ABSTRACT
With the progress of the hierarchical medical system, the top-level design for building medical alliances is exhibiting a clearer picture. The Chinese government sets about trying to build a grid-based medical system layout, covering from national, provincial, municipal, county, township, all the way to village level. Based on the analysis of regional grid-based medical systems in the United States, Switzerland, Singapore and Canada, the authors reviewed the development of grid-based health service system in China, and found out key roadblocks for grid-based medical alliances. These problems include the organizational structure, service content, management mechanism, payment system and other aspects, and corresponding countermeasures and suggestions were proposed in the end.
ABSTRACT
Objective To propose a scoring framework grid-based motion statistics (SF-GMS) feature matching algorithm to improve the poor real-time ability and inaccurate matching in the process of target recognition for medical service robots. Methods The feature point neighborhoods were segmented by SF-GMS algorithm using the grids, and the number of feature points in each neighborhood was counted and the scoring frame function was set to judge the feature matching accuracy according to the number of neighborhood feature points and the scoring threshold. Results and conclusion Compared with random sample consensus algorithm, SF-GMS algorithm effectively improved the successful matching rate, and had better real-time performance. SF-GMS algorithm had better stability to the changes of illumination view, occlusion, affine, scale and rotation, and could meet the demand of autonomous navigation in simulating hospital ward scenario for medical service robots.
ABSTRACT
ABSTRACT The incomplete geometrical coverage of the Global Navigation Satellite System (GNSS) makes the ionospheric tomographic system an ill-conditioned problem for ionospheric imaging. In order to detect the principal limitations of the ill-conditioned tomographic solutions, numerical simulations of the ionosphere are under constant investigation. In this paper, we show an investigation of the accuracy of Algebraic Reconstruction Technique (ART) and Multiplicative ART (MART) for performing tomographic reconstruction of Chapman profiles using a simulated optimum scenario of GNSS signals tracked by ground-based receivers. Chapman functions were used to represent the ionospheric morphology and a set of analyses was conducted to assess ART and MART performance for estimating the Total Electron Content (TEC) and parameters that describes the Chapman function. The results showed that MART performed better in the reconstruction of the electron density peak and ART gave a better representation for estimating TEC and the shape of the ionosphere. Since we used an optimum scenario of the GNSS signals, the analyses indicate the intrinsic problems that may occur with ART and MART to recover valuable information for many applications of Telecommunication, Spatial Geodesy and Space Weather.