Two-dimensional (2D) organic-inorganic hybrid perovskites are widely used in various optoelectronic devices due to their natural quantum well structure and unique optoelectronic properties. Compared with perovskite film materials, perovskite single crystals have fewer grain boundaries and lower defect density. They are ideal materials for studying the fundamental properties of perovskite. However, how to prepare large-sized 2D perovskite single crystals to facilitate their practical applications in photodetectors is still a challenge. Herein, we prepare large-sized and high-quality 2D Ruddlesden-Popper (C6H5CH2CH2NH3)2PbBr4 perovskite single crystals via an anti-solvent vapor-assisted method and deeply study their optoelectronic properties with lateral photodetectors. The device has extremely low dark current (5.72 × 10-11 A), big responsivity (5.78 mA W-1), and high specific detectivity (2.90 ×1010 Jones). This provides new ideas for the development of efficient and stable perovskite photodetectors.
Simultaneous localization and mapping (SLAM) and path planning are key technologies for robot navigation. The grid map generated by SLAM technology is a prerequisite for the path planning algorithm of mobile robots, playing an important role in autonomous robot navigation. 2D laser SLAM relies on distance information perceived by 2D laser radar to perform pose estimation and map construction, with advantages of high mapping accuracy and stable performance. In this article, we built an experimental platform using the ROS robot operating system, simulated and validated the Gmapping algorithm, and applied the validated algorithm to the Autolabor Pro1 robot platform for indoor mapping. The results show that the algorithm requires less computation and has higher accuracy in constructing small-scale maps, with good mapping effects.
In RFID systems, the dynamic frame slot ALOHA algorithm is an efficient and low-cost method to solve the tag anticollision problem. To solve the problem of excessive energy loss of the reader caused by the Q algorithm frequently transmitting frame length adjustment commands, a dynamic frame slot ALOHA algorithm based on BP neural network (BP-DFSA) is proposed, which can estimate the remaining number of unidentified tags based on the quantity of the three slot states of the previous frame, and adjust the frame length of the next frame only at the end of the current frame. The MATLAB simulation results show that the algorithm can make the RFID system send the frame length adjustment command within 13 times in the whole identification process, and the system throughput rate is stable at about 0.345. It is calculated that this algorithm can significantly reduce the number of frame length adjustment commands sent, saving 43% of reader energy consumption compared to the Q anti-collision algorithm.
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