ID 原文 译文
1553 最后，通过实验及算法的时间复杂度分析比较表明:算法是正确且唯一确定结果的，且 WF 算法具有良好的性能。 Finally, through experimental and analyzing the time complexity of the algo-rithm results show that the proposed algorithm is correct and unique to determine the result and the algorithm has good per-formance.
1554 针对短突发通信系统存在的频偏估计相位模糊问题，本文提出了一种宽范围低复杂度的时域相关频偏估计算法———基于相位解模糊的部分互相关算法。 Considering the phase ambiguity problem of frequency offset estimation existed in the short-burst commu-nication, this paper proposes a wide-range low-complexity time-domain correlation frequency offset estimation algorithm—phase-unwrapping based partial cross-correlation algorithm.
1555 该算法利用自相关算法思想和相关代数知识消除了其复乘运算，再利用基于蒙特卡罗仿真的解相位模糊算法解决了其相位模糊问题，从而适应大频偏下的短突发通信环境。 This algorithm uses the idea of the autocorrelation algorithm andknowledge of the algebra to eliminate its complex multiplication operation, and then utilizes a Monte-Carlo simulation basedphase-unwrapping algorithm to solve the problem of phase ambiguity, thus suitable for the short burst communication envi-ronment with large frequency offsets.
1556 最后仿真结果表明，与经典的 M＆M、AC 和 Giugno 时域算法相比，在保证了较大估计范围的同时，该算法具有更高的估计精度和更低的复杂度，适用于低时延高可靠的短数据包传输。 Simulation results show that when maintaining large estimation range, the proposed al-gorithm also exhibits both higher accuracy and lower complexity than the classical M＆M, AC, and Giugno algorithms, thussuitable for low-latency high-reliability short packet transmission.
1557 人类对各种复杂视觉场景的感知通过注视点的不断移动来实现。 The human eyes observe and perceive different external scenes by the continuous movement of fixations.
1558 但在计算机视觉中，如何准确模拟人眼注视点的移动而实现“看向哪儿”这一生物行为是一个难题。 Itis of great difficulty to predict and model“where to look”of human eyes in computer vision research.
1559 为此，本文基于动态显著性提出一种自由观察情形下的注视点移动计算模型。 To address this prob-lem, a computational model including two parallel processes:
1560 模型包括了全局跃迁和局部转移行为， global transition and local movement, is proposed based on vis-ual dynamic saliency.
1561 前者是通过将眼动趋势、向心性和返回抑制性等规则进行融合来预测下一个注视点从而实现相对远距的注视点迁移; The former predicts the long distance migrations of saccades in the scene by combining the eye move-ment bias, visual centrality and inhibition of return;
1562 后者则是通过查找局部最大平均显著性点并将其作为转移的目的位置，二者通过预设准则进行切换。 the latter is used to determine the next destination by searching for the lo-cal largest average salient point.