Search Results (2,462)

Low back pain associated with exposure to whole-body vibration (WBV) is common among agricultural workers, and seated posture significantly affects health outcomes from WBV exposure. Current posture assessment methods rely on manual observation or body-worn sensors, which are labor-intensive and impractical for continuous monitoring. We developed a machine learning approach to classify seated posture using force sensors and accelerometers integrated into a vibration sensing seat pad for use in agricultural machinery, avoiding the need for body-worn sensors. Twenty-four participants were exposed to WBV in different upper body postures while seat pad force and acceleration data were recorded. We compared four machine learning architectures: Logistic Regression, Random Forest, Support Vector Machine, and Recurrent Neural Network with Gated Recurrent Unit (GRU). The GRU architecture substantially outperformed baseline models, achieving 89% accuracy (weighted F1 = 0.89) in classifying forward and backward leaning postures. To our knowledge, this study demonstrates the first application of machine learning to classify seated postures from seat pad force measurements during WBV exposure. Temporal modeling with an 18 s window proved essential for accurate classification, enabling non-invasive, continuous posture monitoring. Full article

In the context of the rapid development of smart horticulture, a deep remote sensing-based dual detection method for horticultural crop growth anomalies and safety risks was proposed to address the limitations of existing remote sensing monitoring approaches. These conventional methods, which predominantly focused on growth vigor assessment or single-task anomaly detection, had difficulty distinguishing anomalies from actual production risks and exhibited insufficient sensitivity to weak anomalies and complex temporal disturbances. Within a unified framework, a growth state modeling branch and an anomaly perception branch were constructed, enabling the joint modeling of normal growth trajectories and anomalous deviation features. By further introducing a risk joint discrimination mechanism, an integrated analysis pipeline from anomaly identification to risk assessment was achieved. Multi-temporal remote sensing features were used as inputs, through which normal crop growth patterns were characterized via trend perception, texture modeling, and temporal aggregation, while sensitivity to local disturbances and weak anomaly signals was enhanced by anomaly embeddings and energy representations. Systematic experiments conducted on multi-regional and multi-crop horticultural remote sensing datasets demonstrated that the proposed method significantly outperformed comparative approaches, including traditional threshold-based methods, support vector machines, random forests, autoencoders, ConvLSTM, and temporal transformer models. In the dual task of horticultural crop growth anomaly detection and safety risk identification, an accuracy of approximately 0.91 and an F1 score of 0.88 were achieved, indicating higher anomaly recognition accuracy and more stable risk discrimination capability. Further anomaly-type awareness experiments showed that consistent performance was maintained across diverse real-world production scenarios, including climate stress, disease-induced anomalies, and management errors. Full article

Wide-field infrared imaging systems are often constrained by detector size, cooling requirements, and payload limitations, leading to the need for multi-FOV detector sharing. However, conventional geometric multiplexing introduces severe spatial aliasing, which significantly degrades target localization performance. This paper proposes a polarization-encoded field-of-view multiplexing method for recovering spatial information from aliased detector measurements. The imaging plane is divided into multiple FOV regions, each assigned a distinct polarization state. After optical folding, the modulated sub-images are superimposed onto a common detector region. Six-channel polarization measurements are used to reconstruct pixel-wise Stokes vectors, and the spatial origin of each pixel is identified through polarization-domain similarity matching and target-level voting. MATLAB-based simulations were conducted using a nine-region multiplexing configuration. The proposed method achieves 97.3% pixel-level classification accuracy under ideal conditions and maintains over 95% accuracy at a noise level of σ = 0.02. The normalized Stokes reconstruction error is below 0.02, and stable performance is observed under polarization modulation deviations within ±10°. By introducing polarization as an additional encoding dimension, the proposed framework enables efficient separation of multiplexed spatial information without increasing detector resources, demonstrating its potential for compact wide-field infrared sensing applications. Full article

Implantable Cardioverter-Defibrillators (ICDs) are life-sustaining devices used in the prevention of sudden death in patients suffering from advanced cardiac diseases. Although improvements in ICD technology and monitoring capabilities have been made, existing techniques are still not effective in predicting accelerated battery drain, thereby causing unplanned generator replacement and clinically significant device-related events. In this study, machine learning techniques were employed in the assessment of the early detection of ICD battery depletion risk using the collected device interrogation reports. The dataset used consisted of 32 devices in the training set and nine in the testing set. In order to mitigate the problem of a small sample size, a GMM-based data augmentation technique was applied solely to the training data, and actual devices were used for the testing data. Five supervised models, including Logistic Regression, Random Forest, SVM, CatBoost, and a Neural Network (MLP), have been utilized using a repeated stratified cross-validation and a separate held-out test data set. All the models have been tested for their performance using classification metrics. All models demonstrated variable performance with wide confidence intervals due to limited sample size. Support vector machines showed the highest cross-validation discrimination 0.889 ± 0.203, though uncertainty remains substantial given the small datasets (n = 41). From the feature analysis, it was found that atrial sensing amplitude, RV/LV capture threshold, output settings, and implant duration were the most important features for the prediction of high battery depletion risk. These findings suggest that changes in device parameters and implant age are associated with elevated battery depletion risk, supporting the feasibility of telemetry-driven risk stratification for device management. Full article

In recent decades, forest disturbances have increased in both frequency and intensity, driven by global warming and urbanization. Remote sensing, together with forest disturbance algorithms, offers broad opportunities for forest disturbance monitoring due to its high temporal and spatial resolution. However, operational methods capable of predicting and classifying disturbances while providing official area estimates suitable for national statistics remain scarce. The Three Indices Three Dimensions (3I3D) algorithm has proven effective in identifying forest changes and providing area estimates in Mediterranean ecosystems using Sentinel-2 imagery. Yet, while suitable for change detection, it does not distinguish among disturbance types. Here, we propose a two-step framework for forest disturbance detection and classification, tested in inland Spain for 2018. First, a binary forest change map is produced through an enhanced version of the 3I3D approach. This step incorporates Receiver Operating Characteristic (ROC) analysis to calibrate the algorithm through data-driven threshold selection, allowing adaptation to specific regional conditions. Second, detected changes are classified into four disturbance types: wildfire, clear-cut, thinning, and non-stand replacing disturbance, using Sentinel-2 spectral bands, 3I3D-derived metrics, and geometric descriptors of disturbance patches. Three machine-learning classifiers were compared: Support Vector Machine, Random Forest, and Neural Network. The detection step reached an overall accuracy of 82%, estimating that 1.43% of Spanish forests (264,900 ha) were disturbed in 2018. In the classification step, Random Forest achieved the best performance, with an overall accuracy of 72%. Of the detected disturbed area, 69% corresponded to non-stand replacing disturbances, while the remaining area was classified as thinnings (19%), wildfires (26%), and clear-cuts (55%). By integrating freely available Sentinel-2 imagery, remote sensing algorithms, and photo-interpreted reference datasets, this study provides a scalable and operational approach capable of producing annual disturbance maps that combine both detection and classification of high- and low-intensity disturbances, supporting official national-scale estimates of forest disturbance areas. Full article

This research presents a machine learning-based vibration signal acquired from aluminum grinding experiment for potential application in smart and intelligent manufacturing. The study addresses the challenges of traditional surface finishing quality inspection by integrating vibration sensing and support vector machine (SVM). A robot manipulator lab grinding experiment consist of a four-axis DOBOT Magician with a handheld cylindrical grinding tool attached on the end-effector of the DOBOT Magician. This customized lab grinding experiment was designed to perform consistent surface finishing experiment for different aluminum work coupon and time duration. Triaxial accelerometer was used to collect the vibration signal and to investigate the most relevant vibration signal direction (x, y, and z) to the surface quality prediction of the aluminum work coupon. The vibration signal was acquired via LabVIEW and NI data acquisition (DAQ) system. The vibration features were extracted and analyzed using Python programming in Google Colab. The SVM algorithm in Python (3.11 and 3.12) is used to classify surface roughness quality into coarse, medium, and fine categories based on the extracted vibration features. Vibration feature parameters such as root mean square (RMS), Peak to RMS, Skewness, and Kurtosis were also investigated to determined which feature pairs are most critical for effective surface roughness monitoring and prediction using SVM classification. The classification model achieved high accuracy across all three vibration axes (x, y, and z), with the z-axis yielding the most consistent results. The proposed system has potential applications in real-time surface quality prediction within smart manufacturing practices aligned with Industry 4.0 principles. Full article

Texture feature extraction plays a crucial role in land-use and land-cover (LULC) classification for the remotely sensed images. However, when these images are quantized to a limited number of gray levels to reduce data volume or noise, conventional texture descriptors often lose discriminative power. This study investigates singular values of the gray-level co-occurrence matrix (GLCM) as novel texture features for image classification, with local binary pattern (LBP), complete LBP (CLBP) statistics, and original GLCM features proposed by Haralick et al. for comparison. Under coarse quantization, texture descriptors of LBP and its variants, which encode micro-texture, lose detail, whereas GLCM, which encodes macro-texture, retains structural co-occurrence patterns. This study thus proposes a new feature set, namely the Singular Values of the gray-level co-occurrence matrix (SVGM), for texture discrimination. Experimental analysis indicates SVGM achieves higher class separability by preserving dominant spatial structure while suppressing noise and redundancy. Quantitative evaluation using classical SVMs with multiple kernels, quantum learning models with different kernels, and neural baselines (ANN and 1D-CNN) further shows that SVGM consistently improves classification performance. Within our tested models, quantum kernel SVMs are competitive and achieve the best results on some datasets, while classical models perform best on others. Full article

Satellite remote sensing offers a cost-effective solution for the continuous monitoring of black and odorous water bodies (BOWs). However, limitations in spatial and spectral resolution hinder the quantitative inversion of water quality parameters and the precise assessment of risk levels using satellite data alone. To address this challenge, this study proposes a synergistic approach combining satellite and Unmanned Aerial Vehicle (UAV) remote sensing to rapidly identify potentially polluted water bodies and quantitatively assess their risk levels. First, a Black and Odorous Water Index (MBOWI) was constructed based on reflectance characteristics in the visible to near-infrared bands to screen for potential black and odorous water bodies using satellite imagery. Subsequently, high-resolution multispectral UAV imagery, integrated with in situ sampling data, was employed to develop machine learning models for inverting key water quality parameters, including Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), Total Phosphorus (TP) and Ammonia Nitrogen (NH₃-N). Comparative analysis of Polynomial Regression (PR), Random Forest (RF), and Simulated Annealing-optimized Support Vector Regression (SA-SVR) revealed that RF and SA-SVR exhibited superior performance in inverting four non-optically active water quality parameters due to their robust nonlinear fitting capabilities, with the mean Adjusted Coefficient of Determination ($R_{adj}^2$) ranging from 0.57 to 0.69. Water quality classification based on the single-factor worst-case method achieved an overall accuracy of 0.70 across validation samples. Notably, for Class V (heavily polluted) water bodies, both classification accuracy and recall rate reached 0.89, demonstrating the model’s high precision in identifying high-risk waters. Finally, the proposed framework was applied to northern Zhejiang Province to assess seven potential black and odorous water bodies, successfully identifying four as high-risk and one as low-risk. This study validates satellite and UAV synergistic remote sensing for the hierarchical risk management of black and odorous water bodies. Full article

Accurate GNSS timing is fundamental to Power Time Synchronization Systems (PTSS). However, conventional substation infrastructures remain vulnerable to sophisticated spoofing attacks. In this research, a sensing-assisted array antenna-based spoofing mitigation method is proposed. The proposed architecture operates at the signal front-end and incorporates a dedicated spoofing sensing path to estimate the Direction-of-Arrival (DoA) of malicious signals, enabling adaptive null steering while preserving authentic satellite reception. To provide reliable spatial reference for DoA estimation, a unified high-precision attitude determination method is developed for compact 10 cm-scale array antennas under single-frequency and environmental error conditions. The method integrates the Constrained Least-squares AMBiguity Decorrelation Adjustment (C-LAMBDA)-based constrained ambiguity resolution, redundant antenna element-based vertical accuracy enhancement, and iterative refinement to mitigate centimeter-level environmental biases. Semi-simulated experiments demonstrate that the proposed method achieves baseline vector Root Mean Square Errors (RMSE) below 5 mm in horizontal components and approximately 10 mm in vertical components. The resulting attitude accuracies reach 2° in heading, 6° in pitch, and 4° in roll, while eliminating over 80% of systematic environmental phase errors with an average convergence within 6 iterations. These results satisfy the spatial accuracy requirements for effective spoofing suppression and front-end signal purification. Consequently, a robust technical approach is established for enhancing the anti-spoofing capabilities of PTSS without modifying existing infrastructure. Full article

Greenhouse cultivation enables year-round vegetable production and high yields through precise environmental regulation. Yet, the same stable microclimate that promotes crop growth also favors the proliferation of pests and diseases. This review synthesizes current knowledge on how greenhouse climate variables govern pest and disease epidemiology in tomato, cucumber, and sweet pepper. Only greenhouse-based studies were included to ensure direct relevance to protected horticulture. Microclimatic stability determines infection probability, vector behavior, and host susceptibility. Warm, humid conditions promote fungal and bacterial pathogens, whereas dry, high vapor pressure deficit (VPD) environments favor mites and thrips and enhance virus transmission. Species-specific traits further modulate vulnerability. Tomato is dominated by virus–bacterium complexes and foliar/stem fungal diseases, cucumber by phytopathogenic fungi favored by high relative humidity (RH) and soilborne pathogens, and sweet pepper by virus–vector systems and long-cycle fungal infections. Temperature exerts the strongest influence, while RH and VPD jointly regulate surface moisture and vector activity. Light intensity and spectral composition also affect pest orientation and fungal sporulation. Integrating environmental sensing, biological control, and adaptive climate regulation offers a pathway toward preventive, climate-smart Integrated Pest Management (IPM). The review highlights the emerging role of climate-informed decision-support systems (DSSs) and the need for greenhouse-specific datasets to improve pest and disease forecasting. Full article

High-spatiotemporal-resolution precipitation data are essential for studies on regional hydrology, meteorology, and ecological conservation. Because the Qaidam Basin is a data-scarce region with a few ground stations and coarse-resolution remote sensing products, its utility in regional research is constrained. Therefore, high-resolution precipitation data are urgently needed. Here, longitude, latitude, the normalized difference vegetation index (NDVI), the digital elevation model (DEM), daytime and nighttime land surface temperature, slope, and aspect were selected as environmental variables. Four machine learning methods, Artificial Neural Network (ANN), Cubist, Random Forest (RF), and Support Vector Machine (SVM), were used to downscale Tropical Rainfall Measuring Mission (TRMM) precipitation data from 25 to 1 km in the Qaidam Basin and validated using ground observation stations. For annual downscaling, the accuracy ranked as Cubist > ANN > RF > SVM, and residual correction further improved performance. The Cubist model produced the best results, generating finer spatial patterns and reducing outliers in both annual and monthly products. Longitude, latitude, the DEM, and the NDVI were important contributors to the Cubist model. The resulting high-resolution dataset provides valuable support for hydrological and climate change research in the Qaidam Basin. Full article

Electric-propulsion (EP) orbit raising from geosynchronous transfer orbit (GTO) to geostationary Earth orbit (GEO) requires long-duration, continuously steered low thrust, for which small pointing deviations may accumulate over time, and practical execution is constrained by spacecraft attitude and momentum management. This study develops a cross-layer command-to-execution framework that couples mission-level thrust-command generation with smooth trajectory planning of an EP vectoring arm. At the orbit layer, an engineering-oriented mission-level transfer model with dominant J₂ secular correction is used to construct a time-tagged sequence of thrust magnitude and direction commands for the GTO–GEO transfer. At the execution layer, a 4-DOF revolute arm is modeled using Denavit–Hartenberg kinematics, and the desired thrust directions are mapped to feasible joint trajectories through a direction-only inverse-kinematics formulation cast as a constrained nonlinear least-squares problem with cross/dot residuals, smoothness regularization, and warm-start propagation. In numerical simulation, the GTO–GEO transfer is completed in approximately 278 days with Δv ≈ 3665 m/s, corresponding to a propellant consumption of 175 kg (spacecraft mass from 1800 kg to 1625 kg). The planned joint trajectories remain smooth over the full horizon, with maximum inter-sample variations of 1.84° and 1.04° for the major and minor motion groups, respectively. The numerical geometric thrust-direction tracking error in the kinematic mapping remains at the millidegree level, with a mean of 7.39 × 10⁻⁴° and a P95 of 0.00101°. The results demonstrate that the proposed cross-layer interface can generate executable, low-bandwidth joint commands while preserving high geometric consistency with the desired thrust directions in the numerical kinematic mapping sense, thereby providing a practical basis for implementation-oriented studies of EP orbit transfer with vectoring manipulators. Full article

The Vietnam Mekong River Delta (VMRD) is a climate-sensitive region characterized by diverse ecosystems, including extensive mangrove forests that protect against sea-level rise and contribute to global carbon sequestration. Accurate land cover classification in the VMRD is essential but remains challenging due to complex landscapes and dynamic environmental conditions. The primary objective of this study is to propose a semi-supervised deep learning framework that integrates satellite indices with multi-temporal remote sensing data to address key classification challenges, particularly in situations where ground truth data is limited, as compared to unsupervised and supervised machine learning methods. Our comparative analysis across different sample sizes (500 to 6000 ground-truth data points) reveals critical insights into model performance and scalability. Supervised models, including Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN), demonstrated strong performance when sufficient labeled data were available, with CNN achieving the highest accuracy (0.97 at 6000 samples). However, at minimal sample sizes (500 sample points), these supervised approaches exhibited substantial limitations, with accuracies dropping dramatically (RF: 0.75, SVM: 0.80, CNN: 0.81). Supervised models also showed overfitting tendencies compared to official land cover statistics. In contrast, the semi-supervised approach (SoC4SS-FGVC) achieves remarkably high performance at small sample sizes (0.92 accuracy with 500 sample points), demonstrating strength under minimal data availability. The framework also showed improved capability in distinguishing spectrally similar land-cover classes and detecting environmentally sensitive types such as mangrove forests. Cross-validation with official statistics confirmed the semi-supervised model’s superior effectiveness in delineating paddy rice fields and its resistance to overfitting. The performance analysis demonstrates that SoC4SS-FGVC provides a practical and cost-effective solution for land cover mapping, particularly in regions where extensive ground-truth data collection is prohibitively expensive or logistically challenging. Full article

Remote sensing object detection is severely hindered by background clutter and uneven object spatial distribution, limiting the performance of traditional algorithms and the original RT-DETR. To address these issues, this paper proposes an improved RT-DETR-based algorithm, CB-DETR. First, a symmetry-guided Density-Adaptive Attention (DAA) module is designed to tackle insufficient intra-scale feature interaction and poor adaptability to uneven density regions in RT-DETR. Centered on a density estimation network, it predicts target density, generates normalized weights via temperature scaling and softmax, and dynamically adjusts receptive fields through a multi-branch structure to symmetrically adapt to high- and low-density regions, outperforming RT-DETR’s fixed receptive field design. Second, a cross-attention-fused Posterior Dynamic Query Decoder (PDQD) is constructed to overcome fixed query interaction and weak small/occluded object detection in the original decoder. A dynamic query update mechanism optimizes vectors via multi-round iterations, breaking fixed-layer limitations and mining detailed features in complex scenarios, thus improving small/occluded target detection accuracy. Comparative experiments on RSOD, DIOR, and DOTA datasets show that CB-DETR outperforms the original RT-DETR comprehensively: ${\mathrm{mAP}}_{50}$/${\mathrm{mAP}}_{50:95}$ improve by 2.8%/2.1% and Precision (P)/Recall (R) by 4%/2.4% on RSOD; ${\mathrm{mAP}}_{50}$ improves by 1.3% on DIOR and 3% on DOTA. All core metrics surpass the original model and mainstream improved algorithms, verifying the effectiveness and innovation of the proposed improvements. Full article

In wetland ecological monitoring, accurate acquisition of water bodies is particularly crucial, especially for hydrological monitoring and eutrophication control. Water bodies can be clearly delineated by using optical remote sensors. Optical sensors can clearly delineate water boundaries and features when extracting water bodies via remote sensing. Meanwhile, synthetic aperture radar (SAR), with its unique microwave capabilities, can easily penetrate vegetation and operate regardless of weather conditions, enabling all-weather monitoring. Each sensor type exhibits distinct advantages in water body monitoring and research. This study focuses on Caohai Wetland in Guizhou Province, utilizing data from the optical satellite Zhuhai-1 (launched by China in 2017) and the radar satellite RadarSat-2 (launched by Canada) at identical resolutions during the same period. Five supervised classification methods were applied to extract water bodies using optical imagery within the wetland area, with results evaluated against SAR data. Results indicate that the optimal water body extraction methods based on optical and SAR data are Random Forest Classification and Support Vector Machine classification, respectively, achieving an overall accuracy of 0.896 and 0.940, with Kappa coefficients of 0.791 and 0.879. The water area extracted using SAR was significantly larger than that based on optical data, thereby identifying areas within Caohai Wetland that were not fully submerged in vegetation during this period. This study holds significant implications for accurate water body extraction and analysis benefited an improved monitoring and conserving the wetland environment. Full article