拓扑光子学是光学领域一个重要的新兴研究方向，它的奇异光学响应特性颠覆了人们对光传播的理解，为人工操控光的传播提供了一种全新方法。Floquet拓扑光子绝缘体是拓扑光子学的一个重要分支，它利用周期性的驱动来探索物质的相变过程，发现了很多违反直觉的新奇物理现象。本文综述了近十年来Floquet拓扑光子绝缘体的主要研究进展。根据实现方式将其分为基于时间调制、基于空间调制、基于耦合谐振调制的三个大类进行详细论述。对于每一类Floquet拓扑光子绝缘体，从基础理论和物理实现平台两个方面论述了Floquet拓扑光子绝缘体的独特光学特性和奇异物理现象，并讨论了其潜在的应用前景。最后，总结了该领域当前面临的主要挑战，并对Floquet拓扑光子绝缘体的未来研究方向进行了展望。

电磁波吸收器是一种能够吸收和湮灭电磁波的装置，广泛应用于**、科技和人民生活的各个领域。基于超材料的吸收器由于其强大的吸收电磁波的能力、超薄特性以及设计的灵活性而受到广泛关注。但此类吸收器存在带宽窄的问题，因此，设计了一种基于金属–介电–金属（metal-dielectric-metal，MDM）结构的宽带超材料吸收器。对其吸收原理和物理机制进行了分析，并对其结构参数进行了仿真模拟。结果表明，超材料吸收器对于490～1 790 nm的入射光的吸收率高于80%，平均吸收率可达90%，最佳工作角度为30°。此外，通过修改单元结构的对称性，可以实现偏振相关调控。所提出的超材料吸收器非常适合于太阳能光伏、光通信、滤波和传感等方面的应用。

To improve the performance of few-shot classification, we present a general and flexible method named Multi-Scale Attention and Domain Adaptation Network (MADA). Firstly, to tackle the problem of limited samples, a masked autoencoder is used to image augmentation. Moreover, it can be inserted as a plug-and-play module into a few-shot classification. Secondly, the multi-scale attention module can adapt feature vectors extracted by embedding function to the current classification task. Multi-scale attention machine strengthens the discriminative image region by focusing on relating samples in both base class and novel class, which makes prototypes more accurate. In addition, the embedding function pays attention to the task-specific feature. Thirdly, the domain adaptation module is used to address the domain shift caused by the difference in data distributions of the two domains. The domain adaptation module consists of the metric module and the margin loss function. The margin loss pushes different prototypes away from each other in the feature space. Sufficient margin space in feature space improves the generalization performance of the method. The experimental results show the classification accuracy of the proposed method is 67.45% for 5-way 1-shot and 82.77% for 5-way 5-shot on the miniImageNet dataset. The classification accuracy is 70.57% for 5-way 1-shot and 85.10% for 5-way 5-shot on the tieredImageNet dataset. The classification accuracy of our method is better than most previous methods. After dimension reduction and visualization of features by using t-SNE, it can be concluded that domain drift is alleviated, and prototypes are more accurate. The multi-scale attention module enhanced feature representations are more discriminative for the target classification task. In addition, the domain adaptation module improves the generalization ability of the model.

针对少量样本条件下模型易过拟合、目标错检与漏检问题，本文基于TFA (two-stage fine-tuning approach)提出了一种在线推断校准的小样本目标检测框架。该框架设计了一种全新的Attention-FPN网络，通过建模特征通道间的依赖关系选择性融合特征，结合分级冻结的学习机制引导RPN模块提取正确的新类前景目标；同时，构建了一种在线校准模块对样本进行实例分割编码，对众多候选目标进行评分重加权处理，纠正误检和漏检的预测目标。结果表明，所提算法在VOC数据集Novel Set1中，五个任务的平均nAP50提升10.16%，在性能上优于目前的主流算法。