ID 原文 译文
19115 在验证阶段，在仲裁的辅助下，验证者依照发送者的经典结果核实签名的有效性。 In the verifying phase, the verifier verifies the validity of the completed signature under the aid of an arbitrator.
19116 此外，随机数和认证的公共板被引进阻止接收方在接收真正信息序列之前的存在性伪造攻击和否认攻击。 Additionally, the applications of random number and public board deter the verifier’s existential forgery and repudiation attacks before the verifier accepts the true message.
19117 安全性分析表明设计的算法满足签名者和接收者的不可抵赖以及任何人的不可伪造。 Analyses show that the suggested arbitrated quantumsignature algorithm satisfies the general two requirements, i.e., impossibility of disavowal from the signer andthe verifier and impossibility of forgery from anyone.
19118 讨论表明方案不能抗击发送者的抵赖攻击，相应的建议被给出。 The discussions demonstrate that the scheme may notprevent disavowal attack from the signer and that the corresponding improvements are presented.
19119 由于实验上已经证明量子游走可以在多个不同的物理系统上实现，因此该签名方案未来是可实现的。 The scheme may be realizable because quantum walks have experimentally proven to be implementable in different physical systems.
19120 自动目标识别(ATR)是雷达信息处理领域的重要研究方向。 Automatic Target Recognition(ATR) is an important research area in the field of radar information processing.
19121 由于卷积神经网络(CNN)无需进行特征工程，图像分类性能优越，因此在雷达自动目标识别领域研究中受到越来越多的关注。 Because the deep Convolution Neural Network(CNN) does not need to carry out feature engineeringand the performance of image classification is superior, it attracts more and more attention in the field of radarautomatic target recognition.
19122 该文综合论述了CNN在雷达图像处理中的应用进展。 The application of CNN to radar image processing is reviewed in this paper.
19123 首先介绍了雷达自动目标识别相关知识，包括雷达图像的特性，并指出了传统的雷达自动目标识别方法局限性。 Firstly, the related knowledges including the characteristics of the radar image is introduced, and thelimitations of traditional radar automatic target recognition methods are pointed out.
19124 给出了CNN卷积神经网络原理、组成和在计算机视觉领域的发展历程。 The principle,composition, development of CNN and the field of computer vision are introduced.