Recently, low-dose computed tomography （CT） has become highly desirable because of the growing concern for the potential risks of excessive radiation. For low-dose CT imaging, it is a significant challenge to guaran...Recently, low-dose computed tomography （CT） has become highly desirable because of the growing concern for the potential risks of excessive radiation. For low-dose CT imaging, it is a significant challenge to guarantee image quality while reducing radiation dosage. Compared with classical filtered backprojection algorithms, compressed sensing-based iterative re- construction has achieved excellent imaging performance, but its clinical application is hindered due to its computational ineffi- ciency. To promote low-dose CT imaging, we propose a promising reconstruction scheme which combines total-variation mini- mization and sparse dictionary learning to enhance the reconstruction performance, and properly schedule them with an adaptive iteration stopping strategy to boost the reconstruction speed. Experiments conducted on a digital phantom and a physical phantom demonstrate a superior performance of our method over other methods in terms of image quality and computational efficiency, which validates its potential for low-dose CT imaging.展开更多
The development of digital twins for geotechnical structures necessitates the real-time updates of threedimensional(3D)virtual models(e.g.numerical finite element method(FEM)model)to accurately predict time-varying ge...The development of digital twins for geotechnical structures necessitates the real-time updates of threedimensional(3D)virtual models(e.g.numerical finite element method(FEM)model)to accurately predict time-varying geotechnical responses(e.g.consolidation settlement)in a 3D spatial domain.However,traditional 3D numerical model updating approaches are computationally prohibitive and therefore difficult to update the 3D responses in real time.To address these challenges,this study proposes a novel machine learning framework called sparse dictionary learning(T-3D-SDL)for real-time updating of time-varying 3D geotechnical responses.In T-3D-SDL,a concerned dataset(e.g.time-varying 3D settlement)is approximated as a linear superposition of dictionary atoms generated from 3D random FEM analyses.Field monitoring data are then used to identify non-trivial atoms and estimate their weights within a Bayesian framework for model updating and prediction.The proposed approach enables the real-time update of temporally varying settlements with a high 3D spatial resolution and quantified uncertainty as field monitoring data evolve.The proposed approach is illustrated using an embankment construction project.The results show that the proposed approach effectively improves settlement predictions along temporal and 3D spatial dimensions,with minimal latency(e.g.within minutes),as monitoring data appear.In addition,the proposed approach requires only a reasonably small number of 3D FEM model evaluations,avoids the use of widely adopted yet often criticized surrogate models,and effectively addresses the limitations(e.g.computational inefficiency)of existing 3D model updating approaches.展开更多
This Letter presents a novel approach to enhance the fringe contrast（visibility） in a digital off-axis hologram digitally, which can save several adjustment procedures. In the approach, we train a pair of coupled di...This Letter presents a novel approach to enhance the fringe contrast（visibility） in a digital off-axis hologram digitally, which can save several adjustment procedures. In the approach, we train a pair of coupled dictionaries from a low fringe contrast hologram and a high one of the same specimen, use the dictionaries to sparse code the input hologram, and finally output a higher fringe contrast hologram. The sparse representation shows good adaptability on holograms. The experimental results demonstrate the benefit of low noise in a three-dimensional profile and prove the effectiveness of the approach.展开更多
To improve the performance of sound source localization based on distributed microphone arrays in noisy and reverberant environments,a sound source localization method was proposed.This method exploited the inherent s...To improve the performance of sound source localization based on distributed microphone arrays in noisy and reverberant environments,a sound source localization method was proposed.This method exploited the inherent spatial sparsity to convert the localization problem into a sparse recovery problem based on the compressive sensing（CS） theory.In this method two-step discrete cosine transform（DCT）-based feature extraction was utilized to cover both short-time and long-time properties of the signal and reduce the dimensions of the sparse model.Moreover,an online dictionary learning（DL） method was used to dynamically adjust the dictionary for matching the changes of audio signals,and then the sparse solution could better represent location estimations.In addition,we proposed an improved approximate l_0norm minimization algorithm to enhance reconstruction performance for sparse signals in low signal-noise ratio（SNR）.The effectiveness of the proposed scheme is demonstrated by simulation results where the locations of multiple sources can be obtained in the noisy and reverberant conditions.展开更多
基金Project supported by the National High-Tech R＆D Program （863） of China （No. 2015AA016704e） and the Zhejiang Provincial Natural Science Foundation, China （No. LY14F020028）
文摘Recently, low-dose computed tomography （CT） has become highly desirable because of the growing concern for the potential risks of excessive radiation. For low-dose CT imaging, it is a significant challenge to guarantee image quality while reducing radiation dosage. Compared with classical filtered backprojection algorithms, compressed sensing-based iterative re- construction has achieved excellent imaging performance, but its clinical application is hindered due to its computational ineffi- ciency. To promote low-dose CT imaging, we propose a promising reconstruction scheme which combines total-variation mini- mization and sparse dictionary learning to enhance the reconstruction performance, and properly schedule them with an adaptive iteration stopping strategy to boost the reconstruction speed. Experiments conducted on a digital phantom and a physical phantom demonstrate a superior performance of our method over other methods in terms of image quality and computational efficiency, which validates its potential for low-dose CT imaging.
基金supported by a grant from the Research Grant Council of Hong Kong Special Administrative Region(Project No.11207724).
文摘The development of digital twins for geotechnical structures necessitates the real-time updates of threedimensional(3D)virtual models(e.g.numerical finite element method(FEM)model)to accurately predict time-varying geotechnical responses(e.g.consolidation settlement)in a 3D spatial domain.However,traditional 3D numerical model updating approaches are computationally prohibitive and therefore difficult to update the 3D responses in real time.To address these challenges,this study proposes a novel machine learning framework called sparse dictionary learning(T-3D-SDL)for real-time updating of time-varying 3D geotechnical responses.In T-3D-SDL,a concerned dataset(e.g.time-varying 3D settlement)is approximated as a linear superposition of dictionary atoms generated from 3D random FEM analyses.Field monitoring data are then used to identify non-trivial atoms and estimate their weights within a Bayesian framework for model updating and prediction.The proposed approach enables the real-time update of temporally varying settlements with a high 3D spatial resolution and quantified uncertainty as field monitoring data evolve.The proposed approach is illustrated using an embankment construction project.The results show that the proposed approach effectively improves settlement predictions along temporal and 3D spatial dimensions,with minimal latency(e.g.within minutes),as monitoring data appear.In addition,the proposed approach requires only a reasonably small number of 3D FEM model evaluations,avoids the use of widely adopted yet often criticized surrogate models,and effectively addresses the limitations(e.g.computational inefficiency)of existing 3D model updating approaches.
文摘This Letter presents a novel approach to enhance the fringe contrast（visibility） in a digital off-axis hologram digitally, which can save several adjustment procedures. In the approach, we train a pair of coupled dictionaries from a low fringe contrast hologram and a high one of the same specimen, use the dictionaries to sparse code the input hologram, and finally output a higher fringe contrast hologram. The sparse representation shows good adaptability on holograms. The experimental results demonstrate the benefit of low noise in a three-dimensional profile and prove the effectiveness of the approach.
基金supported by the Doctoral Program of Higher Education of China(20133207120007)the National Natural Science Foundation of China(61405094)+1 种基金the Open Research Fund of Jiangsu Key Laboratory of Meteorological Observation and Information Processing(KDXS1408)the Science and Technology Support Project of Jiangsu Province-Industry(BE2014139)
文摘To improve the performance of sound source localization based on distributed microphone arrays in noisy and reverberant environments,a sound source localization method was proposed.This method exploited the inherent spatial sparsity to convert the localization problem into a sparse recovery problem based on the compressive sensing（CS） theory.In this method two-step discrete cosine transform（DCT）-based feature extraction was utilized to cover both short-time and long-time properties of the signal and reduce the dimensions of the sparse model.Moreover,an online dictionary learning（DL） method was used to dynamically adjust the dictionary for matching the changes of audio signals,and then the sparse solution could better represent location estimations.In addition,we proposed an improved approximate l_0norm minimization algorithm to enhance reconstruction performance for sparse signals in low signal-noise ratio（SNR）.The effectiveness of the proposed scheme is demonstrated by simulation results where the locations of multiple sources can be obtained in the noisy and reverberant conditions.
