Multi-angle illumination is a widely adopted strategy in various super-resolution imaging systems,where improving computational efficiency and signal-to-noise ratio(SNR)remains a critical challenge.In this study,we pr...Multi-angle illumination is a widely adopted strategy in various super-resolution imaging systems,where improving computational efficiency and signal-to-noise ratio(SNR)remains a critical challenge.In this study,we propose the integration of the iterative kernel correction(IKC)algorithm with a multi-angle(MA)illumination scheme to enhance imaging reconstruction efficiency and SNR.The proposed IKC-MA scheme demonstrates the capability to significantly reduce image acquisition time while achieving high-quality reconstruction within 1 s,without relying on extensive experimental datasets.This ensures broad applicability across diverse imaging scenarios.Experimental results indicate substantial improvements in imaging speed and quality compared to conventional methods,with the IKC-MA model achieving a remarkable reduction in data acquisition time.This approach offers a faster and more generalizable solution for super-resolution microscopic imaging,paving the way for advancements in real-time imaging applications.展开更多
Satellite multi-angle polarimetric(MAP)observations provide crucial insights into the microphysical and optical properties of atmospheric aerosols.Recent advancements in multi-angle,multispectral,and polarized satelli...Satellite multi-angle polarimetric(MAP)observations provide crucial insights into the microphysical and optical properties of atmospheric aerosols.Recent advancements in multi-angle,multispectral,and polarized satellite observations have increased data content and complexity.While traditional methods like look-up tables and optimal estimation face challenges in fully utilizing these advanced datasets,deep learning approaches offer substantial advantages.However,deep learning models also have limitations,particularly regarding physical interpretability and the efficiency of processing highdimensional observational data.To address these challenges,we propose MAP_CapsNet,a deep learning algorithm based on Capsule Networks(CapsNets)for aerosol multi-parameter retrieval.This algorithm combines the multi-dimensional modeling capabilities of CapsNets with vector radiative transfer models to retrieve aerosol optical and microphysical parameters.We applied it to MAP measurements from the Directional Polarimetric Camera(DPC)onboard the Gaofen-5(02)satellite to retrieve different aerosol parameters over China in 2022.The results were validated against Aerosol Robotic Network and Sun/sky-radiometer Observation Network data.The correlation coefficients(R)for aerosol optical depth and fine mode fraction exceed 0.935 and 0.782,respectively.The single scattering albedo also showed a moderate correlation(R=0.691).Compared with Moderate Resolution Imaging Spectroradiometer and Visible Infrared Imaging Radiometer Suite products,the DPC exhibited good spatial consistency and an enhanced ability to characterize aerosol properties due to higher spatial resolution and MAP capability.These findings highlight the DPC instrument’s potential for high-resolution,real-time monitoring of dust and haze pollution events.展开更多
Magnetic soft robots have the characteristics of small size,noncable drive,and motion agility,which are suitable for medical operations in the gastrointestinal tract.However,multiangle folding and reconfigurable magne...Magnetic soft robots have the characteristics of small size,noncable drive,and motion agility,which are suitable for medical operations in the gastrointestinal tract.However,multiangle folding and reconfigurable magnetization have yet to be fully investigated,and thus,the study of magnetic soft robots with morphological changes and medical functions is still challenging.To this end,we propose a magnetic soft sheet robot based on the magnetorheological fluids,which presents a remarkable capability for reversible folding motion,rapid real-time reconfigurable magnetization,and targeted drug delivery functions.Furthermore,the robot has a fully soft sheet structure that is not magnetized in a zero magnetic field.After folding,the surface area of the soft sheet robot can be reduced to one third of its original area to cope with the complex gastrointestinal cavities.Five kinds of soft sheet robot prototypes with different magnetic driving abilities are fabricated,and the movement experiments of these robots are carried out on a smooth surface,a flexible fluff surface,a slope surface,underwater,and under load,respectively.The influence mechanisms of different magnetic field strengths and frequencies on robot movement are analyzed.The effectiveness of the proposed scheme is verified by ex vivo porcine stomach experiments and ultrasonic detection.展开更多
基金National Key Research and Development Program of China(2022YFF0712500,2022YFC3401100)Guangdong Major Project of Basic and Applied Basic Research(2020B0301030009)+5 种基金National Natural Science Foundation of China(62375144,61875092,12204017,12004012,12004013,12041602,91750203,91850111,92150301)Beijing-Tianjin-Hebei Basic Research Cooperation Special Program(19JCZDJC65300)China Postdoctoral Science Foundation(2020M680220,2020M680230)Clinical Medicine Plus X-Young Scholars Project,Peking UniversityFundamental Research Funds for the Central UniversitiesHigh-performance Computing Platform of Peking University。
文摘Multi-angle illumination is a widely adopted strategy in various super-resolution imaging systems,where improving computational efficiency and signal-to-noise ratio(SNR)remains a critical challenge.In this study,we propose the integration of the iterative kernel correction(IKC)algorithm with a multi-angle(MA)illumination scheme to enhance imaging reconstruction efficiency and SNR.The proposed IKC-MA scheme demonstrates the capability to significantly reduce image acquisition time while achieving high-quality reconstruction within 1 s,without relying on extensive experimental datasets.This ensures broad applicability across diverse imaging scenarios.Experimental results indicate substantial improvements in imaging speed and quality compared to conventional methods,with the IKC-MA model achieving a remarkable reduction in data acquisition time.This approach offers a faster and more generalizable solution for super-resolution microscopic imaging,paving the way for advancements in real-time imaging applications.
基金supported by the National Outstanding Youth Foundation of China(Grant No.41925019)the National Key R&D Program of China(Grant No.2023YFB3907405)the National Natural Science Foundation of China(Grant No.42175148).
文摘Satellite multi-angle polarimetric(MAP)observations provide crucial insights into the microphysical and optical properties of atmospheric aerosols.Recent advancements in multi-angle,multispectral,and polarized satellite observations have increased data content and complexity.While traditional methods like look-up tables and optimal estimation face challenges in fully utilizing these advanced datasets,deep learning approaches offer substantial advantages.However,deep learning models also have limitations,particularly regarding physical interpretability and the efficiency of processing highdimensional observational data.To address these challenges,we propose MAP_CapsNet,a deep learning algorithm based on Capsule Networks(CapsNets)for aerosol multi-parameter retrieval.This algorithm combines the multi-dimensional modeling capabilities of CapsNets with vector radiative transfer models to retrieve aerosol optical and microphysical parameters.We applied it to MAP measurements from the Directional Polarimetric Camera(DPC)onboard the Gaofen-5(02)satellite to retrieve different aerosol parameters over China in 2022.The results were validated against Aerosol Robotic Network and Sun/sky-radiometer Observation Network data.The correlation coefficients(R)for aerosol optical depth and fine mode fraction exceed 0.935 and 0.782,respectively.The single scattering albedo also showed a moderate correlation(R=0.691).Compared with Moderate Resolution Imaging Spectroradiometer and Visible Infrared Imaging Radiometer Suite products,the DPC exhibited good spatial consistency and an enhanced ability to characterize aerosol properties due to higher spatial resolution and MAP capability.These findings highlight the DPC instrument’s potential for high-resolution,real-time monitoring of dust and haze pollution events.
基金The support of the National Natural Science Foundation of China(52405081,52575080)the Natural Science Foundation of Jiangsu Province(BK20231066)the Joint Funds of the National Natural Science Foundation of China(U24A20116)in carrying out this research is gratefully acknowledged.
文摘Magnetic soft robots have the characteristics of small size,noncable drive,and motion agility,which are suitable for medical operations in the gastrointestinal tract.However,multiangle folding and reconfigurable magnetization have yet to be fully investigated,and thus,the study of magnetic soft robots with morphological changes and medical functions is still challenging.To this end,we propose a magnetic soft sheet robot based on the magnetorheological fluids,which presents a remarkable capability for reversible folding motion,rapid real-time reconfigurable magnetization,and targeted drug delivery functions.Furthermore,the robot has a fully soft sheet structure that is not magnetized in a zero magnetic field.After folding,the surface area of the soft sheet robot can be reduced to one third of its original area to cope with the complex gastrointestinal cavities.Five kinds of soft sheet robot prototypes with different magnetic driving abilities are fabricated,and the movement experiments of these robots are carried out on a smooth surface,a flexible fluff surface,a slope surface,underwater,and under load,respectively.The influence mechanisms of different magnetic field strengths and frequencies on robot movement are analyzed.The effectiveness of the proposed scheme is verified by ex vivo porcine stomach experiments and ultrasonic detection.
