摘要：The Giant Radio Array for Neutrino Detection(GRAND)is a planned large-scale observatory of ultra-high-energy(UHE)cosmic particles,with energies exceeding 10~8 Ge *** goal is to solve the long-standing mystery of the origin of UHE cosmic *** do this,GRAND will detect an unprecedented number of UHE cosmic rays and search for the undiscovered UHE neutrinos and gamma rays associated to them with unmatched *** will use large arrays of antennas to detect the radio emission coming from extensive air showers initiated by UHE particles in the *** design is modular:20 separate,independent sub-arrays,each of 10000 radio antennas deployed over 10000 km^2.A staged construction plan will validate key detection techniques while achieving important science goals *** we present the science goals,detection strategy,preliminary design,performance goals,and construction plans for GRAND.

摘要：The orchards usually have rough terrain,dense tree canopy and *** is hard to use GNSS for autonomous navigation in orchard due to signal occlusion,multipath effect,and radio frequency *** achieve autonomous navigation in orchard,a visual navigation method based on multiple images at different shooting angles is proposed in this paper.A dynamic image capturing device is designed for camera installation and multiple images can be shot at different ***,the obtained orchard images are classified into sky and soil detection *** image is transformed to HSV space and initially segmented into sky,canopy and soil regions by median filtering and morphological ***,the sky and soil regions are extracted by the maximum connected region algorithm,and the region edges are detected and filtered by the Canny ***,the navigation line in the current frame is extracted by fitting the region coordinate *** the dynamic weighted filtering algorithm is used to extract the navigation line for the soil and sky detection stage,respectively,and the navigation line for the sky detection stage is mirrored to the soil ***,the Kalman filter algorithm is used to fuse and extract the final navigation *** test results on 200 images show that the accuracy of visual navigation path fitting is 95.5%,and single frame image processing costs 60 ms,which meets the real-time and robustness requirements of *** visual navigation experiments in Camellia oleifera orchard show that when the driving speed is 0.6 m/s,the maximum tracking offset of visual navigation in weed-free and weedy environments is 0.14 m and 0.24 m,respectively,and the RMSE is 30 mm and 55 mm,respectively.