ID	原文	译文
5054	仿真结果表明，所提算法可有效补偿无源雷达中机动目标的距离徙动和多普勒徙动，对变加速目标的积累效果和检测概率显著优于现有算法。	Simulation results demonstrate that the proposed methodcan effectively compensate the RM and DFM induced by the target motion parameters in passive radar, and for ma-neuvering targets with jerk motion, the proposed method achieves a better integration and detection performance overexisting methods.
5055	作为高效视频编码（HEVC）的扩展，3D-HEVC 标准有效地提高了 3D 视频的压缩效率，但是也带来了很高的编码计算复杂度。为了显著地降低 3D-HEVC 编码复杂度，提出了一种提前 Merge 模式终止算法。	3D-high efficiency video coding (3D-HEVC) standard is an extension of HEVC. Though 3D-HEVC effective-ly improves the compression efficiency of 3D video, it also brings huge computational complexity. To greatly reduce the3D-HEVC coding complexity, an early Merge mode decision approach was proposed.
5056	首先，提取 Merge 模式编码后的残差信号作为特征信息；	The residual signal that encoded by the Merge mode was firstly extracted as feature information.
5057	然后，根据当前编码帧内已经编码的编码单元（CU）的最优Merge 模式残差信号建立学习模型；最后，提取当前 CU 的 Merge 模式的残差信号，并且利用学习模型预测 Merge模式是否为最优模式。	A learning model was established in terms of the residualsignals of the coding unit (CU) in current frame that used early Merge mode as the optimal mode. The residual signal that encoded by the Merge mode was firstly extracted as feature information. Finally, the residualsignal was extracted for the Merge mode of current CU, and the learning model was used to predict whether the Mergemode was the optimal mode or not.
5058	实验结果表明，提出的提前 Merge 模式终止算法分别将 3D-HEVC 纹理视点和深度图编码的时间降低了 41.9%和 24.3%，且编码性能的降低几乎可忽略不计。	Experimental results show that the proposed early Merge mode decision approachreduces the coding times of 3D-HEVC texture views and depth maps about 41.9% and 24.3%, respectively, and the cod-ing performance degradation is almost negligible.
5059	相较于现有的提前 Merge 模式算法，提出的提前 Merge 模式终止算法能进一步降低 3D-HEVC 的编码时间，并且设计简单，易于集成到 3D-HEVC 测试模型。	Compared with existing early Merge mode decision approaches, the proposed approach further reduces the coding time, and can be easily integrated into the 3D-HEVC test model due to its design simplicity.
5060	提出了一种利用图像深度学习解决无线电信号识别问题的技术思路。	A technical idea was innovatively proposed that uses image deep learning to solve the problem of radio signalrecognition.
5061	首先把无线电信号具象化为一张二维图片，将无线电信号识别问题转化为图像识别领域的目标检测问题；	First, the radio signal was transformed into a two-dimensional picture, and the radio signal recognition prob-lem was transformed into the object detection problem in the field of image recognition.
5062	进而充分利用人工智能在图像识别领域的先进成果，提高无线电信号识别的智能化水平和复杂电磁环境下的识别能力。	Then, the advanced achieve-ments about image recognition were used to improve the intelligence and ability of radio signal recognition in complexelectromagnetic environment.
5063	基于该思路，提出了一种基于图像深度学习的无线电信号识别算法——RadioImageDet 算法。	Based on the proposed idea, a novel radio signal recognition algorithm named RadioIma-geDet was proposed.