A novel low-coherence digital inline holographic microscope for accurate three-dimensional(3D)position estimation and nanoparticle classification is proposed and validated.Two low-coherence digital inline holograms of...A novel low-coherence digital inline holographic microscope for accurate three-dimensional(3D)position estimation and nanoparticle classification is proposed and validated.Two low-coherence digital inline holograms of a sample containing numerous nanoparticles,generated by two illumination light beams forming a small angle with each other from a low-coherence light source,are employed to determine the nanoparticles’actual 3D positions.Each nanoparticle’s sub-holograms,extracted from the holograms of the sample,are used to reconstruct the intensity scattering image at its respective actual position using the Rayleigh–Sommerfeld backpropagation method.The intensity scattering image of each nanoparticle is then used to classify particles with similar sizes and shapes.The advantages of the proposed system include rapid and highly accurate 3D nanoparticle position determination and nanoparticle classification without the need to pre-prepare patterns or have prior knowledge of the nanoparticle characteristics.展开更多
Fire rescue challenges and solutions have evolved from straightfor-ward plane rescue to encompass 3D space due to the rise of high-rise city buildings.Hence,this study facilitates a system with quick and simplified on...Fire rescue challenges and solutions have evolved from straightfor-ward plane rescue to encompass 3D space due to the rise of high-rise city buildings.Hence,this study facilitates a system with quick and simplified on-site launching and generates real-time location data,enabling fire rescuers to arrive at the intended spot faster and correctly for effective and precise rescue.Auto-positioning with step-by-step instructions is proposed when launching the locating system,while no extra measuring instrument like Total Station(TS)is needed.Real-time location tracking is provided via a 3D space real-time locating system(RTLS)constructed using Ultra-wide Bandwidth technology(UWB),which requires electromagnetic waves to pass through concrete walls.A hybrid weighted least squares with a time difference of arrival(WLS/TDOA)positioning method is proposed to address real path-tracking issues in 3D space and to meet RTLS requirements for quick computing in real-world applications.The 3D WLS/TDOA algorithm is theoretically constructed with the Cramer-Rao lower bound(CRLB).The computing complexity is reduced to the lower bound for embedded hardware to directly compute the time differential of the arriving signals using the time-to-digital converter(TDC).The results of the experiments show that the errors are controlled when the positioning algorithm is applied in various complicated situations to fulfill the requirements of engineering applications.The statistical analysis of the data reveals that the proposed UWB RTLS auto-positioning system can track target tags with an accuracy of 0.20 m.展开更多
Reliable and accurate cooperative positioning is vital to intelligent connected vehicles(ICVs),in which vehicle-vehicle relative measurements are integrated to provide stable locationaware services.However,in zero-tru...Reliable and accurate cooperative positioning is vital to intelligent connected vehicles(ICVs),in which vehicle-vehicle relative measurements are integrated to provide stable locationaware services.However,in zero-trust autonomous driving environments,the possibility of measurement failures and malicious communication attacks tends to reduce positioning performance.With this in mind,this paper presents an ultra-wide bandwidth(UWB)based cooperative positioning system with the specific objective of ICV localization in zero-trust driving environments.Firstly,to overcome measurement degradation under non-line-ofsight(NLOS)propagation conditions,this study proposes a decentralized 3D cooperative positioning method based on a distributed Kalman filter(DKF)by integrating relative rangeazimuth-elevation measurements,unlike the state-of-the-art methods that rely on only one single relative range information to update motion states.More specifically,in contrast to pioneering studies that mainly focus on the positioning problem arising from only one single type of communication attack(either false data injection(FDI)or denial of service(DoS)),we consider a more challenging case of secure cooperative state estimation under mixed FDI and DoS attacks.To this end,a singular-value decomposition(SVD)-assisted decoupled DKF algorithm is proposed in this work,in which a novel update-triggered inter-vehicular communication mechanism is introduced to ensure robust positioning performance against communication attacks while maintaining low transmission load between individuals.To verify the effectiveness in practical 3D NLOS scenarios,we design an intelligent connected multi-robot platform based on a robot operating system(ROS)and UWB technology.Consequently,extensive experimental results demonstrate its superiority and feasibility by achieving a high positioning accuracy of 0.68 m under adverse attacks,especially in the case of hybrid FDI and DoS attacks.In addition,several critical discussions,including the impact of attack parameters,resilience assessment,and a comparison with event-triggered methods,are provided in this work.Moreover,a demo video has been uploaded in the supplementary materials for a detailed presentation.展开更多
The mobility and connective capabilities of unmanned aerial vehicles(UAVs)are becoming more and more important in defense,commercial,and research domains.However,their open communication makes UAVs susceptible toundes...The mobility and connective capabilities of unmanned aerial vehicles(UAVs)are becoming more and more important in defense,commercial,and research domains.However,their open communication makes UAVs susceptible toundesirablepassive attacks suchas eavesdroppingor jamming.Recently,the inefficiencyof traditional cryptography-based techniques has led to the addition of Physical Layer Security(PLS).This study focuses on the advanced PLS method for passive eavesdropping in UAV-aided vehicular environments,proposing a solution to complement the conventional cryptography approach.Initially,we present a performance analysis of first-order secrecy metrics in 6G-enabled UAV systems,namely hybrid outage probability(HOP)and secrecy outage probability(SOP)over 2×2 Nakagami-m channels.Later,we propose a novel technique for mitigating passive eavesdropping,which considers first-order secrecy metrics as an optimization problem and determines their lower and upper bounds.Finally,we conduct an analysis of bounded HOP and SOP using the interactive Nakagami-m channel,considering the multiple-input-multiple-output configuration of the UAV system.The findings indicate that 2×2 Nakagami-mis a suitable fadingmodel under constant velocity for trustworthy receivers and eavesdroppers.The results indicate that UAV mobility has some influence on an eavesdropper’s intrusion during line-of-sight-enabled communication and can play an important role in improving security against passive eavesdroppers.展开更多
Using machine vision to identify and sort scattered regular targets is an urgent problem to be solved in automated production lines.This study proposed a three-dimensional(3D)recognition method combining monocular vis...Using machine vision to identify and sort scattered regular targets is an urgent problem to be solved in automated production lines.This study proposed a three-dimensional(3D)recognition method combining monocular vision and machine learning algorithms.According to the color characteristics of the targets,to convert the original color picture into YCbCr mode and use the 2D Otsu algorithm to perform gray level image segmentation on the Cb channel.Then the Haar-feature training was carried out.The comparison of feature training and Haar method for Hough transform showed that the recognized time of Haar-feature AdaBoost trainer reached 31.00 ms,while its false recognized rate was 3.91%.The strong classifier was formed by weight combination,and the Hough contour transformation algorithm was set to correct the normal vector between plane coordinate and camera coordinate system.The monocular vision system ensured that the field of camera view had not obstructed while the dots were being struck.It was measured and calculated angles between targets and the horizontal plane which coordinate points of the identified plane feature.The testing results were compared with the Otsu and AdaBoost trainer where the prediction and training set have an error of no more than 0.25 mm.Its correct rate can reach 95%.It shows that the Otsu and Haar-feature based on AdaBoost algorithm is feasible within a certain error ranges and meet the engineering requirements for solving the poses of automated regular three-dimensional targets.展开更多
基金funded by the Vietnam Ministry of Education and Training(Project No.B2025 BKA-11).
文摘A novel low-coherence digital inline holographic microscope for accurate three-dimensional(3D)position estimation and nanoparticle classification is proposed and validated.Two low-coherence digital inline holograms of a sample containing numerous nanoparticles,generated by two illumination light beams forming a small angle with each other from a low-coherence light source,are employed to determine the nanoparticles’actual 3D positions.Each nanoparticle’s sub-holograms,extracted from the holograms of the sample,are used to reconstruct the intensity scattering image at its respective actual position using the Rayleigh–Sommerfeld backpropagation method.The intensity scattering image of each nanoparticle is then used to classify particles with similar sizes and shapes.The advantages of the proposed system include rapid and highly accurate 3D nanoparticle position determination and nanoparticle classification without the need to pre-prepare patterns or have prior knowledge of the nanoparticle characteristics.
文摘Fire rescue challenges and solutions have evolved from straightfor-ward plane rescue to encompass 3D space due to the rise of high-rise city buildings.Hence,this study facilitates a system with quick and simplified on-site launching and generates real-time location data,enabling fire rescuers to arrive at the intended spot faster and correctly for effective and precise rescue.Auto-positioning with step-by-step instructions is proposed when launching the locating system,while no extra measuring instrument like Total Station(TS)is needed.Real-time location tracking is provided via a 3D space real-time locating system(RTLS)constructed using Ultra-wide Bandwidth technology(UWB),which requires electromagnetic waves to pass through concrete walls.A hybrid weighted least squares with a time difference of arrival(WLS/TDOA)positioning method is proposed to address real path-tracking issues in 3D space and to meet RTLS requirements for quick computing in real-world applications.The 3D WLS/TDOA algorithm is theoretically constructed with the Cramer-Rao lower bound(CRLB).The computing complexity is reduced to the lower bound for embedded hardware to directly compute the time differential of the arriving signals using the time-to-digital converter(TDC).The results of the experiments show that the errors are controlled when the positioning algorithm is applied in various complicated situations to fulfill the requirements of engineering applications.The statistical analysis of the data reveals that the proposed UWB RTLS auto-positioning system can track target tags with an accuracy of 0.20 m.
基金supported in part by the National Natural Science Foundation of China(62273065,62003064,62303386)the Natural Science Foundation of Chongqing(CSTB2023NSCQ-LZX0014)+1 种基金the Science and Technology Research Program of Chongqing Municipal Education Commission(KJZDK201800701,KJQN202000717)Sichuan Science and Technology Program(2024NSFSC0525).
文摘Reliable and accurate cooperative positioning is vital to intelligent connected vehicles(ICVs),in which vehicle-vehicle relative measurements are integrated to provide stable locationaware services.However,in zero-trust autonomous driving environments,the possibility of measurement failures and malicious communication attacks tends to reduce positioning performance.With this in mind,this paper presents an ultra-wide bandwidth(UWB)based cooperative positioning system with the specific objective of ICV localization in zero-trust driving environments.Firstly,to overcome measurement degradation under non-line-ofsight(NLOS)propagation conditions,this study proposes a decentralized 3D cooperative positioning method based on a distributed Kalman filter(DKF)by integrating relative rangeazimuth-elevation measurements,unlike the state-of-the-art methods that rely on only one single relative range information to update motion states.More specifically,in contrast to pioneering studies that mainly focus on the positioning problem arising from only one single type of communication attack(either false data injection(FDI)or denial of service(DoS)),we consider a more challenging case of secure cooperative state estimation under mixed FDI and DoS attacks.To this end,a singular-value decomposition(SVD)-assisted decoupled DKF algorithm is proposed in this work,in which a novel update-triggered inter-vehicular communication mechanism is introduced to ensure robust positioning performance against communication attacks while maintaining low transmission load between individuals.To verify the effectiveness in practical 3D NLOS scenarios,we design an intelligent connected multi-robot platform based on a robot operating system(ROS)and UWB technology.Consequently,extensive experimental results demonstrate its superiority and feasibility by achieving a high positioning accuracy of 0.68 m under adverse attacks,especially in the case of hybrid FDI and DoS attacks.In addition,several critical discussions,including the impact of attack parameters,resilience assessment,and a comparison with event-triggered methods,are provided in this work.Moreover,a demo video has been uploaded in the supplementary materials for a detailed presentation.
基金funded by Taif University,Taif,Saudi Arabia,Project No.(TUDSPP-2024-139).
文摘The mobility and connective capabilities of unmanned aerial vehicles(UAVs)are becoming more and more important in defense,commercial,and research domains.However,their open communication makes UAVs susceptible toundesirablepassive attacks suchas eavesdroppingor jamming.Recently,the inefficiencyof traditional cryptography-based techniques has led to the addition of Physical Layer Security(PLS).This study focuses on the advanced PLS method for passive eavesdropping in UAV-aided vehicular environments,proposing a solution to complement the conventional cryptography approach.Initially,we present a performance analysis of first-order secrecy metrics in 6G-enabled UAV systems,namely hybrid outage probability(HOP)and secrecy outage probability(SOP)over 2×2 Nakagami-m channels.Later,we propose a novel technique for mitigating passive eavesdropping,which considers first-order secrecy metrics as an optimization problem and determines their lower and upper bounds.Finally,we conduct an analysis of bounded HOP and SOP using the interactive Nakagami-m channel,considering the multiple-input-multiple-output configuration of the UAV system.The findings indicate that 2×2 Nakagami-mis a suitable fadingmodel under constant velocity for trustworthy receivers and eavesdroppers.The results indicate that UAV mobility has some influence on an eavesdropper’s intrusion during line-of-sight-enabled communication and can play an important role in improving security against passive eavesdroppers.
基金This work was financially supported by the National Natural Science Foundation of China(Grant No.51705365)The authors also acknowledge the State Key Research Program of China(Grant No.2017YFD0700404)+1 种基金the Guangdong Provincial Department of Education Project(Grant No.2016KZDXM027)the Guangdong Provincial Department of Agriculture(Grant No.2019KJ129).
文摘Using machine vision to identify and sort scattered regular targets is an urgent problem to be solved in automated production lines.This study proposed a three-dimensional(3D)recognition method combining monocular vision and machine learning algorithms.According to the color characteristics of the targets,to convert the original color picture into YCbCr mode and use the 2D Otsu algorithm to perform gray level image segmentation on the Cb channel.Then the Haar-feature training was carried out.The comparison of feature training and Haar method for Hough transform showed that the recognized time of Haar-feature AdaBoost trainer reached 31.00 ms,while its false recognized rate was 3.91%.The strong classifier was formed by weight combination,and the Hough contour transformation algorithm was set to correct the normal vector between plane coordinate and camera coordinate system.The monocular vision system ensured that the field of camera view had not obstructed while the dots were being struck.It was measured and calculated angles between targets and the horizontal plane which coordinate points of the identified plane feature.The testing results were compared with the Otsu and AdaBoost trainer where the prediction and training set have an error of no more than 0.25 mm.Its correct rate can reach 95%.It shows that the Otsu and Haar-feature based on AdaBoost algorithm is feasible within a certain error ranges and meet the engineering requirements for solving the poses of automated regular three-dimensional targets.
