ID 原文 译文
19065 粗糙熵阈值法需要解决两个问题，一是图像信息不完整性的度量，二是图像的粒化。 There are two problems to be considered in the rough entropy based thresholding methods, i.e., measuring the incompleteness of knowledge about an image and granulating the image.
19066 该文基于倒数信息熵，提出一种倒数粗糙熵用来度量图像中信息的不完整性。 In this paper, reciprocal rough entropy, a new form of rough entropy, is defined to measure the incompleteness of the image information.
19067 为了更好地对图像进行粒化，采用一种基于均匀性直方图的粒子选取方式。 In order to granulate the image effectively, a granule size selectionmethod based on the homogeneity histogram is employed.
19068 该文提出的倒数粗糙熵表述简洁，计算简单。 The proposed reciprocal rough entropy is simple in expression and calculation.
19069 实验验证了该文方法的有效性。 The experimental results verify the effectiveness of the proposed algorithm.
19070 为了降低计算任务的时延和系统的成本，移动边缘计算(MEC)被用于车辆网络，以进一步改善车辆服务。 In order to reduce the delay of computing tasks and the total cost of the system, Mobile EedgeComputing (MEC) technology is applied to vehicular networks to improve further the service quality.
19071 该文在考虑计算资源的情况下对车辆网络时延问题进行研究，提出一种多平台卸载智能资源分配算法，对计算资源进行分配，以提高下一代车辆网络的性能。 Thedelay problem of vehicular networks is studied with the consideration of computing resources. In order to improve the performance of the next generation vehicular networks, a multi-platform offloading intelligent resource allocation algorithm is proposed to allocate the computing resources.
19072 该算法首先使用K临近(KNN)算法对计算任务的卸载平台(云计算、移动边缘计算、本地计算)进行选择，然后在考虑非本地计算资源分配和系统复杂性的情况下，使用强化学习方法，以有效解决使用移动边缘计算的车辆网络中的资源分配问题。 In the proposed algorithm, the K-Nearest Neighbor (KNN) algorithm is used to select the offloading platform (i.e., cloud computing, mobile edgecomputing, local computing) for computing tasks. For the computing resource allocation problem and system complexity in non-local computing, reinforcement learning is used to solve the optimization problem of resource allocation in vehicular networks using the mobile edge computing technology.
19073 仿真结果表明，与任务全部卸载到本地或MEC服务器等基准算法相比，提出的多平台卸载智能资源分配算法实现了时延成本的显著降低，平均可节省系统总成本达80%。 Simulation results demonstratethat compared with the baseline algorithms (i.e., all tasks offload to the local or MEC server), the proposedmulti-platform offloading intelligent resource allocation algorithm achieves a significant reduction in latencycost, and the average system cost can be saved by 80%.
19074 原模图低密度奇偶校验(P-LDPC)码已经广泛应用于各种通信系统，为了使其能够满足不同应用场景下系统对纠错性能、硬件资源损耗以及功耗等方面的要求，需要对P-LDPC码进行进一步的设计优化。 Protograph Low Density Parity Check (P-LDPC) code is widely used in various communicationsystems. In order to meet the requirements of error correction performance, hardware resource loss and power consumption in different application scenarios, further design optimization of P-LDPC codes is needed.