A miniaturized， high-precision wavelength measurement technology for near-infrared ultra-wideband single-frequency fiber lasers in the 0.8-2.4 μm range is proposed， based on the principle of the Michelson interferometer. The optical components utilized are compatible with the entire near-infrared band （0.8-2.4 μm）. A servo motor translation stage with a maximum stroke of 50 mm， combined with a double-folded optical path design， enables an optical path difference four times the horizontal displacement. Signal acquisition and processing are performed using a high-speed acquisition card and a LabVIEW-based upper computer program， achieving miniaturization， high precision， and rapid measurement. A novel sampling number equivalent resolution method is introduced to simplify the wavelength resolution design process， establishing a direct numerical relationship between wavelength resolution and the number of sampling points， while allowing other parameters to be adjusted independently. Wavelength measurement experiments were conducted on five typical single-frequency lasers （0.8， 1.06， 1.55， 1.94， and 2.05 μm）， and the results were compared with those obtained using a commercial high-precision wavemeter. The experimental results demonstrate the effectiveness of the proposed method， with a single measurement time of 1.5 s， measurement accuracies of ±1.4， ±1.1， ±1.2， ±1.2， and ±1.3 pm， and a wavelength resolution consistent with the theoretical value of 0.2×10-6. These findings validate the capability of this miniaturized technology to perform high-precision， rapid measurements across the full 0.8-2.4 μm near-infrared spectrum.

Ground-based optical telescopes used in daytime debris detection systems for Geostationary Earth Orbit （GEO） experience significant positioning errors due to thermal fluctuations and solar position variations. Furthermore， their limited field of view precludes the use of conventional astronomical positioning techniques for precise target localization. This study presents a novel swing-scan and fusion positioning methodology utilizing neighboring star matching to address these limitations.The proposed approach incorporates three key steps： first， observing two calibrated stars in proximity to the space debris； second， deriving telescope directional change coefficients by comparing telescope orientation with the stars' theoretical astronomical positions； and third， correcting target positioning errors through spatial position matching between the debris and calibrated stars.Implementation of this methodology on the 500 mm aperture GEO space debris all-day detection system， achieved high-precision positioning across the celestial sphere with positioning errors below 4″. Validation experiments conducted on two Beidou precision orbit targets demonstrated total positioning errors of 1.77″ and 1.63″ for azimuth and elevation， respectively. These results confirm the effectiveness of the proposed positioning technology in error correction and its suitability for all-day detection system requirements.

Subwavelength gratings， as compact， easily integrable polarization-selective devices with high extinction ratios， hold significant potential for applications in remote sensing， material stress detection， anti-scattering imaging， and related fields， garnering widespread attention. In this study， we design a one-dimensional subwavelength grating polarization-selective device with high extinction ratios and high transmittance， along with an efficient fabrication method. Utilizing the effective medium theory and finite-difference time-domain simulations， we developed and modeled a one-dimensional subwavelength grating device featuring a double-layer metal structure optimized for holographic interference processing. This design offers robust duty cycle tolerance and facilitates the direct transfer of photoresist patterns. A holographic laser lithography system was constructed to fabricate one-dimensional gratings with dimensions of 30 mm×30 mm and a period of 800 nm. Compared to conventional techniques， this approach demonstrates significant advantages in processing efficiency， cost-effectiveness， and tunable periodicity. The transfer of photoresist patterns to metal gratings was achieved via silicon substrate etching and metal film deposition. The resulting gratings exhibit an average transmittance exceeding 45% and a maximum extinction ratio of 30 dB for infrared wavelengths in the range of 3-15 μm.

In response to the need for detecting column concentrations of greenhouse gases in the marine atmosphere， a 1.57 µm near-infrared laser heterodyne radiometric detection system suitable for shipborne platforms was developed. An image tracking algorithm was developed based on a low-cost surveillance pan-tilt system. The algorithm delay was reduced by simplifying the image processing and control algorithms. This enabled stable solar tracking on a shipborne dynamic platform. A measurement method with local oscillator laser temperature step tuning was proposed. This reduces the impact of tracking errors and non-short noise， thereby improving the signal-to-noise ratio of the measurements. Sea trials were conducted in the South China Sea using the system， and the maximum measured tracking error was 1.98 mrad， meeting the requirements for laser heterodyne detection. Continuous monitoring of atmospheric CO2 column concentration was achieved during both cruising and drifting modes. The fluctuation in the measured CO2 column concentration was less than ±1.0×10-6， reducing the fluctuation range to 1/12 of that observed with traditional local oscillator laser current tuning. This system provides the technical means for subsequent detection of background greenhouse gas column concentrations in the marine atmosphere.

A real-time imaging data acquisition （DAQ） system for pn-junction Charge-Coupled Devices （pnCCD） was developed， incorporating multi-thread processing and multi-frame image superposition techniques to address the specific operational characteristics of pnCCDs. To fulfill the high-speed DAQ requirements for real-time data acquisition and caching， DDR3 memory was employed to receive scientific data from the detector box. The host computer utilized the queue operations functionality of LabVIEW to implement multi-threaded processing for data acquisition， storage， and image superposition. Additionally， real-time image processing modules with multiple operational modes were designed. Experimental results demonstrated that the DAQ system effectively met the requirements across various pnCCD operational modes. Real-time image processing enabled rapid determination of the focusing camera's imaging position， facilitating efficient completion of key experimental procedures， such as focus adjustment and positive axis determination， within the X-ray calibration facility. The system exhibited stable and reliable communication， showcasing exceptional flexibility and portability.

This study presents the development of a novel MEMS-based Fabry-Perot accelerometer with a composite cavity， designed for high-sensitivity， small-range vibration monitoring in the extreme conditions （500°C， 2 MPa） of lead-bismuth reactors. To optimize the sensor's performance， a detailed analysis was conducted using a mechanical model of a cross-beam and mass block structure to evaluate the influence of structural parameters on sensitivity， measurement range， and resonant frequency. This analysis guided the determination of optimal design parameters for the sensitive structure. The chip structure， featuring a composite cavity， was fabricated through a precise double-sided bonding process， ensuring the achievement of the required mechanical properties and thermal stability. Following fabrication， the sensor was assembled and manufactured to conform to stringent design specifications. A dedicated testing platform was established to evaluate the sensor's dynamic characteristics， sensitivity， operational range， and thermal performance under high-temperature conditions. Experimental results demonstrated the sensor's robust functionality， maintaining performance at temperatures up to 500 ℃， and achieving a high sensitivity of 7.69 nm/g within a frequency range of 2-30 Hz. The device provides a measurement range from -11g to +11g with a resolution of 0.04g， enabling precise detection of flow-induced vibrations critical for wear monitoring in reactor components. These attributes enable effective assessment of the wear state of fuel assemblies， a key factor in reactor safety and operational reliability. The accelerometer's high sensitivity and operational reliability underscore its potential as an advanced tool for early detection of vibration-induced component wear in extreme environments. This development marks a significant contribution to improving reactor safety and efficiency， offering a powerful solution for proactive maintenance and monitoring in lead-bismuth reactors.

To address the challenges posed by environmental factors such as temperature， humidity， air turbulence， station transfer， and line-of-sight obstructions in pose measurement systems like laser-based， iGPS， and visual imagery methods， a novel measurement field employing draw-wire displacement sensors was developed for detecting the relative pose of large aircraft components. The relative positions and orientations of these components were calculated using a back propagation BP neural network-Newton iteration method. By strategically arranging six sets of draw-wire displacement sensors along the docking surfaces of the aircraft components， a theoretical model for pose determination was established. The pose was subsequently resolved through the neural network-Newton algorithm. Numerical simulations demonstrate that the maximum position error is 4.9×10⁻⁷ mm， and the maximum attitude angle error is 1.97×10⁻⁹°. Comparative analyses between error simulations and experimental results confirmed the feasibility and precision of this approach. The findings indicate that the proposed method remains robust against external environmental disturbances and enables highly accurate detection of the relative pose of large components.

To address challenges related to low detection accuracy and poor dimensional measurement precision of small defects on metal additive manufacturing surfaces， this study proposes a novel defect detection method based on the You Only Look Once （YOLO） v8 model. The Efficient Channel Attention （ECA） module is integrated into the detection head of the YOLOv8 framework， and the Complete Intersection Over Union （CIoU） loss function is replaced with the Wise Intersection Over Union （WIoU） loss function， effectively mitigating the impact of low-quality samples and enhancing detection performance. To overcome difficulties associated with training on high-resolution image datasets， which often lead to overfitting， local features containing target defects are cropped during the training phase to generate the training dataset. During inference， high-resolution test images are divided into smaller sub-images using a sliding window approach for defect prediction. Detected defect sub-images are marked as regions of interest， and precise defect size measurement is achieved through edge detection techniques in computer vision. Experimental results demonstrate that the improved model achieves a detection accuracy of 94.3%， a recall rate of 93.4%， and an mAP50 of 97.3%， significantly outperforming traditional methods. Furthermore， the dimensional measurement accuracy for small defects reaches 40 μm， highlighting the effectiveness of the proposed approach.

Aiming at the problems of low accuracy of multiple classification in Knee osteoarthritis （KOA） and insufficient feature extraction of knee joint images， the 3DRes-ViT network model based on multi-modal fusion was proposed in this paper. Firstly， the 3D Convolutional Neural Networks （3D CNN） is designed to extract the 3D shallow features of the two magnetic resonance imaging （MRI） sequences respectively， including dual echo steady state （DESS） and fast spin echo （TSE）. The study found that the two kinds of information are complementary， and then these features are fused. Secondly， the dependencies among the fused feature channels are captured by the Efficient Channel Attention （ECA） module and fed into the Vision Transformer （ViT） encoders， which combines the advantages of 3DCNN and ViT to efficiently aggregate the local and global features of the two modalities. Finally， the output of ViT is then fused with the X-ray image features extracted by the 2D convolutional neural network （2D CNN） to further enhance the classification performance. Experimental results show that our method performs excellently in the KOA four-classification task， with an average classification accuracy of 91.2%， an average precision of 91.6%， an F1 score of 0.914， and a reduction of the average absolute error to 8.8%. The proposed model surpasses the mainstream methods in the current field and significantly improves the multiple classification accuracy of knee osteoarthritis.