Search Results (42)

Satellite-aided multi-unmanned aerial vehicle (UAV) collaborative localization systems combine the extensive coverage of satellites with the flexibility of UAVs, offering new opportunities for locating highly dynamic emitters across large areas. However, the openness of space-air communication links and the increasing complexity of cybersecurity threats make these systems vulnerable to false data injection attacks. Most existing detection approaches focus only on temporal dependencies in time-frequency features and lack diagnostic mechanisms for identifying malicious UAVs, which limits their ability to effectively detect and mitigate such attacks. To address this issue, this paper proposes an intelligent collaborative localization framework that safeguards localization integrity by identifying and correcting false ranging information from malicious UAVs. The framework captures spatio-temporal correlations in multidimensional ranging sequences through a graph attention network (GAT) coupled with a time-attention-based variational autoencoder (VAE) to detect anomalies through anomalous distribution patterns. Malicious UAVs are further diagnosed through an anomaly scoring mechanism based on statistical analysis and reconstruction errors, while detected anomalies are corrected via a K-nearest neighbor-based (KNN) algorithm to enhance localization performance. Simulation results show that the proposed model improves localization accuracy by 25.9%, demonstrating the effectiveness of spatial–temporal feature extraction in securing collaborative localization. Full article

To address the insufficient quantitative understanding of vegetation driving mechanisms across spatio-temporal scales, this study integrated multi-source data and machine learning methods to simulate and analyze Normalized Difference Vegetation Index (NDVI) changes in the Beijing–Tianjin–Hebei (BTH) region over the past two decades. Using the SHapley Additive exPlanations (SHAP) method, we identified the most important predictors of climate and human activities in the XGBoost model and quantified their spatial contributions. We further analyzed the spatio-temporal variation of the main predictors across different land use types The main findings were as follows: (1) The XGBoost model achieved excellent performance (R² > 0.96, MEA < 0.02, RMSE < 0.027) on the datasets from 2000 to 2020, outperforming random forest (RF), support vector machines (SVM), and K-nearest neighbors (KNN) in prediction accuracy. (2) Vegetation showed an overall improving trend, with areas exhibiting significant improvement accounting for 47.96% of the total region. Precipitation, temperature, and human activities were identified as the most significant predictors of NDVI. Their relative importance varied over time, and NDVI responses to these factors exhibited clear spatial heterogeneity. (3) Primary predictors differed by land use type: NDVI in cropland and grassland was mainly driven by precipitation, forest NDVI by temperature, and urban/built-up areas by human activities. This study developed an analytical framework integrating nonlinearity and spatial heterogeneity, achieving a quantitative “overall-categorical” analysis of the important predictors behind NDVI changes. The approach provided a novel methodological reference for attributing vegetation dynamics. The findings contributed to the implementation of classified regulation in the BTH region, promoting the transition of human activities toward ecological restoration. Full article

Background: Eye-tracking technology enables the objective quantification of oculomotor behavior, providing key insights into visuocognitive performance. This study presents a comparative analysis of visual attention patterns between rhythmic gymnasts and school-aged students using an optical eye-tracking system combined with machine learning algorithms. Methods: Eye movement data were recorded during controlled visual tasks using the DIVE system (sampling rate: 120 Hz). Spatiotemporal metrics—including fixation duration, saccadic amplitude, and gaze entropy—were extracted and used as input features for supervised models: Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Decision Tree (CART), Random Forest, XGBoost, and a one-dimensional Convolutional Neural Network (1D-CNN). Data were divided according to a hold-out scheme (70/30) and evaluated using accuracy, F1-macro score, and Receiver Operating Characteristic (ROC) curves. Results: XGBoost achieved the best performance (accuracy = 94.6%; F1-macro = 0.945), followed by Random Forest (accuracy = 94.0%; F1-macro = 0.937). The neural network showed intermediate performance (accuracy = 89.3%; F1-macro = 0.888), whereas SVM and k-NN exhibited lower values. Gymnasts demonstrated more stable and goal-directed gaze patterns than students, reflecting greater efficiency in visuomotor control. Conclusions: Integrating eye-tracking with artificial intelligence provides a robust framework for the quantitative assessment of visuocognitive performance. Ensemble algorithms demonstrated high discriminative power, while neural networks require further optimization. This approach shows promising applications in sports science, cognitive diagnostics, and the development of adaptive human–machine interfaces. Full article

Aboveground biomass (AGB) is a key indicator of forest structure and carbon sequestration, yet its dynamics under concurrent anthropogenic disturbances remain poorly understood. This study investigates the spatiotemporal dynamics and driving mechanisms of AGB in the Jianfengling tropical lowland rainforest (JFLTLR) within Hainan Tropical Rainforest National Park (NRHTR) from 2015 to 2023. Six machine learning models—Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Decision Tree (DT), and Random Forest (RF)—were evaluated, with RF achieving the highest accuracy (R² = 0.83). Therefore, RF was employed to generate high-resolution annual AGB maps based on Sentinel-1/2 data fusion, field surveys, socio-economic indicators, and topographic variables. Human pressure was quantified using the Human Influence Index (HII). Threshold analysis revealed a critical breakpoint at ΔHII ≈ 0.1712: below this level, AGB remained relatively stable, whereas beyond it, biomass declined sharply (≈−2.65 mg·ha⁻¹ per 0.01 ΔHII). Partial least squares structural equation modeling (PLS-SEM) identified plantation forests as the dominant negative driver, while GDP (−0.91) and road (−1.04) exerted strong indirect effects through HII, peaking in 2019 before weakening under ecological restoration policies. Spatially, biomass remained resilient within central core zones but declined in peripheral regions associated with road expansion. Temporally, AGB exhibited a trajectory of decline, partial recovery, and renewed loss, resulting in a net reduction of ≈ 0.0393 × 10⁶ mg. These findings underscore the urgent need for a “core stabilization–peripheral containment” strategy integrating disturbance early-warning systems, transportation planning that minimizes impacts on high-AGB corridors, and the strengthening of ecological corridors to maintain carbon-sink capacity and guide differentiated rainforest conservation. Full article

Despite advances in diagnostics, the objective and repeatable assessment of patients with neurological deficits (e.g., stroke) remains a major challenge. Modern methods based on artificial intelligence (AI) are of interest to researchers and clinicians in this area. This study presents a comparative analysis of different AI approaches used to analyze gait of stroke patients using a retrospective dataset of 120 individuals. The main objective is to evaluate the effectiveness, accuracy, and clinical relevance of machine learning (ML) and deep learning (DL) models in identifying gait abnormalities and predicting rehabilitation outcomes. Multiple AI techniques—including support vector machines (SVM), random forests (RF), k-nearest neighbors (k-NN), and convolutional neural networks (CNN)—were trained and tested on time-series gait data with spatiotemporal parameters. Performance metrics such as accuracy, precision, recall, and area under the curve (AUC) were used to compare model results. Initial results indicate that DL models, particularly CNNs, outperform traditional ML methods in capturing complex gait patterns and providing reliable classification. However, simpler models showed advantages in interpretability and computational efficiency. This study highlights the potential and shortcomings of AI-based gait analysis tools in supporting clinical decision-making and planning personalized stroke rehabilitation. Full article

Elastic optical networks (EONs) must allocate resources dynamically to accommodate heterogeneous, high-bandwidth demands. However, the continuous setup and teardown of connections with different bit rates can fragment the spectrum and lead to blocking. The blocking predictors enable proactive defragmentation and resource reallocation within network controllers. In this paper, we propose two novel deep learning models (based on CNN–BiLSTM and CNN–LSTM) to predict blocking in EONs by combining spatial feature extraction from spectrum snapshots using 2D convolutional layers with temporal sequence modeling. This hybrid spatio-temporal design learns how local fragmentation patterns evolve over time, allowing it to detect impending blocking scenarios more accurately than conventional methods. We evaluate our model on the simulated NSFNET topology and compare it against multiple baselines, namely 1D CNN, 2D CNN, k-nearest neighbors (KNN), and support vector machines (SVMs). The results show that the proposed CNN–BiLSTM/LSTM models consistently achieve higher performance. The CNN–BiLSTM model achieved the highest accuracy in blocking prediction, while the CNN–LSTM model shows slightly lower accuracy; however, it has much lower complexity and a faster learning time. Full article

This study proposes a feature extraction scheme that fuses accelerometric (ACC) and electromyographic (EMG) data to improve shoulder movement identification in individuals with transhumeral amputation, in whom the clinical need for intuitive control strategies enabling reliable activation of full-arm prostheses is underinvestigated. A novel spatiotemporal warping feature extraction architecture was employed to realize EMG and ACC information fusion at the feature level. EMG and ACC data were collected from six participants with intact limbs and four participants with transhumeral amputation using an NI USB-6009 device at 1000 Hz to support the proposed feature extraction scheme. For each participant, a leave-one-trial-out (LOTO) training and testing approach was used for developing pattern recognition models for both the intact-limb (IL) and amputee (AMP) groups. The analysis revealed that the introduction of ACC information has a positive impact when using windows of length (WLs) lower than 150 ms. A linear discriminant analysis (LDA) classifier was able to exceed the accuracy of 90% in each WL condition and for each group. Similar results were observed for an extreme learning machine (ELM), whereas k-nearest neighbors (kNN) and an autonomous learning multi-model classifier showed a mean accuracy of less than 87% for both IL and AMP groups at different WLs, guaranteeing applicability over a large set of shallow pattern-recognition models that can be used in real scenarios. The present work lays the groundwork for future studies involving real-time validation of the proposed methodology on a larger population, acknowledging the current limitation of offline analysis. Full article

Automatic Facial Expression Recognition (AFER) is a key component of affective computing, enabling machines to recognize and interpret human emotions across various applications such as human–computer interaction, healthcare, entertainment, and social robotics. Dynamic AFER systems, which exploit image sequences, can capture the temporal evolution of facial expressions but often suffer from high computational costs, limiting their suitability for real-time use. In this paper, we propose an efficient dynamic AFER approach based on a novel spatio-temporal representation. Facial landmarks are extracted, and all possible Euclidean distances are computed to model the spatial structure. To capture temporal variations, three statistical metrics are applied to each distance sequence. A feature selection stage based on the Extremely Randomized Trees (ExtRa-Trees) algorithm is then performed to reduce dimensionality and enhance classification performance. Finally, the emotions are classified using a linear multi-class Support Vector Machine (SVM) and compared against the k-Nearest Neighbors (k-NN) method. The proposed approach is evaluated on three benchmark datasets: CK+, MUG, and MMI, achieving recognition rates of 94.65%, 93.98%, and 75.59%, respectively. Our results demonstrate that the proposed method achieves a strong balance between accuracy and computational efficiency, making it well-suited for real-time facial expression recognition applications. Full article

Reservoir-induced landslides threaten infrastructures and downstream communities, making continuous deformation monitoring vital. Time-series InSAR, notably the SBAS algorithm, provides high-precision surface-displacement mapping but suffers from voids due to layover/shadow effects and temporal decorrelation. Existing deep-learning approaches often operate on fixed-size patches or ignore irregular spatio-temporal dependencies, limiting their ability to recover missing pixels. With this objective, a hybrid spatio-temporal Graph Attention (ST-GAT) framework was developed and trained on SBAS-InSAR values using 24 influential features. A unified spatio-temporal graph is constructed, where each node represents a pixel at a specific acquisition time. The nodes are connected via inverse distance spatial edges to their K-nearest neighbors, and they have bidirectional temporal edges to themselves in adjacent acquisitions. The two spatial GAT layers capture terrain-driven influences, while the two temporal GAT layers model annual deformation trends. A compact MLP with per-map bias converts the fused node embeddings into normalized LOS estimates. The SBAS-InSAR results reveal LOS deformation, with 48% of missing pixels and 20% located near the Dasu dam. ST D-GAT reconstructed fully continuous spatio-temporal displacement fields, filling voids at critical sites. The model was validated and achieved an overall R² (0.907), ρ (0.947), per-map R² ≥ 0.807 with RMSE ≤ 9.99, and a ROC-AUC of 0.91. It also outperformed the six compared baseline models (IDW, KNN, RF, XGBoost, MLP, simple-NN) in both RMSE and R². By combining observed LOS values with 24 covariates in the proposed model, it delivers physically consistent gap-filling and enables continuous, high-resolution landslide monitoring in radar-challenged mountainous terrain. Full article

In recent years, Academia and industry have conducted extensive and in-depth research on bearing-fault-diagnosis technology. However, the current modeling of time–space coupling characteristics in rolling bearing fault diagnosis remains inadequate, and the integration of multi-modal correlations requires further improvement. To address these challenges, this paper proposes a joint diagnosis framework integrating graph convolutional networks (GCNs) with attention-enhanced bidirectional gated recurrent units (BiGRUs). The proposed framework first constructs an improved K-nearest neighbor-based spatio-temporal graph to enhance multidimensional spatial–temporal feature modeling through GCN-based spatial feature extraction. Subsequently, we design an end-to-end spatio-temporal joint learning architecture by implementing a global attention-enhanced BiGRU temporal modeling module. This architecture achieves the deep fusion of spatio-temporal features through the graph-structural transformation of vibration signals and a feature cascading strategy, thereby improving overall model performance. The experiment demonstrated a classification accuracy of 97.08% on three public datasets including CWRU, verifying that this method decouples bearing signals through dynamic spatial topological modeling, effectively combines multi-scale spatiotemporal features for representation, and accurately captures the impact characteristics of bearing faults. Full article

With the rapid progress of intelligent vehicle technology, the accurate recognition of road surface types and conditions has emerged as a crucial technology for improving the safety and comfort levels in autonomous driving. This paper puts forward a multi-feature fusion approach for road surface identification. Relying on a 24 GHz millimeter-wave radar, statistical features are combined with wavelet transform techniques. This combination enables the efficient classification of diverse road surface types and conditions. Firstly, the discriminability of radar echo signals corresponding to different road surface types is verified via statistical analysis. During this process, six-dimensional statistical features that display remarkable differences are extracted. Subsequently, a novel radar data reconstruction approach is presented. This method involves fitting discrete echo signals into coordinate curves. Then, discrete wavelet transform is utilized to extract both low-frequency and high-frequency features, thereby strengthening the spatio-temporal correlation of the signals. The low-frequency information serves to capture general characteristics, whereas the high-frequency information reflects detailed features. The statistical features and wavelet transform features are fused at the feature level, culminating in the formation of a 56-dimensional feature vector. Four machine learning models, namely the Wide Neural Network (WNN), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Kernel methods, are employed as classifiers for both training and testing purposes. Experiments were executed with 8865 samples obtained from a real-vehicle platform. These samples comprehensively represented 12 typical road surface types and conditions. The experimental outcomes clearly indicate that the proposed method is capable of attaining a road surface type identification accuracy as high as 94.2%. As a result, it furnishes an efficient and cost-efficient road perception solution for intelligent driving systems. This research validates the potential application of millimeter-wave radar in intricate road environments and offers both theoretical underpinning and practical support for the advancement of autonomous driving technology. Full article

Axial piston pumps play a vital role in fluid power systems, which are widely employed in diverse fields such as aerospace, ocean engineering, and rail transit. It is essential to accurately diagnose faults in these pumps since their reliable operation hinges on it. A graph isomorphic network with a spatio-temporal attention mechanism (GIN-ST) is proposed in this paper for fault diagnosis of hydraulic axial piston pumps; GIN-AM addresses the problem of traditional intelligent fault diagnosis methods being limited to nonlinear mapping and transformation in Euclidean space. Initially, the weighted graphs are constructed from a univariate time series through K-nearest neighbor graph methods. Subsequently, a spatio-temporal attention-based module used to learn the graph representation of piston pump faults is presented, where a novel READOUT function and Transformer encoder provide spatial and temporal interpretability, respectively. Finally, the proposed (GIN-ST) model is compared against other intelligent fault diagnosis methods, and the superiority of the proposed method is proven. Full article

Sleep posture detection is a potentially important component of sleep quality assessment and health monitoring. Accurate identification of sleep postures can offer valuable insights into an individual’s sleep patterns, comfort levels, and potential health risks. For example, improper sleep postures may lead to musculoskeletal issues, respiratory disturbances, and even worsen conditions like sleep apnea. Additionally, for long-term bedridden patients, continuous monitoring of sleep postures is essential to prevent pressure ulcers and other complications. Traditional methods for sleep posture detection have several limitations: wearable sensors can disrupt natural sleep and cause discomfort, camera-based systems raise privacy concerns and are sensitive to environmental conditions, and pressure-sensing mats are often complex and costly. To address these issues, we have developed a low-cost non-contact sleeping posture detection system. Our system features eight optimally distributed triaxial accelerometers, providing a comfortable and non-contact front-end data acquisition unit. For sleep posture classification, we employ an improved density peak clustering algorithm that incorporates the K-nearest neighbor mechanism. Additionally, we have constructed a Parallel Convolutional Spatiotemporal Network (PCSN) by integrating Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (Bi-LSTM) modules. Experimental results demonstrate that the PCSN can accurately distinguish six sleep postures: prone, supine, left log, left fetus, right log, and right fetus. The average accuracy is 98.42%, outperforming most state-of-the-art deep learning models. The PCSN achieves the highest scores across all metrics: 98.64% precision, 98.18% recall, and 98.10% F1 score. The proposed system shows considerable promise in various applications, including sleep studies and the prevention of diseases like pressure ulcers and sleep apnea. Full article

This study proposes a mixed-integer programming-based hierarchical collaborative optimization (MIP-HCO) model to optimize the scheduling and execution of emergency launch missions, ensuring rapid response and performance maximization under constrained time and resources. The key innovation lies in integrating k-Nearest Neighbor (KNN) with Branch and Bound (B&B) to enhance computational efficiency and global optimality. The first layer constructs a spatiotemporal optimization model, considering launch sites, storage proximity, and process duration. The B&B algorithm solves mission scheduling, while a dynamic adjustment strategy optimizes launch vehicle reutilization. The second layer refines mission selection based on contribution assessment and re-optimizes scheduling using integer programming. KNN classification approximates scheduling quality, reducing B&B complexity and accelerating convergence. Results from simulation data and experimental simulations confirm that the KNN + B&B hybrid strategy optimizes scheduling efficiency, enabling launch systems to respond swiftly under emergencies while maximizing mission effectiveness. Full article

The uncontrolled expansion of mining activities has caused severe environmental impacts in semi-arid regions, endangering fragile ecosystems and water resources. This study aimed to propose a decision-making model to identify land use and land cover changes in the semi-arid region of Pernambuco, Brazil, caused by mining through a spatiotemporal analysis using high-resolution images from the PlanetScope satellite constellation. The methodology consisted of monitoring and evaluating environmental impacts using the k-Nearest Neighbors (kNN) algorithm, spectral indices (Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)), and hydrological data, covering the period from 2018 to 2023. As a result, a 3.28% reduction in vegetated areas and a 6.62% increase in urban areas were identified over five years, suggesting landscape transformation, possibly influenced by the expansion of mining and development activities. The application of kNN yielded an Overall Accuracy (OA) greater than 99% and a Kappa index of 0.98, demonstrating the effectiveness of the adopted methodology. However, challenges were encountered in distinguishing between constructions and bare soil, with the Jeffries–Matusita distance (JMD) analysis indicating a value below 0.34, while the similarity between water and vegetation highlights the need for more comprehensive training data. The results indicated that between 2018 and 2023, there was a marked degradation of vegetation and a significant increase in built-up areas, especially near water bodies. This trend reflects the intense human intervention in the region and reinforces the need for public policies aimed at mitigating these impacts, as well as promoting environmental recovery in the affected areas. This approach proves the potential of remote sensing and machine learning techniques to effectively monitor environmental changes, reinforcing strategies for sustainable management in mining areas. Full article