ID 原文 译文
51907 由分析可知，一方面，UVLC在水平方向上的光学衰减建模趋于完善， It can be seen from the analysis that the modeling of optical attenuation in the horizontal direction of UVLC tends to be perfect.
51908 目前还未建立准确且完整的水下可见光湍流与垂直信道模型; At present, there is no accurate and complete model of underwater visible light turbulence and vertical channel.
51909 另一方面，强度调制在水下可见光调制技术中占据主流，相干调制则通常用于提高系统的可靠性。 On the other hand, intensity modulation is the mainstream in underwater visible light modulation technology, while coherent modulation is usually used to improve the reliability of the system.
51910 最后分别从构建UVLC网络和自适应调制编码的角度对未来UVLC可能的发展方向进行了论述。 The possible development direction of UVLC in the future is also discussed from the perspective of constructing UVLC network and adaptive modulation coding.
51911 屏幕通信是以动态条码为信息载体，以可见光为媒介的近场通信方式， Screen communication is a near-field communication mode using dynamic bar code as the information carrier and visible light as the media.
51912 因其抗干扰能力强、不占用频谱资源和部署简单等特点，具有广泛的应用场景和极大的发展潜力。 Due to its strong anti-interference ability, spectrum resources free, and simple deployment, it has a wide range of application scenarios and great development potential.
51913 文章提出了一种基于深度学习方法的屏幕通信定位跟踪算法， We have proposed a screen communication location tracking algorithm based on the method of deep learning.
51914 采用快速区域卷积神经网络(Faster R-CNN)算法对信息区域进行处理，使光学摄像头在不依赖传统定位寻像图形的情况下智能定位携信区域，进而提高单帧携信量; The information area is processed by Faster Region with Convolutional Neural Networks Feature(Faster R-CNN) algorithm which makes the optical camera can intelligently locate the carrier area without relying on the traditional positioning finder pattern, resulting in the improvement of the single frame carrying.
51915 采用卢卡斯-卡纳德(LK)光流法对抖动引入的帧间信息区域位置变化进行估计，提升了连续帧处理速率从而提升了系统通信速率。 The Lucas-Kanade(LK) optical flow is used for position change estimation of the subsequent frame information region introduced by the jitter, which improves the continuous frame processing rate and the system communication rate.
51916 实验结果表明，Faster R-CNN算法的平均精度均值(mAP)达到了90.91%，同时引入LK光流法提升系统的处理效率，相比于仅采用Faster R-CNN的算法处理时间缩短59.5%以上，解决了终端系统处理能力瓶颈问题。 Experimental results show that the mean Average Precision(mAP) of the Faster R-CNN is reached up to 90. 91% while the LK optical flow was introduced to improve the processing efficiency of the system, and overcome the bottleneck of the processing power of the terminal system. The processing time was shortened by 59. 5% compared with the Faster R-CNN algorithm.