 64 GDD, operating in electrical detection modes and processing signals using Fast Fourier Transform (FFT) algorithms. Recent advancements in millimeter-wave imaging have focused on improving image resolution and acquisition speed through various techniques, including lock-in amplifiers and electrical detection methods. However, these methods introduce complexity, cost, and extended acquisition times. Our approach mitigates these challenges by implementing a simplified FPA design that eliminates the need for external signal conditioning elements, providing faster and more efficient image acquisition. The primary contributions include significant improvements in the speed and automation of image acquisition achieved through a coordinated control mechanism for efficient row scanning. Compared to previous generations of GDD FPAs, this system achieves a notable reduction in image acquisition time by up to 75%, while maintaining high fidelity. These enhancements make the system particularly suitable for time-sensitive applications. Additionally, future research directions include the incorporation of 3D imaging using FMCW radar. Results from the FMCW measurements using the single GDD circuit demonstrate the system’s ability to accurately capture and process MMW radiation, even at low intensities. The combined strengths of GDD FPA and chirped FMCW radar underscore the system’s effectiveness in MMW detection, laying the groundwork for advanced MMW imaging capabilities across diverse applications. Full article

Sound speed profiles (SSPs) must be detected simultaneously to perform a multibeam depth survey. Accurate real-time sound speed profile (SSP) acquisition remains a critical challenge in deep-sea multibeam bathymetry due to the limitations regarding direct measurements under harsh operational conditions. To address the issue, we propose a joint inversion framework integrating World Ocean Atlas 2023 (WOA23) temperature–salinity model data, historical in situ SSPs, and surface sound speed measurements. By constructing a high-resolution regional sound speed field through WOA23 and historical SSP fusion, this method effectively mitigates spatiotemporal heterogeneity and seasonal variability. The artificial lemming algorithm (ALA) is introduced to optimize the inversion of empirical orthogonal function (EOF) coefficients, enhancing global search efficiency while avoiding local optimization. An experimental validation in the northwest Pacific Ocean demonstrated that the proposed method has a better performance than that of conventional substitution, interpolation, and WOA23-only approaches. The results indicate that the mean absolute error (MAE), root mean square error (RMSE), and maximum error (ME) of SSP reconstruction are reduced by 41.5%, 46.0%, and 49.4%, respectively. When the reconstructed SSPs are applied to multibeam bathymetric correction, depth errors are further reduced to 0.193 m (MAE), 0.213 m (RMSE), and 0.394 m (ME), effectively suppressing the “smiley face” distortion caused by sound speed gradient anomalies. The dynamic selection of the first six EOF modes balances computational efficiency and reconstruction fidelity. This study provides a robust solution for real-time SSP estimation in data-scarce deep-sea environments, particularly for underwater autonomous vehicles. This method effectively mitigates the seabed distortion caused by missing real-time SSPs, significantly enhancing the accuracy and efficiency of deep-sea multibeam surveys. Full article