对国内外关军用及民用飞机平台、分系统及设备雷电标准进行分析，针对雷电直接效应和雷电间接效应的所有测试项目，详述每个测试项目的适用区域、波形要求、测试配置等。结合现有国内雷电设计验证标准及测试存在的不足，提出提升测试设备与试验验证技术匹配性、扩展军用标准测试领域、统一同军种同一平台要求等建议。通过对军用机载平台、设备及分系统关于雷电设计验证标准及测试的分析，为相关产品设计师及试验人员提供设计指标参考，明确产品关于雷电防护的设计要求及验证要求，做到有的放矢，提高设计费效比。

Structured illumination microscopy (SIM) is a popular and powerful super-resolution (SR) technique in biomedical research. However, the conventional reconstruction algorithm for SIM heavily relies on the accurate prior knowledge of illumination patterns and signal-to-noise ratio (SNR) of raw images. To obtain high-quality SR images, several raw images need to be captured under high fluorescence level, which further restricts SIM’s temporal resolution and its applications. Deep learning (DL) is a data-driven technology that has been used to expand the limits of optical microscopy. In this study, we propose a deep neural network based on multi-level wavelet and attention mechanism (MWAM) for SIM. Our results show that the MWAM network can extract high-frequency information contained in SIM raw images and accurately integrate it into the output image, resulting in superior SR images compared to those generated using wide-field images as input data. We also demonstrate that the number of SIM raw images can be reduced to three, with one image in each illumination orientation, to achieve the optimal tradeoff between temporal and spatial resolution. Furthermore, our MWAM network exhibits superior reconstruction ability on low-SNR images compared to conventional SIM algorithms. We have also analyzed the adaptability of this network on other biological samples and successfully applied the pretrained model to other SIM systems.

当前基于深度学习的目标检测算法已较为成熟。然而，基于少量样本检测新类仍具有挑战性，因为少样本条件下的深度学习容易导致特征空间退化。现有工作采用整体微调范式在丰富样本的基类上进行预训练，在此基础上构建新类的特征空间。然而，新类基于多个基类隐式地构造特征空间，其结构较为分散，导致基类与新类之间可分性较差。采用对新类和与其相似的基类进行关联再识别的方法进行少样本目标检测。通过引入动态感兴趣区域头，提升模型对训练样本的利用率，基于二者间的语义相似度，显式地为新类构建特征空间。通过解耦基类和新类的分类分支、添加通道注意力模块及增加边界损失函数，提升二者间的可分性。在标准PASCAL VOC数据集上的实验结果表明，所提方法的nAP50均值较TFA、MPSR及DiGeo分别提升10.2、5.4、7.8。

为了在保持帧结构完整性的同时，低代价地传输管理和控制信号，提出面向高速频分复用相干无源光网络（FDM-CPON）的两种传输管理和控制信号传输机制，即数字端辅助管理和控制通道（AMCC）和数据通道的相加和相乘。通过将AMCC传输的通断键控（OOK）信号映射为数据通道信号幅值的变化，完成数据通道信号幅值再调制，成功将AMCC与数据通道相结合，实现了管理和控制信号与数据通道信号的同步传输。实验结果表明，在基于16QAM传输20 km光纤的200 Gbit/s FDM-CPON系统中，当AMCC的带宽和调制因子（MI）相同时，乘性AMCC对于信号性能的影响更小，自身传输信号的质量也更高。在AMCC的MI为26.1%、带宽为24.4 MHz时，乘性AMCC对信号灵敏度的惩罚比加性AMCC小3 dB。以上研究为未来高速相干频分复用无源光网络AMCC传输与系统设计提供重要参考。