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A Vision-aided Localization and Geo-registration Method for Urban ARGIS Based on 2D Maps
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作者 Chen DENG Xiong YOU +3 位作者 Weiwei ZHANG Meixia ZHI Diao LIN Wang XU 《Journal of Geodesy and Geoinformation Science》 2022年第3期93-110,共18页
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
关键词 augmented reality ARGIS geo-registration vision-aided localization hybrid localization portable sensors
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Development of a sensor for the detection of Escherichia coli in brackish waters
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作者 Mancuso Monique Grossi Marco +4 位作者 Rappazzo Alessandro Ciro Zaccone Renata Caruso Gabriella RiccòBruno Bergamasco Alessandro 《Journal of Coastal Life Medicine》 2016年第3期200-202,共3页
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
关键词 MONITORING WATERS Escherichia coli portable sensor Fecal pollution ENVIRONMENT
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Artificial intelligence reinforced upconversion nanoparticle-based lateral flow assay via transfer learning 被引量:1
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作者 Wei Wang Kuo Chen +1 位作者 Xing Ma Jinhong Guo 《Fundamental Research》 CAS CSCD 2023年第4期544-556,共13页
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
关键词 Upconverting nanoparticles Lateral flow assays Transfer learning Internet of medical things portable fluorescent sensor
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