Inspired by the function of crucial components in photosystemⅡ(PSⅡ),electrochemical and dyesensitized photoelectrochemical(DSPEC)water oxidation devices were constructed by the selfassembly of well-designed amphipat...Inspired by the function of crucial components in photosystemⅡ(PSⅡ),electrochemical and dyesensitized photoelectrochemical(DSPEC)water oxidation devices were constructed by the selfassembly of well-designed amphipathic Ru(bda)-based catalysts(bda=2,2'-bipyrdine-6,6'-dicarbonoxyl acid)and aliphatic chain decorated electrode surfaces,forming lipid bilayer membrane(LBM)-like structures.The Ru(bda)catalysts on electrode-supported LBM films demonstrated remarkable water oxidation performance with different O-O formation mechanisms.However,compared to the slow charge transfer process,the O-O formation pathways did not determine the PEC water oxidation efficiency of the dyesensitized photoanodes,and the different reaction rates for similar catalysts with different catalytic paths did not determine the PEC performance of the DSPECs.Instead,charge transfer plays a decisive role in the PEC water oxidation rate.When an indolo[3,2-b]carbazole derivative was introduced between the Ru(bda)catalysts and aliphatic chain-modified photosensitizer in LBM films,serving as a charge transfer mediator for the tyrosine-histidine pair in PSⅡ,the PEC water oxidation performance of the corresponding photoanodes was dramatically enhanced.展开更多
为了完成线性调频(linear frequency modulation,LFM)信号的稀疏采样,并利用稀疏数据对原始信号参数进行估计,本文提出了一种基于Z变换和改进有限新息率(finite rate of innovation,FRI)的LFM信号参数估计方法。以Z变换理论为基础,设计...为了完成线性调频(linear frequency modulation,LFM)信号的稀疏采样,并利用稀疏数据对原始信号参数进行估计,本文提出了一种基于Z变换和改进有限新息率(finite rate of innovation,FRI)的LFM信号参数估计方法。以Z变换理论为基础,设计了一种数学模型,一旦信号能够表达成该数学模型的结构形式,就能通过Z变换和零化滤波器的方法估计信号参数。然后,利用了自相关延迟的FRI结构对LFM信号采样,该结构不仅完成了LFM信号的稀疏采样,而且稀疏采样结果能够与数学模型结构相符。在理论上通过数学论证的方式证明了所提方法能够用于获取LFM信号参数信息,并通过仿真和实测数据验证了所提方法的有效性,理论和实验结果表明该方法只需要4个采样点就能实现对LFM信号的参数估计,并且实验中的参数估计误差均在3%以内,极大的提高有限新息率采样的参数估计效率。展开更多
AIM:To propose an algorithm for automatic detection of diabetic retinopathy(DR)lesions based on ultra-widefield scanning laser ophthalmoscopy(SLO).METHODS:The algorithm utilized the FasterRCNN(Faster Regions with CNN ...AIM:To propose an algorithm for automatic detection of diabetic retinopathy(DR)lesions based on ultra-widefield scanning laser ophthalmoscopy(SLO).METHODS:The algorithm utilized the FasterRCNN(Faster Regions with CNN features)+ResNet50(Residua Network 50)+FPN(Feature Pyramid Networks)method for detecting hemorrhagic spots,cotton wool spots,exudates,and microaneurysms in DR ultra-widefield SLO.Subimage segmentation combined with a deeper residual network FasterRCNN+ResNet50 was employed for feature extraction to enhance intelligent learning rate.Feature fusion was carried out by the feature pyramid network FPN,which significantly improved lesion detection rates in SLO fundus images.RESULTS:By analyzing 1076 ultra-widefield SLO images provided by our hospital,with a resolution of 2600×2048 dpi,the accuracy rates for hemorrhagic spots,cotton wool spots,exudates,and microaneurysms were found to be 87.23%,83.57%,86.75%,and 54.94%,respectively.CONCLUSION:The proposed algorithm demonstrates intelligent detection of DR lesions in ultra-widefield SLO,providing significant advantages over traditional fundus color imaging intelligent diagnosis algorithms.展开更多
基金conducted by the Fundamental Research Center of Artificial Photosynthesis(FReCAP)financially supported by the National Natural Science Foundation of China(22172011 and 22088102)+1 种基金the National Key R&D Program of China(2022YFA0911904)the Fundamental Research Funds for the Central Universities(DUT22LK06,DUT22QN213 and DUT23LAB611)。
文摘Inspired by the function of crucial components in photosystemⅡ(PSⅡ),electrochemical and dyesensitized photoelectrochemical(DSPEC)water oxidation devices were constructed by the selfassembly of well-designed amphipathic Ru(bda)-based catalysts(bda=2,2'-bipyrdine-6,6'-dicarbonoxyl acid)and aliphatic chain decorated electrode surfaces,forming lipid bilayer membrane(LBM)-like structures.The Ru(bda)catalysts on electrode-supported LBM films demonstrated remarkable water oxidation performance with different O-O formation mechanisms.However,compared to the slow charge transfer process,the O-O formation pathways did not determine the PEC water oxidation efficiency of the dyesensitized photoanodes,and the different reaction rates for similar catalysts with different catalytic paths did not determine the PEC performance of the DSPECs.Instead,charge transfer plays a decisive role in the PEC water oxidation rate.When an indolo[3,2-b]carbazole derivative was introduced between the Ru(bda)catalysts and aliphatic chain-modified photosensitizer in LBM films,serving as a charge transfer mediator for the tyrosine-histidine pair in PSⅡ,the PEC water oxidation performance of the corresponding photoanodes was dramatically enhanced.
文摘为了完成线性调频(linear frequency modulation,LFM)信号的稀疏采样,并利用稀疏数据对原始信号参数进行估计,本文提出了一种基于Z变换和改进有限新息率(finite rate of innovation,FRI)的LFM信号参数估计方法。以Z变换理论为基础,设计了一种数学模型,一旦信号能够表达成该数学模型的结构形式,就能通过Z变换和零化滤波器的方法估计信号参数。然后,利用了自相关延迟的FRI结构对LFM信号采样,该结构不仅完成了LFM信号的稀疏采样,而且稀疏采样结果能够与数学模型结构相符。在理论上通过数学论证的方式证明了所提方法能够用于获取LFM信号参数信息,并通过仿真和实测数据验证了所提方法的有效性,理论和实验结果表明该方法只需要4个采样点就能实现对LFM信号的参数估计,并且实验中的参数估计误差均在3%以内,极大的提高有限新息率采样的参数估计效率。
基金Supported by Hunan Provincial Science and Technology Department Clinical Medical Technology Innovation Guidance Project(No.2021SK50103)。
文摘AIM:To propose an algorithm for automatic detection of diabetic retinopathy(DR)lesions based on ultra-widefield scanning laser ophthalmoscopy(SLO).METHODS:The algorithm utilized the FasterRCNN(Faster Regions with CNN features)+ResNet50(Residua Network 50)+FPN(Feature Pyramid Networks)method for detecting hemorrhagic spots,cotton wool spots,exudates,and microaneurysms in DR ultra-widefield SLO.Subimage segmentation combined with a deeper residual network FasterRCNN+ResNet50 was employed for feature extraction to enhance intelligent learning rate.Feature fusion was carried out by the feature pyramid network FPN,which significantly improved lesion detection rates in SLO fundus images.RESULTS:By analyzing 1076 ultra-widefield SLO images provided by our hospital,with a resolution of 2600×2048 dpi,the accuracy rates for hemorrhagic spots,cotton wool spots,exudates,and microaneurysms were found to be 87.23%,83.57%,86.75%,and 54.94%,respectively.CONCLUSION:The proposed algorithm demonstrates intelligent detection of DR lesions in ultra-widefield SLO,providing significant advantages over traditional fundus color imaging intelligent diagnosis algorithms.
