Sensors based on optical resonators often have their measurement range limited by their cavity linewidth, particularly in the measurement of time-varying signals. This paper introduces a method for optical frequency shift detection using multiple harmonics to expand the dynamic range of sensors based on optical resonators. The proposed method expands the measurement range of optical frequency shift beyond the cavity linewidth while maintaining measurement accuracy. The theoretical derivation of this method is carried out based on the equation of motion for an optical resonator and the recursive relationship of the Bessel function. Experimental results show that the dynamic range is expanded to 4 times greater than the conventional first harmonic method while still maintaining accuracy. Furthermore, we present an objective analysis of the correlation between the expansion factor of the method and the linewidth and free spectrum of the optical resonator.

认知偏差是认知电子战中的客观存在。基于动态博弈方法，针对认知雷达对抗过程中因不完整信息和测量误差导致的认知偏差，研究雷达对抗建模方法：以雷达抗干扰改善因子和干扰机干扰效益因子计算博弈双方的效益，采用精炼贝叶斯均衡建立动态雷达对抗模型，分析认知偏差造成的影响。仿真实验结果证明了所提方法的有效性。

为了提高动态场景RGB-D SLAM中相机位姿精度，基于实例分割与光流算法，提出一种高精度RGB-D SLAM方法。首先，通过实例分割算法检测出场景中的物体，删除非刚性物体并构造语义地图。接着，通过光流信息计算运动残差，检测场景中动态刚性物体，并在语义地图中追踪这些动态刚性物体。然后，删除每一帧中非刚性物体和动态刚性物体上的动态特征点，利用其他稳定的特征点优化相机位姿。最后，通过TSDF模型重建静态背景，并以点云的形式显示动态刚性物体。在TUM和Bonn数据集中测试表明，本文方法与当前最先进的SLAM工作ACEFusion相比相机精度提升约43%。消融实验结果表明，保留动态刚性物体处于静止状态下的特征点对相机位姿估计结果提升约37%。稠密建图实验结果表明，本文方法在动态场景中重建结果优于当前先进的工作，平均重建误差为0.042 m。代码开源在https：//github.com/wawcg/dy_wcg。

Active metasurfaces have recently attracted more attention since they can make the light manipulation be versatile and real-time. Metasurfaces-based holography possesses the advantages of high spatial resolution and enormous information capacity for applications in optical displays and encryption. In this work, a tunable polarization multiplexing holographic metasurface controlled by an external magnetic field is proposed. The elaborately designed nanoantennas are arranged on the magneto-optical intermediate layer, which is placed on the metallic reflecting layer. Since the non-diagonal elements of the dielectric tensor of the magneto-optical material become non-zero values once the external magnetic field is applied, the differential absorption for the left and right circularly polarized light can be generated. Meanwhile, the amplitude and phase can be flexibly modulated by changing the sizes of the nanoantennas. Based on this, the dynamic multichannel holographic display of metasurface in the linear and circular polarization channels is realized via magnetic control, and it can provide enhanced security for optical information storage. This work paves the way for the realization of magnetically controllable phase modulation, which is promising in dynamic wavefront control and optical information encryption.

Increasing bandwidth requirements have posed significant challenges for traditional access networks. It is difficult for intensity modulation/direct detection to meet the power budget and flexibility requirements of the next-generation passive optical network (PON) at 100G and beyond considering the new requirements. This is driving researchers to develop novel optical access technologies. Low-cost, wide-coverage, and high-flexibility coherent PON is emerging as a strong contender in the competition. In this article, we will review technologies that reduce the complexity of coherent PON (CPON), enabling it to meet the commercial requirements. Also, advanced algorithms and architectures that can enhance system coverage and flexibility are also discussed.

初烤烟叶等级的快速准确检测对开发烟叶智能分级设备以促进农产品精细化管理有着重要意义。针对相似度较高但等级不同的初烤烟叶难以区分的问题，本文提出多感受野特征自适应融合及动态损失调整的初烤烟叶等级检测网络（Flue-cured Tobacco Leaf Grade Detection Network，FTGDNet）。首先，FTGDNet采用CSPNet作为特征提取主干网络，采用GhostNet作为辅助特征提取网络以增强模型的特征提取能力；其次，在主干网络末端嵌入显式视觉中心瓶颈模块（Explicit Visual Center Bottleneck module，EVCB）以实现全局特征信息与局部细节特征信息融合；然后，构建多感受野特征自适应融合模块（Multi-Receptive Field Feature Adaptive Fusion module，MRFA_d），利用注意力特征融合机制（Attention Feature Fusion，AFF）将不同感受野特征图进行自适应加权融合，在增强模型局部感受野的同时突出有效通道信息；最后，设计了一种新的定位损失函数（More Complete IoU Loss，MCIoU_Loss），结合预测框与真实框面积损失以解决在回归定位过程中二者宽高比相等且中心点重合时CIoU_Loss性能退化导致定位精度下降问题，此外，引入矩形相似度衰减系数在训练过程中对真实框与预测框的相似度判别项进行动态调整，加快模型拟合。实验结果表明，FTGDNet对十个等级的初烤烟叶的验证精度达到90.0%，测试精度达到87.4%，且推理时间仅为12.6 ms。相较于多种先进目标检测算法，FTGDNet具有更高的检测精度和更快的检测速度，可为高精度初烤烟叶等级检测提供关键技术支撑。