Augmented Reality Geographic Information System(ARGIS) applications can only provide users accurate content services with a highly precise geo-registration.However,the absolute 6DOF(Degree of Freedom) pose provided by...Augmented Reality Geographic Information System(ARGIS) applications can only provide users accurate content services with a highly precise geo-registration.However,the absolute 6DOF(Degree of Freedom) pose provided by the portable sensors is usually inaccurate in urban outdoors,resulting in poorly geo-registration accuracy for ARGIS applications.Aiming at this issue,an automatic vision-aided localization method based on the 2D map is proposed to improve the initial localization accuracy of the portable sensors,and an overall geo-registration optimization framework for outdoor ARGIS is proposed.Based on the initial pose provided by the sensors,the basic principles of the vision-aided localization method are expounded in detail.The experimental results show that the proposed method can effectively correct the initial pose obtained by the pose sensors,and improve the geo-registration accuracy of outdoor ARGIS applications ultimately.展开更多
Monitoring of bacterial pathogens is important for marine environmental protection,because the presence of these microorganisms can be a serious risk for human health.For this reason,a portable sensor implemented as a...Monitoring of bacterial pathogens is important for marine environmental protection,because the presence of these microorganisms can be a serious risk for human health.For this reason,a portable sensor implemented as an electronic embedded system featuring disposable measurement cells was used to evaluate the ability and sensitivity of detection of Escherichia coli(E.coli)as an indicator of fecal pollution in transitional environments and a water sample added with E.coli(10^(2) CFU/mL)was assayed.The first result obtained from the laboratory experiment seems promising for the determination of E.coli in environmental samples,though further improvements will be needed for the field application of this sensor in marine and brackish waters.展开更多
The combination of upconverting nanoparticles(UCNPs)and immunochromatography has become a widely used and promising new detection technique for point-of-care testing(POCT).However,their low luminescence efficiency,non...The combination of upconverting nanoparticles(UCNPs)and immunochromatography has become a widely used and promising new detection technique for point-of-care testing(POCT).However,their low luminescence efficiency,non-specific adsorption,and image noise have always limited their progress toward practical applications.Recently,artificial intelligence(AI)has demonstrated powerful representational learning and generalization capabilities in computer vision.We report for the first time a combination of AI and upconversion nanoparticle-based lateral flow assays(UCNP-LFAs)for the quantitative detection of commercial internet of things(IoT)devices.This universal UCNPs quantitative detection strategy combines high accuracy,sensitivity,and applicability in the field detection environment.By using transfer learning to train AI models in a small self-built database,we not only significantly improved the accuracy and robustness of quantitative detection,but also efficiently solved the actual problems of data scarcity and low computing power of POCT equipment.Then,the trained AI model was deployed in IoT devices,whereby the detection process does not require detailed data preprocessing to achieve real-time inference of quantitative results.We validated the quantitative detection of two detectors using eight transfer learning models on a small dataset.The AI quickly provided ultra-high accuracy prediction results(some models could reach 100%accuracy)even when strong noise was added.Simultaneously,the high flexibility of this strategy promises to be a general quantitative detection method for optical biosensors.We believe that this strategy and device have a scientific significance in revolutionizing the existing POCT technology landscape and providing excellent commercial value in the in vitro diagnostics(IVD)industry.展开更多
文摘Augmented Reality Geographic Information System(ARGIS) applications can only provide users accurate content services with a highly precise geo-registration.However,the absolute 6DOF(Degree of Freedom) pose provided by the portable sensors is usually inaccurate in urban outdoors,resulting in poorly geo-registration accuracy for ARGIS applications.Aiming at this issue,an automatic vision-aided localization method based on the 2D map is proposed to improve the initial localization accuracy of the portable sensors,and an overall geo-registration optimization framework for outdoor ARGIS is proposed.Based on the initial pose provided by the sensors,the basic principles of the vision-aided localization method are expounded in detail.The experimental results show that the proposed method can effectively correct the initial pose obtained by the pose sensors,and improve the geo-registration accuracy of outdoor ARGIS applications ultimately.
基金Supported by decision support system for sustainable fisheries management in the regions of Southern Italy"(Workpackage 1-CNR-IAMC Messina)(Law 191,December 23,2009,article 44).
文摘Monitoring of bacterial pathogens is important for marine environmental protection,because the presence of these microorganisms can be a serious risk for human health.For this reason,a portable sensor implemented as an electronic embedded system featuring disposable measurement cells was used to evaluate the ability and sensitivity of detection of Escherichia coli(E.coli)as an indicator of fecal pollution in transitional environments and a water sample added with E.coli(10^(2) CFU/mL)was assayed.The first result obtained from the laboratory experiment seems promising for the determination of E.coli in environmental samples,though further improvements will be needed for the field application of this sensor in marine and brackish waters.
基金The authors thank the financial support from the National Natural Science Foundation of China(61905033 and 62122017).
文摘The combination of upconverting nanoparticles(UCNPs)and immunochromatography has become a widely used and promising new detection technique for point-of-care testing(POCT).However,their low luminescence efficiency,non-specific adsorption,and image noise have always limited their progress toward practical applications.Recently,artificial intelligence(AI)has demonstrated powerful representational learning and generalization capabilities in computer vision.We report for the first time a combination of AI and upconversion nanoparticle-based lateral flow assays(UCNP-LFAs)for the quantitative detection of commercial internet of things(IoT)devices.This universal UCNPs quantitative detection strategy combines high accuracy,sensitivity,and applicability in the field detection environment.By using transfer learning to train AI models in a small self-built database,we not only significantly improved the accuracy and robustness of quantitative detection,but also efficiently solved the actual problems of data scarcity and low computing power of POCT equipment.Then,the trained AI model was deployed in IoT devices,whereby the detection process does not require detailed data preprocessing to achieve real-time inference of quantitative results.We validated the quantitative detection of two detectors using eight transfer learning models on a small dataset.The AI quickly provided ultra-high accuracy prediction results(some models could reach 100%accuracy)even when strong noise was added.Simultaneously,the high flexibility of this strategy promises to be a general quantitative detection method for optical biosensors.We believe that this strategy and device have a scientific significance in revolutionizing the existing POCT technology landscape and providing excellent commercial value in the in vitro diagnostics(IVD)industry.