ID 原文 译文
26065 在 3mm 波段进行了仿真测试及分析，仿真结果表明，所设计的基于贝塞尔波束的毫米波介质透镜天线的 3dB 宽度为 3. 23mm，景深约为 228mm，相较于传统的基于高斯波束的毫米波介质透镜天线，基于贝塞尔波束的毫米波介质透镜天线可实现的景深提升至 5 倍以上。 Further, the simulation experiment and analysis are conducted in the 3mm band. The results of simulation indicate that the 3dB beam-width is 3. 23mmand the achievable DOF is around 228mm. Compared with the traditional MMW dielectric lens antenna based on Gaussian beam, the achievable DOF can be improved more than five times by MMW dielectric lens antenna based on Bessel beam．
26066 针对宽禁带半导体 SiC APD 紫外单光子探测器，本文提出了一种 1 × 8 线阵型单光子计数读出电路。 Based on the wide bandgap semiconductor SiC APD ultraviolet single photon detector, a 1 × 8 linear arrayphoton counting readout circuit is proposed in this paper．
26067 根据光强条件并通过合适的时序控制，可选取固定门控或互补门控探测方式，实现宽动态范围紫外光子信号的探测计数。 According to the condition of the light intensity and through suitable timing control, the unitary fixed mode or complementary gating detection mode can be selected to implement the functions of sensing and counting for the wide dynamic range ultraviolet photon signals．
26068 读出电路采用 TSMC 0. 18μm CMOS 工艺制备，测试结果表明，读出电路具备单光子探测功能，性能与仿真分析预期结果吻合。 The readout integrated circuit is fabrica-ted by TSMC 0. 18μm CMOS process, the test results show that the readout circuit is capable of single photon detection, and the performance is in agreement with the expected prediction by the simulation results．
26069 最终，借助微动系统二维转台完成对日盲紫外单波长光子的探测与成像，实现对多个独立紫外光源的准确区分与目标定位。 Finally, the two-dimensional turntable of the micro-motion system is utilized to complete the solar blind single-wavelength photon detection and imaging, so as toimplement accurate discrimination and target localization on the multiple independent ultraviolet light sources．
26070 残差神经网络(ＲesNet)是近几年来深度学习研究中的热点，在计算机视觉领域取得较好成就。 Ｒesidual neural network (ＲesNet) has witnessed tremendous amount of attention in deep learning researchover the last few years and has made great achievements in computer vision．
26071 本文对残差神经网络从以下几个方面进行总结:第一，阐述残差神经网络的基本结构和工作原理; In this paper, the ＲesNet is summarized in the following aspects: Firstly, the basic structure and working principle of the ＲesNet are expounded;
26072 第二，在模型发展方面，以时间为顺序总结了残差神经网络的 8 种网络模型; Secondly, in model development, the eight network models of the ＲesNet are summarized in time sequence;
26073 第三，在结构优化方面，从残差神经网络的卷积层、池化层、残差单元、全连接层以及整个网络 5 个方面进行总结; Thirdly, in structural optimization, the research progress is described from five aspects of ＲesNet, including convolutional layer, pooling layer, residual unit, fully con-nected layer and the whole network;
26074 最后，将 ＲesNet 应用到医学图像处理领域，主要从图像识别和图像分割 2 个方面探讨。 Finally, the application of ＲesNet in medical images processing is mainly discussed fromtwo aspects of image recognition and image segmentation．