Abstract:

Abstract:

Abstract:

Abstract:The fundamental principles of single?photon imaging technology are introduced, along with an analysis of its advantages of high sensitivity, high temporal resolution and high photon utilization efficiency. The technical characteri?stics of single?point scanning and multi?pixel single?photon imaging are elaborated, with discussions on their applications in scenarios such as long?range imaging, underwater imaging, and imaging in complex environments. Principles of traditional single?photon imaging algorithms and deep learning?based algorithms are presented, followed by a comparative analysis of their application effects under conditions of sparse echoes, strong noise and multi?peak signals. The outlook for the future development of single?photon imaging technology is proposed, highlighting that innovative hardware systems, optimized imaging algorithms and interdisciplinary technology integration will further advance the field toward higher accuracy, efficiency and intelligence.

Abstract:To achieve fast and high?accuracy detection with single?photon light detection and ranging（LiDAR), this paper designs a high?flux and high?repetition?rate single?photon LiDAR system and proposes a waveform correction method tailored for this system. By increasing the photon counting rate, the system significantly reduces the single?pixel acquisition time. Meanwhile, the waveform correction method effectively addresses the issues of waveform distortion caused by the dead time of single?photon detectors under high?flux and high?repetition?rate conditions, thereby enhancing the inversion accuracy of target signal strength and depth. The system employs a free?running single?photon detector in the near?infrared band with a dead time of 1 200 ns and a laser repetition rate of 3 MHz, and the single?pixel acquisition time is set to 1 ms. Simulation and experimental results demonstrate that the proposed method achieves a distance inversion accuracy of 4.9 mm and a photon flux inversion accuracy of 0.16 photons. In the 3D imaging experiment, using a 50 × 50 point?to?point scanning pattern, the imaging plane fitting accuracy reaches 8 mm, enabling high?precision 3D imaging of small UAVs at close range. This study provides a new technical approach for the application of single?photon LiDAR in fast imaging fields such as target detection and resource mapping.

Abstract:Aiming at the lack of more in?depth quantitative data for the study of the systematic errors of Littrow?type grating interferometers, the systematic errors of Littrow?type grating interferometers caused by the positioning accuracy of the components, namely, the systematic errors of the interferometers caused by the additional optical path differences due to grating rotation around the x, y, and z axes as well as mirror rotation around the y axis, were investigated in terms of the impact of the systematic errors on the displacement measurements of the interferometers. A mathematical model of the error caused by the change in optical path difference when the grating and mirror rotate around the axes was established, quantitatively analyzed, and the accuracy of the mathematical model was verified by experiments. The results show that: when the grating and mirror rotate around the x and z axes, no additional optical path difference is generated; when the grating rotates around the y axis, the systematic error will be generated and increase with the increase of the grating constant and the rotation angle; when the mirror rotates around the y axis, the error will be generated only when the rotation angles of the two mirrors are different, and the error will increase with the increase of the rotation angle of the two mirrors. After synthesizing the errors of the whole system, the undefined system error is ± 3.12 μm in high assembly level, and ± 17.75 μm in general level, which verifies the correctness of the theoretical simulation, and provides technical reference and theoretical support for the system design of the Littrow?type grating interferometer.

Abstract:In laser ranging scenarios involving non?cooperative targets, the complex and diverse surface characteristics of these targets often result in low reflectivity and scattering of reflected light in various directions. Consequently, the optical energy returning to the measurement system is weak. To effectively collect the return optical energy and achieve precise focusing of the laser spot under such conditions, a high?precision laser zoom optical?mechanical system and auto?focusing control system have been designed. The optical structure of the system is optimized by incorporating a combination of collimating lens group, front lens group, movable lens group, rear lens group, and compensating lens group. This design ensures efficient beam focusing and maximizes energy, thereby enhancing the signal?to?noise ratio and stability across different ranging distances. Additionally, the focusing consistency of the system is improved by optimizing optical axis stability and mechanical structure layout. In terms of control methodology, an image recognition?based auto?focusing strategy is introduced. A high?resolution camera captures real?time images of the target laser spot. Image processing techniques are employed to extract key features such as spot diameter, shape, and clarity. These features are used to dynamically calculate optimal focal length adjustment parameters, enabling automatic closed?loop focusing via a stepper motor. Experimental results indicate that the system has a light spot centroid offset of no more than 65 μm within a working distance of 0.5 ~ 30 m, which meets the design requirements and can effectively achieve spot focusing.

Abstract:To solve the measurement problem of stroke error for heavy?duty screw pairs with diameters of 100 ~ 300 mm in key industries, building upon the experience of small and medium?sized screw measurement instruments, this study has optimized the overall configuration, critical components, and measurement?control systems while exploring manufacturing and assembly processes. A specialized measurement machine for heavy?duty screw pairs was successfully developed, enabling dynamic measurement of both lead error in heavy?duty screws and travel error in screw pair assemblies. Experimental results demonstrate that within a 4 m travel range, the instrument's optical axis exhibits maximum indication errors of 0.5 μm and 1.3 μm when positioned at 50 mm and 150 mm from the screw centerline, respectively. For the tested screw pair samples, the maximum deviations between the extreme values of all measured parameters do not exceed one?third of the tolerance specified for P1?grade screw pairs. These findings confirm that the measurement accuracy of this heavy?duty screw pair stroke measuring machine meets P1?grade requirements. The successful development of this instrument plays an important role in promoting the development of heavy?duty screw pairs and screw detection fields.

Abstract:To enhance the demodulation accuracy of vernier spectral signals in Fabry?Pérot (F?P) sensors, this study proposes a direct deep learning?based demodulation method for spectral data. The method involves preprocessing spectral data to convert complex vernier spectral information into formats compatible with Convolutional Neural Network (CNN), followed by training and testing deep learning models on the processed full?spectrum data. The CNN architecture was employed for feature extraction and classification of spectral data, enabling accurate demodulation of target signals. Experimental validation was conducted utilizing spectral data collected from a dual?cavity F?P sensor with 112.5 nm / MPa sensitivity. The results demonstrate that the CNN model achieved an average accuracy of 92.49% with 10?fold cross?validation, accompanied by a Root Mean Square Error (RMSE) of 0.039 2 MPa and a mean relative error of 3.31%. The hybrid Convolutional Neural Network?Long Short Term Memory (CNN?LSTM) model exhibited superior performance with an average accuracy of 96.98%, an RMSE of 0.039 0 MPa, and a mean relative error of 3.28%. Notably, the CNN?LSTM approach attained high precision using only 256 sampled data points, demonstrating remarkable efficiency. This method provides an effective technical pathway for advancing spectral signal demodulation technology, offering significant reference value for developing intelligent optical sensing systems.

Abstract:To improve the identification accuracy of the static model of quartz flexible accelerometer, this study proposes a static model parameter identification method based on a Particle Swarm Optimization?Back Propagation (PSO?BP) neural network. This approach addresses the local optima susceptibility of Back Propagation (BP) neural networks through Particle Swarm Optimization (PSO) integration. The neural architecture is configured according to accelerometer input?output dimensions, where the PSO's global exploration capability optimizes the initial weight for the BP network. Precision centrifuge?based calibration experiments were conducted to validate the proposed method. Experimental results demonstrate that the PSO?BP neural network exhibits significantly enhanced capability in resolving nonlinear coefficients compared to the standard BP network, achieving a reduction of the mean squared error (MSE) by two orders of magnitude, which provides technical support for advancing the development of high?precision navigation technologies in airborne inertial navigation systems.

Abstract:In response to the need for rapid and highly sensitive detection of tuberculosis antigens and novel coronavirus proteins, a highly?sensitivity optical microfiber sensor for detecting tuberculosis antigens (MPT64 protein, Ag85B protein) and the nucleocapsid (N) protein of severe acute respiratory syndrome coronavirus 2 (SARS?CoV?2) has been developed. The optical microfiber sensor is coated with graphene oxide (GO) which offers extremely high surface area and excellent optical properties, and can significantly enhance the conical optical fiber's immobilization capacity; the GO?coated conical optical fiber is further functionalized with single?stranded DNA (ssDNA) aptamers, enabling efficient capture of target proteins and facilitating real?time detection in vitro. The prepared sensor is employed to detect the target analytes. The experimental results reveal that the sensor can rapidly detect MPT64 and Ag85B in complex samples within 10 s, achieving detection limits of 4.23 × 10?2? M and 3.11 × 10?1? M, respectively. Additionally, the sensor exhibits a detection limit of 6.25 × 10?1? M for the N protein of SARS?CoV?2. The optical microfiber sensor possesses the advantages of high sensitivity and rapid detection, and is expected to play an important role in medical fields such as tuberculosis diagnosis and coronavirus detection.

Abstract:The key technologies of low?altitude aircraft navigation are introduced, encompassing fundamental navigation techniques and comprehensive augmentation methodologies. A thorough analysis is conducted regarding the present state of navigation system testing technologies, with particular emphasis on the categorization and evaluation of various testing methodologies for critical navigation parameters. These include position?velocity measurement, heading determination, attitude assessment, and verification approaches for integrated enhancement technologies. It is pointed out that the test technology faces the deficiency in verification capabilities in two aspects: adaptability to complex scenarios and anti?interference ability, the lag between standardization and supervision, and the contradiction between test cost and scale, and it presents the trends of intelligence, standardization and globalization, scenario diversification, low?carbon and sustainability, which provides a reference for the further development of low?altitude aircraft navigation system test technology.

Abstract:To address the inefficiency, error?proneness, and safety risks associated with traditional manual meter reading for pressure instruments, as well as the limited adaptability of automated meter?reading technologies based on sensors and 3D vision, this study integrates computer vision and artificial intelligence technologies to develop a metering system that combines data acquisition, real?time monitoring, and data analysis. By improving the fast region?convolutional neural network（Fast R?CNN) algorithm through data augmentation and a lightweight feature extraction network, the system optimizes instrument positioning accuracy in complex environments. Additionally, the DeepLabv3+ model is enhanced by incorporating channel attention and spatial attention mechanisms, along with a hybrid loss function, to improve character segmentation efficiency. Experimental results demonstrate that the improved algorithm achieves an average positioning accuracy of 84% for instrument dial positioning and a mean Intersection over union of 78.6% for character segmentation in challenging industrial environments. Furthermore, the system reduces the time required for a single measurement by 85% compared to manual reading, confirming its high efficiency and strong adaptability. This research provides a scalable technical framework for intelligent monitoring of industrial equipment, offering the practical value for advancing digital and intelligent metering.

Abstract:In light of the challenges posed by the requirement of numerous parameters calibration, the involvement of multiple metrology specialties, and the inadequacies in existing calibration methods for coherent Doppler wind lidar. Considering the characteristics of laser wind measurement, such as non?contact, high spatiotemporal resolution and large detection range, the method of "feature analysis and multi?method integration" is adopted to break through the difficulties of quantitative evaluation of controversial parameters such as wind speed, wind direction, maximum measurement distance and detection blind zone. The advantages, disadvantages and applicable conditions of three different wind speed calibration methods, namely the wind speed calibrations using a calibration turntable, the radio frequency signal simulation of Doppler frequency shift and using a standard anemometer, are analyzed. Moreover, the calibration turntable wind speed calibration method was improved to enhance the data reliability of the wind speed parameter calibration. Aiming at the difficult problem of wind direction calibration, the calibration method under different scanning modes is presented dialectically to improve its universality. Based on specific measurement cases, the calibration results of wind speed and wind direction are given, and the uncertainty analysis of wind speed and wind direction parameters are performed, effectively illustrating the feasibility of the calibration method. The research results provide standardized technical support for the precise detection of wind fields in such fields as meteorological observation, aviation safety, and new energy, and have positive significance for promoting the development of atmospheric remote sensing metrology technology.