Overview: The space gravitational wave detection telescope is one of the core payloads of the gravitational wave detection satellite, simultaneously expanding and contracting the transmitted beam. Optical path stability is one of the core indices for the telescope, closely related to its structural stability. To meet the ultra-high path stability and structural stability requirements posed by the gravitational wave detection mission, it is essential to study the structural deformation measurement of the telescope. Currently, there are still several shortcomings in the research of multi-degree-of-freedom deformation measurement methods for gravitational wave detection telescopes, such as inaccurate selection of measurement points, inability to decouple multi-degree-of-freedom coupling, and unclear identification of error sources in multi-degree-of-freedom measurement. This paper deeply investigates the high-precision measurement of structural deformation of space-borne telescopes designed for space gravitational wave detection. It preliminarily establishes a framework and method system for measuring the structural deformation of space-borne telescopes, theoretically describing the measurement principle of the method. The feasibility of this method applied to space gravitational wave detection is verified through simulation analysis and error decomposition. The paper focuses on resolving the issue of decoupling multiple degrees of freedom, establishing a mathematical model using analytical methods, and conducting preliminary validation using Zemax. Finally, noise analysis of the measurement system is carried out, with experimental testing of the main noise components in the measurement system, validating the correctness of the theoretical noise model proposed in this paper. The experimental results show that near 1 Hz, the displacement noise background of the single-link interferometer is 100 pm/Hz^1/2. At 1 mHz in the low-frequency range, the displacement noise background reaches 10 nm/Hz^1/2. The noise level of the measurement system below 1 mHz is mainly limited by environmental temperature noise, while above 10 mHz, it is primarily constrained by laser frequency noise, phase acquisition background noise, and vibration noise. During the development phase of the space gravitational wave detection telescope, the research on this measurement method is expected to fulfill the telescope's multi-degree-of-freedom deformation measurement needs. It also provides data feedback for telescope design and offers guidance for the study of the telescope's optical path stability.

Overview: In order to achieve the measurement of gravitational wave signals in the millihertz frequency band, the space-based gravitational wave detection projects such as LISA, TianQin, and Taiji projects, which are based on laser interference systems, require the hardware noise floor of the interferometers to be lower than the interstellar weak light shot noise limit. This imposes stringent engineering specifications on the optical-mechanical design and the corresponding interferometer payload. This paper approaches the issue from the perspective of detection mode selection and derives the expressions of readout noise and stray light noise in the interference signal under the single detector mode and the balanced mode. Furthermore, a detailed discussion is provided on the weak-light interference process of the scientific interferometer. The results demonstrate that the balanced mode is capable of suppressing the interference phase noise caused by laser power fluctuations and backscattered stray light across multiple orders of magnitude. However, the suppression capability is constrained by the unequal splitting property of the beam combiner. To address this, a relative gain factor is introduced to compensate for the unequal splitting property of the beam combiner. Further analysis reveals that electronic gain compensation can only eliminate the impact of unequal splitting on one of the two noises rather than both simultaneously. Therefore, a balance must be struck in selecting gain compensation between the suppression of laser power fluctuation noise and stray light noise. Even with this consideration, the balanced mode still offers significant noise suppression capabilities at a magnitude difference, thus potentially reducing the engineering requirements for laser power fluctuations and telescope backscattered stray light.

针对现有图像重定向方法视觉效果差和处理速度慢的问题，提出一种基于主成分分析法和分块的内容感知图像重定向方法。首先，利用主成分分析法融合梯度图和显著图来提取更加丰富的图像特征，避免主体信息失真；其次，相邻裁缝线由均值代替，避免像素不连贯；最后，根据能量图中列能量值的大小将图像分为显著区域和非显著区域，并行缩放分块，更加注重图像特征并提高运行效率。在MIT RetargetMe、DUT-OMRON和NJU2000数据集上进行实验分析，以主观感受和客观因子运行时间、SIFT-flow作为评价指标，与几种常用算法对比。实验结果表明，该方法保证了图像主体信息的完整性，平均运行时间为线裁剪算法的1/3。本文提出的方法不仅具有较优的视觉效果，而且可降低运算量。

为充分利用高光谱影像中蕴含的空谱特征，提出了一种半监督空谱局部判别分析的高光谱影像特征提取算法（S₄LFDA）。鉴于高光谱数据集具有空间一致性，首先将像元进行空间重构，保存高光谱数据的近邻关系；其次引入光谱信息散度重构像元间的相似度；为了充分利用大量无标签样本提高算法性能，采用模糊C均值聚类算法对样本进行聚类分析得到伪标签；然后通过增加规范化项到局部力导引算法（FDA）的类内散度矩阵和类间散度矩阵中，以此保持无标签样本的聚类结构一致性；最后通过局部FDA算法来保持有标签样本类间散度最大化和类内散度最小化并求解最佳投影向量。S₄LFDA算法既保持了数据集在光谱域的可分性，又保持了像元在空间区域内的近邻关系，合理利用有标签样本及无标签样本，提高了算法的分类性能。在Pavia University和Indian Pines数据集上进行实验，总体分类精度达到95.60%和94.38%。与其他维数约简算法相比，该算法有效提高了地物分类性能。

随着电磁环境的日益复杂，电子设备面临的电磁威胁愈加严峻。光电系统作为高灵敏集成化电子设备，强电磁脉冲能量耦合进入系统内部，影响防护能力本就薄弱的光电系统的正常运行。为明晰典型光电系统强电磁耦合过程，通过仿真分析不同强电磁辐照条件下筒型、侧窗型和多窗口型三种典型光电系统的强电磁耦合情况，提取了光电系统强电磁耦合特征及其制约因素，验证了光电系统进行强电磁防护加固的必要性和紧迫性。为解决光电系统强电磁防护能力薄弱的问题，通过仿真分析，验证了透明电磁防护窗口的强电磁加固效能；开展了基于支撑台阶与导电侧壁的电磁缝隙防护加固方法研究，分析了透明防护窗口缝隙耦合泄露的关键安装结构参数，提出了一种非电接触式装配缝隙强电磁防护加固方法。经测试，当缝隙防护结构长度为6 mm时，在0.2～4 GHz频率范围光电系统平均强电磁防护效能提升4.51 dB。研究结果为光电系统强电磁防护能力提升提供了理论指导和具体解决方案。

针对低信噪比条件下，现有的雷达辐射源信号识别方法存在识别正确率低、时效性差的问题，提出了一种基于压缩残差网络的雷达辐射源信号识别方法。首先，利用Choi-Williams分布的时频分析方法将时域信号转换为二维时频图像；然后，根据应用场景特点，选择卷积神经网络（Convolutional Neural Networks, CNN）“压缩”范围；最后，构建压缩残差网络来自动提取图像特征并完成分类。仿真实验结果表明，在同等体量的设计下，与当前较为常用的标准CNN以及ResNet模型相比，所提模型能够降低信号识别运行时间约88%，在信噪比为−14 dB条件下对14种雷达辐射源信号的平均识别率高约5%。提供了一种高效的雷达辐射源信号智能识别方法，具有潜在的工程应用前景。

Recently, deep learning has yielded transformative success across optics and photonics, especially in optical metrology. Deep neural networks (DNNs) with a fully convolutional architecture (e.g., U-Net and its derivatives) have been widely implemented in an end-to-end manner to accomplish various optical metrology tasks, such as fringe denoising, phase unwrapping, and fringe analysis. However, the task of training a DNN to accurately identify an image-to-image transform from massive input and output data pairs seems at best na?ve, as the physical laws governing the image formation or other domain expertise pertaining to the measurement have not yet been fully exploited in current deep learning practice. To this end, we introduce a physics-informed deep learning method for fringe pattern analysis (PI-FPA) to overcome this limit by integrating a lightweight DNN with a learning-enhanced Fourier transform profilometry (LeFTP) module. By parameterizing conventional phase retrieval methods, the LeFTP module embeds the prior knowledge in the network structure and the loss function to directly provide reliable phase results for new types of samples, while circumventing the requirement of collecting a large amount of high-quality data in supervised learning methods. Guided by the initial phase from LeFTP, the phase recovery ability of the lightweight DNN is enhanced to further improve the phase accuracy at a low computational cost compared with existing end-to-end networks. Experimental results demonstrate that PI-FPA enables more accurate and computationally efficient single-shot phase retrieval, exhibiting its excellent generalization to various unseen objects during training. The proposed PI-FPA presents that challenging issues in optical metrology can be potentially overcome through the synergy of physics-priors-based traditional tools and data-driven learning approaches, opening new avenues to achieve fast and accurate single-shot 3D imaging.