Kun Liang

The rapid development of quantum technology and single-photon LiDAR places ever-increasing demands on detection systems in terms of bandwidth suppression, wavelength stability, and transmission accuracy. Conventional narrowband filters, however, are falling short in sub-nanometer bandwidth control and high-temperature stability, thereby failing to support high-precision measurements. Consequently, ultra-narrowband optical filters have emerged as a key approach to overcoming these performance bottlenecks. This paper focuses on the core technologies involved in the development of such filters. In the design phase, multiparameter optimization based on a Fabry–Perot (F-P) etalon translates application requirements into fabrication parameters. For substrate fabrication, a sequential process of computer numerical control (CNC) milling, lapping, chemical–mechanical polishing (CMP), and ion-beam polishing, followed by atomic layer deposition (ALD) step formation and ion-beam evaporation coating, is employed to achieve a nanometer-level surface figure and roughness. Temperature control is achieved via a dual-tank hybrid circulation system, maintaining stability within ±0.1 °C. Test results show that the fabricated filter exhibits a full width at a half maximum (FWHM) of 30–60 pm, a free spectral range (FSR) of 260 ± 5 pm, a temperature stability of ≤3 pm/°C, and a peak transmittance of ≥80%. These results preliminarily confirm the device’s excellent overall performance.

The mechanical state of gas-insulated switchgear (GIS) is often reflected by changes in vibration response, which require both reliable frequency readout and effective characterization of local response variations. We propose a single-optical-path dual-mode speckle measurement method for GIS vibration monitoring. By incorporating a tunable liquid lens into the same imaging system, the proposed method enables rapid switching between focused and defocused states and organizes them into two complementary readout paths. The focused mode preserves speckle structural information and supports correlation-based displacement and frequency extraction, whereas the defocused mode uses the ROI-averaged inverse-squared speckle contrast, denoted here as the speckle vibration index (SVI), for statistical-response characterization. Experiments demonstrate the complementary advantage of the two focal states: all focal states achieved frequency extraction with mean errors below 0.5 Hz under baseline conditions, whereas the focused mode maintained higher matching robustness after ROI reduction and numerical low-signal degradation; by contrast, moderate defocus produced the highest mean SVI under different excitation conditions. Application to a GIS fault-simulation platform demonstrates the potential of the dual-mode framework for localized structural readout and statistical-response characterization in engineering vibration monitoring.

Conformal Frequency Selective Surface (FSS) radomes have become increasingly crucial in modern radar systems due to their excellent band-pass characteristics and ability to reduce radar cross-section (RCS). The accurate simulation of the conformal radome requires large computational resources due to their electrically large size and complex geometric structure. In this article, an efficient finite element modeling method based on the first principles of symmetry is proposed, applying it to the electromagnetic simulation of conformal FSS radomes exemplified by cN rotational symmetry. Utilizing projection operators from group theory, the method decomposes excitations into sub-excitations that adhere to the symmetry. This approach is versatile, extending beyond single rotational symmetry to accommodate any spatial symmetries and their combinations, thus sur passing the discrete Fourier expansion method's capabilities. The technique significantly reduces the degrees of freedom (DOFs) to 1/N of the original size, thus enhancing computational efficiency and simplifying geometric modeling and mesh complexity. With the proposed method, key metrics of conformal radomes, such as the RCS and transmittance/reflectance, can be effectively evaluated, thus supporting the design of conformal FSS radomes with arbitrary high rotational symmetry

This research presents a novel, to our knowledge, simultaneous measurement method of atmospheric temperature and pressure profiles based on the lidar scattering spectrum under low-SNR conditions. Departing from classical retrieval processing based on energy, the method exploits the bistable nature of the pure spectral dimension characteristics (Brillouin shift and Rayleigh–Brillouin spectrum linewidth) and exploits their robust effects to enhance temperature and pressure inversion accuracy and suppress Mie scattering noise effects. Key optimizations include retrieval model revisions, aiming to improve accuracy and efficiency, and the strategic manipulation of determining the optimal solution of the analytical model. Collectively, these refinements address the challenges in the simultaneous retrieval of atmospheric temperature and pressure profiles caused by aerosol effects and analytical model limitations, thereby advancing the development of atmospheric lidar technology. The effectiveness of the proposed method is demonstrated through a comprehensive review of recent advances in theoretical analysis, simulation, and lidar ground experiments that validate its performance in low-altitude scenarios. As the results show, the retrieved temperature and pressure show good agreement with the reference temperature and pressure, the absolute temperature difference is less than 2.0 K, and the relative pressure difference is less than 0.21%. This innovative approach, encapsulated within an atmospheric lidar system framework, promises to overcome SNR challenges in multi-parameter remote sensing of the atmospheric environment, paving the way for improved measurement accuracy in low-SNR conditions.

In precision agriculture, the application of artificial intelligence and high-power laser technology for weed control offers significant efficiency and accuracy advantages. Current laser-based weed control systems encounter limitations in data quality, annotation efficiency, and the spatial precision of weed stem localization. In addressing these challenges, this study introduces an innovative framework for weed detection and automated annotation, alongside a localization algorithm for weed stems. The methodology enhances the accuracy and automation of weed annotation across sequential imagery through the optimization of the weed object detection algorithm and zero-shot segmentation techniques, coupled with spatial correlation analysis utilizing three-dimensional (3D) weed reconstruction data. For stem localization, regression predictions of weed and stem positions are obtained via a two-stage segmentation network. Spatial association and constraint mechanisms are then applied based on motion cues and tracking data to correct the results. Experimental evaluations on the dataset demonstrate that the automated annotation accuracy reaches 89.2%, with an average pixel error within 5.3 pixels. The positional accuracy for weed stem localization attains 92.7% within the permissible pixel error margin, representing improvements of 5.34% and 1.2% over UniStemNet and EffiStemNet, respectively. These results validate the efficacy of the proposed algorithm and offer valuable insights for the advancement of laser-based robotic weed control systems in precision agriculture.

Lidar plays an important role in the detection of atmospheric environmental parameters due to its property of high detection accuracy, good real-time performance and high spatiotemporal resolution. Measuring the spectral properties of molecular scattered light enables to determine profiles of atmospheric parameters such as wind speed, temperature, and pressure with high vertical resolution (~100 m) by using sophisticated multi-parameter inversion algorithms. So far, atmospheric measurements of Rayleigh Brillouin (RB) spectrum were used to only determine temperature, using pressure from respective models or from measurements such as radiosondes. In this paper, we propose a new simultaneous inversion method of atmospheric temperature and pressure based on lidar echo energy and the information contained in the RB spectrum. The accuracy and uncertainty of the proposed method are analysed based on numerical simulations and atmospheric measurements and compared to state-of-the-art retrieval methods. It is shown that the accuracy of retrieved temperature can be improved from 2.0 K to 1.1 K and the one of the pressures from 1.2 % to 0.8 %. Furthermore, the potential of additionally retrieving wind speed and the aerosol extinction coefficient based on spectral characteristics and intensity of the scattered light is investigated and discussed. This lays a foundation for the future development and application lidar instruments capable for multi-parameter retrieval.

This article presents a LiDAR system that utilizes a Fizeau interferometer and photomultiplier tube array to detect the water Rayleigh–Brillouin spectrum, utilized to obtain underwater temperature and salinity synchronizing measurements based on the Brillouin spectral linewidth and shift. Temperature and salinity measurements were conducted in the laboratory to verify the efficiency of the system. The results demonstrate that the LiDAR system can accurately obtain the Rayleigh–Brillouin spectral backscattering profiles of water. Following linear fitting and reconstruction, the retrieved temperature accuracy is ±0.13 °C and salinity accuracy is ±0.16‰. By effectively leveraging the multiparameter information contained in the Rayleigh–Brillouin spectrum, the system achieved precise temperature and salinity measurements. This study provides a reference for marine remote sensing applications