With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods ...With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.展开更多
Forests are vital ecosystems that play a crucial role in sustaining life on Earth and supporting human well-being.Traditional forest mapping and monitoring methods are often costly and limited in scope,necessitating t...Forests are vital ecosystems that play a crucial role in sustaining life on Earth and supporting human well-being.Traditional forest mapping and monitoring methods are often costly and limited in scope,necessitating the adoption of advanced,automated approaches for improved forest conservation and management.This study explores the application of deep learning-based object detection techniques for individual tree detection in RGB satellite imagery.A dataset of 3157 images was collected and divided into training(2528),validation(495),and testing(134)sets.To enhance model robustness and generalization,data augmentation was applied to the training part of the dataset.Various YOLO-based models,including YOLOv8,YOLOv9,YOLOv10,YOLOv11,and YOLOv12,were evaluated using different hyperparameters and optimization techniques,such as stochastic gradient descent(SGD)and auto-optimization.These models were assessed in terms of detection accuracy and the number of detected trees.The highest-performing model,YOLOv12m,achieved a mean average precision(mAP@50)of 0.908,mAP@50:95 of 0.581,recall of 0.851,precision of 0.852,and an F1-score of 0.847.The results demonstrate that YOLO-based object detection offers a highly efficient,scalable,and accurate solution for individual tree detection in satellite imagery,facilitating improved forest inventory,monitoring,and ecosystem management.This study underscores the potential of AI-driven tree detection to enhance environmental sustainability and support data-driven decision-making in forestry.展开更多
Urban environments offer a wealth of opportunities for residents to respite from their hectic life.Outdoor running or jogging becomes increasingly popular of an option.Impacts of urban environments on outdoor running,...Urban environments offer a wealth of opportunities for residents to respite from their hectic life.Outdoor running or jogging becomes increasingly popular of an option.Impacts of urban environments on outdoor running,despite some initial studies,remain underexplored.This study aims to establish an analytical framework that can holistically assess the urban environment on the healthy vitality of running.The proposed framework is applied to two modern Chinese cities,i.e.,Guangzhou and Shenzhen.We construct three interpretable random forest models to explore the non-linear relationship between environmental variables and running intensity(RI)through analyzing the runners'trajectories and integrating with multi-source urban big data(e.g.,street view imagery,remote sensing,and socio-economic data)across the built,natural,and social dimensions,The findings uncover that road density has the greatest impact on RI,and social variables(e.g.,population density and housing price)and natural variables(e.g.,slope and humidity)all make notable impact on outdoor running.Despite these findings,the impact of environmental variables likely change across different regions due to disparate regional construction and micro-environments,and those specific impacts as well as optimal thresholds also alter.Therefore,construction of healthy cities should take the whole urban environment into account and adapt to local conditions.This study provides a comprehensive evaluation on the influencing variables of healthy vitality and guides sustainable urban planning for creating running-friendly cities.展开更多
Accurate fine-grained geospatial scene classification using remote sensing imagery is essential for a wide range of applications.However,existing approaches often rely on manually zooming remote sensing images at diff...Accurate fine-grained geospatial scene classification using remote sensing imagery is essential for a wide range of applications.However,existing approaches often rely on manually zooming remote sensing images at different scales to create typical scene samples.This approach fails to adequately support the fixed-resolution image interpretation requirements in real-world scenarios.To address this limitation,we introduce the million-scale fine-grained geospatial scene classification dataset(MEET),which contains over 1.03 million zoom-free remote sensing scene samples,manually annotated into 80 fine-grained categories.In MEET,each scene sample follows a scene-in-scene layout,where the central scene serves as the reference,and auxiliary scenes provide crucial spatial context for fine-grained classification.Moreover,to tackle the emerging challenge of scene-in-scene classification,we present the context-aware transformer(CAT),a model specifically designed for this task,which adaptively fuses spatial context to accurately classify the scene samples.CAT adaptively fuses spatial context to accurately classify the scene samples by learning attentional features that capture the relationships between the center and auxiliary scenes.Based on MEET,we establish a comprehensive benchmark for fine-grained geospatial scene classification,evaluating CAT against 11 competitive baselines.The results demonstrate that CAT significantly outperforms these baselines,achieving a 1.88%higher balanced accuracy(BA)with the Swin-Large backbone,and a notable 7.87%improvement with the Swin-Huge backbone.Further experiments validate the effectiveness of each module in CAT and show the practical applicability of CAT in the urban functional zone mapping.The source code and dataset will be publicly available at https://jerrywyn.github.io/project/MEET.html.展开更多
Objective:The use of technology is growing rapidly.It can also be used in nursing interventions.A technology pack can support nursing interventions.An application called guided imagery in Indonesian(GIANESIA)has been ...Objective:The use of technology is growing rapidly.It can also be used in nursing interventions.A technology pack can support nursing interventions.An application called guided imagery in Indonesian(GIANESIA)has been developed to reduce anxiety in preoperative patients.Methods:A total of 42 participants joined this research.The respondents were those who would undergo surgery.We used the numeric visual analog anxiety scale(NVAAS)as the instrument to measure their anxiety levels.The participants were first given informed consent.Then,they open the application that has been installed.The process begins with participants choosing their initial anxiety score.Later,they start the therapy session,and immediately after finishing it,a pop-up bar prompts them to enter their final,posttherapy anxiety score.Results:This study shows the effectiveness of therapy given by GIANESIA in reducing anxiety in preoperative patients with p-value=0.000(a<0.05).Also,61.9%of the participants had decreased anxiety levels after therapy with GIANESIA.Conclusions:This study proves that providing therapy via a mobile application is effective in easing uncomfortable feelings,especially anxiety,in preoperative patients.Moving forward,the app can and should be expanded with new features and further developmental goals.展开更多
Crop-yield is a crucial metric in agriculture,essential for effective sector management and improving the overall production process.This indicator is heavily influenced by numerous environmental factors,particularly ...Crop-yield is a crucial metric in agriculture,essential for effective sector management and improving the overall production process.This indicator is heavily influenced by numerous environmental factors,particularly those related to soil and climate,which present a challenging task due to the complex interactions involved.In this paper,we introduce a novel integrated neurosymbolic framework that combines knowledge-based approaches with sensor data for crop-yield prediction.This framework merges predictions from vectors generated by modeling environmental factors using a newly developed ontology focused on key elements and evaluates this ontology using quantitative methods,specifically representation learning techniques,along with predictions derived from remote sensing imagery.We tested our proposed methodology on a public dataset centered on corn,aiming to predict crop-yield.Our developed smart model achieved promising results in terms of crop-yield prediction,with a root mean squared error(RMSE)of 1.72,outperforming the baseline models.The ontologybased approach achieved an RMSE of 1.73,while the remote sensing-based method yielded an RMSE of 1.77.This confirms the superior performance of our proposed approach over those using single modalities.This in-tegrated neurosymbolic approach demonstrates that the fusion of statistical and symbolic artificial intelligence(AI)represents a significant advancement in agricultural applications.It is particularly effective for crop-yield prediction at the field scale,thus facilitating more informed decision-making in advanced agricultural prac-tices.Additionally,it is acknowledged that results might be further improved by incorporating more detailed ontological knowledge and testing the model with higher-resolution imagery to enhance prediction accuracy.展开更多
Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive functions.Effective rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recove...Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive functions.Effective rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by artifacts.Accurate classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific variability.To address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and robustness.GACL-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal dynamics.Evaluated on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art methods.Additionally,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive rehabilitation.These findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.展开更多
While algorithms have been created for land usage in urban settings,there have been few investigations into the extraction of urban footprint(UF).To address this research gap,the study employs several widely used imag...While algorithms have been created for land usage in urban settings,there have been few investigations into the extraction of urban footprint(UF).To address this research gap,the study employs several widely used image classification method classified into three categories to evaluate their segmentation capabilities for extracting UF across eight cities.The results indicate that pixel-based methods only excel in clear urban environments,and their overall accuracy is not consistently high.RF and SVM perform well but lack stability in object-based UF extraction,influenced by feature selection and classifier performance.Deep learning enhances feature extraction but requires powerful computing and faces challenges with complex urban layouts.SAM excels in medium-sized urban areas but falters in intricate layouts.Integrating traditional and deep learning methods optimizes UF extraction,balancing accuracy and processing efficiency.Future research should focus on adapting algorithms for diverse urban landscapes to enhance UF extraction accuracy and applicability.展开更多
A brain-computer interface(BCI)based on motor imagery(MI)provides additional control pathways by decoding the intentions of the brain.MI ability has great intra-individual variability,and the majority of MI-BCI system...A brain-computer interface(BCI)based on motor imagery(MI)provides additional control pathways by decoding the intentions of the brain.MI ability has great intra-individual variability,and the majority of MI-BCI systems are unable to adapt to this variability,leading to poor training effects.Therefore,prediction of MI ability is needed.In this study,we propose an MI ability predictor based on multi-frequency EEG features.To validate the performance of the predictor,a video-guided paradigm and a traditional MI paradigm are designed,and the predictor is applied to both paradigms.The results demonstrate that all subjects achieved>85%prediction precision in both applications,with a maximum of 96%.This study indicates that the predictor can accurately predict the individuals’MI ability in different states,provide the scientific basis for personalized training,and enhance the effect of MI-BCI training.展开更多
The classification of motor imagery electroencephalogram(MI-EEG)signals is one of the key challenges in brain-computer interface(BCI)technology.Existing Riemannian geometry-based methods for MI-EEG signal analysis,whi...The classification of motor imagery electroencephalogram(MI-EEG)signals is one of the key challenges in brain-computer interface(BCI)technology.Existing Riemannian geometry-based methods for MI-EEG signal analysis,which rely on a single symmetric positive definite(SPD)manifold,often provide a limited geometric structure,making it difficult to fully capture the complex geometric characteristics of the signals.To address this issue,this paper proposes an innovative classification method for MI-EEG signals based on multi-Riemannian kernel fusion features(MRKFF).This method extends the classical SPD manifold by incorporating the Gaussian SPD manifold and the Grassmann manifold,extracting more discriminative kernel features from these heterogeneous manifolds for fusion-based classification.The proposed method is validated on the OpenBMI binary classification dataset and the BCI Competition IV-2a four-class dataset,achieving average classification accuracies of 75.6%and 71.0%,with Kappa values of 0.50 and 0.61,respectively.The proposed MRKFF method provides a new perspective for the geometric analysis of MI-EEG signals,enabling a deeper understanding and analysis of the complex geometric structure of these signals,thereby achieving more accurate signal classification in BCI applications.展开更多
Drone photography is an essential building block of intelligent transportation,enabling wide-ranging monitoring,precise positioning,and rapid transmission.However,the high computational cost of transformer-based metho...Drone photography is an essential building block of intelligent transportation,enabling wide-ranging monitoring,precise positioning,and rapid transmission.However,the high computational cost of transformer-based methods in object detection tasks hinders real-time result transmission in drone target detection applications.Therefore,we propose mask adaptive transformer (MAT) tailored for such scenarios.Specifically,we introduce a structure that supports collaborative token sparsification in support windows,enhancing fault tolerance and reducing computational overhead.This structure comprises two modules:a binary mask strategy and adaptive window self-attention (A-WSA).The binary mask strategy focuses on significant objects in various complex scenes.The A-WSA mechanism is employed to self-attend for balance perfomance and computational cost to select objects and isolate all contextual leakage.Extensive experiments on the challenging CarPK and VisDrone datasets demonstrate the effectiveness and superiority of the proposed method.Specifically,it achieves a mean average precision (mAP@0.5) improvement of 1.25%over car detector based on you only look once version 5 (CD-YOLOv5) on the CarPK dataset and a 3.75%average precision(AP@0.5) improvement over cascaded zoom-in detector (CZ Det) on the VisDrone dataset.展开更多
Rapidly obtaining spatial distribution maps of secondary disasters triggered by strong earthquakes is crucial for understanding the disaster-causing processes in the earthquake hazard chain and formulating effective e...Rapidly obtaining spatial distribution maps of secondary disasters triggered by strong earthquakes is crucial for understanding the disaster-causing processes in the earthquake hazard chain and formulating effective emergency response measures and post-disaster reconstruction plans.On April 3,2024,a M_(W)7.4 earthquake struck offshore east of Hualien,Taiwan,China,which triggered numerous coseismic landslides in bedrock mountain regions and severe soil liquefaction in coastal areas,resulting in significant economic losses.This study utilized postearthquake emergency data from China's high-resolution optical satellite imagery and applied visual interpretation method to establish a partial database of secondary disasters triggered by the 2024 Hualien earthquake.A total of 5348 coseismic landslides were identified,which were primarily distributed along the eastern slopes of the Central Mountain Range watersheds.In high mountain valleys,these landslides mainly manifest as localized bedrock collapses or slope debris flows,causing extensive damage to highways and tourism facilities.Their distribution partially overlaps with the landslide concentration zones triggered by the 1999 Chi-Chi earthquake.Additionally,6040 soil liquefaction events were interpreted,predominantly in the Hualien Port area and the lowland valleys of the Hualien River and concentrated within the IX-intensity zone.Widespread surface subsidence and sand ejections characterized soil liquefaction.Verified against local field investigation data in Taiwan,rapid imaging through post-earthquake remote sensing data can effectively assess the distribution of coseismic landslides and soil liquefaction within high-intensity zones.This study provides efficient and reliable data for earthquake disaster response.Moreover,the results are critical for seismic disaster mitigation in high mountain valleys and coastal lowlands.展开更多
Deep learning methods have been widely applied in motor imagery(MI)-based brain-computer interfaces(BCI)for decoding electroencephalogram(EEG)signals.High temporal resolution and asymmetric spatial activation are fund...Deep learning methods have been widely applied in motor imagery(MI)-based brain-computer interfaces(BCI)for decoding electroencephalogram(EEG)signals.High temporal resolution and asymmetric spatial activation are fundamental properties of EEG during MI processes.However,due to the limited receptive field of convolutional kernels,traditional convolutional neural networks(CNNs)often focus only on local features,and are insufficient to cover neural processes across different frequency bands and duration scales.This limitation hinders the effective characterization of rhythmic activity changes in MI-EEG signals over time.Additionally,MI-EEG signals exhibit significant asymmetric activation between the left and right hemispheres.Traditional spatial feature extraction methods overlook the interaction between global and local regions at the spatial scale of EEG signals,resulting in inadequate spatial representation and ultimately limiting decoding accuracy.To address these limitations,in this study,a novel deep learning network that integrates multi-modal temporal features with spatially asymmetric feature modeling was proposed.The network first extracts multi-modal temporal information from EEG data channels,and then captures global and hemispheric spatial features in the spatial dimension and fuses them through an advanced fusion layer.Global dependencies are captured using a self-attention module,and a multi-scale convolutional fusion module is introduced to explore the relationships between the two types of temporal features.The fused features are classified through a classification layer to accomplish motor imagery task classification.To mitigate the issue of limited sample size,a data augmentation strategy based on signal segmentation and recombination is designed.Experimental results on the BCI Competition IV-2a(bbic-IV-2a)and BCI Competition IV-2b(bbic-IV-2a)datasets demonstrated that the proposed method achieved superior accuracy in multi-class motor imagery classification compared with existing models.On the BCI-IV-2a dataset,it attained an average classification accuracy of 84.36%,while also showing strong performance on the binary classification BCI-IV-2b dataset.These outcomes validate the capability of the proposed network to enhance MI-EEG classification accuracy.展开更多
Spartina alterniflora is now listed among the world’s 100 most dangerous invasive species,severely affecting the ecological balance of coastal wetlands.Remote sensing technologies based on deep learning enable large-...Spartina alterniflora is now listed among the world’s 100 most dangerous invasive species,severely affecting the ecological balance of coastal wetlands.Remote sensing technologies based on deep learning enable large-scale monitoring of Spartina alterniflora,but they require large datasets and have poor interpretability.A new method is proposed to detect Spartina alterniflora from Sentinel-2 imagery.Firstly,to get the high canopy cover and dense community characteristics of Spartina alterniflora,multi-dimensional shallow features are extracted from the imagery.Secondly,to detect different objects from satellite imagery,index features are extracted,and the statistical features of the Gray-Level Co-occurrence Matrix(GLCM)are derived using principal component analysis.Then,ensemble learning methods,including random forest,extreme gradient boosting,and light gradient boosting machine models,are employed for image classification.Meanwhile,Recursive Feature Elimination with Cross-Validation(RFECV)is used to select the best feature subset.Finally,to enhance the interpretability of the models,the best features are utilized to classify multi-temporal images and SHapley Additive exPlanations(SHAP)is combined with these classifications to explain the model prediction process.The method is validated by using Sentinel-2 imageries and previous observations of Spartina alterniflora in Chongming Island,it is found that the model combining image texture features such as GLCM covariance can significantly improve the detection accuracy of Spartina alterniflora by about 8%compared with the model without image texture features.Through multiple model comparisons and feature selection via RFECV,the selected model and eight features demonstrated good classification accuracy when applied to data from different time periods,proving that feature reduction can effectively enhance model generalization.Additionally,visualizing model decisions using SHAP revealed that the image texture feature component_1_GLCMVariance is particularly important for identifying each land cover type.展开更多
In order to improve the performance of the braincomputer interface system of motor imagery(MI),the optimization of the MI strategy is an effective means.Therefore,this paper proposes a motor-tactile imagery(MTI)strate...In order to improve the performance of the braincomputer interface system of motor imagery(MI),the optimization of the MI strategy is an effective means.Therefore,this paper proposes a motor-tactile imagery(MTI)strategy to improve the classification accuracy of the MI-based brain-computer interface(BCI)system by adding a corresponding strong correlation of tactile imagery to each imaginary action of the MI.Electroencephalogram(EEG)signals generated by different strategies were collected,and the corresponding classification accuracy was obtained.The experimental results showed that the performance of the MTI-training group was significantly better than that of the MI group and the MTI-without-training group.The MTI strategy proposed in this study can significantly improve the performance of BCI.展开更多
Application of continuous repetition of motor imagery can improve the performance of exercise tasks.However,there is a lack of more detailed neurophysiological evidence to support the formulation of clear standards fo...Application of continuous repetition of motor imagery can improve the performance of exercise tasks.However,there is a lack of more detailed neurophysiological evidence to support the formulation of clear standards for interventions using motor imagery.Moreover,identification of motor imagery intervention time is necessary because it exhibits possible central fatigue.Therefore,the purpose of this study was to elucidate the development of fatigue during continuous repetition of motor imagery through objective and subjective evaluation.The study involved two experiments.In experiment 1,14 healthy young volunteers were required to imagine grasping and lifting a 1.5-L plastic bottle using the whole hand.Each participant performed the motor imagery task 100 times under each condition with 48 hours interval between two conditions:500 mL or 1500 mL of water in the bottle during the demonstration phase.Mental fatigue and a decrease in pinch power appeared under the 1500-mL condition.There were changes in concentration ability or corticospinal excitability,as assessed by motor evoked potentials,between each set with continuous repetition of motor imagery also under the 1500-mL condition.Therefore,in experiment 2,12 healthy volunteers were required to perform the motor imagery task 200 times under the 1500-mL condition.Both concentration ability and corticospinal excitability decreased.This is the first study to show that continuous repetition of motor imagery can decrease corticospinal excitability in addition to producing mental fatigue.This study was approved by the Institutional Ethics Committee at the Nagasaki University Graduate School of Biomedical and Health Sciences(approval No.18121302)on January 30,2019.展开更多
The concise and informative representation of hyperspectral imagery is achieved via the introduced diffusion geometric coordinates derived from nonlinear dimension reduction maps - diffusion maps. The huge-volume high...The concise and informative representation of hyperspectral imagery is achieved via the introduced diffusion geometric coordinates derived from nonlinear dimension reduction maps - diffusion maps. The huge-volume high- dimensional spectral measurements are organized by the affinity graph where each node in this graph only connects to its local neighbors and each edge in this graph represents local similarity information. By normalizing the affinity graph appropriately, the diffusion operator of the underlying hyperspectral imagery is well-defined, which means that the Markov random walk can be simulated on the hyperspectral imagery. Therefore, the diffusion geometric coordinates, derived from the eigenfunctions and the associated eigenvalues of the diffusion operator, can capture the intrinsic geometric information of the hyperspectral imagery well, which gives more enhanced representation results than traditional linear methods, such as principal component analysis based methods. For large-scale full scene hyperspectral imagery, by exploiting the backbone approach, the computation complexity and the memory requirements are acceptable. Experiments also show that selecting suitable symmetrization normalization techniques while forming the diffusion operator is important to hyperspectral imagery representation.展开更多
By the brief introduction of Kate Chopin and her achievement, this paper elaborates the awakening of consciousness of the feminism of the protagonist in Kate Chopin's The Story of an Hour. In the short story, the ...By the brief introduction of Kate Chopin and her achievement, this paper elaborates the awakening of consciousness of the feminism of the protagonist in Kate Chopin's The Story of an Hour. In the short story, the author uses many literary elements to describe the characters, especially the irony and imagery. This thesis uses those rhetorical devices to vividly describe the characters and to criticize the inequality between men and women in the late 19th century.[1]展开更多
Purpose:This study aimed to investigate the construct of external visual imagery(EVI)vs.internal visual imagery(IV/)by comparing the athletes'imagery ability with their levels of skill and types of sports.Methods:...Purpose:This study aimed to investigate the construct of external visual imagery(EVI)vs.internal visual imagery(IV/)by comparing the athletes'imagery ability with their levels of skill and types of sports.Methods:Seventy-two young athletes in open(n=45)or closed(n=27)sports and with different skill levels completed 2 custom-designed tasks.The EVI task involved the subject generating and visualizing the rotated images of different body parts,whereas the IVI task involved the subject visualizing himself or herself performing specific movements.Results:The significant Skill-Level x Sport Type interactions for the EVI task revealed that participants who specialized in open sports and had higher skill-levels had a higher accuracy rate as compared to the other subgroups.For the IVI task,the differences between the groups were less clear:those with higher skill-levels or open sports had a higher accuracy rate than those with lower skill-levels or closed sports.Conclusion:EVI involves the visualization of others and the environment,and would be relevant to higher skill-level athletes who engage in open sports.IVI,in contrast,tends to be more self-oriented and would be relevant for utilization by higher skill-level athletes regardless of sport type.展开更多
文摘With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.
基金funding from Horizon Europe Framework Programme(HORIZON),call Teaming for Excellence(HORIZON-WIDERA-2022-ACCESS-01-two-stage)-Creation of the centre of excellence in smart forestry“Forest 4.0”No.101059985funded by the EuropeanUnion under the project FOREST 4.0-“Ekscelencijos centras tvariai miško bioekonomikai vystyti”No.10-042-P-0002.
文摘Forests are vital ecosystems that play a crucial role in sustaining life on Earth and supporting human well-being.Traditional forest mapping and monitoring methods are often costly and limited in scope,necessitating the adoption of advanced,automated approaches for improved forest conservation and management.This study explores the application of deep learning-based object detection techniques for individual tree detection in RGB satellite imagery.A dataset of 3157 images was collected and divided into training(2528),validation(495),and testing(134)sets.To enhance model robustness and generalization,data augmentation was applied to the training part of the dataset.Various YOLO-based models,including YOLOv8,YOLOv9,YOLOv10,YOLOv11,and YOLOv12,were evaluated using different hyperparameters and optimization techniques,such as stochastic gradient descent(SGD)and auto-optimization.These models were assessed in terms of detection accuracy and the number of detected trees.The highest-performing model,YOLOv12m,achieved a mean average precision(mAP@50)of 0.908,mAP@50:95 of 0.581,recall of 0.851,precision of 0.852,and an F1-score of 0.847.The results demonstrate that YOLO-based object detection offers a highly efficient,scalable,and accurate solution for individual tree detection in satellite imagery,facilitating improved forest inventory,monitoring,and ecosystem management.This study underscores the potential of AI-driven tree detection to enhance environmental sustainability and support data-driven decision-making in forestry.
基金National Natural Science Foundation of China,No.42171455The Hong Kong RGC Research Impact Fund,No.R5011-23The Hong Kong General Research Fund,No.15204121。
文摘Urban environments offer a wealth of opportunities for residents to respite from their hectic life.Outdoor running or jogging becomes increasingly popular of an option.Impacts of urban environments on outdoor running,despite some initial studies,remain underexplored.This study aims to establish an analytical framework that can holistically assess the urban environment on the healthy vitality of running.The proposed framework is applied to two modern Chinese cities,i.e.,Guangzhou and Shenzhen.We construct three interpretable random forest models to explore the non-linear relationship between environmental variables and running intensity(RI)through analyzing the runners'trajectories and integrating with multi-source urban big data(e.g.,street view imagery,remote sensing,and socio-economic data)across the built,natural,and social dimensions,The findings uncover that road density has the greatest impact on RI,and social variables(e.g.,population density and housing price)and natural variables(e.g.,slope and humidity)all make notable impact on outdoor running.Despite these findings,the impact of environmental variables likely change across different regions due to disparate regional construction and micro-environments,and those specific impacts as well as optimal thresholds also alter.Therefore,construction of healthy cities should take the whole urban environment into account and adapt to local conditions.This study provides a comprehensive evaluation on the influencing variables of healthy vitality and guides sustainable urban planning for creating running-friendly cities.
基金supported by the National Natural Science Foundation of China(42030102,42371321).
文摘Accurate fine-grained geospatial scene classification using remote sensing imagery is essential for a wide range of applications.However,existing approaches often rely on manually zooming remote sensing images at different scales to create typical scene samples.This approach fails to adequately support the fixed-resolution image interpretation requirements in real-world scenarios.To address this limitation,we introduce the million-scale fine-grained geospatial scene classification dataset(MEET),which contains over 1.03 million zoom-free remote sensing scene samples,manually annotated into 80 fine-grained categories.In MEET,each scene sample follows a scene-in-scene layout,where the central scene serves as the reference,and auxiliary scenes provide crucial spatial context for fine-grained classification.Moreover,to tackle the emerging challenge of scene-in-scene classification,we present the context-aware transformer(CAT),a model specifically designed for this task,which adaptively fuses spatial context to accurately classify the scene samples.CAT adaptively fuses spatial context to accurately classify the scene samples by learning attentional features that capture the relationships between the center and auxiliary scenes.Based on MEET,we establish a comprehensive benchmark for fine-grained geospatial scene classification,evaluating CAT against 11 competitive baselines.The results demonstrate that CAT significantly outperforms these baselines,achieving a 1.88%higher balanced accuracy(BA)with the Swin-Large backbone,and a notable 7.87%improvement with the Swin-Huge backbone.Further experiments validate the effectiveness of each module in CAT and show the practical applicability of CAT in the urban functional zone mapping.The source code and dataset will be publicly available at https://jerrywyn.github.io/project/MEET.html.
基金supported by Ministry of Research and Technology/National Research and Innovation Agency Program scheme PDP(Research for Beginner Lecturers)2021(No.B/112/E3/RA.00/2021).
文摘Objective:The use of technology is growing rapidly.It can also be used in nursing interventions.A technology pack can support nursing interventions.An application called guided imagery in Indonesian(GIANESIA)has been developed to reduce anxiety in preoperative patients.Methods:A total of 42 participants joined this research.The respondents were those who would undergo surgery.We used the numeric visual analog anxiety scale(NVAAS)as the instrument to measure their anxiety levels.The participants were first given informed consent.Then,they open the application that has been installed.The process begins with participants choosing their initial anxiety score.Later,they start the therapy session,and immediately after finishing it,a pop-up bar prompts them to enter their final,posttherapy anxiety score.Results:This study shows the effectiveness of therapy given by GIANESIA in reducing anxiety in preoperative patients with p-value=0.000(a<0.05).Also,61.9%of the participants had decreased anxiety levels after therapy with GIANESIA.Conclusions:This study proves that providing therapy via a mobile application is effective in easing uncomfortable feelings,especially anxiety,in preoperative patients.Moving forward,the app can and should be expanded with new features and further developmental goals.
基金partially funded by the JSPS KAKENHI Grant Number JP22K18004.
文摘Crop-yield is a crucial metric in agriculture,essential for effective sector management and improving the overall production process.This indicator is heavily influenced by numerous environmental factors,particularly those related to soil and climate,which present a challenging task due to the complex interactions involved.In this paper,we introduce a novel integrated neurosymbolic framework that combines knowledge-based approaches with sensor data for crop-yield prediction.This framework merges predictions from vectors generated by modeling environmental factors using a newly developed ontology focused on key elements and evaluates this ontology using quantitative methods,specifically representation learning techniques,along with predictions derived from remote sensing imagery.We tested our proposed methodology on a public dataset centered on corn,aiming to predict crop-yield.Our developed smart model achieved promising results in terms of crop-yield prediction,with a root mean squared error(RMSE)of 1.72,outperforming the baseline models.The ontologybased approach achieved an RMSE of 1.73,while the remote sensing-based method yielded an RMSE of 1.77.This confirms the superior performance of our proposed approach over those using single modalities.This in-tegrated neurosymbolic approach demonstrates that the fusion of statistical and symbolic artificial intelligence(AI)represents a significant advancement in agricultural applications.It is particularly effective for crop-yield prediction at the field scale,thus facilitating more informed decision-making in advanced agricultural prac-tices.Additionally,it is acknowledged that results might be further improved by incorporating more detailed ontological knowledge and testing the model with higher-resolution imagery to enhance prediction accuracy.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT under Grant NRF-2022R1A2C1005316.
文摘Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive functions.Effective rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by artifacts.Accurate classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific variability.To address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and robustness.GACL-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal dynamics.Evaluated on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art methods.Additionally,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive rehabilitation.These findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.
文摘While algorithms have been created for land usage in urban settings,there have been few investigations into the extraction of urban footprint(UF).To address this research gap,the study employs several widely used image classification method classified into three categories to evaluate their segmentation capabilities for extracting UF across eight cities.The results indicate that pixel-based methods only excel in clear urban environments,and their overall accuracy is not consistently high.RF and SVM perform well but lack stability in object-based UF extraction,influenced by feature selection and classifier performance.Deep learning enhances feature extraction but requires powerful computing and faces challenges with complex urban layouts.SAM excels in medium-sized urban areas but falters in intricate layouts.Integrating traditional and deep learning methods optimizes UF extraction,balancing accuracy and processing efficiency.Future research should focus on adapting algorithms for diverse urban landscapes to enhance UF extraction accuracy and applicability.
基金supported by the Natural Science Foundation of Hebei Province(F2024202019)the National Natural Science Foundation of China(32201072).
文摘A brain-computer interface(BCI)based on motor imagery(MI)provides additional control pathways by decoding the intentions of the brain.MI ability has great intra-individual variability,and the majority of MI-BCI systems are unable to adapt to this variability,leading to poor training effects.Therefore,prediction of MI ability is needed.In this study,we propose an MI ability predictor based on multi-frequency EEG features.To validate the performance of the predictor,a video-guided paradigm and a traditional MI paradigm are designed,and the predictor is applied to both paradigms.The results demonstrate that all subjects achieved>85%prediction precision in both applications,with a maximum of 96%.This study indicates that the predictor can accurately predict the individuals’MI ability in different states,provide the scientific basis for personalized training,and enhance the effect of MI-BCI training.
基金Supported by the Natural Science Foundation of Shandong Province(No.ZR2022MF309)the Science and Technology Small and Mediumsized Enterprise Innovation Ability Enhancement Projec of Shandong Province(No.2022TSGC2554).
文摘The classification of motor imagery electroencephalogram(MI-EEG)signals is one of the key challenges in brain-computer interface(BCI)technology.Existing Riemannian geometry-based methods for MI-EEG signal analysis,which rely on a single symmetric positive definite(SPD)manifold,often provide a limited geometric structure,making it difficult to fully capture the complex geometric characteristics of the signals.To address this issue,this paper proposes an innovative classification method for MI-EEG signals based on multi-Riemannian kernel fusion features(MRKFF).This method extends the classical SPD manifold by incorporating the Gaussian SPD manifold and the Grassmann manifold,extracting more discriminative kernel features from these heterogeneous manifolds for fusion-based classification.The proposed method is validated on the OpenBMI binary classification dataset and the BCI Competition IV-2a four-class dataset,achieving average classification accuracies of 75.6%and 71.0%,with Kappa values of 0.50 and 0.61,respectively.The proposed MRKFF method provides a new perspective for the geometric analysis of MI-EEG signals,enabling a deeper understanding and analysis of the complex geometric structure of these signals,thereby achieving more accurate signal classification in BCI applications.
文摘Drone photography is an essential building block of intelligent transportation,enabling wide-ranging monitoring,precise positioning,and rapid transmission.However,the high computational cost of transformer-based methods in object detection tasks hinders real-time result transmission in drone target detection applications.Therefore,we propose mask adaptive transformer (MAT) tailored for such scenarios.Specifically,we introduce a structure that supports collaborative token sparsification in support windows,enhancing fault tolerance and reducing computational overhead.This structure comprises two modules:a binary mask strategy and adaptive window self-attention (A-WSA).The binary mask strategy focuses on significant objects in various complex scenes.The A-WSA mechanism is employed to self-attend for balance perfomance and computational cost to select objects and isolate all contextual leakage.Extensive experiments on the challenging CarPK and VisDrone datasets demonstrate the effectiveness and superiority of the proposed method.Specifically,it achieves a mean average precision (mAP@0.5) improvement of 1.25%over car detector based on you only look once version 5 (CD-YOLOv5) on the CarPK dataset and a 3.75%average precision(AP@0.5) improvement over cascaded zoom-in detector (CZ Det) on the VisDrone dataset.
基金funded by the Basic Research program from the Institute of Earthquake Forecasting,China Earthquake Administration(Grant No.CEAIEF20240302)the National Natural Science Foundation of China(Grant Nos.42072248)the National Key Research and Development Program of China(Grant Nos.2021YFC3000600 and 2019YFE0108900)。
文摘Rapidly obtaining spatial distribution maps of secondary disasters triggered by strong earthquakes is crucial for understanding the disaster-causing processes in the earthquake hazard chain and formulating effective emergency response measures and post-disaster reconstruction plans.On April 3,2024,a M_(W)7.4 earthquake struck offshore east of Hualien,Taiwan,China,which triggered numerous coseismic landslides in bedrock mountain regions and severe soil liquefaction in coastal areas,resulting in significant economic losses.This study utilized postearthquake emergency data from China's high-resolution optical satellite imagery and applied visual interpretation method to establish a partial database of secondary disasters triggered by the 2024 Hualien earthquake.A total of 5348 coseismic landslides were identified,which were primarily distributed along the eastern slopes of the Central Mountain Range watersheds.In high mountain valleys,these landslides mainly manifest as localized bedrock collapses or slope debris flows,causing extensive damage to highways and tourism facilities.Their distribution partially overlaps with the landslide concentration zones triggered by the 1999 Chi-Chi earthquake.Additionally,6040 soil liquefaction events were interpreted,predominantly in the Hualien Port area and the lowland valleys of the Hualien River and concentrated within the IX-intensity zone.Widespread surface subsidence and sand ejections characterized soil liquefaction.Verified against local field investigation data in Taiwan,rapid imaging through post-earthquake remote sensing data can effectively assess the distribution of coseismic landslides and soil liquefaction within high-intensity zones.This study provides efficient and reliable data for earthquake disaster response.Moreover,the results are critical for seismic disaster mitigation in high mountain valleys and coastal lowlands.
文摘Deep learning methods have been widely applied in motor imagery(MI)-based brain-computer interfaces(BCI)for decoding electroencephalogram(EEG)signals.High temporal resolution and asymmetric spatial activation are fundamental properties of EEG during MI processes.However,due to the limited receptive field of convolutional kernels,traditional convolutional neural networks(CNNs)often focus only on local features,and are insufficient to cover neural processes across different frequency bands and duration scales.This limitation hinders the effective characterization of rhythmic activity changes in MI-EEG signals over time.Additionally,MI-EEG signals exhibit significant asymmetric activation between the left and right hemispheres.Traditional spatial feature extraction methods overlook the interaction between global and local regions at the spatial scale of EEG signals,resulting in inadequate spatial representation and ultimately limiting decoding accuracy.To address these limitations,in this study,a novel deep learning network that integrates multi-modal temporal features with spatially asymmetric feature modeling was proposed.The network first extracts multi-modal temporal information from EEG data channels,and then captures global and hemispheric spatial features in the spatial dimension and fuses them through an advanced fusion layer.Global dependencies are captured using a self-attention module,and a multi-scale convolutional fusion module is introduced to explore the relationships between the two types of temporal features.The fused features are classified through a classification layer to accomplish motor imagery task classification.To mitigate the issue of limited sample size,a data augmentation strategy based on signal segmentation and recombination is designed.Experimental results on the BCI Competition IV-2a(bbic-IV-2a)and BCI Competition IV-2b(bbic-IV-2a)datasets demonstrated that the proposed method achieved superior accuracy in multi-class motor imagery classification compared with existing models.On the BCI-IV-2a dataset,it attained an average classification accuracy of 84.36%,while also showing strong performance on the binary classification BCI-IV-2b dataset.These outcomes validate the capability of the proposed network to enhance MI-EEG classification accuracy.
基金The National Key Research and Development Program of China under contract No.2023YFC3008204the National Natural Science Foundation of China under contract Nos 41977302 and 42476217.
文摘Spartina alterniflora is now listed among the world’s 100 most dangerous invasive species,severely affecting the ecological balance of coastal wetlands.Remote sensing technologies based on deep learning enable large-scale monitoring of Spartina alterniflora,but they require large datasets and have poor interpretability.A new method is proposed to detect Spartina alterniflora from Sentinel-2 imagery.Firstly,to get the high canopy cover and dense community characteristics of Spartina alterniflora,multi-dimensional shallow features are extracted from the imagery.Secondly,to detect different objects from satellite imagery,index features are extracted,and the statistical features of the Gray-Level Co-occurrence Matrix(GLCM)are derived using principal component analysis.Then,ensemble learning methods,including random forest,extreme gradient boosting,and light gradient boosting machine models,are employed for image classification.Meanwhile,Recursive Feature Elimination with Cross-Validation(RFECV)is used to select the best feature subset.Finally,to enhance the interpretability of the models,the best features are utilized to classify multi-temporal images and SHapley Additive exPlanations(SHAP)is combined with these classifications to explain the model prediction process.The method is validated by using Sentinel-2 imageries and previous observations of Spartina alterniflora in Chongming Island,it is found that the model combining image texture features such as GLCM covariance can significantly improve the detection accuracy of Spartina alterniflora by about 8%compared with the model without image texture features.Through multiple model comparisons and feature selection via RFECV,the selected model and eight features demonstrated good classification accuracy when applied to data from different time periods,proving that feature reduction can effectively enhance model generalization.Additionally,visualizing model decisions using SHAP revealed that the image texture feature component_1_GLCMVariance is particularly important for identifying each land cover type.
文摘In order to improve the performance of the braincomputer interface system of motor imagery(MI),the optimization of the MI strategy is an effective means.Therefore,this paper proposes a motor-tactile imagery(MTI)strategy to improve the classification accuracy of the MI-based brain-computer interface(BCI)system by adding a corresponding strong correlation of tactile imagery to each imaginary action of the MI.Electroencephalogram(EEG)signals generated by different strategies were collected,and the corresponding classification accuracy was obtained.The experimental results showed that the performance of the MTI-training group was significantly better than that of the MI group and the MTI-without-training group.The MTI strategy proposed in this study can significantly improve the performance of BCI.
文摘Application of continuous repetition of motor imagery can improve the performance of exercise tasks.However,there is a lack of more detailed neurophysiological evidence to support the formulation of clear standards for interventions using motor imagery.Moreover,identification of motor imagery intervention time is necessary because it exhibits possible central fatigue.Therefore,the purpose of this study was to elucidate the development of fatigue during continuous repetition of motor imagery through objective and subjective evaluation.The study involved two experiments.In experiment 1,14 healthy young volunteers were required to imagine grasping and lifting a 1.5-L plastic bottle using the whole hand.Each participant performed the motor imagery task 100 times under each condition with 48 hours interval between two conditions:500 mL or 1500 mL of water in the bottle during the demonstration phase.Mental fatigue and a decrease in pinch power appeared under the 1500-mL condition.There were changes in concentration ability or corticospinal excitability,as assessed by motor evoked potentials,between each set with continuous repetition of motor imagery also under the 1500-mL condition.Therefore,in experiment 2,12 healthy volunteers were required to perform the motor imagery task 200 times under the 1500-mL condition.Both concentration ability and corticospinal excitability decreased.This is the first study to show that continuous repetition of motor imagery can decrease corticospinal excitability in addition to producing mental fatigue.This study was approved by the Institutional Ethics Committee at the Nagasaki University Graduate School of Biomedical and Health Sciences(approval No.18121302)on January 30,2019.
基金The National Key Technologies R & D Program during the 11th Five-Year Plan Period (No.2006BAB15B01)
文摘The concise and informative representation of hyperspectral imagery is achieved via the introduced diffusion geometric coordinates derived from nonlinear dimension reduction maps - diffusion maps. The huge-volume high- dimensional spectral measurements are organized by the affinity graph where each node in this graph only connects to its local neighbors and each edge in this graph represents local similarity information. By normalizing the affinity graph appropriately, the diffusion operator of the underlying hyperspectral imagery is well-defined, which means that the Markov random walk can be simulated on the hyperspectral imagery. Therefore, the diffusion geometric coordinates, derived from the eigenfunctions and the associated eigenvalues of the diffusion operator, can capture the intrinsic geometric information of the hyperspectral imagery well, which gives more enhanced representation results than traditional linear methods, such as principal component analysis based methods. For large-scale full scene hyperspectral imagery, by exploiting the backbone approach, the computation complexity and the memory requirements are acceptable. Experiments also show that selecting suitable symmetrization normalization techniques while forming the diffusion operator is important to hyperspectral imagery representation.
文摘By the brief introduction of Kate Chopin and her achievement, this paper elaborates the awakening of consciousness of the feminism of the protagonist in Kate Chopin's The Story of an Hour. In the short story, the author uses many literary elements to describe the characters, especially the irony and imagery. This thesis uses those rhetorical devices to vividly describe the characters and to criticize the inequality between men and women in the late 19th century.[1]
基金supported by the Dr.and Mrs.Sui Kau Chan donation grant for sports training and rehabilitation held by Chetwyn C.H.Chan and Amy S.N.Fuand by an internal grant awarded to Chetwyn C.H.Chan by The Hong Kong Polytechnic University
文摘Purpose:This study aimed to investigate the construct of external visual imagery(EVI)vs.internal visual imagery(IV/)by comparing the athletes'imagery ability with their levels of skill and types of sports.Methods:Seventy-two young athletes in open(n=45)or closed(n=27)sports and with different skill levels completed 2 custom-designed tasks.The EVI task involved the subject generating and visualizing the rotated images of different body parts,whereas the IVI task involved the subject visualizing himself or herself performing specific movements.Results:The significant Skill-Level x Sport Type interactions for the EVI task revealed that participants who specialized in open sports and had higher skill-levels had a higher accuracy rate as compared to the other subgroups.For the IVI task,the differences between the groups were less clear:those with higher skill-levels or open sports had a higher accuracy rate than those with lower skill-levels or closed sports.Conclusion:EVI involves the visualization of others and the environment,and would be relevant to higher skill-level athletes who engage in open sports.IVI,in contrast,tends to be more self-oriented and would be relevant for utilization by higher skill-level athletes regardless of sport type.
