为了提高二氧化碳气体检测系统的测量空间分辨率并减小系统体积，设计了一种基于2 μm激光二极管和Herriott多光程吸收池的高灵敏二氧化碳气体传感器。设计并加工了有效光程为2.6 m的Herriott池来进行光路折叠。使用中心波长为2 μm的激光二极管，覆盖二氧化碳分子在4 989.9 cm^-1处的较强吸收线。采用波长调制技术减小系统的噪声。此外，为系统加载Kalman滤波技术来进一步提高探测灵敏度。实验结果表明，采用该传感器，系统的探测极限在1 s的积分时间下可达到0.18×10^-6，而经过自编程实时Kalman滤波后探测极限可达到0.13×10^-6，提高了27%。采用该传感器对室内二氧化碳浓度进行长达8 h的连续监测，并在暨南大学理工学院楼顶进行了24 h的二氧化碳浓度监测，证明了仪器的稳定性。

吸热管表面能流密度分布的均匀性对线性菲涅耳式聚光器的光热性能及安全运行有着重要影响。基于Tonatiuh光学仿真软件构建了线性菲涅耳式聚光器模型，研究了圆柱面形反射镜焦距及瞄准点位置与光斑宽度之间的关系，在此基础上探讨了不同瞄准策略下线性菲涅耳式聚光器的光学效率及能流分布的均匀性。结果表明：采用瞄准点均匀分布的瞄准策略，聚光器在保持较高光学效率的同时能够提升集热管表面能流分布的均匀性。采用优化后的瞄准策略，线性菲涅耳式聚光器光学效率可达到87.4%，集热管表面能流密度标准差由45.3%降低到30.7%，集热管顶部能流密度提升了10.2%。集热管空管预热采用该优化瞄准策略，在变占空比跟踪模式下，集热管弯曲变形程度远小于现有瞄准策略。该结果可为线性菲涅耳式聚光集热系统的优化设计提供理论支撑。

Single-pixel imaging (SPI) is a typical computational imaging modality that allows two- and three-dimensional image reconstruction from a one-dimensional bucket signal acquired under structured illumination. It is in particular of interest for imaging under low light conditions and in spectral regions where good cameras are unavailable. However, the resolution of the reconstructed image in SPI is strongly dependent on the number of measurements in the temporal domain. Data-driven deep learning has been proposed for high-quality image reconstruction from a undersampled bucket signal. But the generalization issue prohibits its practical application. Here we propose a physics-enhanced deep learning approach for SPI. By blending a physics-informed layer and a model-driven fine-tuning process, we show that the proposed approach is generalizable for image reconstruction. We implement the proposed method in an in-house SPI system and an outdoor single-pixel LiDAR system, and demonstrate that it outperforms some other widespread SPI algorithms in terms of both robustness and fidelity. The proposed method establishes a bridge between data-driven and model-driven algorithms, allowing one to impose both data and physics priors for inverse problem solvers in computational imaging, ranging from remote sensing to microscopy.