ID 原文 译文
25415 通过仿真试验，验证了利用本文提出的算法，能够准确地估计高动态飞行器平台终端通信中的频偏，满足时频同步的要求。 It is verified that the algorithm proposed in this paper can accurately estimate the frequency offset in terminal communication of high dynamic aircraft platform, which can meet requirement of time-frequency synchronization．
25416 为了提高感兴趣区域音源的声场合成质量，提出了一种非均匀二次声源分布的声场分析与合成方法。 In order to improve the sound field synthesis quality of the sound source in the region of interest, a non-uniform secondary source distribution method for sound field analysis and synthesis is proposed．
25417 首先，将连续二次声源的采样分为两个不同的采样区域，在总扬声器数目一定的情况下，增大感兴趣区域的样点数目并减少感兴趣区域外样点数目。 First, the sampling of the continuous secondary source is divided into two different sampling regions. With a certain number of loudspeakers, the number of samples in the region of interest is increased and the number of samples outside the region of interest is reduced．
25418 然后，利用采样混叠矩阵的 Hermitian 特性，使用瑞利熵来估计不同非均匀采样方案的最大误差，以指导非均匀采样方案的选择。 Then, using the Hermitian characteristic of the sampling aliasing matrix, Ｒayleigh entropy is used to estimate the maximum error of different non-uniform sampling schemes to guide the selection of non-uniform sampling schemes．
25419 最终得到非均匀二次声源驱动信号的频域计算方法。 Finally, the frequency domain calculation method of the driving signal for the non-uniform secondary source distribution is obtained．
25420 实验结果表明在 2. 5D圆形区域边界下，所提非均匀排布方法与典型的均匀排布的最佳模态带宽相比，对感兴趣区域音源的无混叠半径提高了 12. 9% ～ 22. 6% ，验证了提出方法的有效性。 The experimental results show that the proposed non-uniform distribution method improves the aliasing-free radius by 12. 9% ～22. 6% for the sound sources from the region of interest under the 2. 5D circular boundary condition, compared with the optimal modal bandwidth of the uniform distribution. The effectiveness of the proposed method is verified．
25421 提出了一种采用深度学习与图像融合混合实现策略的低照度图像增强算法。 An improved low-light image enhancement (LLIE) algorithm based on the hybrid strategy of deep learning and image fusion was proposed in this paper．
25422 首先，利用照射分量预测模型直接基于输入的低照度图像快速地估计出其最佳照射分量并在 Ｒetinex 模型框架下获得一张整体上适度曝光图像; We first adopted illumination prediction model to quickly estimate the optimal illumination component from a given low-light image and obtain its corresponding moderately exposed image within the framework of the Ｒetinex model．
25423 其次，将低照度图像本身及它的过曝光图像作为适度曝光图像的修正补充图像参与融合; Then the low-light image and its over-exposed image were used as supplementary images for the moderately exposed image．
25424 最后，采用局部结构化融合和色度加权融合机制技术将制备好的 3 张待融合图像进行融合以获得最终的增强图像。 Finally, the three images were fused within the framework of the local structured fusion and the chrominance weighted fusion mechanism to obtain the final enhanced image．