Saved Queries

Accurate field-scale surface soil moisture (SSM) measures are important for agricultural water management. Conventional satellite SSM products remain too coarse for within-field applications. Here, we developed sequence-aware deep learning models for growing-season SSM estimation by fusing data from Sentinel-1 C-band SAR, harmonized Landsat-8/Sentinel-2 (HLS), and local ancillary datasets. We assembled a multi-source dataset on Sentinel-1 overpass time for 2016–2024 (May–September), yielding 1469 samples and 65 features per sample, including SAR and optical features, meteorological data, soil texture and bulk density, topography, crop labels, irrigation-likelihood flag, and irregular-time-step encoding. We compared long short-term memory (LSTM) and temporal convolutional neural network (TCN) architectures together with attention-augmented variants, including feature attention (FA), temporal attention (TA), and the combined feature–temporal attention (FTA). Models were trained and tested on seven years of data and were validated based on a temporal generalization using combined data of a wet year (2016) and a dry year (2023). The best model, FTA-TCN, achieved R² = 0.851, RMSE = 0.024 m³.m⁻³, and MAE = 0.020 m³.m⁻³ on the withheld validation years, outperforming the base LSTM (R² = 0.422; RMSE = 0.053 m³.m⁻³; MAE = 0.043 m³.m⁻³) and the base TCN (R² = 0.746; RMSE = 0.034 m³.m⁻³; MAE = 0.022 m³.m⁻³). Shapley additive explanations (SHAP) analysis indicated that antecedent precipitation and short-term rainfall accumulations were dominant forcings, while soil texture, elevation, incidence angle, and vegetation indices modulated SSM variability. Satellite-derived features accounted for ~28.5% of aggregated SHAP importance. Overall, the results show that dual-attention temporal convolution can capture field-scale SSM dynamics across wet and dry seasons when satellite signals are coupled with local soil-meteorological-management context. Full article

(This article belongs to the Special Issue Automated Mapping and Monitoring of Soil Key Components and Functions Using Satellite Imagery and Artificial Intelligence Learning)

►▼ Show Figures

Figure 1

20 pages, 318 KB

Open AccessArticle

Artificial Intelligence Adoption, Internet Penetration, and Subjective Well-Being in the GCC Region: A Panel ARDL Analysis

by Mohamed Sharif Bashir, Awadelkarim Elamin Altahir Ahmed, Ehab Ebrahim Mohamed Ebrahim and Mohamed Abdelmohsen

Economies 2026, 14(7), 258; https://doi.org/10.3390/economies14070258 (registering DOI) - 5 Jul 2026

Abstract

This paper examines the long-run relationship between subjective well-being and digital transformation in the six Gulf Cooperation Council (GCC) countries—Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates—over the period 2011–2025 using a balanced country-year panel dataset. Subjective well-being is measured by the national average Cantril Ladder score from the Gallup World Poll as reported in the World Happiness Report. Explanatory variables include a binary AI Readiness Period Indicator (AI) distinguishing the pre-AI-readiness phase (2011–2018, AI = 0) from the post-AI-readiness phase (2019–2025, AI = 1), anchored by the Oxford Insights Government AI Readiness Index, Internet penetration from the International Telecommunication Union (ITU), and real GDP per capita. After accounting for cross-sectional dependence and non-stationarity, the analysis employs a panel autoregressive distributed lag (ARDL) framework estimated via the Pooled Mean Group (PMG) approach. The results indicate the existence of a stable long-run cointegrating relationship among the variables. The baseline PMG estimates suggest positive long-run associations between GDP per capita and the AI Readiness Period Indicator with subjective well-being, and a negative association between Internet penetration and well-being in a high-connectivity regional context. Short-run effects are generally weak, while the error-correction term confirms adjustment toward the long-run equilibrium. Robustness checks based on alternative estimators confirm the positive long-run effect of income, while the estimated effects of the AI Readiness Period Indicator and Internet penetration show sensitivity in sign and significance across specifications and should therefore be interpreted as indicative rather than definitive. Overall, the findings suggest that digital transformation is not a homogeneous driver of subjective well-being. Instead, the AI Readiness Period Indicator and Internet penetration operate through distinct mechanisms, with potentially different welfare implications in highly connected rentier-state economies. Full article

25 pages, 20683 KB

Open AccessArticle

Frequency–Geometry-Guided Network for Depth Map Super-Resolution

by Zhiqiang Feng and Chong Zhang

Sensors 2026, 26(13), 4282; https://doi.org/10.3390/s26134282 (registering DOI) - 5 Jul 2026

Abstract

Depth super-resolution reconstructs high-resolution (HR) depth maps from low-resolution (LR) inputs with the aid of HR RGB guidance, but RGB edges often do not coincide with true depth discontinuities, causing texture copying and degraded geometric consistency. To address this problem, we propose Frequency–Geometry-Guided Network (FGGNet), a spatial–frequency fusion framework for RGB-guided depth map super-resolution. FGGNet introduces Multi-branch RGB-guided Convolution (MRGConv) to enhance RGB structural representations, a Geometry Prior-guided Fusion Module (GPFM) to filter geometrically inconsistent RGB responses using depth-derived priors, and radial complex spectral loss (RCSL) to emphasize boundary-related high-frequency components in the complex spectral domain. Experiments on NYU v2, Middlebury, Lu, and RGB-D-D show that FGGNet achieves competitive or superior reconstruction accuracy under synthetic and real-world degradation settings. Under the ×16 setting, FGGNet reduces RMSE by 13.7%, 22.8%, 18.5%, and 11.4% on the four datasets, respectively, compared with the average RMSE of five representative state-of-the-art methods. These results validate the effectiveness of combining geometric prior filtering with frequency-domain supervision for reliable depth reconstruction. Full article

(This article belongs to the Special Issue Deep Learning Technology and Image Sensing: 3rd Edition)

►▼ Show Figures

Figure 1

27 pages, 11400 KB

Open AccessArticle

Characterizing Short-Duration Summer Rainstorms in Nanjing, China, Using Multi-Source Remote Sensing and Explainable AI

by Yiding Wang, Ningxin Yong, Siyu Zhu and Yang Hong

Remote Sens. 2026, 18(13), 2212; https://doi.org/10.3390/rs18132212 (registering DOI) - 5 Jul 2026

Abstract

With global warming and rapid urbanization, short-duration summer rainstorms are becoming more intense and localized, posing growing challenges to urban flood resilience. However, their spatiotemporal characteristics, vertical structures, and environmental drivers remain poorly understood. Here, we combine multi-source remote sensing datasets and China’s new-generation satellite-borne dual-frequency precipitation radar observations to investigate summer rainstorms in Nanjing, China, during 2017–2024. Results reveal pronounced spatiotemporal heterogeneity, with higher rainfall intensities concentrated over urban and adjacent areas. During the study period, rainstorm intensity and duration increased by 7.44% and 38.63%, respectively, while the affected area decreased by 8.18%, indicating a transition toward more localized yet more intense rainfall events. Environmental analyses suggest that large-scale thermodynamic conditions and regional topographic forcing provide a favorable background for convection development, while local urban thermal effects may further modulate rainfall enhancement. Three-dimensional radar detection of an illustrative rainstorm event indicates an inverted-cone vertical structure, suggesting a mixed convective-stratiform precipitation structure involving both warm-rain and ice-phase processes. An Explainable Bayesian-Optimized XGBoost (EBOX) model further identifies near-surface air temperature and specific humidity as the primary environmental factors associated with rainstorm occurrence and development. Overall, this study highlights the value of integrating satellite remote sensing with explainable artificial intelligence to improve understanding of urban extreme rainfall and provide new insights into how climate change, topography, and urbanization jointly shape precipitation extremes in rapidly urbanizing monsoon regions. Full article

(This article belongs to the Special Issue Monitoring and Early Warning for Heavy Precipitation, Flash Floods and Waterlogging Disasters Using Remote Sensing)

►▼ Show Figures

Figure 1

29 pages, 4564 KB

Open AccessArticle

Robust Real-Time DOA Estimation for Outdoor Vehicle Acoustic Sources Using Dynamic-Pruning GCC-PHAT and Adaptive Forgetting Factor OPAST-MUSIC

by Xueheng Hu, Jianxin Zhang, Hong Ma, Jiaqing Shi and Yanyan Du

Sensors 2026, 26(13), 4281; https://doi.org/10.3390/s26134281 (registering DOI) - 5 Jul 2026

Abstract

In outdoor road environments, vehicle acoustic source direction-of-arrival (DOA) estimation is challenged by a low signal-to-noise ratio (SNR), dynamic-noise interference, and stringent real-time requirements. Under such conditions, conventional methods often struggle to achieve an effective balance among estimation accuracy, computational efficiency, and robustness against noise. To address this issue, this paper proposes a DOA estimation method that integrates a dynamic-pruning strategy with an adaptive subspace tracking mechanism. The proposed approach reduces computational complexity while enhancing algorithmic stability in complex and time-varying noise environments. Extensive experiments conducted on simulated data, the LOCATA dataset, and real-world outdoor road measurements demonstrate that the proposed method achieves comparable or superior DOA accuracy relative to conventional approaches, while significantly reducing computational cost. Furthermore, it exhibits stronger stability and robustness in real-world static and dynamic vehicle localization scenarios. Our method achieves a more favorable trade-off among multiple performance metrics. The results show that this method has good engineering application potential in complex outdoor environments, and can provide a practical solution for real-world vehicle monitoring. Full article

(This article belongs to the Section Navigation and Positioning)

►▼ Show Figures

Figure 1

21 pages, 1281 KB

Open AccessArticle

Credit Card Fraud Detection Under Extreme Class Imbalance Using Leakage-Safe Feature Selection and GA-Based Hyperparameter Optimization

by Chen Ma, Lihong Zhang, Zhi Xing and Junjing Su

Appl. Sci. 2026, 16(13), 6734; https://doi.org/10.3390/app16136734 (registering DOI) - 5 Jul 2026

Abstract

Credit card fraud detection is a typical rare-event classification problem because fraudulent transactions usually account for only a very small proportion of all transactions. Conventional evaluation on balanced or resampled test data may lead to overly optimistic performance estimates. To address this issue, this study proposes a leakage-safe credit card fraud detection framework integrating Random Forest Gini impurity-based feature selection, resampling strategy evaluation, and Genetic Algorithm (GA)-based hyperparameter optimization. The framework was evaluated on the public European credit card fraud dataset containing 284,807 transactions, of which only 492 were fraudulent. The original dataset was first divided into a stratified training set and an untouched original-distribution test set. Feature selection, standardization, resampling, GA optimization, and threshold tuning were performed only on the training data or training folds. The final test set contained 85,443 transactions, including 148 fraudulent transactions, and was used only once for final evaluation. Experimental results show that GA-XGBoost achieved the best overall balance among the optimized models, with a PR-AUC of 0.798, ROC-AUC of 0.967, MCC of 0.814, balanced accuracy of 0.865, fraud-class precision of 0.908, fraud-class recall of 0.730, and fraud-class F1-score of 0.809. Compared with baseline XGBoost, GA-XGBoost improved PR-AUC from 0.741 to 0.798, MCC from 0.766 to 0.814, and fraud-class F1-score from 0.764 to 0.809, while reducing false positives from 22 to 11 and false negatives from 43 to 40. The ablation results further indicate that resampling strategies are not universally beneficial and should be evaluated under the original test distribution. These findings suggest that leakage-safe evaluation and fraud-class-oriented metrics provide a more reliable basis for practical credit card fraud detection. Full article

(This article belongs to the Special Issue Intelligent Data Processing and Management: Technologies and Applications)

32 pages, 26857 KB

Open AccessData Descriptor

Comprehensive Dataset of Unidirectional Carbon Fiber Pultruded Composites and Their Constituents (Fibers and Matrix)

by Pinelopi Mageira, Jens W. Andreasen, Vedrana A. Dahl, Carsten Gundlach and Lars P. Mikkelsen

Data 2026, 11(7), 166; https://doi.org/10.3390/data11070166 (registering DOI) - 5 Jul 2026

Abstract

A comprehensive experimental dataset for unidirectional carbon fiber pultruded composites is presented, including mechanical testing results, microscopy images, and X-ray computed tomography volumes. In contrast to typical datasets, all measurements consistently describe a single material system, encompassing both the composite and its constituents (carbon fibers and vinyl ester matrix), thereby enabling a comprehensive and coherent multiscale material characterization. X-ray-computed tomography images of samples extracted from three pultruded composite profiles were acquired with a voxel size of 0.55 µm and analyzed to determine the fiber orientation distribution. Scanning electron microscopy with a pixel size of 0.098 µm was used to determine the overall and local fiber volume fractions. Compression testing of 17 composite specimens provided the compressive properties. The tensile and shear properties of the matrix were obtained from tensile and shear tests on seven and four matrix specimens, respectively. The Ramberg-Osgood model was fitted to the matrix’s tensile stress–strain response. Single-fiber tensile testing was conducted on 255 carbon fibers with three gauge lengths to determine fiber properties and Weibull parameters. All mechanical tests were performed up to material failure. The dataset is suitable for semi-analytical predictions and numerical finite-element modeling of composite mechanical behavior. Full article

(This article belongs to the Section Data Science for Chemistry, Energy and Materials)

►▼ Show Figures

Figure 1

31 pages, 7447 KB

Open AccessArticle

MSIA-YOLO: A Multi-Scale Semantic Interaction and Alignment Network for Small Object Detection in Low-Altitude UAV Remote Sensing Images

by Wen Zhang, Xiaorong Xue, Bingyan Lu, Yishuo Tian, Jingtong Yang, Xin Zhao and Wancheng Wang

Remote Sens. 2026, 18(13), 2210; https://doi.org/10.3390/rs18132210 (registering DOI) - 5 Jul 2026

Abstract

Small object detection is fundamentally constrained by the lack of discriminative fine-grained features. Although introducing higher resolution detection scales can improve performance, it also amplifies background noise. In addition, the independently decoupled design of conventional detection heads is insufficient to address the persistent challenges of missed detections and false positives for small objects. To this end, we propose MSIA-YOLO, a YOLOv11-based detector with multi-scale semantic interaction and alignment, optimized from three complementary perspectives: feature modeling, high resolution semantic compensation, and task coordinated alignment. First, Receptive Field Attention Convolution (RFAConv) is integrated into the backbone to enhance critical local details, such as edge and texture cues, via receptive field aware attention. Second, to alleviate fine detail attenuation caused by repeated downsampling, we construct a CHSP-P2 small object detection framework with an additional P2 branch. A scale sequence fusion mechanism is further introduced to perform high resolution semantic compensation through cross scale hybrid inputs. Finally, we design a DTIA-Head (Dynamic Task Interaction and Alignment Head), which promotes joint optimization of classification and localization through dynamic task interaction and spatial alignment. Extensive experiments on the public datasets VisDrone, TinyPerson, and RSOD show that, compared with the YOLOv11n baseline, MSIA-YOLO improves mAP50 by 7.7%, 10.3%, and 1.0%, respectively, while also outperforming several advanced detectors. These results demonstrate the effectiveness and generalization capability of the proposed method in small object, dense object, and complex scene object detection scenarios. Full article

(This article belongs to the Special Issue Target Detection, Recognition, Tracking, and Positioning Using Remote Sensing and AI Techniques (Second Edition))

26 pages, 4327 KB

Open AccessArticle

A Comparative Analysis of EWT and EMD Techniques in the Diagnosis of Major Depressive Disorder Using EEG: Asymmetry Features and Explainability via SHAP

by Nadide Gulsah Gulenc, Gokce Koc and Mahmut Ozturk

Diagnostics 2026, 16(13), 2107; https://doi.org/10.3390/diagnostics16132107 (registering DOI) - 5 Jul 2026

Abstract

Background/Objectives: Major Depressive Disorder is a serious mental disorder that negatively affects an individual’s health and quality of life. The diagnosis of this disease is based on clinical interviews, questionnaires, and the patient’s self-reports. The objective of this study is to develop a biological diagnostic system based on the analysis of EEG signals and brain regions, rather than relying on self-reports. Methods: In this study, the EEG signals in the Multimodal Open Mental Disorder Analysis (MODMA) dataset were divided into six anatomical regions: prefrontal, frontal, central, parietal, temporal, and occipital. Empirical Wavelet Transform and Empirical Mode Decomposition methods were applied separately to the channels in each region, resulting in three IMF components. A total of 23 features, including statistical, nonlinear, spectral, and model-based (AR) features, were extracted from each IMF component. In addition to these features, asymmetry features between the left and right hemispheres were also included. Feature dimensions ranging from 10 to 40 were selected via the mRMR method, and the extracted feature sets were classified using SVM, k-NN, RUSBoost, Random Forest, and Meta-Ensemble machine learning models with Leave-One-Subject-Out (LOSO) validation. Results: According to the analysis results, the highest accuracy rate in Major Depressive Disorder (MDD) diagnosis was achieved by classifying features extracted from the frontal and prefrontal regions. The EMD signal processing method demonstrated superior performance compared to the EWT method. An accuracy rate of 98.11% was achieved using Random Forest and Meta-Ensemble models. Conclusions: In the proposed method, Explainable Artificial Intelligence (XAI) based SHAP analysis was applied to provide reliable and interpretable features for MDD diagnosis based on brain regional analysis. Full article

(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)

►▼ Show Figures

Figure 1

20 pages, 1844 KB

Open AccessArticle

Deep Multiscale Learning for Robust Image Detection and Tracking in Dynamic Environments

by Obai Alashram, Obada Al-Khatib and Abeer Elkhouly

Computers 2026, 15(7), 429; https://doi.org/10.3390/computers15070429 (registering DOI) - 5 Jul 2026

Abstract

Deep multiscale learning has emerged as a promising venue for robust image detection and multi-object tracking in adverse conditions, but the current solutions tend to be impacted by the issues of occlusion, scale variation, and background clutter, focusing on each of them separately and restricting the generalization. In a direction to address these gaps, this piece of writing proposes a unified model that incorporates HRNet to extract high-resolution features, DETR to make use of transformers for detection, and TrackFormer to identify in an identity-preserving manner. Data was based on the MOT17 benchmark dataset, which provides various urban video sequences, including annotated bounding boxes and identities, to guarantee a test that is rigorous. The approaches were selected due to their complementary advantages: HRNet keeps fine-grained spatial information, DETR allows us to locate the objects in an accurate way, and TrackFormer tracks the trajectories across fragments. Experiments show good performance, with a mean detection AP of 70.9, precision of 76.5, recall of 72.8, MOTA of 74.8, IDF1 of 70.2, and HOTA of 63.6, maintaining real-time performance of 26 FPS with a latency of 38.5 ms per frame. In general, this work offers a globally scalable, end-to-end system for problems like surveillance and self-driving, and future work aims to address outrageously dense scenes, enhance cross-dataset generalization, and come up with lightweight systems to deploy these edges. Full article

►▼ Show Figures

Figure 1

34 pages, 2615 KB

Open AccessArticle

Liver Disease Prediction Using Hybrid Feature Selection: A Comparative Analysis of Machine Learning Models

by Osman Eray

Appl. Sci. 2026, 16(13), 6726; https://doi.org/10.3390/app16136726 (registering DOI) - 5 Jul 2026

Abstract

Liver disease represents a major global health burden, and early diagnosis is essential for reducing mortality. Machine learning (ML) approaches offer non-invasive alternatives to conventional diagnostics, yet existing studies on liver disease prediction often lack systematic feature selection, apply resampling before data splitting (introducing leakage), and report results from single train-test splits without statistical testing. This study proposes a Hybrid Feature Selection (HFS) framework combining Pearson-correlation-based redundancy elimination with a weighted Information Gain–Gain Ratio scoring function, integrated with SMOTE within a leakage-free pipeline. The framework is evaluated on two benchmarks—the Indian Liver Patient Dataset (ILPD, n = 583) and the BUPA Liver Disorders Dataset (n = 345)—across ten classifiers and ten independent train-test splits, with significance assessed via paired Wilcoxon signed-rank tests. On ILPD, the HFS + SMOTE pipeline produced statistically significant ROC-AUC improvements (p < 0.05) in five of ten classifiers and resolved majority-class collapse, raising mean Specificity from 0.00–0.33 to 0.61–0.92. A 2 × 2 ablation study confirmed that HFS and SMOTE contribute independently, with SMOTE driving the Specificity transformation and HFS reducing feature-space noise. Sensitivity analyses demonstrated robustness to the weighting parameter w and confirmed k = 6 as the optimal feature count. Replication on BUPA—which exhibits near-perfect class balance and no feature redundancy—produced a principled null result, confirming that the pipeline’s effectiveness is mechanistically linked to dataset characteristics. The HFS algorithm consistently identified four clinically meaningful core features (AST, ALT, Total Bilirubin, Age) across all runs, validated by SHAP and Permutation Importance stability analysis. Full article

24 pages, 2262 KB

Open AccessReview

Reframing Weed Detection: From Feature-Based Vision to Crop-Guided Intelligence in Precision Agriculture

by Yanjun Duan, Wenpeng Zhu, Shugui Ding, Mian Li, Kang Han, Xiaoyue Lai, Yuxin Liao, Fuhao Gong, Zhong Li, Maocheng Zhao, Bin Wu and Xiaojun Jin

Agronomy 2026, 16(13), 1291; https://doi.org/10.3390/agronomy16131291 (registering DOI) - 5 Jul 2026

Abstract

Weeds remain one of the primary constraints on crop productivity, making accurate detection and spatial localization essential for precision weeding systems. Over the past decades, weed detection has evolved from traditional feature-based image processing to deep learning-driven visual recognition, substantially improving detection accuracy under controlled and semi-controlled conditions. However, most existing approaches still follow a weed-centric paradigm in which models are trained to explicitly recognize diverse weed species or weed classes. Such strategies face persistent limitations caused by extreme weed morphological variability, crop-weed similarity, high annotation cost, and spatial-temporal heterogeneity across fields, seasons, and cropping systems. This review therefore reframes weed detection as a broader transition from feature-based vision and direct weed recognition toward crop-guided, context-aware, and decision-oriented intelligence. Specifically, we synthesize the literature from three perspectives: (i) methodological evolution, including handcrafted features, machine learning, deep learning, segmentation, and multimodal sensing; (ii) paradigm transformation, from weed-centric detection to crop-guided inference based on crop structure, crop rows, and non-crop vegetation; and (iii) deployment-oriented integration, including edge devices, latency-accuracy-energy trade-offs, and robotic actuation. We further summarize representative public datasets, method categories, crop-guided studies, and edge-platform reporting requirements. Finally, we outline a decision-aware hybrid framework in which crop-guided perception provides low-latency weed localization, while species-level recognition is conditionally activated when required by herbicide selection, resistance management, or high-risk weed control. This synthesis clarifies both the value and the limitations of crop-guided weed detection and outlines actionable directions for scalable, robust, and field-deployable intelligent weeding systems. Full article

(This article belongs to the Section Precision and Digital Agriculture)

►▼ Show Figures

Figure 1

18 pages, 15335 KB

Open AccessArticle

Cytogenetics Study of Habenaria janellehayneana Choltco, B.Moloney & Yong, H. rhodocheila Hance and Nervilia khaoyaica Suddee, Watthana & S.W.Gale, Rare Species of Family Orchidaceae from Thailand

by Santi Watthana, Chuthapond Musimun, Surapon Saensouk, Kamonwan Koompoot, Piyaporn Saensouk and Nooduan Muangsan

Taxonomy 2026, 6(3), 42; https://doi.org/10.3390/taxonomy6030042 (registering DOI) - 5 Jul 2026

Abstract

This study presents a cytogenetic and comparative analysis of three rare terrestrial orchid species from Thailand: Habenaria janellehayneana, H. rhodocheila, and Nervilia khaoyaica. Somatic chromosome numbers, karyotype structures, and chromosomal morphology were investigated using conventional cytological techniques. The results revealed that both H. janellehayneana and H. rhodocheila possess a chromosome number of 2n = 42 with a fundamental number (NF) of 84, whereas N. khaoyaica has 2n = 36 and NF = 72. Karyotype formulas were determined as 11m + 10sm for H. janellehayneana, 21m for H. rhodocheila, and 2m + 16sm for N. khaoyaica, all indicating symmetrical karyotypes. Multivariate analyses, including Principal Component Analysis (PCA) and hierarchical clustering, were applied to both morphological and karyological datasets. The results demonstrated clear separation between genera along the primary component axis, while species within Habenaria were distinguished along the secondary axis. Karyotype-based PCA further supported distinct chromosomal patterns among all three species, despite identical chromosome numbers in the two Habenaria taxa. This study provides the first cytogenetic report for these species and highlights the importance of chromosome structure, particularly centromere position and arm ratio, in species delimitation. The findings contribute valuable baseline data for taxonomy, evolutionary studies, and conservation of Orchidaceae in Thailand. Full article

►▼ Show Figures

Figure 1

15 pages, 2459 KB

Open AccessArticle

Driver Attention Region Prediction Based on Multi-Attention Mechanism Multi-Scale Fusion Network

by Yunxing Chen, Guo Yu, Kunhui Li and Xingyu Yuan

Vehicles 2026, 8(7), 152; https://doi.org/10.3390/vehicles8070152 (registering DOI) - 5 Jul 2026

Abstract

In driver attention zone prediction tasks, accurately identifying and locating the driver’s attention zone is crucial. Traditional models have significant limitations in complex driving scenarios due to their failure to fully utilize multidimensional driving environment information. To address these issues, this paper proposes a multi-attention feature fusion network (MAFF-HRNet) for driver attention region prediction. The proposed network combines high-resolution feature extraction with bimodal RGB–semantic inputs, multi-scale feature fusion, attention-based feature refinement, and temporal modeling. The experimental results on the DR(eye)VE dataset show that MAFF-HRNet improves driver attention region prediction under the current evaluation protocol. These results indicate that semantic scene information, multi-scale spatial representation, and temporal context are beneficial for generating more accurate driver attention heatmaps in complex driving scenes. Full article

(This article belongs to the Special Issue The Application of Deep Learning in Intelligent Vehicle Perception Systems)

►▼ Show Figures

Figure 1

21 pages, 3273 KB

Open AccessArticle

Few-Shot Cross-Domain Fault Diagnosis via Wavelet Convolution Embedding and BDC-Based Metric Meta-Learning

by Zaiyou Xu, Jiale Kai and Jun Wang

Sensors 2026, 26(13), 4276; https://doi.org/10.3390/s26134276 (registering DOI) - 5 Jul 2026

Abstract

Few-shot cross-domain bearing fault diagnosis is challenging because labeled fault samples are limited and signals collected by vibration sensors under different operating conditions often show significant distribution shifts. To improve bearing fault identification under limited-sample and cross-condition scenarios, this paper proposes a wavelet convolution (WC) and Brownian distance covariance (BDC)-based metric meta-learning framework, termed WCBDC. In this framework, the WC is inserted into the feature extraction process to capture multiscale time–frequency information from vibration signals. The BDC is then applied to model nonlinear inter-channel statistical dependencies and improve the discriminability of fault embeddings. The obtained feature embeddings are further organized within a prototypical-network-based classifier, in which category prototypes are estimated from support samples and query instances are assigned by prototype-distance comparison. The proposed method is evaluated on the Paderborn University (PU) and Beijing Jiaotong University (BJTU) bearing datasets under both 5-way 5-shot and 5-way 1-shot scenarios. On the PU dataset, WCBDC reaches average accuracies of 92.19% and 84.13%, while the corresponding results on the BJTU dataset are 77.24% and 62.57%. These results exceed those of representative meta-learning baselines, demonstrating that WCBDC provides improved diagnostic performance for sensor-based bearing fault recognition when labeled samples are scarce and operating conditions vary. Full article

(This article belongs to the Special Issue Deep Learning Based Intelligent Fault Diagnosis)

►▼ Show Figures

Figure 1

Show export options Show export options

Select all

Export citation of selected articles as:

Error

Oops... you haven't selected anything for export.

Displaying article 1-50 on page 1 of 1811.

Go to page 1 2 3 4 5

Search Results (90,530)

Further Information

Guidelines

MDPI Initiatives

Follow MDPI