The aim of the present study was to assess spatial features of tuberculosis prevalence and their relationships with four main ethnic communities in Taiwan. Methods of spatial analysis were clustering pattern determina...The aim of the present study was to assess spatial features of tuberculosis prevalence and their relationships with four main ethnic communities in Taiwan. Methods of spatial analysis were clustering pattern determination (such as global version of Moran’s test and local version of Gi*(d) statistic), using logistic regression calculations to identify spatial distributions over a contiguous five years and identify significant similarities, discriminant analysis to classify variables, and geographically weighted regression (GWR) to determine the strength of relationships between tuberculosis prevalence and ethnic variables in spatial features. Tuberculosis demonstrated decreasing trends in prevalence in both genders during 2005 to 2009. All results of the global Moran’s tests indicated spatial heterogeneity and clusters in the plain and mountainous Aboriginal townships. The Gi*(d) statistic calculated z-score outcomes, categorized as clusters or non-clusters, at at 5% significance level. According to the stepwise Wilks’ lambda discriminant analysis, in the Aborigines and Hoklo communities townships with clusters of tuberculosis cases differentiated from townships without cluster cases, to a greater extent than in the other communities. In the GWR models, the explanatory variables demonstrated significant and positive signs of parameter estimates in clusters occurring in plain and mountainous aboriginal townships. The explanatory variables of both the Hoklo and Hakka communities demonstrated significant, but negative, signs of parameter estimates. The Mainlander community did not significantly associate with cluster patterns of tuberculosis in Taiwan. Results indicated that locations of high tuberculosis prevalence closely related to areas containing higher proportions of the Aboriginal community in Taiwan. This information is relevant for assessment of spatial risk factors, which, in turn, can facilitate the planning of the most advantageous types of health care policies, and implementation of effective health care services.展开更多
The recent fast development in computer vision and mobile sensor technology such as mobile LiDAR and RGB-D cameras is pushing the boundary of the technology to suit the need of real-life applications in the fields of ...The recent fast development in computer vision and mobile sensor technology such as mobile LiDAR and RGB-D cameras is pushing the boundary of the technology to suit the need of real-life applications in the fields of Augmented Reality(AR),robotics,indoor GIS and self-driving.Camera localization is often a key and enabling technology among these applications.In this paper,we developed a novel camera localization workflow based on a highly accurate 3D prior map optimized by our RGBD SLAM method in conjunction with a deep learning routine trained using consecutive video frames labeled with high precision camera pose.Furthermore,an AR registration method tightly coupled with a game engine is proposed,which incorporates the proposed localization algorithm and aligns the real Kinetic camera with a virtual camera of the game engine to facilitate AR application development in an integrated manner.The experimental results show that the localization accuracy can achieve an average error of 35 cm based on a fine-tuned prior 3D feature database at 3 cm accuracy compared against the ground-truth 3D LiDAR map.The influence of the localization accuracy on the visual effect of AR overlay is also demonstrated and the alignment of the real and virtual camera streamlines the implementation of AR fire emergency response demo in a Virtual Geographic Environment.展开更多
文摘The aim of the present study was to assess spatial features of tuberculosis prevalence and their relationships with four main ethnic communities in Taiwan. Methods of spatial analysis were clustering pattern determination (such as global version of Moran’s test and local version of Gi*(d) statistic), using logistic regression calculations to identify spatial distributions over a contiguous five years and identify significant similarities, discriminant analysis to classify variables, and geographically weighted regression (GWR) to determine the strength of relationships between tuberculosis prevalence and ethnic variables in spatial features. Tuberculosis demonstrated decreasing trends in prevalence in both genders during 2005 to 2009. All results of the global Moran’s tests indicated spatial heterogeneity and clusters in the plain and mountainous Aboriginal townships. The Gi*(d) statistic calculated z-score outcomes, categorized as clusters or non-clusters, at at 5% significance level. According to the stepwise Wilks’ lambda discriminant analysis, in the Aborigines and Hoklo communities townships with clusters of tuberculosis cases differentiated from townships without cluster cases, to a greater extent than in the other communities. In the GWR models, the explanatory variables demonstrated significant and positive signs of parameter estimates in clusters occurring in plain and mountainous aboriginal townships. The explanatory variables of both the Hoklo and Hakka communities demonstrated significant, but negative, signs of parameter estimates. The Mainlander community did not significantly associate with cluster patterns of tuberculosis in Taiwan. Results indicated that locations of high tuberculosis prevalence closely related to areas containing higher proportions of the Aboriginal community in Taiwan. This information is relevant for assessment of spatial risk factors, which, in turn, can facilitate the planning of the most advantageous types of health care policies, and implementation of effective health care services.
基金This work was funded by the National Key Research and Development Program of China[grant number 2016YFB0502102]It was also partially funded by National Natural Science Foundation of China[grant number 41101436]the Scientific Research Foundation for the Returned Overseas Chinese Scholars,State Education Ministry。
文摘The recent fast development in computer vision and mobile sensor technology such as mobile LiDAR and RGB-D cameras is pushing the boundary of the technology to suit the need of real-life applications in the fields of Augmented Reality(AR),robotics,indoor GIS and self-driving.Camera localization is often a key and enabling technology among these applications.In this paper,we developed a novel camera localization workflow based on a highly accurate 3D prior map optimized by our RGBD SLAM method in conjunction with a deep learning routine trained using consecutive video frames labeled with high precision camera pose.Furthermore,an AR registration method tightly coupled with a game engine is proposed,which incorporates the proposed localization algorithm and aligns the real Kinetic camera with a virtual camera of the game engine to facilitate AR application development in an integrated manner.The experimental results show that the localization accuracy can achieve an average error of 35 cm based on a fine-tuned prior 3D feature database at 3 cm accuracy compared against the ground-truth 3D LiDAR map.The influence of the localization accuracy on the visual effect of AR overlay is also demonstrated and the alignment of the real and virtual camera streamlines the implementation of AR fire emergency response demo in a Virtual Geographic Environment.