High-resolution transmission electron microscopy(HRTEM)promises rapid atomic-scale dynamic structure imaging.Yet,the precision limitations of aberration parameters and the challenge of eliminating aberrations in Cs-co...High-resolution transmission electron microscopy(HRTEM)promises rapid atomic-scale dynamic structure imaging.Yet,the precision limitations of aberration parameters and the challenge of eliminating aberrations in Cs-corrected transmission electron microscopy constrain resolution.A machine learning algorithm is developed to determine the aberration parameters with higher precision from small,lattice-periodic crystal images.The proposed algorithm is then validated with simulated HRTEM images of graphene and applied to the experimental images of a molybdenum disulfide(MoS_(2))monolayer with 25 variables(14 aberrations)resolved in wide ranges.Using these measured parameters,the phases of the exit-wave functions are reconstructed for each image in a focal series of MoS_(2)monolayers.The images were acquired due to the unexpected movement of the specimen holder.Four-dimensional data extraction reveals time-varying atomic structures and ripple.In particular,the atomic evolution of the sulfur-vacancy point and line defects,as well as the edge structure near the amorphous,is visualized as the resolution has been improved from about 1.75?to 0.9 A.This method can help salvage important transmission electron microscope images and is beneficial for the images obtained from electron microscopes with average stability.展开更多
The specific detection of tumor markers is crucial in early tumor screening and subsequent treatment processes.To ac-curately distinguish the signal response caused by trace markers,the high demodulation resolution of...The specific detection of tumor markers is crucial in early tumor screening and subsequent treatment processes.To ac-curately distinguish the signal response caused by trace markers,the high demodulation resolution of the sensor is necessary.In this paper,we propose a dual-wavelength fiber laser sensing system enhanced with microwave photonics de-modulation technology to achieve high-resolution tumor marker detection.This sensing system can simultaneously perform spectral wavelength-domain and frequency-domain analyses.Experimental results demonstrate that this system's refractive index(RI)sensitivity reaches 1083 nm/RIU by wavelength analysis and-1902 GHz/RIU by frequency analysis,with ideal detection resolutions of 1.85×10^(-5)RIU and 5.26×10^(-8)RIU,respectively.Compared with traditional wavelength domain analysis,the demodulation resolution is improved by three orders of magnitude,based on the same sensing structure.To validate its biosensing performance,carcinoembryonic antigen-related cell adhesion molecule 5(CEACAM5)is selected as the detection target.Experimental results show that the improved sensing system has a limit of detection(LOD)of 0.076 ng/mL and a detection resolution of 0.008 ng/mL.Experimental results obtained from human serum samples are consistent with clinical data,highlighting the strong clinical application potential of the proposed sens-ing system and analysis method.展开更多
为了克服传统马尔可夫随机场模型在海洋溢油识别中对合成孔径雷达(Synthetic Aperture Radar,SAR)图像相干斑噪声高敏感性以及溢油边界识别模糊等问题,文章提出一种超像素尺度下边缘约束隐马尔可夫随机场(Hidden Markov Random Fields,H...为了克服传统马尔可夫随机场模型在海洋溢油识别中对合成孔径雷达(Synthetic Aperture Radar,SAR)图像相干斑噪声高敏感性以及溢油边界识别模糊等问题,文章提出一种超像素尺度下边缘约束隐马尔可夫随机场(Hidden Markov Random Fields,HMRF)的SAR图像溢油识别算法(Edge-Corrected HMRF at the Super-Pixel Scale,SE-HMRF)。利用简单线性迭代聚类(Simple Linear Iterative Clustering,SLIC)对SAR图像进行超像素分割,克服SAR图像中相干斑噪声的影响。为了提高溢油识别的准确性,在超像素分割基础上构建HMRF描述图像的空间关系,根据贝叶斯定理将溢油识别问题转化为能量函数最小化问题;为了克服SLIC对溢油边缘过分割或欠分割,将溢油边缘信息引入到能量函数中约束溢油识别结果。为了验证本文提出算法对溢油识别的准确性,选取Sentinel-1卫星SAR图像进行对比实验,本文提出算法溢油识别结果的Kappa系数和概率兰德指数分别达到0.951和0.954,全局一致性误差仅为0.024,定性评价与定量评价的结果均优于对比算法,说明文章提出算法能够在保持识别效率的同时获得准确的溢油识别结果。展开更多
基金financial support from the National Natural Science Foundation of China(Grant No.61971201)。
文摘High-resolution transmission electron microscopy(HRTEM)promises rapid atomic-scale dynamic structure imaging.Yet,the precision limitations of aberration parameters and the challenge of eliminating aberrations in Cs-corrected transmission electron microscopy constrain resolution.A machine learning algorithm is developed to determine the aberration parameters with higher precision from small,lattice-periodic crystal images.The proposed algorithm is then validated with simulated HRTEM images of graphene and applied to the experimental images of a molybdenum disulfide(MoS_(2))monolayer with 25 variables(14 aberrations)resolved in wide ranges.Using these measured parameters,the phases of the exit-wave functions are reconstructed for each image in a focal series of MoS_(2)monolayers.The images were acquired due to the unexpected movement of the specimen holder.Four-dimensional data extraction reveals time-varying atomic structures and ripple.In particular,the atomic evolution of the sulfur-vacancy point and line defects,as well as the edge structure near the amorphous,is visualized as the resolution has been improved from about 1.75?to 0.9 A.This method can help salvage important transmission electron microscope images and is beneficial for the images obtained from electron microscopes with average stability.
基金supported in part by the Science and Technology Department of Guangdong Province(2021A0505080002)Department of Natural Resources of Guangdong Province(GDNRC[2022]No.22)+2 种基金Science,Technology and Innovation Commission of Shenzhen Municipality(20220815121807001)Hunan Provincial Natural Science Foundation of China(under Grant Nos.2023JJ30209)Hunan Provincial Education Department Science Research Fund of China(under Grant Nos.22B0862).
文摘The specific detection of tumor markers is crucial in early tumor screening and subsequent treatment processes.To ac-curately distinguish the signal response caused by trace markers,the high demodulation resolution of the sensor is necessary.In this paper,we propose a dual-wavelength fiber laser sensing system enhanced with microwave photonics de-modulation technology to achieve high-resolution tumor marker detection.This sensing system can simultaneously perform spectral wavelength-domain and frequency-domain analyses.Experimental results demonstrate that this system's refractive index(RI)sensitivity reaches 1083 nm/RIU by wavelength analysis and-1902 GHz/RIU by frequency analysis,with ideal detection resolutions of 1.85×10^(-5)RIU and 5.26×10^(-8)RIU,respectively.Compared with traditional wavelength domain analysis,the demodulation resolution is improved by three orders of magnitude,based on the same sensing structure.To validate its biosensing performance,carcinoembryonic antigen-related cell adhesion molecule 5(CEACAM5)is selected as the detection target.Experimental results show that the improved sensing system has a limit of detection(LOD)of 0.076 ng/mL and a detection resolution of 0.008 ng/mL.Experimental results obtained from human serum samples are consistent with clinical data,highlighting the strong clinical application potential of the proposed sens-ing system and analysis method.
文摘为了克服传统马尔可夫随机场模型在海洋溢油识别中对合成孔径雷达(Synthetic Aperture Radar,SAR)图像相干斑噪声高敏感性以及溢油边界识别模糊等问题,文章提出一种超像素尺度下边缘约束隐马尔可夫随机场(Hidden Markov Random Fields,HMRF)的SAR图像溢油识别算法(Edge-Corrected HMRF at the Super-Pixel Scale,SE-HMRF)。利用简单线性迭代聚类(Simple Linear Iterative Clustering,SLIC)对SAR图像进行超像素分割,克服SAR图像中相干斑噪声的影响。为了提高溢油识别的准确性,在超像素分割基础上构建HMRF描述图像的空间关系,根据贝叶斯定理将溢油识别问题转化为能量函数最小化问题;为了克服SLIC对溢油边缘过分割或欠分割,将溢油边缘信息引入到能量函数中约束溢油识别结果。为了验证本文提出算法对溢油识别的准确性,选取Sentinel-1卫星SAR图像进行对比实验,本文提出算法溢油识别结果的Kappa系数和概率兰德指数分别达到0.951和0.954,全局一致性误差仅为0.024,定性评价与定量评价的结果均优于对比算法,说明文章提出算法能够在保持识别效率的同时获得准确的溢油识别结果。
