ID 原文 译文
16935 经验证该文算法建立的模型能够实现机器人的空间位置表征，提高了机器人在实验场景下的定位精度，表现出良好的位置估计性能。 It is verified that the model established by the algorithmcan realize the spatial position representation of the robot, improves the positioning accuracy of the objectunder the experimental scene, and shows good position estimation performance.
16936 图像缩放技术要求对图像缩放的同时保证重要信息不丢失且物体边缘不发生扭曲。 Image retargeting technologies require important information preservation and less edge distortionduring increasing/decreasing image size.
16937 近年来，SeamCarving及其改进算法得到了广泛的关注和研究。 The seam carving based algorithms, as the classic retargeting model,receive widespread attention in recent years.
16938 由于采用了离散式最小能量线迭代搜索策略，缩放信息无法在迭代过程中传递导致扭曲现象普遍存在。 However, because of the discrete least energy seam searchingstrategy, the retargeting information can not be passed generation by generation, which causes retargetingdistortions to prevail.
16939 该文针对上述问题提出最小位移可视差(JND)检测算法，能够有效地检测每一次迭代中出现的潜在扭曲信息。 To solve this problem, the Just Noticeable Distortion (JND) algorithm is proposed todetect the potential distribution of distortion information.
16940 能量权重能够将JND信息累加传递给后续的迭代过程，从而抑制缩放过程中的边缘扭曲现象。 Through the proposed energy weight Ew, the JNDinformation can be passed to the following retargeting iteration for distortion reduction.
16941 通过JND算法和能量权重，该文首次将离散的Seam Carving模型转变为连续缩放模型。最后，在公共数据集RetargetMe上与最新的图像缩放算法进行多组对比实验，验证了所提方法的有效性和先进性。 According to the bestknowledge, it is the first time to propose the seam carving algorithm in continuous way by the JND algorithmand energy weight, are the promising results also demonstrated compared with several new approaches atpublic database ‘Retarget Me’, qualitatively and quantitatively.
16942 该文研究到达角度(AOA)协同定位下无人机路径优化问题。 An optimal path planning problem is investigated for Angle-Of-Arrival (AOA) source localizationusing Unmanned Aerial Vehicles (UAVs) equipped with passive sensors.
16943 考虑实际AOA量测噪声方差是目标-传感器距离的函数，距离相关噪声特性使得AOA定位难度增加。 The more realistic model is considered where the variance of AOA measurement noises is a function of the source-to-sensor distances, which complicates AOA-based source localization.
16944 为了更好地适应量测噪声随距离变化特性，该文提出一种变增益无迹卡尔曼滤波算法。 A modified Variable Gain Unscented Kalman Filter (VG-UKF)isdeveloped to adapt to distance-dependent variance of AOA measurement noises.