Because of the growing concern over the radiation dose delivered to patients, X-ray cone-beam CT(CBCT) imaging of low dose is of great interest. It is difficult for traditional reconstruction methods such as Feldkamp ...Because of the growing concern over the radiation dose delivered to patients, X-ray cone-beam CT(CBCT) imaging of low dose is of great interest. It is difficult for traditional reconstruction methods such as Feldkamp to reduce noise and keep resolution at low doses. A typical method to solve this problem is using optimizationbased methods with careful modeling of physics and additional constraints. However, it is computationally expensive and very time-consuming to reach an optimal solution. Recently, some pioneering work applying deep neural networks had some success in characterizing and removing artifacts from a low-dose data set. In this study,we incorporate imaging physics for a cone-beam CT into a residual convolutional neural network and propose a new end-to-end deep learning-based method for slice-wise reconstruction. By transferring 3D projection to a 2D problem with a noise reduction property, we can not only obtain reconstructions of high image quality, but also lower the computational complexity. The proposed network is composed of three serially connected sub-networks: a cone-to-fan transformation sub-network, a 2D analytical inversion sub-network, and an image refinement sub-network. This provides a comprehensive solution for end-to-end reconstruction for CBCT. The advantages of our method are that the network can simplify a 3D reconstruction problem to a 2D slice-wise reconstruction problem and can complete reconstruction in an end-to-end manner with the system matrix integrated into the network design. Furthermore, reconstruction can be less computationally expensive and easily parallelizable compared with iterative reconstruction methods.展开更多
Chromium(VI)(Cr(VI)),a toxic metal ion,is widely present in industrial wastewater.To reduce the contamination of Cr(VI),many technologies for the photocatalytic reduction of Cr(VI)to Cr(III)have been developed in the ...Chromium(VI)(Cr(VI)),a toxic metal ion,is widely present in industrial wastewater.To reduce the contamination of Cr(VI),many technologies for the photocatalytic reduction of Cr(VI)to Cr(III)have been developed in the past decades.However,the practical application of photocatalysts for the reduction of Cr(VI)inwastewater treatment is often hindered by the complicated photoreduction processes due to the sedimentation and stratification of catalyst particles that present during the treatment of the wastewater.Probing and understanding the influences of the sedimentation and stratification of the catalyst particles on the photoreduction processes are long-term challenges in the field.Herein,we demonstrate that this issue can be solved by using layer location integrated low-field time-domain nuclear magnetic resonance(LF-NMR)relaxometry.With paramagnetic Cr(III)cation as the molecular probe,we successfully monitored the Cr(VI)photoreduction processes by operando probing the 1 H T2 relaxation time of the photoreduction systems.The influences of catalyst sedimentation and the light wavelength on photocatalysis were studied and discussed.The results showed the great potential of LF-NMR relaxometry in the study of Cr(VI)photoreduction processes during industrial wastewater treatments.展开更多
Program slicing is a technique for simplifying programs by focusing on selected aspects of their behavior.Current mainstream static slicing methods operate on dependence graph PDG(program dependence graph)or SDG(syste...Program slicing is a technique for simplifying programs by focusing on selected aspects of their behavior.Current mainstream static slicing methods operate on dependence graph PDG(program dependence graph)or SDG(system dependence graph),but these friendly graph representations may be a bit expensive for some users.In this paper we attempt to study a light-weight approach of static program slicing,called Symbolic Program Slicing(SymPas),which works as a dataflow analysis on LLVM(low-level virtual machine).In our SymPas approach,slices are stored in symbolic forms,not in procedures being re-analyzed(cf.procedure summaries).Instead of re-analyzing a procedure multiple times to find its slices for each callling context,we calculate a single symbolic slice which can be instantiated at call sites avoiding re-analysis;SymPas is implemented with LLVM to perform slicing on LLVM intermediate representation(IR).For comparison,we systematically adapt IFDS(interprocedural finite distributive subset)analysis and the SDG-based slicing method(SDGIFDS)to statically slice IR programs.Evaluated on open-source and benchmark programs,our backward SymPas shows a factor-of-6 reduction in time cost and a factor-of-4 reduction in space cost,compared with backward SDG-IFDS,thus being more efficient.In addition,the result shows that after studying slices from 66 programs,ranging up to 336800 IR instructions in size,SymPas is highly size-scalable.展开更多
基于深度置信网络(DBN)对信号双谱对角切片(BDS)结构特征进行学习,实现低截获概率(LPI)雷达信号识别。该方法首先建立基于受限玻尔兹曼机(RBM)的DBN模型,对LPI雷达信号的BDS数据进行逐层无监督贪心学习,然后运用后向传播(BP)机制在有监...基于深度置信网络(DBN)对信号双谱对角切片(BDS)结构特征进行学习,实现低截获概率(LPI)雷达信号识别。该方法首先建立基于受限玻尔兹曼机(RBM)的DBN模型,对LPI雷达信号的BDS数据进行逐层无监督贪心学习,然后运用后向传播(BP)机制在有监督学习方式下根据学习误差对DBN模型参数进行微调,最后基于该BDS-DBN模型实现未知信号的分类和识别。理论分析和仿真结果表明,信噪比高于8 d B时,基于BDS和DBN的识别方法对调频连续波(FMCW),Frank,Costas,FSK/PSK 4类LPI信号的综合识别率保持在93.4%以上,高于传统的主成分分析加支持向量机法(PCA-SVM)和主成分分析加线性判别分析法(PCA-LDA)。展开更多
基金supported by the National Natural Science Foundation of China(Nos.61771279,11435007)the National Key Research and Development Program of China(No.2016YFF0101304)
文摘Because of the growing concern over the radiation dose delivered to patients, X-ray cone-beam CT(CBCT) imaging of low dose is of great interest. It is difficult for traditional reconstruction methods such as Feldkamp to reduce noise and keep resolution at low doses. A typical method to solve this problem is using optimizationbased methods with careful modeling of physics and additional constraints. However, it is computationally expensive and very time-consuming to reach an optimal solution. Recently, some pioneering work applying deep neural networks had some success in characterizing and removing artifacts from a low-dose data set. In this study,we incorporate imaging physics for a cone-beam CT into a residual convolutional neural network and propose a new end-to-end deep learning-based method for slice-wise reconstruction. By transferring 3D projection to a 2D problem with a noise reduction property, we can not only obtain reconstructions of high image quality, but also lower the computational complexity. The proposed network is composed of three serially connected sub-networks: a cone-to-fan transformation sub-network, a 2D analytical inversion sub-network, and an image refinement sub-network. This provides a comprehensive solution for end-to-end reconstruction for CBCT. The advantages of our method are that the network can simplify a 3D reconstruction problem to a 2D slice-wise reconstruction problem and can complete reconstruction in an end-to-end manner with the system matrix integrated into the network design. Furthermore, reconstruction can be less computationally expensive and easily parallelizable compared with iterative reconstruction methods.
基金supported by the National Natural Science Foundation of China(grant nos.22072045,21603073,and 21574043)the Ministry of Science and Technology of the People's Republic of China(grant no.2018YFC1602800)Xing-FuZhi-Hua Foundation of ECNU and Microscale Magnetic Resonance Platform of ECNU and the Microscale Magnetic Resonance Platform of ECNU.
文摘Chromium(VI)(Cr(VI)),a toxic metal ion,is widely present in industrial wastewater.To reduce the contamination of Cr(VI),many technologies for the photocatalytic reduction of Cr(VI)to Cr(III)have been developed in the past decades.However,the practical application of photocatalysts for the reduction of Cr(VI)inwastewater treatment is often hindered by the complicated photoreduction processes due to the sedimentation and stratification of catalyst particles that present during the treatment of the wastewater.Probing and understanding the influences of the sedimentation and stratification of the catalyst particles on the photoreduction processes are long-term challenges in the field.Herein,we demonstrate that this issue can be solved by using layer location integrated low-field time-domain nuclear magnetic resonance(LF-NMR)relaxometry.With paramagnetic Cr(III)cation as the molecular probe,we successfully monitored the Cr(VI)photoreduction processes by operando probing the 1 H T2 relaxation time of the photoreduction systems.The influences of catalyst sedimentation and the light wavelength on photocatalysis were studied and discussed.The results showed the great potential of LF-NMR relaxometry in the study of Cr(VI)photoreduction processes during industrial wastewater treatments.
基金The work was supported by the National Natural Science Foundation of China under Grant Nos.60973046 and 61300054the Qing Lan Project of Jiangsu Province of Chinathe 1311 Talent Program Funding of Nanjing University of Posts and Telecommunications.
文摘Program slicing is a technique for simplifying programs by focusing on selected aspects of their behavior.Current mainstream static slicing methods operate on dependence graph PDG(program dependence graph)or SDG(system dependence graph),but these friendly graph representations may be a bit expensive for some users.In this paper we attempt to study a light-weight approach of static program slicing,called Symbolic Program Slicing(SymPas),which works as a dataflow analysis on LLVM(low-level virtual machine).In our SymPas approach,slices are stored in symbolic forms,not in procedures being re-analyzed(cf.procedure summaries).Instead of re-analyzing a procedure multiple times to find its slices for each callling context,we calculate a single symbolic slice which can be instantiated at call sites avoiding re-analysis;SymPas is implemented with LLVM to perform slicing on LLVM intermediate representation(IR).For comparison,we systematically adapt IFDS(interprocedural finite distributive subset)analysis and the SDG-based slicing method(SDGIFDS)to statically slice IR programs.Evaluated on open-source and benchmark programs,our backward SymPas shows a factor-of-6 reduction in time cost and a factor-of-4 reduction in space cost,compared with backward SDG-IFDS,thus being more efficient.In addition,the result shows that after studying slices from 66 programs,ranging up to 336800 IR instructions in size,SymPas is highly size-scalable.
文摘基于深度置信网络(DBN)对信号双谱对角切片(BDS)结构特征进行学习,实现低截获概率(LPI)雷达信号识别。该方法首先建立基于受限玻尔兹曼机(RBM)的DBN模型,对LPI雷达信号的BDS数据进行逐层无监督贪心学习,然后运用后向传播(BP)机制在有监督学习方式下根据学习误差对DBN模型参数进行微调,最后基于该BDS-DBN模型实现未知信号的分类和识别。理论分析和仿真结果表明,信噪比高于8 d B时,基于BDS和DBN的识别方法对调频连续波(FMCW),Frank,Costas,FSK/PSK 4类LPI信号的综合识别率保持在93.4%以上,高于传统的主成分分析加支持向量机法(PCA-SVM)和主成分分析加线性判别分析法(PCA-LDA)。
