Search Results (4,202)

Unmanned Aerial Vehicles are increasingly used for urban photogrammetry, yet best-practice guidance on flight-planning parameters in dense city blocks remains limited. However, the quality of generated orthomosaics is strongly dependent on the proper configuration of flight parameters, highlighting the need for evidence-based guidance in consolidated urban environments. This study evaluated the impact of various flight configurations on orthomosaic visual quality and operational efficiency. A total of 96 automated flights were conducted over a 1.5-hectare urban area, systematically varying height, frontal overlap, lateral overlap, camera angle, and flight pattern. Orthomosaic photogrammetric reconstructions were generated and assessed using a multi-criteria scoring system based on the image processing time and the visual clarity of control targets. Results show that a flight height of 60 m, 70% frontal overlap, 80% lateral overlap, nadir $90°$ camera angle, and a grid flight pattern provide the best balance between image quality and operational efficiency. Lower heights improved visual detail but increased processing time, while excessive overlaps did not necessarily enhance final image quality. Full article

Detection of changes in remote sensing relies on clean multi-temporal images, but cloud cover may considerably degrade image quality. Cloud removal, a critical image-restoration task, demands effective modeling of long-range spatial dependencies to reconstruct information under cloud occlusions. While Transformer-based models excel at handling such spatial modeling, their quadratic computational complexity limits practical application. The recently proposed Mamba, a state space model, offers a computationally efficient alternative for long-range modeling, but its inherent 1D sequential processing is ill-suited to capturing complex 2D spatial contexts in images. To bridge this gap, we propose the multi-pattern scanning Mamba (MPSM) block. Our MPSM block adapts the Mamba architecture for vision tasks by introducing a set of diverse scanning patterns that traverse features along horizontal, vertical, and diagonal paths. This multi-directional approach ensures that each feature aggregates comprehensive contextual information from the entire spatial domain. Furthermore, we introduce a dynamic path-aware (DPA) mechanism to adaptively recalibrate feature contributions from different scanning paths, enhancing the model’s focus on position-sensitive information. To effectively capture both global structures and local details, our MPSM blocks are embedded within a U-Net architecture enhanced with multi-scale supervision. Extensive experiments on the RICE1, RICE2, and T-CLOUD datasets demonstrate that our method achieves state-of-the-art performance while maintaining favorable computational efficiency. Full article

Machine harvesting of cotton in Xinjiang has significantly improved harvesting efficiency; however, it has also resulted in a considerable increase in residual mulch content within the cotton, which has severely affected the quality and market value of cotton textiles. Existing mulch detection algorithms based on machine vision generally suffer from complex parameterization and insufficient real-time performance. To overcome these limitations, this study proposes a novel mulch detection algorithm, Mulch-YOLO, developed on the YOLOv11 framework. Specifically, an improved CBAM (Convolutional Block Attention Module) is incorporated into the BiFPN (Bidirectional Feature Pyramid Network) to achieve more effective fusion of multi-scale mulch features. To enhance the semantic representation of mulch features, a modified Content-Aware ReAssembly of Features module, CARAFE-Mulch (Content-Aware ReAssembly of Features), is designed to reorganize feature maps, resulting in stronger feature expressiveness compared with the original representations. Furthermore, the MobileOne module is optimized by integrating the DECA Dilated Efficient Channel Attention (Dilated Efficient Channel Attention) module, thereby reducing both the parameter count and computational load while improving detection efficiency in real time. To verify the effectiveness of the proposed approach, experiments were conducted on a real-world dataset containing 20,134 images of low-visual-saliency plastic mulch. The results indicate that Mulch-YOLO achieves a lightweight architecture and high detection accuracy. Compared with YOLOv11n, the proposed method improves mAP@0.5 by 4.7% and mAP@0.5:0.95 by 3.3%, with a 24% reduction in model parameters. Full article

(1) Background: Digital Elevation Models (DEMs) encompass digital bare earth surface representations that are essential for spatial data analysis, such as hydrological and geological modelling, as well as for other applications, such as agriculture and environmental management. However, available bare-earth DEMs can have limited coverage or accessibility. Moreover, the majority of available global DEMs have lower spatial resolutions (∼30–90 m) and contain errors introduced by surface features such as buildings and vegetation. (2) Methods: This research presents an innovative method to convert global DEMs to bare-earth DEMs while enhancing their spatial resolution as measured by the improved vertical accuracy of each pixel, combined with reduced pixel size. We propose the Joint Spatial Propagation Super-Resolution network (JSPSR), which integrates Guided Image Filtering (GIF) and Spatial Propagation Network (SPN). By leveraging guidance features extracted from remote sensing images with or without auxiliary spatial data, our method can correct elevation errors and enhance the spatial resolution of DEMs. We developed a dataset for real-world bare-earth DEM Super-Resolution (SR) problems in low-relief areas utilising open-access data. Experiments were conducted on the dataset using JSPSR and other methods to predict 3 m and 8 m spatial resolution DEMs from 30 m spatial resolution Copernicus GLO-30 DEMs. (3) Results: JSPSR improved prediction accuracy by 71.74% on Root Mean Squared Error (RMSE) and reconstruction quality by 22.9% on Peak Signal-to-Noise Ratio (PSNR) compared to bicubic interpolated GLO-30 DEMs, and achieves 56.03% and 13.8% improvement on the same items against a baseline Single Image Super Resolution (SISR) method. Overall RMSE was 1.06 m at 8 m spatial resolution and 1.1 m at 3 m, compared to 3.8 m for GLO-30, 1.8 m for FABDEM and 1.3 m for FathomDEM, at either resolution. (4) Conclusions: JSPSR outperforms other methods in bare-earth DEM super-resolution tasks, with improved elevation accuracy compared to other state-of-the-art globally available datasets. Full article

Multifunctional metasurfaces, capable of flexible electromagnetic wave manipulation, have become a focus of research for their high integration and utility. In particular, those operating simultaneously in transmission and reflection modes have attracted growing interest, as they integrate multiple functions within a single aperture, save physical space, and further expand wave control capabilities across full space. In this work, an inspiring strategy of transmission-reflection-integrated bifunctional metasurface by hybridizing geometric phase and propagation phase is proposed. The transmission and reflection modes can be independently and flexibly controlled in full space: the co-polarized reflection under left-handed circular polarization (LCP) incidence is governed by rotation-induced geometric phase modulation, while the co-polarized transmission under right-handed circular polarization (RCP) incidence is modulated through scaling-induced propagation phase modulation. Moreover, arbitrary amplitude modulation of the co-polarized transmission under RCP incidence can be realized by incorporating lumped resistors. As a proof of concept, a bifunctional meta-device is constructed, which can generate vortex beam carrying arbitrary topological charge for LCP reflected wave and achieve high-quality holographic imaging for RCP transmitted wave. Both the simulated and experimental results validate the feasibility of the proposed strategy, which significantly enhances the integration density of multifunctional metasurfaces while reducing inter-functional crosstalk, expanding its potential applications in electronic engineering. Moreover, it can also serve as a fundamental machine learning platform, facilitating multimodal fusion and cross-modal learning in radar signals and visual imaging. Full article

Image captioning, as a representative cross-modal task, faces significant challenges, including high annotation costs and modality alignment difficulties. To address these issues, this paper proposes CMSA, an image captioning framework that does not require paired image-text data. The framework integrates a generator, a discriminator, and a reward module, employing a collaborative multi-module optimization strategy to enhance caption quality. The generator builds multi-level joint feature representations based on a contrastive language-image pretraining model, effectively mitigating the modality alignment problem and guiding the language model to generate text highly consistent with image semantics. The discriminator learns linguistic styles from external corpora and evaluates textual naturalness, providing critical reward signals to the generator. The reward module combines image-text relevance and textual quality metrics, optimizing the generator parameters through reinforcement learning to further improve semantic accuracy and language expressiveness. CMSA adopts a progressive multi-stage training strategy that, combined with joint feature modeling and reinforcement learning mechanisms, significantly reduces reliance on costly annotated data. Experimental results demonstrate that CMSA significantly outperforms existing methods across multiple evaluation metrics on the MSCOCO and Flickr30k datasets, exhibiting superior performance and strong cross-dataset generalization ability. Full article

Background/Objectives: Utilization of polyetheretherketone (PEEK) cages for spinal fusion has surged in the U.S., yet comprehensive comparisons evaluating its postoperative effectiveness with alternative materials remain limited. This systematic review investigates the efficacy of PEEK cages against traditional fusion materials across various surgery types, elucidating PEEK’s impact on fusion rates, postoperative outcomes, and long-term success. Methods: A systematic search of PubMed, CINAHL, Scopus, Embase, and Web of Science was conducted through 14 October 2024. Included studies were randomized controlled trials (RCTs) comparing PEEK cages with titanium, silicon nitride, and metal-coated PEEK cages for anterior cervical discectomy and fusion (ACDF), posterior lumbar interbody fusion (PLIF), and transforaminal lumbar interbody fusion (TLIF). Article quality was assessed using GRADE criteria. Results: From 288 initially screened articles, 25 RCTs involving 2046 patients (mean follow-up 23.1 ± 18.2 months) met inclusion criteria and were determined as moderate (n = 21) or high (n = 4) quality. Fusion rates by cage material for PEEK (n = 1041), Ti-PEEK (n = 291), and titanium (n = 53) were 85.63 ± 18.00%, 80.05 ± 19.9%, and 92.75 ± 11.31%, respectively. In ACDF, titanium cages achieved higher fusion rates than PEEK (100% vs. 94%). In PLIF and TLIF, coated PEEK outperformed uncoated PEEK (75% vs. 71% and 94% vs. 84%, respectively). Uncoated PEEK achieved fusion rates of 94.04 ± 5.04% for ACDF, 71.21 ± 21.93% for PLIF, and 83.50 ± 24.66% for TLIF, with titanium outperforming PEEK in early fusion outcomes. Coated PEEK demonstrated potential improvements in fusion rates over uncoated PEEK in PLIFs and TLIFs. Conclusions: Selection of cage material for spinal fusions should be tailored to surgical requirements and patient needs. While titanium and PEEK are effective, their performance varies across contexts. New materials and surface modifications may enhance these outcomes further, warranting future research in long-term studies and development of novel materials. These findings can help surgeons choose cage materials according to procedure type, patient characteristics, and imaging needs. Full article

Background: Current fMRI (functional magnetic resonance imaging)-driven brain information decoding for visual image reconstruction techniques faces issues such as poor structural fidelity, inadequate model generalization, and unnatural visual image reconstruction in complex scenarios. Methods: To address these challenges, this study proposes a Dynamic Confidence Bayesian Adaptive Network (DCBAN). In this network model, deep nested Singular Value Decomposition is introduced to embed low-rank constraints into the deep learning model layers for fine-grained feature extraction, thus improving structural fidelity. The proposed Bayesian Adaptive Fractional Ridge Regression module, based on singular value space, dynamically adjusts the regularization parameters, significantly enhancing the decoder’s generalization ability under complex stimulus conditions. The constructed Dynamic Confidence Adaptive Diffusion Model module incorporates a confidence network and time decay strategy, dynamically adjusting the semantic injection strength during the generation phase, further enhancing the details and naturalness of the generated images. Results: The proposed DCBAN method is applied to the NSD, outperforming state-of-the-art methods by 8.41%, 0.6%, and 4.8% in PixCorr (0.361), Incep (96.0%), and CLIP (97.8%), respectively, achieving the current best performance in both structural and semantic fMRI visual image reconstruction. Conclusions: The DCBAN proposed in this thesis offers a novel solution for reconstructing visual images from fMRI signals, significantly enhancing the robustness and generative quality of the reconstructed images. Full article

The swift advancement of renewable energy technology has highlighted the need for effective photovoltaic (PV) solar energy tracking systems. Deep learning (DL) has surfaced as a promising method to improve the precision and efficacy of photovoltaic (PV) solar tracking by utilising complicated patterns in meteorological and PV system data. This systematic literature review (SLR) seeks to offer a thorough examination of the progress in deep learning architectures for photovoltaic solar energy tracking over the last decade (2016–2025). The review was structured around four research questions (RQs) aimed at identifying prevalent deep learning architectures, datasets, performance metrics, and issues within the context of deep learning-based PV solar tracking systems. The present research utilised SLR methodology to analyse 64 high-quality publications from reputed academic databases like IEEE Xplore, Science Direct, Springer, and MDPI. The results indicated that deep learning architectures, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Transformer-based models, are extensively employed to improve the accuracy and efficiency of photovoltaic solar tracking systems. Widely utilised datasets comprised meteorological data, photovoltaic system data, time series data, temperature data, and image data. Performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE), were employed to assess model efficacy. Identified significant challenges encompass inadequate data quality, restricted availability, high computing complexity, and issues in model generalisation. Future research should concentrate on enhancing data quality and accessibility, creating generalised models, minimising computational complexity, and integrating deep learning with real-time photovoltaic systems. Resolving these challenges would facilitate advancements in efficient, reliable, and sustainable photovoltaic solar tracking systems, hence promoting the wider adoption of renewable energy technology. This review emphasises the capability of deep learning to transform photovoltaic solar tracking and stresses the necessity for interdisciplinary collaboration to address current limitations. Full article

As urbanization accelerates, the growing needs of children have led to a significant imbalance between supply and demand in urban spaces. Creating child-friendly environments is crucial for enhancing urban resilience and promoting sustainable development. However, there is currently a lack of sufficient quantitative methods to assess child-friendliness and analyze the complex interactions between children’s perceptions and spatial factors. This study uses the central area of Xiamen as a case study to explore how different street environment characteristics influence perceptions of child-friendliness. This study integrates empathy-based stories (MEBS), street scene image analysis, XGBoost machine learning, and GeoSHapley spatial analysis to explore children’s perceptions of urban spaces. The study reveals that: (1) The child-friendly resources in the central urban area of Xiamen are concentrated in the northeastern and Huli districts, while a supply–demand mismatch exists in Siming District, which has a higher population density; (2) Greenness and pavement coverage are critical in shaping child-friendliness, with greenness having the greatest positive impact; (3) Some areas with child-friendly renovations have a lower child-friendliness index, whereas regions like Guanyinshan, which did not undergo renovations, scored higher; (4) The interaction between greenness and openness positively influences perceptions, while enclosure and visual complexity have a negative effect. Building on the need for child-friendly environments, this study develops a spatial analysis framework to quantify the alignment of child-friendly supply and demand in Xiamen’s central urban area, identify regions with mismatched supply and demand, and offer spatial decision support to improve urban environmental quality and promote sustainable development. Full article

As an important part of the urban ecological environment, urban green space plays a crucial and irreplaceable role in improving air quality, promoting sustainable development, and enhancing residents’ quality of life. This study takes Beijing’s urban green space as the research object. Based on Landsat series satellite remote sensing images, the land use distribution of Beijing is obtained through supervised classification. Combined with data such as PM_2.5 concentration and wind speed, the dry deposition efficiency of PM_2.5 is quantitatively analyzed. The results show that: (1) Beijing’s urban green space has significant advantages in PM_2.5 dry deposition. In terms of dry deposition flux, the order of annual average deposition of different land types is: forest land > farm land > grassland > impervious surface > water body = unutilized land. Among them, forest land has the best dry deposition effect, with an annual average dry deposition of 1.13 g/m², which is 188.41 times that of impervious surface; cultivated land and grassland are 0.22 g/m² and 0.19 g/m² respectively, which are 37.13 times and 32.34 times that of impervious surface. (2) From 2000 to 2020, the PM_2.5 removal rate of green space continued to rise, but the reduction amount showed a trend of first increasing and then decreasing. There are significant seasonal differences. The reduction amount is the highest in autumn (reaching 449.90 tons in October), followed by summer, spring, and winter (the lowest in August, at 190.27 tons). (3) In terms of spatial distribution, the high-value areas of dry deposition are concentrated in the suburbs, showing a “southwest-northeast” axial distribution, while the low-value areas are mainly located in the outer suburbs, reflecting the imbalance of green space layout and the regional differences in PM_2.5 reduction. Combined with the current situation of green space in Beijing, the study puts forward targeted optimization suggestions, providing theoretical support and scientific basis for the construction of Beijing as a “garden city”. Full article

The fusion of infrared and visible images provides critical value in computer vision by integrating their complementary information, especially in the field of industrial detection, which provides a more reliable data basis for subsequent defect recognition. This paper presents STGAN, a novel Generative Adversarial Network framework based on a Swin Transformer for high-quality infrared and visible image fusion. Firstly, the generator employs a Swin Transformer as its backbone for feature extraction, which adopts a U-Net architecture, and the improved W-MSA is introduced into the bottleneck layer to enhance local attention and improve the expression ability of cross-modal features. Secondly, the discriminator uses a Markov discriminator to distinguish the difference. Then, the core GAN framework is leveraged to guarantee the retention of both infrared thermal radiation and visible-light texture details in the generated image so as to improve the clarity and contrast of the fused image. Finally, simulation verification showed that six out of seven indicators ranked in the top two, especially in key indicators such as PSNR, VIF, MI, and EN, which achieved optimal or suboptimal values. The experimental results on the general dataset show that this method is superior to the advanced method in terms of subjective vision and objective indicators, and it can effectively enhance the fine structure and thermal anomaly information in the image, which gives it great potential in the application of industrial surface defect detection. Full article

In high-density commercial farms, the timely detection and removal of dead bodies are essential to maintain the well-being of animals and ensure farm productivity. This review systematically synthesizes 128 published studies, 52 of which are highly related to the death detecting topic, covering diverse animal species and farming scenarios. The review systematically synthesizes existing research on death detection methods, dead body removal systems, and their integration. The death detection process is divided into three key stages: data acquisition, dataset establishment, and data processing. Inspection systems are categorized into fixed and mobile inspection systems, enabling autonomous imaging for death detection. Regarding death removal systems, current research predominantly focuses on hardware design for poultry and aquaculture, but real-farm validation remains limited. Key focuses for future development include enhancing the robustness and adaptability of detection models with high-quality datasets, brainstorming for more feasible designs of removal systems to enhance adaptability to diverse farm conditions, and improving the integration of inspection systems with removal systems to conduct fully automated detection-removal operations. Ultimately, the successful application of these technologies will reduce labor dependence, enhance biosecurity, and support sustainable, high-density large-scale animal farming while ensuring both satisfying production and the welfare of animals. Full article

Fresnel diffraction plays a critical role in coherent diffraction imaging and holography. Experimental setups for these techniques are often designed based on plane-wave illumination. However, two key issues arise in practical applications: on the one hand, it is difficult to obtain an ideal plane wave in experiments, which inevitably introduces wavefront curvature; on the other hand, the use of spherical waves enhances the quality of reconstruction results, while it also imposes additional requirements for the calibration of both the illumination wavefront and experimental parameters. To address these issues, we introduce a diffraction-adapted propagation model that integrates both the spherical wavefront effects and sampling variations within the diffraction model. The parameters of this model can be estimated through prior-free optimization, thereby eliminating the need for prior knowledge of system parameters or specific experimental setups. Our approach enables robust reconstruction across a wide range of Fresnel diffraction patterns. It also allows for the automatic calibration of experimental parameters using only the measured data. The effectiveness of the proposed method has been validated through both theoretical analysis and experimental results. Full article

Background/Objectives: Menopause, marked by permanent cessation of menstruation, is a universal transition associated with vasomotor, genitourinary, psychological, and metabolic changes. These conditions significantly affect health-related quality of life (HRQoL) and increase the risk of chronic diseases. Despite their impact, timely diagnosis and individualized management are often limited by delayed care, fragmented health systems, and cultural barriers. Methods: This review summarizes current applications of artificial intelligence (AI) in postmenopausal health, focusing on risk prediction, early detection, and personalized treatment. Evidence was compiled from studies using biomarkers, imaging, wearable sensors, electronic health records, natural language processing, and digital health platforms. Results: AI enhances disease prediction and diagnosis, including improved accuracy in breast cancer and osteoporosis screening through imaging analysis, and cardiovascular risk stratification via machine learning models. Wearable devices and natural language processing enable real-time monitoring of underreported symptoms such as hot flushes and mood disorders. Digital technologies further support individualized interventions, including lifestyle modification and optimized medication regimens. By improving access to telemedicine and reducing bias, AI also has the potential to narrow healthcare disparities. Conclusions: AI can transform postmenopausal care from reactive to proactive, offering personalized strategies that improve outcomes and quality of life. However, challenges remain, including algorithmic bias, data privacy, and clinical implementation. Ethical frameworks and interdisciplinary collaboration among clinicians, data scientists, and policymakers are essential for safe and equitable adoption. Full article