To prepare a conductive polymer actuator with decent performance,a self-built experimental platform for the preparation of polypyrrole film is employed.One of the essential goals is to examine the mechanical character...To prepare a conductive polymer actuator with decent performance,a self-built experimental platform for the preparation of polypyrrole film is employed.One of the essential goals is to examine the mechanical characteristics of the actuator in the presence of various combinations of process parameters,combined with the orthogonal test method of"four factors and three levels".The bending and sensing characteristics of actuators of various sizes are methodically examined using a self-made bending polypyrrole actuator.The functional relationship between the bending displacement and the output voltage signal is established by studying the characteristics of the actuator sensor subjected to various degrees of bending.The experimental results reveal that the bending displacement of the actuator tip almost exhibits a linear variation as a function of length and width.When the voltage reaches 0.8 V,the bending speed of the actuator tends to be stable.Finally,the mechanical properties of the self-assembled polypyrrole actuator are verified by the design and fabrication of the microgripper.展开更多
With the rapid development of science and technology,new sensing technology has been used increasingly in mechatronics system,for the system of intelligent,automation and efficiency,provide strong support.Emerging sen...With the rapid development of science and technology,new sensing technology has been used increasingly in mechatronics system,for the system of intelligent,automation and efficiency,provide strong support.Emerging sensor technology in electromechanical integration system of innovative applications not only promote the system of intelligent upgrade,also for its wide application in the field of multiple provides a strong support,and along with the advance of technology and application scenario development,emerging sensor technology in electromechanical integration system to play a more important role.In this regard,this paper first expounds the overview of emerging sensing technology,then analyzes the innovation and integration of emerging sensing technology and mechatronics system,and finally further explores the practical application of emerging sensing technology in mechatronics system,in order to provide some reference for relevant researchers.展开更多
The growing demand for the miniaturization and multifunctionality of optoelectronic devices has promoted the development of transparent ferroelectrics.However,it is difficult for the superior multiple optical properti...The growing demand for the miniaturization and multifunctionality of optoelectronic devices has promoted the development of transparent ferroelectrics.However,it is difficult for the superior multiple optical properties of these materials to be compatible with the excellent ferroelectricity and piezoelectricity in transparent ceramics.Here,we successfully synthesized Bi/Eu codoped eco-friendly K0.5Na0.5NbO3transparent-ferroelectric ceramics with photo luminescence(PL)behavior,photochromic(PC)reactions and temperature-responsive PL.Based on the distinct optical properties of ceramics at different temperature ranges(room temperature and ultralow temperature),high utilization of multiple optical functions was realized.At room temperature,the PC behavior induced PL modulation contrast reaches 75.2%(at 592 nm),which can be applied in the optical information storage field.In the ultralow temperature range,the ceramics exhibit excellent sensitivity(with a maximum relative sensitivity of26.32%/K)via fluorescence intensity ratio technology and exhibit great application potential in noncontact optical temperature measurements.Additionally,the change in the PL intensity at different wavelengths(I_(614)/I_(592))can serve as a reliable indicator for detecting the occurrence of the phase transition from rhombohedral to orthorhombic at low temperature.This work provides a feasible paradigm for realizing the integration of ferroelectricity and multifarious optical properties in a single optoelectronic material.展开更多
The Jhelum River Basin in Pakistan has experienced recurrent and severe flooding over the past several decades,leading to substantial economic losses,infrastructure damage,and socio-environmental disruptions.This stud...The Jhelum River Basin in Pakistan has experienced recurrent and severe flooding over the past several decades,leading to substantial economic losses,infrastructure damage,and socio-environmental disruptions.This study uses multi-temporal satellite remote sensing data with historical hydrological records to map the spatial and temporal dynamics of major flood events occurring between 1988 and 2019.By utilizing satellite imagery from Landsat 5,Landsat 8,and Sentinel-2,key flood events were analyzed through the application of water indices such as the Normalized DifferenceWater Index(NDWI)and theModified NDWI(MNDWI)to delineate flood extents.Historical discharge data from key hydrological control points,including Mangla Dam and Rasul Barrage,were incorporated to validate and interpret flood intensity and inundation patterns.Flood footprints were extracted and mapped using preand post-flood images in Google Earth Engine,while land use and land cover(LULC)analysis revealed a consistent increase in built-up areas and a corresponding decline in vegetative cover in flood-prone tehsils from 1988 to 2023.Findings indicated that the flood years 1992 and 1997were themost catastrophic,with over 180 km2 of land submerged.A substantial proportion of inundated zones consisted of agricultural land and low-lying urban settlements,underscoring the vulnerability of these areas.This study proved the effectiveness of integrating satellite imagery and historical hydrological data for spatio-temporal flood monitoring and provides essential insights for future flood risk assessment and the development of site-specific mitigation strategies in vulnerable areas of the Jhelum River Basin.展开更多
Motor imbalance is a critical failure mode in rotating machinery,potentially causing severe equipment damage if undetected.Traditional vibration-based diagnostic methods rely on direct sensor contact,leading to instal...Motor imbalance is a critical failure mode in rotating machinery,potentially causing severe equipment damage if undetected.Traditional vibration-based diagnostic methods rely on direct sensor contact,leading to installation challenges and measurement artifacts that can compromise accuracy.This study presents a novel radar-based framework for non-contact motor imbalance detection using 24 GHz continuous-wave radar.A dataset of 1802 experimental trials was sourced,covering four imbalance levels(0,10,20,30 g)across varying motor speeds(500–1500 rpm)and load torques(0–3 Nm).Dual-channel in-phase and quadrature radar signals were captured at 10,000 samples per second for 30-s intervals,preserving both amplitude and phase information for analysis.A multi-domain feature extraction methodology captured imbalance signatures in time,frequency,and complex signal domains.From 65 initial features,statistical analysis using Kruskal–Wallis tests identified significant descriptors,and recursive feature elimination with Random Forest reduced the feature set to 20 dimensions,achieving 69%dimensionality reduction without loss of performance.Six machine learning algorithms,Random Forest,Extra Trees Classifier,Extreme Gradient Boosting,Categorical Boosting,Support Vector Machine with radial basis function kernel,and k-Nearest Neighbors were evaluated with grid-search hyperparameter optimization and five-fold cross-validation.The Extra Trees Classifier achieved the best performance with 98.52%test accuracy,98%cross-validation accuracy,and minimal variance,maintaining per-class precision and recall above 97%.Its superior performance is attributed to its randomized split selection and full bootstrapping strategy,which reduce variance and overfitting while effectively capturing the nonlinear feature interactions and non-normal distributions present in the dataset.The model’s average inference time of 70 ms enables near real-time deployment.Comparative analysis demonstrates that the radar-based framework matches or exceeds traditional contact-based methods while eliminating their inherent limitations,providing a robust,scalable,and noninvasive solution for industrial motor condition monitoring,particularly in hazardous or space-constrained environments.展开更多
Azobenzene-winged phenanthrolines(L1 and L2)were designed,synthesized,and fully characterized.Ligand L1 forms an in-situ cobalt complex,which has been effectively employed as a circular dichroism(CD)-active chiral sen...Azobenzene-winged phenanthrolines(L1 and L2)were designed,synthesized,and fully characterized.Ligand L1 forms an in-situ cobalt complex,which has been effectively employed as a circular dichroism(CD)-active chiral sensor.The resulting ternary complex(L1-Co^(2+)-amino alcohol)exhibits pronounced exciton-coupled circular dichroism(ECCD)signals at the characteristic azobenzene absorption bands.These signals arise from efficient chirality transfer from the chiral amino alcohol to the azobenzene chromophores,enabling the determination of the absolute configuration of chiral amino alcohols.Accordingly,the L1-Co^(2+)coordination system demonstrates considerably potential in chirality sensing applications.Remarkably,the induced ECCD signals are highly responsive to multiple external stimuli,including photoirradiation,solvent polarity,temperature,and redox conditions.In particular,temperature and redox changes can induce a reversible inversion of the ECCD signal,thereby establishing this system as a multifunctional,stimuli-responsive chiroptical molecular switch.展开更多
Interferometry is a crucial investigative technique used across diverse fields to achieve highprecision measurements.It works by analyzing the phase difference between two interfering waves,which results from variatio...Interferometry is a crucial investigative technique used across diverse fields to achieve highprecision measurements.It works by analyzing the phase difference between two interfering waves,which results from variations in optical path lengths within an interferometer.We introduce a novel method for directly measuring changes in the phase difference within an optical interferometer,importantly,with the added advantage of a controllable enhancement factor.This approach is achieved through a two-step process:first,the optical phase difference is encoded into a sub-GHz radiofrequency(RF)signal using microwave-photonic manipulation;then,RF interferometry-assisted phase amplification is implemented at the destructive interference point.In our experiments,we demonstrate a phase sensitivity of 2.14 rad∕nm operating at 140 MHz using a miniature in-fiber Fabry-Pérot interferometer for sub-nanometer displacement sensing,which reveals a sensitivity magnification factor of 258.6.With further refinement,we anticipate that even higher enhancement factors can be achieved,paving the way for the development of cost-effective,ultrasensitive interferometry-based instruments for high-precision optical measurements.展开更多
Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectra...Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability.展开更多
Point-of-care diagnostics and inline quantitative phase imaging(QPI)drive the demand for portable,ultra-miniaturized,and robust optical imaging and metrology systems.We propose and demonstrate a wavefront sensor integ...Point-of-care diagnostics and inline quantitative phase imaging(QPI)drive the demand for portable,ultra-miniaturized,and robust optical imaging and metrology systems.We propose and demonstrate a wavefront sensor integrated into a photonic integrated circuit,enabling single-shot optical phase retrieval.We implemented an integrated wavefront sensor array with a spatial resolution of 17μm and a numerical aperture of 0.1.Furthermore,we experimentally demonstrated the reconstruction of wavefronts defined by Zernike polynomials,specifically the first 14 terms(Z_(1)to Z_(14)),achieving an average root mean square error below 0.07.This advancement paves the way for fully integrated,portable,and robust optical imaging systems,facilitating integrated wavefront sensors in demanding applications such as point-of-care diagnostics,endoscopy,in situ QPI,and inline surface profile measurement.展开更多
High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes an...High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.展开更多
As a vital food crop,rice is an important part of global food crops.Studying the spatiotemporal changes in rice cultivation facilitates early prediction of production risks and provides support for agricultural policy...As a vital food crop,rice is an important part of global food crops.Studying the spatiotemporal changes in rice cultivation facilitates early prediction of production risks and provides support for agricultural policy decisions related to rice.With the increasing application of satellite remote sensing technology in crop monitoring,remote sensing for rice cultivation has emerged as a novel approach,offering new perspectives for monitoring rice planting.This paper briefly outlined the current research and development status of satellite remote sensing for monitoring rice cultivation both at home and abroad.Foreign scholars have made innovations in data sources and methodologies for satellite remote sensing monitoring,and utilized multi-source satellite information and machine learning algorithms to enhance the accuracy of rice planting monitoring.Scholars in China have achieved significant results in the study of satellite remote sensing for monitoring rice cultivation.Their research and application in monitoring rice planting areas provide valuable references for agricultural production management.However,satellite remote sensing monitoring of rice still faces challenges such as low spatiotemporal resolution and difficulties related to cloud cover and data fusion,which require further in-depth investigation.Additionally,there are shortcomings in the accuracy of remote sensing monitoring for fragmented farmland plots and smallholder farming.To address these issues,future efforts should focus on developing multi-source heterogeneous data fusion analysis technologies and researching monitoring systems.These advancements are expected to enable high-precision large-scale acquisition of rice planting information,laying a foundation for future smart agriculture.展开更多
基金Funded by the National Natural Science Foundation of Hunan Province,Chinal(No.2021JJ60012)。
文摘To prepare a conductive polymer actuator with decent performance,a self-built experimental platform for the preparation of polypyrrole film is employed.One of the essential goals is to examine the mechanical characteristics of the actuator in the presence of various combinations of process parameters,combined with the orthogonal test method of"four factors and three levels".The bending and sensing characteristics of actuators of various sizes are methodically examined using a self-made bending polypyrrole actuator.The functional relationship between the bending displacement and the output voltage signal is established by studying the characteristics of the actuator sensor subjected to various degrees of bending.The experimental results reveal that the bending displacement of the actuator tip almost exhibits a linear variation as a function of length and width.When the voltage reaches 0.8 V,the bending speed of the actuator tends to be stable.Finally,the mechanical properties of the self-assembled polypyrrole actuator are verified by the design and fabrication of the microgripper.
文摘With the rapid development of science and technology,new sensing technology has been used increasingly in mechatronics system,for the system of intelligent,automation and efficiency,provide strong support.Emerging sensor technology in electromechanical integration system of innovative applications not only promote the system of intelligent upgrade,also for its wide application in the field of multiple provides a strong support,and along with the advance of technology and application scenario development,emerging sensor technology in electromechanical integration system to play a more important role.In this regard,this paper first expounds the overview of emerging sensing technology,then analyzes the innovation and integration of emerging sensing technology and mechatronics system,and finally further explores the practical application of emerging sensing technology in mechatronics system,in order to provide some reference for relevant researchers.
基金Project supported by the National Natural Science Foundation of China(52072075,52102126,12104093)the Natural Science Foundation of Fujian Province(2021J05122,2021J05123,2022J01087,2022J01552,2023J01259)。
文摘The growing demand for the miniaturization and multifunctionality of optoelectronic devices has promoted the development of transparent ferroelectrics.However,it is difficult for the superior multiple optical properties of these materials to be compatible with the excellent ferroelectricity and piezoelectricity in transparent ceramics.Here,we successfully synthesized Bi/Eu codoped eco-friendly K0.5Na0.5NbO3transparent-ferroelectric ceramics with photo luminescence(PL)behavior,photochromic(PC)reactions and temperature-responsive PL.Based on the distinct optical properties of ceramics at different temperature ranges(room temperature and ultralow temperature),high utilization of multiple optical functions was realized.At room temperature,the PC behavior induced PL modulation contrast reaches 75.2%(at 592 nm),which can be applied in the optical information storage field.In the ultralow temperature range,the ceramics exhibit excellent sensitivity(with a maximum relative sensitivity of26.32%/K)via fluorescence intensity ratio technology and exhibit great application potential in noncontact optical temperature measurements.Additionally,the change in the PL intensity at different wavelengths(I_(614)/I_(592))can serve as a reliable indicator for detecting the occurrence of the phase transition from rhombohedral to orthorhombic at low temperature.This work provides a feasible paradigm for realizing the integration of ferroelectricity and multifarious optical properties in a single optoelectronic material.
文摘The Jhelum River Basin in Pakistan has experienced recurrent and severe flooding over the past several decades,leading to substantial economic losses,infrastructure damage,and socio-environmental disruptions.This study uses multi-temporal satellite remote sensing data with historical hydrological records to map the spatial and temporal dynamics of major flood events occurring between 1988 and 2019.By utilizing satellite imagery from Landsat 5,Landsat 8,and Sentinel-2,key flood events were analyzed through the application of water indices such as the Normalized DifferenceWater Index(NDWI)and theModified NDWI(MNDWI)to delineate flood extents.Historical discharge data from key hydrological control points,including Mangla Dam and Rasul Barrage,were incorporated to validate and interpret flood intensity and inundation patterns.Flood footprints were extracted and mapped using preand post-flood images in Google Earth Engine,while land use and land cover(LULC)analysis revealed a consistent increase in built-up areas and a corresponding decline in vegetative cover in flood-prone tehsils from 1988 to 2023.Findings indicated that the flood years 1992 and 1997were themost catastrophic,with over 180 km2 of land submerged.A substantial proportion of inundated zones consisted of agricultural land and low-lying urban settlements,underscoring the vulnerability of these areas.This study proved the effectiveness of integrating satellite imagery and historical hydrological data for spatio-temporal flood monitoring and provides essential insights for future flood risk assessment and the development of site-specific mitigation strategies in vulnerable areas of the Jhelum River Basin.
基金funded by Princess Nourah bint Abdulrahman University Researchers Support-ing Project number(PNURSP2026R346)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Motor imbalance is a critical failure mode in rotating machinery,potentially causing severe equipment damage if undetected.Traditional vibration-based diagnostic methods rely on direct sensor contact,leading to installation challenges and measurement artifacts that can compromise accuracy.This study presents a novel radar-based framework for non-contact motor imbalance detection using 24 GHz continuous-wave radar.A dataset of 1802 experimental trials was sourced,covering four imbalance levels(0,10,20,30 g)across varying motor speeds(500–1500 rpm)and load torques(0–3 Nm).Dual-channel in-phase and quadrature radar signals were captured at 10,000 samples per second for 30-s intervals,preserving both amplitude and phase information for analysis.A multi-domain feature extraction methodology captured imbalance signatures in time,frequency,and complex signal domains.From 65 initial features,statistical analysis using Kruskal–Wallis tests identified significant descriptors,and recursive feature elimination with Random Forest reduced the feature set to 20 dimensions,achieving 69%dimensionality reduction without loss of performance.Six machine learning algorithms,Random Forest,Extra Trees Classifier,Extreme Gradient Boosting,Categorical Boosting,Support Vector Machine with radial basis function kernel,and k-Nearest Neighbors were evaluated with grid-search hyperparameter optimization and five-fold cross-validation.The Extra Trees Classifier achieved the best performance with 98.52%test accuracy,98%cross-validation accuracy,and minimal variance,maintaining per-class precision and recall above 97%.Its superior performance is attributed to its randomized split selection and full bootstrapping strategy,which reduce variance and overfitting while effectively capturing the nonlinear feature interactions and non-normal distributions present in the dataset.The model’s average inference time of 70 ms enables near real-time deployment.Comparative analysis demonstrates that the radar-based framework matches or exceeds traditional contact-based methods while eliminating their inherent limitations,providing a robust,scalable,and noninvasive solution for industrial motor condition monitoring,particularly in hazardous or space-constrained environments.
基金the support of this work by the National Natural Science Foundation of China(Nos.22471182,22271201,22422108,22171194)the Science&Technology Department of Sichuan Province(No.2025zNSFSC0125)+1 种基金the Fundamental Research Funds for the Central Universities(No.20826041D4117)the Comprehensive Training Platform of Specialized Laboratory,College of Chemistry,Prof.Peng Wu of the Analytical&Testing Center,Sichuan University.
文摘Azobenzene-winged phenanthrolines(L1 and L2)were designed,synthesized,and fully characterized.Ligand L1 forms an in-situ cobalt complex,which has been effectively employed as a circular dichroism(CD)-active chiral sensor.The resulting ternary complex(L1-Co^(2+)-amino alcohol)exhibits pronounced exciton-coupled circular dichroism(ECCD)signals at the characteristic azobenzene absorption bands.These signals arise from efficient chirality transfer from the chiral amino alcohol to the azobenzene chromophores,enabling the determination of the absolute configuration of chiral amino alcohols.Accordingly,the L1-Co^(2+)coordination system demonstrates considerably potential in chirality sensing applications.Remarkably,the induced ECCD signals are highly responsive to multiple external stimuli,including photoirradiation,solvent polarity,temperature,and redox conditions.In particular,temperature and redox changes can induce a reversible inversion of the ECCD signal,thereby establishing this system as a multifunctional,stimuli-responsive chiroptical molecular switch.
基金support from the Roy A.Wilkens Professorship Endowment。
文摘Interferometry is a crucial investigative technique used across diverse fields to achieve highprecision measurements.It works by analyzing the phase difference between two interfering waves,which results from variations in optical path lengths within an interferometer.We introduce a novel method for directly measuring changes in the phase difference within an optical interferometer,importantly,with the added advantage of a controllable enhancement factor.This approach is achieved through a two-step process:first,the optical phase difference is encoded into a sub-GHz radiofrequency(RF)signal using microwave-photonic manipulation;then,RF interferometry-assisted phase amplification is implemented at the destructive interference point.In our experiments,we demonstrate a phase sensitivity of 2.14 rad∕nm operating at 140 MHz using a miniature in-fiber Fabry-Pérot interferometer for sub-nanometer displacement sensing,which reveals a sensitivity magnification factor of 258.6.With further refinement,we anticipate that even higher enhancement factors can be achieved,paving the way for the development of cost-effective,ultrasensitive interferometry-based instruments for high-precision optical measurements.
基金supported by the Henan Province Key R&D Project under Grant 241111210400the Henan Provincial Science and Technology Research Project under Grants 252102211047,252102211062,252102211055 and 232102210069+2 种基金the Jiangsu Provincial Scheme Double Initiative Plan JSS-CBS20230474,the XJTLU RDF-21-02-008the Science and Technology Innovation Project of Zhengzhou University of Light Industry under Grant 23XNKJTD0205the Higher Education Teaching Reform Research and Practice Project of Henan Province under Grant 2024SJGLX0126。
文摘Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability.
基金supported by the National Natural Science Foundation of China(Grant Nos.52175509 and 52450158)the National Key Research and Development Program of China(Grant No.2023YFF1500900)+2 种基金the Shenzhen Fundamental Research Program(Grant No.JCYJ20220818100412027)the Guangdong-Hong Kong Technology Cooperation Funding Scheme Category C Platform(Grant No.SGDX20230116093543005)the Innovation Project of Optics Valley Laboratory(Grant No.OVL2023PY003)。
文摘Point-of-care diagnostics and inline quantitative phase imaging(QPI)drive the demand for portable,ultra-miniaturized,and robust optical imaging and metrology systems.We propose and demonstrate a wavefront sensor integrated into a photonic integrated circuit,enabling single-shot optical phase retrieval.We implemented an integrated wavefront sensor array with a spatial resolution of 17μm and a numerical aperture of 0.1.Furthermore,we experimentally demonstrated the reconstruction of wavefronts defined by Zernike polynomials,specifically the first 14 terms(Z_(1)to Z_(14)),achieving an average root mean square error below 0.07.This advancement paves the way for fully integrated,portable,and robust optical imaging systems,facilitating integrated wavefront sensors in demanding applications such as point-of-care diagnostics,endoscopy,in situ QPI,and inline surface profile measurement.
基金provided by the Science Research Project of Hebei Education Department under grant No.BJK2024115.
文摘High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.
基金Supported by Natural Science Foundation General Project of Heilongjiang Province(C2018050).
文摘As a vital food crop,rice is an important part of global food crops.Studying the spatiotemporal changes in rice cultivation facilitates early prediction of production risks and provides support for agricultural policy decisions related to rice.With the increasing application of satellite remote sensing technology in crop monitoring,remote sensing for rice cultivation has emerged as a novel approach,offering new perspectives for monitoring rice planting.This paper briefly outlined the current research and development status of satellite remote sensing for monitoring rice cultivation both at home and abroad.Foreign scholars have made innovations in data sources and methodologies for satellite remote sensing monitoring,and utilized multi-source satellite information and machine learning algorithms to enhance the accuracy of rice planting monitoring.Scholars in China have achieved significant results in the study of satellite remote sensing for monitoring rice cultivation.Their research and application in monitoring rice planting areas provide valuable references for agricultural production management.However,satellite remote sensing monitoring of rice still faces challenges such as low spatiotemporal resolution and difficulties related to cloud cover and data fusion,which require further in-depth investigation.Additionally,there are shortcomings in the accuracy of remote sensing monitoring for fragmented farmland plots and smallholder farming.To address these issues,future efforts should focus on developing multi-source heterogeneous data fusion analysis technologies and researching monitoring systems.These advancements are expected to enable high-precision large-scale acquisition of rice planting information,laying a foundation for future smart agriculture.
