With the rapid advancements in edge computing and artificial intelligence,federated learning(FL)has gained momentum as a promising approach to collaborative data utilization across organizations and devices,while ensu...With the rapid advancements in edge computing and artificial intelligence,federated learning(FL)has gained momentum as a promising approach to collaborative data utilization across organizations and devices,while ensuring data privacy and information security.In order to further harness the energy efficiency of wireless networks,an integrated sensing,communication and computation(ISCC)framework has been proposed,which is anticipated to be a key enabler in the era of 6G networks.Although the advantages of pushing intelligence to edge devices are multi-fold,some challenges arise when incorporating FL into wireless networks under the umbrella of ISCC.This paper provides a comprehensive survey of FL,with special emphasis on the design and optimization of ISCC.We commence by introducing the background and fundamentals of FL and the ISCC framework.Subsequently,the aforementioned challenges are highlighted and the state of the art in potential solutions is reviewed.Finally,design guidelines are provided for the incorporation of FL and ISCC.Overall,this paper aims to contribute to the understanding of FL in the context of wireless networks,with a focus on the ISCC framework,and provide insights into addressing the challenges and optimizing the design for the integration of FL into future 6G networks.展开更多
Point-of-care diagnostics and inline quantitative phase imaging(QPI)drive the demand for portable,ultra-miniaturized,and robust optical imaging and metrology systems.We propose and demonstrate a wavefront sensor integ...Point-of-care diagnostics and inline quantitative phase imaging(QPI)drive the demand for portable,ultra-miniaturized,and robust optical imaging and metrology systems.We propose and demonstrate a wavefront sensor integrated into a photonic integrated circuit,enabling single-shot optical phase retrieval.We implemented an integrated wavefront sensor array with a spatial resolution of 17μm and a numerical aperture of 0.1.Furthermore,we experimentally demonstrated the reconstruction of wavefronts defined by Zernike polynomials,specifically the first 14 terms(Z_(1)to Z_(14)),achieving an average root mean square error below 0.07.This advancement paves the way for fully integrated,portable,and robust optical imaging systems,facilitating integrated wavefront sensors in demanding applications such as point-of-care diagnostics,endoscopy,in situ QPI,and inline surface profile measurement.展开更多
In recent years,computational intelligence has been widely used in many fields and achieved remarkable performance.Evolutionary computing and deep learning are important branches of computational intelligence.Many met...In recent years,computational intelligence has been widely used in many fields and achieved remarkable performance.Evolutionary computing and deep learning are important branches of computational intelligence.Many methods based on evolutionary computation and deep learning have achieved good performance in remote sensing image registration.This paper introduces the application of computational intelligence in remote sensing image registration from the two directions of evolutionary computing and deep learning.In the part of remote sensing image registration based on evolutionary calculation,the principles of evolutionary algorithms and swarm intelligence algorithms are elaborated and their application in remote sensing image registration is discussed.The application of deep learning in remote sensing image registration is also discussed.At the same time,the development status and future of remote sensing image registration are summarized and their prospects are examined.展开更多
Studies on land use and land cover changes (LULCC) have been a great concern due to their contribution to the policies formulation and strategic plans in different areas and at different scales. The LULCC when intense...Studies on land use and land cover changes (LULCC) have been a great concern due to their contribution to the policies formulation and strategic plans in different areas and at different scales. The LULCC when intense and on a global scale can be catastrophic if not detected and monitored affecting the key aspects of the ecosystem’s functions. For decades, technological developments and tools of geographic information systems (GIS), remote sensing (RS) and machine learning (ML) since data acquisition, processing and results in diffusion have been investigated to access landscape conditions and hence, different land use and land cover classification systems have been performed at different levels. Providing coherent guidelines, based on literature review, to examine, evaluate and spread such conditions could be a rich contribution. Therefore, hundreds of relevant studies available in different databases (Science Direct, Scopus, Google Scholar) demonstrating advances achieved in local, regional and global land cover classification products at different spatial, spectral and temporal resolutions over the past decades were selected and investigated. This article aims to show the main tools, data, approaches applied for analysis, assessment, mapping and monitoring of LULCC and to investigate some associated challenges and limitations that may influence the performance of future works, through a progressive perspective. Based on this study, despite the advances archived in recent decades, issues related to multi-source, multi-temporal and multi-level analysis, robustness and quality, scalability need to be further studied as they constitute some of the main challenges for remote sensing.展开更多
In general, there is a demand for real-time processing of mass quantity remote sensing images. However, the task is not only data-intensive but also computating-intensive. Distributed processing is a hot topic in remo...In general, there is a demand for real-time processing of mass quantity remote sensing images. However, the task is not only data-intensive but also computating-intensive. Distributed processing is a hot topic in remote sensing processing and image deblurring is also one of the most important needs. In order to satisfy the demand for quick proc- essing and deblurring of mass quantity satellite images, we developed a distributed, grid computation-based platform as well as a corresponding middleware for grid computation. Both a constrained power spectrum equalization algorithm and effective block processing measures, which can avoid boundary effect, were applied during the processing. The re- sult is satisfactory since computation efficiency and visual effect were greatly improved. It can be concluded that the technology of spatial information grids is effective for mass quantity remote sensing image processing.展开更多
With the development of multi-signal monitoring technology,the research on multiple signal analysis and processing has become a hot subject.Mechanical equipment often works under variable working conditions,and the ac...With the development of multi-signal monitoring technology,the research on multiple signal analysis and processing has become a hot subject.Mechanical equipment often works under variable working conditions,and the acquired vibration signals are often non-stationary and nonlinear,which are difficult to be processed by traditional analysis methods.In order to solve the noise reduction problem of multiple signals under variable speed,a COT-DCS method combining the Computed Order Tracking(COT)based on Chirplet Path Pursuit(CPP)and Distributed Compressed Sensing(DCS)is proposed.Firstly,the instantaneous frequency(IF)is extracted by CPP,and the speed is obtained by fitting.Then,the speed is used for equal angle sampling of time-domain signals,and angle-domain signals are obtained by COT without a tachometer to eliminate the nonstationarity,and the angledomain signals are compressed and reconstructed by DCS to achieve noise reduction of multiple signals.The accuracy of the CPP method is verified by simulated,experimental signals and compared with some existing IF extraction methods.The COT method also shows good signal stabilization ability through simulation and experiment.Finally,combined with the comparative test of the other two algorithms and four noise reduction effect indicators,the COT-DCS based on the CPP method combines the advantages of the two algorithms and has better noise reduction effect and stability.It is shown that this method is an effective multi-signal noise reduction method.展开更多
Computational ghost imaging(CGI)provides an elegant framework for indirect imaging,but its application has been restricted by low imaging performance.Herein,we propose a novel approach that significantly improves the ...Computational ghost imaging(CGI)provides an elegant framework for indirect imaging,but its application has been restricted by low imaging performance.Herein,we propose a novel approach that significantly improves the imaging performance of CGI.In this scheme,we optimize the conventional CGI data processing algorithm by using a novel compressed sensing(CS)algorithm based on a deep convolution generative adversarial network(DCGAN).CS is used to process the data output by a conventional CGI device.The processed data are trained by a DCGAN to reconstruct the image.Qualitative and quantitative results show that this method significantly improves the quality of reconstructed images by jointly training a generator and the optimization process for reconstruction via meta-learning.Moreover,the background noise can be eliminated well by this method.展开更多
Spectral imaging is an important tool for a wide variety of applications. We present a technique for spectral imaging using computational imaging pattern based on compressive sensing （CS）. The spectral and spatial i...Spectral imaging is an important tool for a wide variety of applications. We present a technique for spectral imaging using computational imaging pattern based on compressive sensing （CS）. The spectral and spatial infor- mation is simultaneously obtained using a fiber spectrometer and the spatial light modulation without mechanical scanning. The method allows high-speed, stable, and sub sampling acquisition of spectral data from specimens. The relationship between sampling rate and image quality is discussed and two CS algorithms are compared.展开更多
Source and mask joint optimization(SMO)is a widely used computational lithography method for state-of-the-art optical lithography process to improve the yield of semiconductor wafers.Nowadays,computational efficiency ...Source and mask joint optimization(SMO)is a widely used computational lithography method for state-of-the-art optical lithography process to improve the yield of semiconductor wafers.Nowadays,computational efficiency has become one of the most challenging issues for the development of pixelated SMO techniques.Recently,compressive sensing(CS)theory has be explored in the area of computational inverse problems.This paper proposes a CS approach to improve the computational efficiency of pixel-based SMO algorithms.To our best knowledge,this paper is the first to develop fast SMO algorithms based on the CS framework.The SMO workflow can be separated into two stages,i.e.,source optimization(SO)and mask optimization(MO).The SO and MO are formulated as the linear CS and nonlinear CS reconstruction problems,respectively.Based on the sparsity representation of the source and mask patterns on the predefined bases,the SO and MO procedures are implemented by sparse image reconstruction algorithms.A set of simulations are presented to verify the proposed CS-SMO methods.The proposed CS-SMO algorithms are shown to outperform the traditional gradient-based SMO algorithm in terms of both computational efficiency and lithography imaging performance.展开更多
This paper tries to address the problem of binary CT image reconstruction in non-destructive detection with an algorithm based on compressed sensing(CS) and Otsu's method, which could reconstruct binary CT image o...This paper tries to address the problem of binary CT image reconstruction in non-destructive detection with an algorithm based on compressed sensing(CS) and Otsu's method, which could reconstruct binary CT image of test object from incomplete detection data. According to binary CT image characteristics, we employ Splitbregman method based on L1/2regularization to solve piecewise constant region reconstruction. To improve the reconstructed image quality from incomplete detection data, we utilize a priori knowledge and Otsu's method as the optimization constraint. In our study, we make numerical simulation to investigate our proposed method,and compare reconstructed results from different reconstruction methods. Finally, the experimental results demonstrate that the proposed method could effectively reduce noise and suppress artifacts, and reconstruct high-quality binary image from incomplete detection data.展开更多
It has been over a decade since the first coded aperture video compressive sensing(CS)system was reported.The underlying principle of this technology is to employ a high-frequency modulator in the optical path to modu...It has been over a decade since the first coded aperture video compressive sensing(CS)system was reported.The underlying principle of this technology is to employ a high-frequency modulator in the optical path to modulate a recorded high-speed scene within one integration time.The superimposed image captured in this manner is modulated and compressed,since multiple modulation patterns are imposed.Following this,reconstruction algorithms are utilized to recover the desired high-speed scene.One leading advantage of video CS is that a single captured measurement can be used to reconstruct a multi-frame video,thereby enabling a low-speed camera to capture high-speed scenes.Inspired by this,a number of variants of video CS systems have been built,mainly using different modulation devices.Meanwhile,in order to obtain high-quality reconstruction videos,many algorithms have been developed,from optimization-based iterative algorithms to deep-learning-based ones.Recently,emerging deep learning methods have been dominant due to their high-speed inference and high-quality reconstruction,highlighting the possibility of deploying video CS in practical applications.Toward this end,this paper reviews the progress that has been achieved in video CS during the past decade.We further analyze the efforts that need to be made—in terms of both hardware and algorithms—to enable real applications.Research gaps are put forward and future directions are summarized to help researchers and engineers working on this topic.展开更多
Noninvasive detection of human physiology plays a key role for diagnosis or therapeutic assessment of various diseases.In the past,many functional modalities,such as electrocardiograph(ECG),electroencephalogram(EEG),f...Noninvasive detection of human physiology plays a key role for diagnosis or therapeutic assessment of various diseases.In the past,many functional modalities,such as electrocardiograph(ECG),electroencephalogram(EEG),fluorescence microscope,and positron emission computed tomography(PETS)have been applied to clinic for probing human heart,brain waves or tissue metabolism,owing to rapid development in fields of electromagnetism,optics or particle physics.Nowadays,a few smart sensing technologies are emerging for human physiology detection in more wide range.展开更多
Intelligent Transportation Systems(ITS)leverage Integrated Sensing and Communications(ISAC)to enhance data exchange between vehicles and infrastructure in the Internet of Vehicles(IoV).This integration inevitably incr...Intelligent Transportation Systems(ITS)leverage Integrated Sensing and Communications(ISAC)to enhance data exchange between vehicles and infrastructure in the Internet of Vehicles(IoV).This integration inevitably increases computing demands,risking real-time system stability.Vehicle Edge Computing(VEC)addresses this by offloading tasks to Road Side Units(RSUs),ensuring timely services.Our previous work,the FLSimCo algorithm,which uses local resources for federated Self-Supervised Learning(SSL),has a limitation:vehicles often can’t complete all iteration tasks.Our improved algorithm offloads partial tasks to RSUs and optimizes energy consumption by adjusting transmission power,CPU frequency,and task assignment ratios,balancing local and RSU-based training.Meanwhile,setting an offloading threshold further prevents inefficiencies.Simulation results show that the enhanced algorithm reduces energy consumption and improves offloading efficiency and accuracy of federated SSL.展开更多
为了突破工程结构数字孪生系统中物理传感覆盖度有限的瓶颈,提出了一种基于人工智能增强型降阶模型(AI-Reduced Order Model, AI-ROM)的虚拟传感技术,能够融合多源物理传感数据,实现复杂结构全域响应的实时推演。方法上,基于降阶模型理...为了突破工程结构数字孪生系统中物理传感覆盖度有限的瓶颈,提出了一种基于人工智能增强型降阶模型(AI-Reduced Order Model, AI-ROM)的虚拟传感技术,能够融合多源物理传感数据,实现复杂结构全域响应的实时推演。方法上,基于降阶模型理论将结构响应视为一组降阶基的线性组合,从而将虚拟传感等效为实测响应数据约束下的降阶基组合系数优化问题,依此构造了可考虑跨力学场虚拟传感的深度学习损失函数,提出了基于标准注意力机制的组合系数智能预测模型,利用局部监测数据完成结构全域响应的精准重构。依托狮子洋大桥钢板-混凝土组合塔壁足尺压弯试验,验证智能虚拟传感方法的有效性。利用精细数值模型生成压弯工况下下全过程响应数据训练部署AI-ROM模型,将试验中的6个位移计与17个应变计实测数据作为响应重构约束条件。结果表明:AI-ROM模型成功实现了基于有限传感数据的跨力学场全域响应重构,即使对于离散性较高的应变场,AI-ROM重构的相对误差为9.1%,相较于精细有限元结果,精度提升了63.5%;借助智能虚拟传感技术,进一步提出了考虑分析区域分片聚类的传感器优化布置算法,通过迭代评估响应重构精度确定关键监测点空间分布,在塔壁足尺试验中,该算法可使应变传感器数量减少58.8%。提出的智能虚拟传感技术有助于实现工程结构数字孪生广域感知-实时仿真一体化要求,为建筑与基础设施运维安全风险评价提供了更为全面的信息支持。展开更多
文摘With the rapid advancements in edge computing and artificial intelligence,federated learning(FL)has gained momentum as a promising approach to collaborative data utilization across organizations and devices,while ensuring data privacy and information security.In order to further harness the energy efficiency of wireless networks,an integrated sensing,communication and computation(ISCC)framework has been proposed,which is anticipated to be a key enabler in the era of 6G networks.Although the advantages of pushing intelligence to edge devices are multi-fold,some challenges arise when incorporating FL into wireless networks under the umbrella of ISCC.This paper provides a comprehensive survey of FL,with special emphasis on the design and optimization of ISCC.We commence by introducing the background and fundamentals of FL and the ISCC framework.Subsequently,the aforementioned challenges are highlighted and the state of the art in potential solutions is reviewed.Finally,design guidelines are provided for the incorporation of FL and ISCC.Overall,this paper aims to contribute to the understanding of FL in the context of wireless networks,with a focus on the ISCC framework,and provide insights into addressing the challenges and optimizing the design for the integration of FL into future 6G networks.
基金supported by the National Natural Science Foundation of China(Grant Nos.52175509 and 52450158)the National Key Research and Development Program of China(Grant No.2023YFF1500900)+2 种基金the Shenzhen Fundamental Research Program(Grant No.JCYJ20220818100412027)the Guangdong-Hong Kong Technology Cooperation Funding Scheme Category C Platform(Grant No.SGDX20230116093543005)the Innovation Project of Optics Valley Laboratory(Grant No.OVL2023PY003)。
文摘Point-of-care diagnostics and inline quantitative phase imaging(QPI)drive the demand for portable,ultra-miniaturized,and robust optical imaging and metrology systems.We propose and demonstrate a wavefront sensor integrated into a photonic integrated circuit,enabling single-shot optical phase retrieval.We implemented an integrated wavefront sensor array with a spatial resolution of 17μm and a numerical aperture of 0.1.Furthermore,we experimentally demonstrated the reconstruction of wavefronts defined by Zernike polynomials,specifically the first 14 terms(Z_(1)to Z_(14)),achieving an average root mean square error below 0.07.This advancement paves the way for fully integrated,portable,and robust optical imaging systems,facilitating integrated wavefront sensors in demanding applications such as point-of-care diagnostics,endoscopy,in situ QPI,and inline surface profile measurement.
基金National Natural Science Foundation of China(Nos.61702392 and 61772393)Key Research and Development Program of Shaanxi Province(Nos.2018ZDXM-GY-045 and 2019JQ-189).
文摘In recent years,computational intelligence has been widely used in many fields and achieved remarkable performance.Evolutionary computing and deep learning are important branches of computational intelligence.Many methods based on evolutionary computation and deep learning have achieved good performance in remote sensing image registration.This paper introduces the application of computational intelligence in remote sensing image registration from the two directions of evolutionary computing and deep learning.In the part of remote sensing image registration based on evolutionary calculation,the principles of evolutionary algorithms and swarm intelligence algorithms are elaborated and their application in remote sensing image registration is discussed.The application of deep learning in remote sensing image registration is also discussed.At the same time,the development status and future of remote sensing image registration are summarized and their prospects are examined.
文摘Studies on land use and land cover changes (LULCC) have been a great concern due to their contribution to the policies formulation and strategic plans in different areas and at different scales. The LULCC when intense and on a global scale can be catastrophic if not detected and monitored affecting the key aspects of the ecosystem’s functions. For decades, technological developments and tools of geographic information systems (GIS), remote sensing (RS) and machine learning (ML) since data acquisition, processing and results in diffusion have been investigated to access landscape conditions and hence, different land use and land cover classification systems have been performed at different levels. Providing coherent guidelines, based on literature review, to examine, evaluate and spread such conditions could be a rich contribution. Therefore, hundreds of relevant studies available in different databases (Science Direct, Scopus, Google Scholar) demonstrating advances achieved in local, regional and global land cover classification products at different spatial, spectral and temporal resolutions over the past decades were selected and investigated. This article aims to show the main tools, data, approaches applied for analysis, assessment, mapping and monitoring of LULCC and to investigate some associated challenges and limitations that may influence the performance of future works, through a progressive perspective. Based on this study, despite the advances archived in recent decades, issues related to multi-source, multi-temporal and multi-level analysis, robustness and quality, scalability need to be further studied as they constitute some of the main challenges for remote sensing.
基金Project 2003AA135010 supported by the National High Technology Research and Development Program of China
文摘In general, there is a demand for real-time processing of mass quantity remote sensing images. However, the task is not only data-intensive but also computating-intensive. Distributed processing is a hot topic in remote sensing processing and image deblurring is also one of the most important needs. In order to satisfy the demand for quick proc- essing and deblurring of mass quantity satellite images, we developed a distributed, grid computation-based platform as well as a corresponding middleware for grid computation. Both a constrained power spectrum equalization algorithm and effective block processing measures, which can avoid boundary effect, were applied during the processing. The re- sult is satisfactory since computation efficiency and visual effect were greatly improved. It can be concluded that the technology of spatial information grids is effective for mass quantity remote sensing image processing.
基金the financial support of this work by the National Natural Science Foundation of Hebei Province China under Grant E2020208052.
文摘With the development of multi-signal monitoring technology,the research on multiple signal analysis and processing has become a hot subject.Mechanical equipment often works under variable working conditions,and the acquired vibration signals are often non-stationary and nonlinear,which are difficult to be processed by traditional analysis methods.In order to solve the noise reduction problem of multiple signals under variable speed,a COT-DCS method combining the Computed Order Tracking(COT)based on Chirplet Path Pursuit(CPP)and Distributed Compressed Sensing(DCS)is proposed.Firstly,the instantaneous frequency(IF)is extracted by CPP,and the speed is obtained by fitting.Then,the speed is used for equal angle sampling of time-domain signals,and angle-domain signals are obtained by COT without a tachometer to eliminate the nonstationarity,and the angledomain signals are compressed and reconstructed by DCS to achieve noise reduction of multiple signals.The accuracy of the CPP method is verified by simulated,experimental signals and compared with some existing IF extraction methods.The COT method also shows good signal stabilization ability through simulation and experiment.Finally,combined with the comparative test of the other two algorithms and four noise reduction effect indicators,the COT-DCS based on the CPP method combines the advantages of the two algorithms and has better noise reduction effect and stability.It is shown that this method is an effective multi-signal noise reduction method.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11704221,11574178,and 61675115)the Taishan Scholar Project of Shandong Province,China(Grant No.tsqn201812059)。
文摘Computational ghost imaging(CGI)provides an elegant framework for indirect imaging,but its application has been restricted by low imaging performance.Herein,we propose a novel approach that significantly improves the imaging performance of CGI.In this scheme,we optimize the conventional CGI data processing algorithm by using a novel compressed sensing(CS)algorithm based on a deep convolution generative adversarial network(DCGAN).CS is used to process the data output by a conventional CGI device.The processed data are trained by a DCGAN to reconstruct the image.Qualitative and quantitative results show that this method significantly improves the quality of reconstructed images by jointly training a generator and the optimization process for reconstruction via meta-learning.Moreover,the background noise can be eliminated well by this method.
基金Supported by the National Major Scientific Instruments Development Project of China under Grant No 2013YQ030595the National Natural Science Foundation of China under Grant Nos 11675014,61601442,61605218,61474123 and 61575207+2 种基金the Science and Technology Innovation Foundation of Chinese Academy of Sciences under Grant No CXJJ-16S047,the National Defense Science and Technology Innovation Foundation of Chinese Academy of Sciencesthe Program of International S&T Cooperation under Grant No 2016YFE0131500the Advance Research Project under Grant No 30102070101
文摘Spectral imaging is an important tool for a wide variety of applications. We present a technique for spectral imaging using computational imaging pattern based on compressive sensing （CS）. The spectral and spatial infor- mation is simultaneously obtained using a fiber spectrometer and the spatial light modulation without mechanical scanning. The method allows high-speed, stable, and sub sampling acquisition of spectral data from specimens. The relationship between sampling rate and image quality is discussed and two CS algorithms are compared.
基金the National Natural Science Foundation of China(NSFC)(61675021)the Fundamental Research Funds for the Central Universities(2018CX01025).
文摘Source and mask joint optimization(SMO)is a widely used computational lithography method for state-of-the-art optical lithography process to improve the yield of semiconductor wafers.Nowadays,computational efficiency has become one of the most challenging issues for the development of pixelated SMO techniques.Recently,compressive sensing(CS)theory has be explored in the area of computational inverse problems.This paper proposes a CS approach to improve the computational efficiency of pixel-based SMO algorithms.To our best knowledge,this paper is the first to develop fast SMO algorithms based on the CS framework.The SMO workflow can be separated into two stages,i.e.,source optimization(SO)and mask optimization(MO).The SO and MO are formulated as the linear CS and nonlinear CS reconstruction problems,respectively.Based on the sparsity representation of the source and mask patterns on the predefined bases,the SO and MO procedures are implemented by sparse image reconstruction algorithms.A set of simulations are presented to verify the proposed CS-SMO methods.The proposed CS-SMO algorithms are shown to outperform the traditional gradient-based SMO algorithm in terms of both computational efficiency and lithography imaging performance.
基金Supported by the National Natural Science Foundation of China(Nos.61401049 and 61201346)Postdoctoral Science Foundation of China(No.2014M560703)+1 种基金Chongqing Postdoctoral Science Foundation(No.Xm2014105)the Fundamental Research Funds for the Central Universities(Nos.CDJZR14125501 and 106112015CDJRC121103)
文摘This paper tries to address the problem of binary CT image reconstruction in non-destructive detection with an algorithm based on compressed sensing(CS) and Otsu's method, which could reconstruct binary CT image of test object from incomplete detection data. According to binary CT image characteristics, we employ Splitbregman method based on L1/2regularization to solve piecewise constant region reconstruction. To improve the reconstructed image quality from incomplete detection data, we utilize a priori knowledge and Otsu's method as the optimization constraint. In our study, we make numerical simulation to investigate our proposed method,and compare reconstructed results from different reconstruction methods. Finally, the experimental results demonstrate that the proposed method could effectively reduce noise and suppress artifacts, and reconstruct high-quality binary image from incomplete detection data.
基金supported by the National Natural Science Foundation of China(61931012,62171258,62088102,and 62271414)the Zhejiang Provincial Outstanding Youth Science Foundation(LR23F010001)the Key Project of Westlake Institute for Optoelectronics(2023GD007).
文摘It has been over a decade since the first coded aperture video compressive sensing(CS)system was reported.The underlying principle of this technology is to employ a high-frequency modulator in the optical path to modulate a recorded high-speed scene within one integration time.The superimposed image captured in this manner is modulated and compressed,since multiple modulation patterns are imposed.Following this,reconstruction algorithms are utilized to recover the desired high-speed scene.One leading advantage of video CS is that a single captured measurement can be used to reconstruct a multi-frame video,thereby enabling a low-speed camera to capture high-speed scenes.Inspired by this,a number of variants of video CS systems have been built,mainly using different modulation devices.Meanwhile,in order to obtain high-quality reconstruction videos,many algorithms have been developed,from optimization-based iterative algorithms to deep-learning-based ones.Recently,emerging deep learning methods have been dominant due to their high-speed inference and high-quality reconstruction,highlighting the possibility of deploying video CS in practical applications.Toward this end,this paper reviews the progress that has been achieved in video CS during the past decade.We further analyze the efforts that need to be made—in terms of both hardware and algorithms—to enable real applications.Research gaps are put forward and future directions are summarized to help researchers and engineers working on this topic.
文摘Noninvasive detection of human physiology plays a key role for diagnosis or therapeutic assessment of various diseases.In the past,many functional modalities,such as electrocardiograph(ECG),electroencephalogram(EEG),fluorescence microscope,and positron emission computed tomography(PETS)have been applied to clinic for probing human heart,brain waves or tissue metabolism,owing to rapid development in fields of electromagnetism,optics or particle physics.Nowadays,a few smart sensing technologies are emerging for human physiology detection in more wide range.
文摘Intelligent Transportation Systems(ITS)leverage Integrated Sensing and Communications(ISAC)to enhance data exchange between vehicles and infrastructure in the Internet of Vehicles(IoV).This integration inevitably increases computing demands,risking real-time system stability.Vehicle Edge Computing(VEC)addresses this by offloading tasks to Road Side Units(RSUs),ensuring timely services.Our previous work,the FLSimCo algorithm,which uses local resources for federated Self-Supervised Learning(SSL),has a limitation:vehicles often can’t complete all iteration tasks.Our improved algorithm offloads partial tasks to RSUs and optimizes energy consumption by adjusting transmission power,CPU frequency,and task assignment ratios,balancing local and RSU-based training.Meanwhile,setting an offloading threshold further prevents inefficiencies.Simulation results show that the enhanced algorithm reduces energy consumption and improves offloading efficiency and accuracy of federated SSL.
文摘为了突破工程结构数字孪生系统中物理传感覆盖度有限的瓶颈,提出了一种基于人工智能增强型降阶模型(AI-Reduced Order Model, AI-ROM)的虚拟传感技术,能够融合多源物理传感数据,实现复杂结构全域响应的实时推演。方法上,基于降阶模型理论将结构响应视为一组降阶基的线性组合,从而将虚拟传感等效为实测响应数据约束下的降阶基组合系数优化问题,依此构造了可考虑跨力学场虚拟传感的深度学习损失函数,提出了基于标准注意力机制的组合系数智能预测模型,利用局部监测数据完成结构全域响应的精准重构。依托狮子洋大桥钢板-混凝土组合塔壁足尺压弯试验,验证智能虚拟传感方法的有效性。利用精细数值模型生成压弯工况下下全过程响应数据训练部署AI-ROM模型,将试验中的6个位移计与17个应变计实测数据作为响应重构约束条件。结果表明:AI-ROM模型成功实现了基于有限传感数据的跨力学场全域响应重构,即使对于离散性较高的应变场,AI-ROM重构的相对误差为9.1%,相较于精细有限元结果,精度提升了63.5%;借助智能虚拟传感技术,进一步提出了考虑分析区域分片聚类的传感器优化布置算法,通过迭代评估响应重构精度确定关键监测点空间分布,在塔壁足尺试验中,该算法可使应变传感器数量减少58.8%。提出的智能虚拟传感技术有助于实现工程结构数字孪生广域感知-实时仿真一体化要求,为建筑与基础设施运维安全风险评价提供了更为全面的信息支持。
