期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到19,695篇文章
< 1 2 … 250 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
Fracture response patterns in deep to ultra-deep tight sandstones:A comparison based on core and borehole images 认领 引用
1
作者 Yu Du Hu-Cheng Deng +7 位作者 Xiao-Fei Hu Hao-Tian Zhang Hong-Hui Wang Cui-Li Wang Mao-Xin Liu Chen-Yang Zhao Shang-Rong Guo Zi-Yun Zheng 《Petroleum Science》 SCIE EI CAS CSCD 2026年第2期541-562,共22页
Natural fractures serve as the primary storage spaces and flow pathways in deep to ultra-deep tight sandstone reservoirs,directly influencing hydrocarbon accumulation,preservation,and production.Borehole images offer ... Natural fractures serve as the primary storage spaces and flow pathways in deep to ultra-deep tight sandstone reservoirs,directly influencing hydrocarbon accumulation,preservation,and production.Borehole images offer intuitive,continuous,and high-resolution identification of natural fractures along the entire borehole.However,relying solely on complete sinusoidal curves from borehole images for fracture identification may lead to omissions,as it overlooks cases where these curves are incomplete or truncated.To address the problems and deficiencies in fracture identification,this study systematically classifies borehole image feature patterns based on core-to-log spatial position restoring.A bidirectional comparison is conducted between natu ral fractures in cores and the fracture image features in borehole images.A quantitative relationship between fracture dip angle,thin layer thickness and borehole radius was established,accompanied by a mathematical expression describing the fracture curve morphology was proposed.These findings enabled the development of an imaging response pattern for natural fractures in deep and ultra-deep tight sandstone reservoirs,incorporating key parameters such as dip angle,through-layer connectivity,and spatial position within the borehole.In the Bashijiqike-Baxigai tight-sandstone reservoirs of the Bozi-Dabei area,we estimate that approximately 24%of coreobserved fractures display distinct linear-pattern features on borehole images,whereas approximately 91%of borehole images features can be correlated with fractures observed in core.Fracture identification rates for natural fractures increased by 17%in water-based mud and by 3%in oil-based mud through the application of the natural fracture image response pattern.Moreover,this study analyzes the deviations in the matching between core fractures and image features.Finally,we further discuss the common sources of error in natural fracture identification using borehole images from multiple perspectives,including missing core responses,inconsistencies between core and borehole image features,distortion of fracture chord curve,inaccurate fracture count,misclassification of fractures,and variations in interpretation under different mud systems.The research addresses the blind spots of traditional methods in fracture identification within thin layers,not only enhancing the detection rate of natural fractu res but also further improving the accuracy of fractu re recognitio n.At the same time,it will contribute to the optimization of fracture characterization,reservoir evaluation,and production forecasting,providing a more reliable data foundation for exploration and development under complex geological conditions. 展开更多
关键词 Borehole images Natural fracture Fracture image features Fracture response pattern Fracture identification Tight sandstone reservoir
暂未订购 下载PDF
An Exploratory Study on Prognostic Prediction and Interpretability Analysis in Early-stage Triple-negative Breast Cancer Using Pathological Images 认领 引用
2
作者 Zixuan Yang Yaping Lyu +4 位作者 Liuliu Quan Shuyue Chen Yuying Sun Jie Ju Peng Yuan 《Biomedical and Environmental Sciences》 SCIE CAS CSCD 2026年第3期310-326,共17页
Objective To develop a prognostic prediction model for early-stage triple-negative breast cancer(TNBC)using H&E-stained pathological images and to investigate its underlying biological interpretability.Methods A d... Objective To develop a prognostic prediction model for early-stage triple-negative breast cancer(TNBC)using H&E-stained pathological images and to investigate its underlying biological interpretability.Methods A deep learning model was trained on 340 WSIs and externally validated using 81 TCGA cases.Image-derived features extracted through convolutional neural networks were integrated with clinicopathological variables.Model performance was assessed using ROC curve analysis,and interpretability was evaluated by correlating image features with mRNA-seq data and characteristics of the immune microenvironment.Results The model achieved AUCs of 0.86 and 0.75 in the training and validation cohorts,respectively.Analysis using HoVer-Net indicated that lymphocyte abundance was associated with recurrence risk.Texture-related features showed significant correlations with immune cell infiltration and prognostic gene expression profiles.Conclusion This study demonstrates that deep learning can enable accurate prognostic prediction in early-stage TNBC,with interpretable image features that reflect the tumor immune microenvironment and gene expression profiles. 展开更多
关键词 Triple-negative breast cancer Prognostic prediction model Deep learning H&E-stained pathological images Model interpretability
暂未订购 下载PDF
Lithology identification using borehole images by contrast-limited adaptive histogram equalization and machine learning models 认领 引用
3
作者 Enming Li Pablo Segarra +4 位作者 José A.Sanchidrián Zahir Ahmed Ignacio Catalán Alberto Fernández Santiago Gómez 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第3期1698-1718,共21页
Agile lithology identification can assist mining by providing important information in the exploration and production of mineral resources.This study proposes a new lithology recognition procedure using video-logging ... Agile lithology identification can assist mining by providing important information in the exploration and production of mineral resources.This study proposes a new lithology recognition procedure using video-logging of boreholes with an endoscope,applied to six production blocks in a limestone quarry.Images are automatically extracted from the videos and the lithology is classified into three classes based on clay content,i.e.massive limestone,brecciated limestone,and high amount of clay.The image quality is evaluated with a gray pixel intensity threshold and three no-reference image quality metrics,i.e.perception-based image quality evaluator,natural image quality evaluator,and blindeferenceless image spatial quality evaluator.After removing low-quality images,7583 images are retained and used for developing lithology classification models using six optimized classification techniques.The contrast-limited adaptive histogram equalization(CLAHE)technique is used to improve image quality.Ten color characteristics involving three percentiles of red,green and blue pixel intensities,together with color counting and five texture characteristics-correlation,entropy,homogeneity,contrast and energy-are used as inputs.Bayesian optimized light gradient boosting machine model performs best,with an overall accuracy of 88.04%,and a precision on the classes of massive limestone,brecciated limestone and high amount of clay of 90.72%,83.52%and 85.29%,respectively,for the testing set.The feature importance scores show that the color counting is the most significant parameter for the development of the classification model.Compared with previous image-based methodologies,this study provides a more flexible and cheaper procedure to identify lithology. 展开更多
关键词 Lithology identification Borehole images Endoscope Light gradient boosting machine Contrast-limited adaptive histogram equalization(CLAHE)
暂未订购 下载PDF
MSFFNet:Multiscale Feature Fusion Network for Small Target Detection in Remote Sensing Images 认领 引用
4
作者 Hui Zong Chenbin Wang +3 位作者 Hao Wang Xin Cheng Shangbing Gao Yunyang Yan 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第2期592-609,共18页
With the advancement of satellite remote sensing technology,object detection based on high-resolution remote sensing imagery has emerged as a prominent research focus in the field of computer vision.Although numerous ... With the advancement of satellite remote sensing technology,object detection based on high-resolution remote sensing imagery has emerged as a prominent research focus in the field of computer vision.Although numerous algorithms have been developed for remote sensing image object detection,they still suffer from challenges such as low detection accuracy and high false positive rates.To address these issues,we propose a novel architecture,the multiscale feature fusion network(MSFFNet).MSFFNet is composed of three key components:the Large Selective Kernel Block(LSKBlock),the Space-to-Depth ADown(SPDA)module and the Double Feature Aggregation Neck(DFAN).Specifically,the LSKBlock adaptively captures salient target features by dynamically adjusting the receptive field size,thereby enhancing detection precision.The SPDA module converts spatial correlations into channel-wise dependencies by segmenting and reordering the feature maps,which helps preserve finegrained information,suppress background interference and reduce false detections.Furthermore,the DFAN integrates shallow and deep features through a multiscale feature fusion module(MSFFM),enabling the extraction of multiscale target representations and improving overall detection performance.Extensive experiments on public datasets,SIMD,VisDrone2019 and DIOR,demonstrate the effectiveness of our approach.Compared with the YOLOv9s baseline model,MSFFNet achieves improvements in mAP50%of 0.6%,1.9%and 3.5%,respectively. 展开更多
关键词 computer vision object recognition remote sensing images
暂未订购 下载PDF
Pedestrian detection of infrared images based on an improved FCOS algorithm 认领 引用
5
作者 Fuzhen ZHU Hao HAN +1 位作者 Hengfei JIA Bing ZHU 《Optoelectronics Letters》 EI 2026年第2期105-110,共6页
The current infrared image pedestrian detectors have problems with high rates of false positives and false negatives. To solve these problems, we proposed an improved anchor-free fully convolutional one-stage object d... The current infrared image pedestrian detectors have problems with high rates of false positives and false negatives. To solve these problems, we proposed an improved anchor-free fully convolutional one-stage object detection(FCOS) algorithm. Firstly, we introduced the channel attention module squeeze excitation(SE)-Block in the FCOS backbone network, which was used to learn how to model the relative importance between different feature channels, and to achieve the weight recalibration of the features extracted from the convolution neural network, and improve the weight values that are more important for pedestrian target detection. Secondly, soft non-maximum suppression(Soft-NMS) replaced the conventional NMS within the algorithm's post-processing phase, which was used to reduce the probability of missed detection for occluded pedestrians. The experimental results show that our improved FCOS algorithm improves the average precision(AP) by 6.71% on the original dataset and 7.97% on the augmented KAIST pedestrian dataset compared with the original FCOS algorithm. Our improvements effectively meet the real-time requirements and there is no significant decrease in speed compared with the original FCOS algorithm, and decreased the false positives and false negatives for infrared image pedestrian detection. 展开更多
关键词 weight recalibration pedestrian detection infrared image pedestrian detectors channel attention module learn how model relative importance different feature channels improved FCOS algorithm infrared images Squeeze Excitation SE block
暂未订购 下载PDF
A Hybrid Deep Learning Multi-Class Classification Model for Alzheimer’s Disease Using Enhanced MRI Images 认领 引用
6
作者 Ghadah Naif Alwakid 《Computers, Materials & Continua》 SCIE EI 2026年第1期797-821,共25页
Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often stru... Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often struggle with low-contrast MRI images,class imbalance,and suboptimal feature extraction.This paper develops a Hybrid DL system that unites MobileNetV2 with adaptive classification methods to boost Alzheimer’s diagnosis by processing MRI scans.Image enhancement is done using Contrast-Limited Adaptive Histogram Equalization(CLAHE)and Enhanced Super-Resolution Generative Adversarial Networks(ESRGAN).A classification robustness enhancement system integrates class weighting techniques and a Matthews Correlation Coefficient(MCC)-based evaluation method into the design.The trained and validated model gives a 98.88%accuracy rate and 0.9614 MCC score.We also performed a 10-fold cross-validation experiment with an average accuracy of 96.52%(±1.51),a loss of 0.1671,and an MCC score of 0.9429 across folds.The proposed framework outperforms the state-of-the-art models with a 98%weighted F1-score while decreasing misdiagnosis results for every AD stage.The model demonstrates apparent separation abilities between AD progression stages according to the results of the confusion matrix analysis.These results validate the effectiveness of hybrid DL models with adaptive preprocessing for early and reliable Alzheimer’s diagnosis,contributing to improved computer-aided diagnosis(CAD)systems in clinical practice. 展开更多
关键词 Alzheimer’s disease deep learning MRI images MobileNetV2 contrast-limited adaptive histogram equalization(CLAHE) enhanced super-resolution generative adversarial networks(ESRGAN) multi-class classification
暂未订购 下载PDF
A multi-instance learning framework combining CNN and transformer for noninvasive coronary heart disease diagnosis using scleral images 认领 引用 被引量：1
7
作者 Chuxiang Gao Zongqing Ma +5 位作者 Hantao Bai Yixuan Shi Fan Fan Guoliang Huang Xiaoqing Zhang Jiang Zhu 《Journal of Innovative Optical Health Sciences》 SCIE EI CSCD 2026年第3期132-145,共14页
Coronary heart disease(CHD)is a prevalent and life-threatening chronic cardiovascular condition,and early screening and diagnosis are essential for improving patient outcomes.Traditional diagnostic methods,such as ele... Coronary heart disease(CHD)is a prevalent and life-threatening chronic cardiovascular condition,and early screening and diagnosis are essential for improving patient outcomes.Traditional diagnostic methods,such as electrocardiography,echocardiography,and coronary angiography,typically require specialized equipment,expert interpretation,and,in some cases,invasive procedures,thereby limiting their accessibility and scalability for population-wide screening.In this paper,we propose a novel,painless,noninvasive,and rapid auxiliary diagnostic approach for CHD based on scleral images.Specifically,we present the ScleraMIL model,a multi-instance learning framework that integrates the strengths of Convolutional Neural Networks(CNNs)and Vision Transformer(ViT)architectures to capture both local and global representations across multiple scleral images from each individual.To ensure the model focuses on scleral features,the Mamba-UNet segmentation model is first employed to precisely extract the scleral region from the raw ocular image.Then,each subject’s ten segmented scleral images(five per eye)are treated as instances forming a bag with a single patient-level label.These instances are processed through a CNN-based feature extractor,followed by a Transformer encoder that models inter-image dependencies for discriminative feature fusion,and finally passed to a classification head to predict the probability of CHD.Experimental results demonstrate that ScleraMIL achieves superior performance on key metrics such as accuracy,AUC,F1-score,and recall,significantly outperforming other deep learning and multiple instance learning methods.This work explores the feasibility of using scleral imaging as a potential biomarker for CHD classification and represents a promising step toward intelligent,accessible,and noninvasive cardiovascular risk assessment. 展开更多
关键词 Multiple instance learning auxiliary diagnosis sclera images
暂未订购 下载PDF
Precision organoid segmentation technique(POST):accurate organoid segmentation in challenging bright-field images 认领 引用 被引量：2
8
作者 Xuan Du Yuchen Li +5 位作者 Jiaping Song Zilin Zhang Jing Zhang Yanhui Li Zaozao Chen Zhongze Gu 《Bio-Design and Manufacturing》 SCIE EI CAS CSCD 2026年第1期80-93,I0013-I0016,共14页
Organoids possess immense potential for unraveling the intricate functions of human tissues and facilitating preclinical disease treatment.Their applications span from high-throughput drug screening to the modeling of... Organoids possess immense potential for unraveling the intricate functions of human tissues and facilitating preclinical disease treatment.Their applications span from high-throughput drug screening to the modeling of complex diseases,with some even achieving clinical translation.Changes in the overall size,shape,boundary,and other morphological features of organoids provide a noninvasive method for assessing organoid drug sensitivity.However,the precise segmentation of organoids in bright-field microscopy images is made difficult by the complexity of the organoid morphology and interference,including overlapping organoids,bubbles,dust particles,and cell fragments.This paper introduces the precision organoid segmentation technique(POST),which is a deep-learning algorithm for segmenting challenging organoids under simple bright-field imaging conditions.Unlike existing methods,POST accurately segments each organoid and eliminates various artifacts encountered during organoid culturing and imaging.Furthermore,it is sensitive to and aligns with measurements of organoid activity in drug sensitivity experiments.POST is expected to be a valuable tool for drug screening using organoids owing to its capability of automatically and rapidly eliminating interfering substances and thereby streamlining the organoid analysis and drug screening process. 展开更多
关键词 Organoid Drug screening Deep learning Image segmentation
暂未订购 下载PDF
A Survey of Generative Adversarial Networks for Medical Images 认领 引用
9
作者 Sameera V.Mohd Sagheer U.Nimitha +3 位作者 P.M.Ameer Muneer Parayangat MohamedAbbas Krishna Prakash Arunachalam 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期130-185,共56页
Over the years,Generative Adversarial Networks(GANs)have revolutionized the medical imaging industry for applications such as image synthesis,denoising,super resolution,data augmentation,and cross-modality translation... Over the years,Generative Adversarial Networks(GANs)have revolutionized the medical imaging industry for applications such as image synthesis,denoising,super resolution,data augmentation,and cross-modality translation.The objective of this review is to evaluate the advances,relevances,and limitations of GANs in medical imaging.An organised literature review was conducted following the guidelines of PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses).The literature considered included peer-reviewed papers published between 2020 and 2025 across databases including PubMed,IEEE Xplore,and Scopus.The studies related to applications of GAN architectures in medical imaging with reported experimental outcomes and published in English in reputable journals and conferences were considered for the review.Thesis,white papers,communication letters,and non-English articles were not included for the same.CLAIM based quality assessment criteria were applied to the included studies to assess the quality.The study classifies diverse GAN architectures,summarizing their clinical applications,technical performances,and their implementation hardships.Key findings reveal the increasing applications of GANs for enhancing diagnostic accuracy,reducing data scarcity through synthetic data generation,and supporting modality translation.However,concerns such as limited generalizability,lack of clinical validation,and regulatory constraints persist.This review provides a comprehensive study of the prevailing scenario of GANs in medical imaging and highlights crucial research gaps and future directions.Though GANs hold transformative capability for medical imaging,their integration into clinical use demands further validation,interpretability,and regulatory alignment. 展开更多
关键词 Generative adversarial networks medical images denoising segmentation translation
暂未订购 下载PDF
Study on automatic recognition of stone composition in intraoperative endoscopic images—a single center study 认领 引用
10
作者 Daxun Luo Bixiao Wang +6 位作者 Haifeng Song Chaoyue Ji Weiguo Hu Bo Xiao Boxing Su Yubao Liu Jianxing Li 《The Canadian Journal of Urology》 SCIE 2026年第3期623-634,共12页
Objectives:Urinary stone composition critically influences treatment selection and recurrence prevention,yet current intraoperative assessment remains imprecise.This study aims to achieve intraoperative prediction of ... Objectives:Urinary stone composition critically influences treatment selection and recurrence prevention,yet current intraoperative assessment remains imprecise.This study aims to achieve intraoperative prediction of stone composition by applying a deep convolutional neural network(CNN)to routinely captured endoscopic images.Methods:We retrospectively studied endoscopic images from stone-breaking surgeries in Beijing Tsinghua Changgung Hospital during 2022-12-2024-12.Images were captured before and after laser lithotripsy.Based on postoperative infrared spectroscopy,stones were divided intove categories.In total,1780 images(1167 from RIRS,613 from PCNL)were included and split into training and testing sets at an 8:2 ratio.Using ResNet-50 as the base model,only endoscopic digital images and stone classication data were input for minimalsupervision learning.After training,the model accuracy for each stone category surpassed 95%.The model was then tested on 20%of RIRS and RIRS+PCNL images,with 3D PCA and Grad-cam for visual analysis.Results:For the RIRS image test set:The precision was 93.8%for the calcium oxalate group(n=147),96.3%for the calcium oxalate mixed with a uric acid group(n=30),91.8%for the calcium oxalate mixed with carbonate apatite group(n=106),88.9%for the struvite mixed with calcium oxalate and carbonate apatite group(n=16),and 100%for the stone free control group(n=26)(Table 1).Total accuracy for CNN modeling is:94.16%,AUC:0.99,weighted F1-Score:0.9353,weighted F1-score 95%CI:(0.9089,0.9599),weighted Kappa:0.9122,weighted Kappa 95%CI:(0.8523,0.9569).For the RIRS+PCNL image test set:The precision was 94.9%for the calcium oxalate group(n=195),98.7%for the calcium oxalate mixed with a uric acid group(n=80),92.2%for the calcium oxalate mixed with carbonate apatite group(n=111),100%for the struvite mixed with calcium oxalate and carbonate apatite group(n=29),and 93.8%for the stone free control group(n=26)(Table 2).Total accuracy:95.92%,AUC:0.99,weighted F1-Score:0.9508,weighted F1-score 95%CI:(0.9304,0.9708)weighted Kappa:0.9357,weighted Kappa 95%CI:(0.8907,0.9678).The 3D PCAprojection results are as follows:PC1:0.2723(27.23%),PC2:0.1102(11.02%),PC3:0.0801(8.01%),Cumulative:46.26%.Conclusions:This study shows deep CNNs can identify renal stone compositions from intraoperative endoscopic images,differentiating pure and mixed components.This analysis is an alternative to traditional methods and has the potential to improve treatment effectiveness. 展开更多
关键词 deep convolutional neural network urolithiasis composition prediction endoscopic images ResNet-50 model diagnostic and therapeutic effectiveness
暂未订购 下载PDF
Research on an intelligent encryption method for remote sensing images with variable dimension employing dual-chaotic system 认领 引用
11
作者 Jiahui Yu Zhenlong Man +1 位作者 Fan Zhang Xiangfu Meng 《Optoelectronics Letters》 EI 2026年第1期41-45,共5页
The rapid advancement of remote sensing technology has heightened concerns over the security of sensitive information.This paper presents an intelligent encryption scheme for remote sensing images using dimensionality... The rapid advancement of remote sensing technology has heightened concerns over the security of sensitive information.This paper presents an intelligent encryption scheme for remote sensing images using dimensionality variation.The scheme employs two high-dimensional chaotic systems to generate keys for simultaneous row-column scrambling and diffusion.By mapping a two-dimensional(2D)plain-image to a three-dimensional(3D)space,pixels are rearranged within a 3D cube using a chaotic key,followed by auto-correlation cyclic diffusion.Experimental results demonstrate that this approach significantly enhances encryption security,making it suitable for secure remote sensing image communication. 展开更多
关键词 d cube variable dimension dimensionality variationthe chaotic keyfollowed intelligent encryption scheme intelligent encryption remote sensing images remote sensing technology
暂未订购 下载PDF
A self-supervised learning-based method for simultaneous motion artifacts removal and denoising of OCTA images 认领 引用 被引量：1
12
作者 Yuzeng Xu Songwen Xu +1 位作者 Di Yang Yanmei Liang 《Journal of Innovative Optical Health Sciences》 SCIE EI CSCD 2026年第4期123-139,共17页
Motion artifacts and noise are key factors that determine image quality in optical coherence tomography angiography(OCTA).Although deep learning has emerged as an effective method for artifacts removal and denoising,i... Motion artifacts and noise are key factors that determine image quality in optical coherence tomography angiography(OCTA).Although deep learning has emerged as an effective method for artifacts removal and denoising,its generalization capability remains limited,and it is difficult to handle images with both motion artifacts and noise.To address this issue,we designed a Swin Transformer-based multi-scale motion artifacts and noise parallel removal network(ST-MANPR)to learn the nonlinear mapping between images with motion artifacts and noise and images without them,thereby achieving simultaneous suppression of motion artifacts and noise.The proposed network integrates the Swin window attention module,channel attention module(CAM),and adaptive multi-scale convolution denoising module(AMS-CNN)to enhance its capability in processing complex image features.At the same time,a hybrid loss function combining wavelet transform(WT)and mean square error(MSE)was introduced to facilitate high-frequency detail restoration.In addition,we constructed a dataset of OCTA images with and without motion artifacts and noise at multiple intensity levels.The created dataset was applied to train the network and the test results were evaluated both visually and numerically.The experimental results show that the proposed network can effectively remove motion artifacts and noise in OCTA images simultaneously. 展开更多
关键词 Optical coherence tomographic angiography self-supervised learning motion artifacts removal image denoising generalization
暂未订购 下载PDF
A synchronous encryption scheme for remote sensing images using 3D cross-coupled chaotic maps and fractal cubes in the internet of things 认领 引用
13
作者 Ze YU Ying ZHOU Zhenlong MAN 《Optoelectronics Letters》 EI 2026年第5期289-294,共6页
With the rapid development and wide application of internet of things(IoT)technology,optical equipment is being promoted to collect and store multi-scale remote sensing images,and to apply them in various fields such ... With the rapid development and wide application of internet of things(IoT)technology,optical equipment is being promoted to collect and store multi-scale remote sensing images,and to apply them in various fields such as industry,agriculture,and ecological and environmental protection.However,the resulting security risks have also caused widespread concern.This paper designs a remote sensing image synchronization encryption scheme based on 3D cross-coupled chaotic map and block-based cubes for multi-scale remote sensing images.First,a new 3D chaotic map is constructed by coupling the traditional single-node sinusoidal map,which provides support for building complex chaotic mappings for devices with limited resources.Second,a fractal square based on the Hilbert curve combined with chaos achieves simultaneous pixel scrambling and diffusion operations,improving multi-scale graphics encryption.Finally,a multi-type remote sensing graphics dataset is used for testing and a series of analysis experiments are performed to prove the feasibility of the algorithm. 展开更多
关键词 Internet Things remote sensing image synchronization Fractal Cubes D Cross Coupled Chaotic Maps Remote Sensing Images security risks internet things iot technologyoptical equipment Synchronous Encryption
暂未订购 下载PDF
Enhanced Capacity Reversible Data Hiding Based on Pixel Value Ordering in Triple Stego Images 认领 引用
14
作者 Kim Sao Nguyen Ngoc Dung Bui 《Computers, Materials & Continua》 SCIE EI 2026年第1期1571-1586,共16页
Reversible data hiding(RDH)enables secret data embedding while preserving complete cover image recovery,making it crucial for applications requiring image integrity.The pixel value ordering(PVO)technique used in multi... Reversible data hiding(RDH)enables secret data embedding while preserving complete cover image recovery,making it crucial for applications requiring image integrity.The pixel value ordering(PVO)technique used in multi-stego images provides good image quality but often results in low embedding capability.To address these challenges,this paper proposes a high-capacity RDH scheme based on PVO that generates three stego images from a single cover image.The cover image is partitioned into non-overlapping blocks with pixels sorted in ascending order.Four secret bits are embedded into each block’s maximum pixel value,while three additional bits are embedded into the second-largest value when the pixel difference exceeds a predefined threshold.A similar embedding strategy is also applied to the minimum side of the block,including the second-smallest pixel value.This design enables each block to embed up to 14 bits of secret data.Experimental results demonstrate that the proposed method achieves significantly higher embedding capacity and improved visual quality compared to existing triple-stego RDH approaches,advancing the field of reversible steganography. 展开更多
关键词 RDH reversible data hiding PVO RDH base three stego images
暂未订购 下载PDF
Transcendence of Mental Images in Intangible Cultural Heritage:On the Spiritual Dialectics of“Artistic Immateriality” 认领 引用
15
作者 Tan Wei 《Contemporary Social Sciences》 2026年第2期35-51,共17页
Focusing on the core characteristics of intangible cultural heritage(ICH)-its living,generative,and transmissive nature-this paper emphasizes ICH’s unique value as the most exemplary domain for demonstrating the conc... Focusing on the core characteristics of intangible cultural heritage(ICH)-its living,generative,and transmissive nature-this paper emphasizes ICH’s unique value as the most exemplary domain for demonstrating the concept of“artistic immateriality.”Compared with other art forms,ICH is distinguished by the intergenerational transmission of techniques,preservation of collective memory,and ongoing dynamic presentations.The enduring spiritual significance of ICH,juxtaposed with the transient and mutable nature of its material carriers,generates a pronounced tension,providing an authentic case for examining the dialectical relationship between spirit and matter.This paper systematically explicates the theoretical foundations of“artistic immateriality”and,through multidimensional case studies including traditional performing arts,mask and facial makeup cultures,and digital technology applications,demonstrates how ICH mental images,as formal actualizations of“Nenone”(being in non-being),dynamically transcend material carriers through technical inheritance and technological innovation.This study seeks to transcend the traditional“hylomorphic”framework in art theory,establishing a theoretical dimension for the study of objects and materiality in the arts that incorporates Eastern perspectives and contemporary values. 展开更多
关键词 ICH artistic immateriality transcendence of mental images spiritual dialectics living inheritance
暂未订购 下载PDF
UDAMSR Net:An Unsupervised Degradation-Aware Network for Enhancing the Spatial Resolution of Spectral Images for Crop Sensing 认领 引用
16
作者 Weijie Tang Ruomei Zhao +6 位作者 Hong Sun Minzan Li Lang Qiao Mingjia Liu Guohui Liu Yang Liu Di Song 《Engineering》 SCIE EI CSCD 2026年第5期31-48,共18页
Low spatial resolution(LR)remote sensing data is widely adopted because of its lower cost,although its limited analytical precision constrains its full use in precision agriculture.By contrast,the acquisition of high ... Low spatial resolution(LR)remote sensing data is widely adopted because of its lower cost,although its limited analytical precision constrains its full use in precision agriculture.By contrast,the acquisition of high spatial resolution(HR)data often requires substantial expense.To address this limitation,this study proposes an unsupervised degradation-aware multi-channel super-resolution network(UDAMSR)to enhance LR spectral images without requiring paired HR-LR training data.The main contributions are as follows:(1)the original framework is extended with dedicated queue and reconstruction layers to process multispectral and hyperspectral image(HIS)cubes,and a contrast-learning-based degradationaware module is integrated to address unknown real-world degradation;(2)comprehensive evaluation is conducted using image quality metrics,spectral consistency analysis,and performance in crop remote sensing tasks,such as chlorophyll content estimation;(3)the generalization capability of the model is assessed using data from three imaging devices,two spatial scales(near-ground and unmanned aerial vehicle(UAV)),and two geographic regions.The results show that the proposed method achieves the best overall performance in the comprehensive evaluation,with a mean peak signal-to-noise ratio(PSNR)of 32.78,a mean root mean squared error(RMSE)of 6.93,a mean structural similarity index of(SSIM)0.89,and a mean spectral angle mapper(SAM)of 0.131.The method effectively reduces the degradation in chlorophyll detection accuracy caused by spatial resolution reduction.The evaluation of generalization capability further shows that the proposed method demonstrates strong generalization across different spatial scales,geographic regions,devices,and data types.These results indicate that UDAMSR provides a robust,efficient,and cost-effective software solution that can compensate for hardware limitations and support high-quality crop phenotyping detection in diverse application scenarios. 展开更多
关键词 UDAMSR Net Spatial resolution enhancement Remote sensing image Crop phenotyping Crop sensing Transfer learning
暂未订购 下载PDF
Super-resolution reconstruction of UAV-borne gamma-ray spectrum images based on Real-ESRGAN algorithm 认领 引用
17
作者 Xin Wang Yuan Yuan +4 位作者 Xuan Zhao Guang-Hao Luo Qi-Qiao Wei He-Xi Wu Chao Xiong 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2026年第2期42-54,共13页
Unmanned aerial vehicle(UAV)-borne gamma-ray spectrum survey plays a crucial role in geological mapping,radioactive mineral exploration,and environmental monitoring.However,raw data are often compromised by flight and... Unmanned aerial vehicle(UAV)-borne gamma-ray spectrum survey plays a crucial role in geological mapping,radioactive mineral exploration,and environmental monitoring.However,raw data are often compromised by flight and instrument background noise,as well as detector resolution limitations,which affect the accuracy of geological interpretations.This study aims to explore the application of the Real-ESRGAN algorithm in the super-resolution reconstruction of UAV-borne gamma-ray spectrum images to enhance spatial resolution and the quality of geological feature visualization.We conducted super-resolution reconstruction experiments with 2×,4×and 6×magnification using the Real-ESRGAN algorithm,comparing the results with three other mainstream algorithms(SRCNN,SRGAN,FSRCNN)to verify the superiority in image quality.The experimental results indicate that Real-ESRGAN achieved a structural similarity index(SSIM)value of 0.950 at 2×magnification,significantly higher than the other algorithms,demonstrating its advantage in detail preservation.Furthermore,Real-ESRGAN effectively reduced ringing and overshoot artifacts,enhancing the clarity of geological structures and mineral deposit sites,thus providing high-quality visual information for geological exploration. 展开更多
关键词 UAV-borne gamma-ray spectrum Super-resolution reconstruction Real-ESRGAN Image processing
暂未订购 下载PDF
Harnessing speckle images:efficient extraction of hidden information 认领 引用
18
作者 Weiru Fan Xiaobin Tang +5 位作者 Xingqi Xu Huizhu Hu Vladislav V.Yakovlev Shi-Yao Zhu Da-Wei Wang Delong Zhang 《Advanced Photonics Nexus》 CSCD 2026年第1期211-223,共13页
Scattering obscures information carried by waves by producing speckle patterns,posing a fundamental challenge across diverse fields,from microscopy to astronomy.Although machine learning has recently shown promise in ... Scattering obscures information carried by waves by producing speckle patterns,posing a fundamental challenge across diverse fields,from microscopy to astronomy.Although machine learning has recently shown promise in speckle analysis,existing approaches are hindered by their dependence on large,labeled datasets—a significant bottleneck in many real-world applications.Here,we introduce speckle unsupervised recognition and evaluation(SURE),a groundbreaking unsupervised learning strategy for speckle recognition that eliminates the need for labeled training data.SURE's distinctive feature lies in its ability to extract invariant features through advanced clustering algorithms to enable direct classification of high-level information from speckle patterns without prior knowledge.We demonstrate the transformative potential of this approach in two key applications:(1)a noninvasive glucose monitoring system that accurately tracks glucose concentrations over time without extensive calibration and(2)a high-throughput communication system using multimode fibers,achieving improved performance in dynamic environments.In addition,we showcase SURE's unprecedented capability to classify objects hidden behind obstacles using scattered light,further broadening its scope.This versatile approach opens new frontiers in biomedical diagnostics,quantum network decoupling,and remote sensing,unlocking a transformative new paradigm for extracting information from seemingly random optical patterns. 展开更多
关键词 scattering unsupervised learning speckle interpretation pattern recognition image sensing
暂未订购 下载PDF
Deep learning for subtype recognition of Yang deficiency tongue images in traditional Chinese medicine 认领 引用
19
作者 Tongbin Zhang Haoran Xu +3 位作者 Ziyi Wang Chuanjun Pan Zheng Wang Lei Wang 《Digital Chinese Medicine》 CAS CSCD 2026年第2期197-210,共14页
Objective To address the lack of fine-grained clinical recognition for specific Yang deficiency syndrome subtypes and the limitations of conventional object detection models in extracting irregular,low-contrast tongue... Objective To address the lack of fine-grained clinical recognition for specific Yang deficiency syndrome subtypes and the limitations of conventional object detection models in extracting irregular,low-contrast tongue phenotypes.This study aims to develop an objective subtype recognition framework based on an improved You Only Look Once nano(YOLO11n)architecture,using a standardized visual phenotype matrix to translate macroscopic traditional Chinese medicine(TCM)descriptions into quantifiable clinical targets.Methods This cross-sectional diagnostic study consecutively enrolled adult inpatients admitted to the Department of Thyroid and Breast Surgery,The First Affiliated Hospital of Wannan Medical University(Yijishan Hospital),between September 1,2024 and June 1,2025,who were suspected of having Yang deficiency constitution based on initial TCM consultation.Clinical tongue image data were collected for analysis.Based on an Expert Visual Phenotype Annotation Matrix,a five-category recognition system was established,including the following TCM syndrome subtypes:spleen-dampness exuberance syndrome,mild kidney Yang deficiency syndrome,upper heat and lower cold syndrome,simultaneous Yin-Yang deficiency syndrome,and Yin deficiency and fluid depletion syndrome(negative control).The proposed Yang deficiency YOLO(YD-YOLO)model,built upon the YOLO11n baseline,integrates the Cross Stage Partial with kernel size 2(C3k2)-GhostBottleneck-Dynamic Convolution(GBDC)module into the backbone to adaptively extract low-contrast features,and embeds the multipath aggregation coordinate attention(MACA)mechanism into the neck to suppress background interference through multi-scale spatial coordination.Gradient-weighted class activation mapping(Grad-CAM)was used to visualize feature attribution and evaluate the biological plausibility of the model’s focus.Model performance was evaluated through ablation and comparative experiments using mean average precision(mAP),precision,recall,F1 score,inference speed(frames per second,FPS),overall accuracy,Cohen’s kappa,and the area under the receiver operating characteristic(ROC)curve(AUC).Results Based on the final inclusion of 1186 clinical cases,the YD-YOLO model had an overall accuracy of 91.5%,a Cohen’s kappa of 0.912,and an mAP@50 of 0.731[higher than the YOLO11n baseline(0.681)],with AUC ranging from 0.91 to 0.97 across all TCM syndrome subtypes.Among the TCM syndrome subtypes,the mild kidney Yang deficiency syndrome had the highest mAP@50(0.900),and the inference speed reached 89.00 FPS.Grad-CAM analysis showed that the model localized activation to key TCM pathological features,such as marginal tooth marks and focal root coatings,while suppressing non-diagnostic oral background noise.Conclusion The YD-YOLO model demonstrates the feasibility of deep learning for the finegrained classification of TCM Yang deficiency subtypes.By integrating visual phenotype quantification with model interpretability,the proposed framework provides an objective basis for syndrome differentiation,supporting the development of standardized digital diagnostic systems and the provision of clinical decision support in TCM practice. 展开更多
关键词 Yang deficiency syndrome Tongue image analysis YD-YOLO Feature extraction Syndrome differentiation
暂未订购 下载PDF
A novel method for automatic river channel morphology extraction from remote sensing satellite images:A case study of the Golmud fluvial fan 认领 引用
20
作者 Shao-hua Zhao Chang-min Zhang +1 位作者 Xiang-hui Zhang Jia-le Liu 《China Geology》 CAS CSCD 2026年第2期333-348,共16页
Distributive Fluvial Systems(DFS)are critical sedimentary systems governing fluvial dynamics,sediment transport,and ecosystem sustainability in modern and ancient basins.Accurate quantification of DFS channel morpholo... Distributive Fluvial Systems(DFS)are critical sedimentary systems governing fluvial dynamics,sediment transport,and ecosystem sustainability in modern and ancient basins.Accurate quantification of DFS channel morphology is essential for advancing sedimentary modeling,optimizing water resource management,and mitigating fluvial hazards.Here,the authors present a novel automated framework that extracts DFS channel networks from remote sensing imagery by integrating multiscale image segmentation,fractal network evolution,and region-merging algorithms.Through hierarchically multiresolution feature processing,this method overcomes limitations of traditional single-scale analysis,enabling adaptive extraction while reducing segmentation heterogeneity.Specifically,the workflow consists of three stages:Image segmentation,feature extraction,and image classification.When applied to the Golmud fluvial fan(Qinghai,China),this approach achieves 90.2%overall channel extraction accuracy using 0.5 m resolution imagery,significantly outperforming traditional DEM-based(81.7%)and water spectral methods(85.4%)in resolving fine-scale channel networks.Crucially,the framework demonstrates robust adaptability to complex sedimentary environments with variable vegetation cover(<30%density)and spectral noise,providing a time-efficient,data-agnostic solution for DFS characterization. 展开更多
关键词 Channel morphology Distributive fluvial system Golmud fluvial fan Image segmentation Remote sensing imagery Ecosystem sustainability Fluvial hazards Water resource management Qinghai-Xizang Plateau
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 2 … 250 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈