Spectral beam combining (SBC) is a method of combining multiple laser beams of different wavelengths to achieve high-power laser. According to the current research status of SBC with external cavity for semiconductor lasers, the research progress of various schemes by using non-off-axis and off-axis approaches is reviewed firstly, and then the design ideas of different methods are summarized. Further, the advantages of each scheme for raising power and beam quality of combined laser beam are analyzed, and the possible development trends in the future is prospected for improving beam performance.

Amplified spontaneous emission (ASE) is an unavoidable effect in fiber lasers, which not only reduces the pumping efficiency of 1.5 m laser, but also limits the power enhancement of 1.5 m laser. In order to reduce the ASE noise energy in the output signal, a new fiber laser amplification system structure is proposed, based on the 1×2 AOM for signal optical cycling pumping amplification, and the simulation of continuous and pulsed signals is carried out using OptiSystem software. Compared with the pulsed fiber amplifier based on MOPA structure, the noise power decreases by 1.7 dB when the signal light is a continuous signal with a power of -30 dBm, and a pump energy of 350 mW, while the noise power decreases by 1.27 dB when the signal light is a pulse signal with a power of -30 dBm, and a pumping energy of 600 mW.

The image recognition of ice thickness on transmission line towers is easily affected by subjective factors and adverse weather, resulting in inconsistent recognition results with reality. To address this issue, a method for image recognition of ice thickness on transmission line towers based on airborne laser point clouds is proposed. Taking into account the influence of temperature, humidity, and wind speed meteorological factors, LiDAR point cloud data of transmission line towers is obtained through airborne LiDAR. The three-dimensional coordinate conversion algorithm is used to convert it into three-dimensional coordinates in a unified coordinate system, thereby constructing a three-dimensional space for transmission line towers. By constructing a three-dimensional space, the three-dimensional coordinates of the ground target are obtained. Through grayscale processing and edge extraction of the image, projection is selected and a projection model is constructed. Obtain actual imaging based on the point cloud pixel projection relationship. Compare the pixel widths of the upper and lower boundaries of the iron tower images before and after icing, and obtain the recognition results of the icing thickness image. According to the experimental verification results, it can be seen that this method has the maximum error with the experimental data at a wind speed of 2 m/s, which is 0.5 cm. The rest are consistent, and it has accurate recognition effect.

Simultaneous Localization and Mapping (SLAM) as a key technology for environment sensing and map building, is flexible and efficient. However, the traditional backpack l LiDAR has problems such as incomplete point cloud features and point cloud stratification caused by large data noise, which makes the system unable to perform accurate positioning and map building. In order to improve the accuracy of autonomous positioning, a robot operating system is adopted to build an autonomous positioning framework, integrating multi-sensor data such as LiDAR, inertial measurement unit and global navigation satellite system (GNSS), and using a SLAM algorithm based on graph optimization to achieve environmental map construction and system pose estimation. The experimental results show that compared with the traditional SLAM algorithm, the improved SLAM algorithm reduces the trajectory X-axis error by 39%, the Y-axis error by 30%, and the plane positioning accuracy by 73% after integrating GNSS data. At the same time, there is a 63% enhancement in elevation positioning accuracy, and a significant increase in building accuracy, which provides new ideas for the application of backpack measurement systems in complex environments.

Welding seam identification is an important step to realize automatic welding. In view of the lack of automatic identification methods in the existing weld recognition algorithm, an algorithm of weld recognition based on laser vision guidance for various types of welds is proposed. The point cloud image of the weld seam is collected using a laser vision system, and invalid point clouds are removed through point cloud preprocessing. The point cloud segmentation and fitting algorithm is improved to quickly segment the feature areas of the weld seam. And the straight-line models are obtained through segmentation and fitting to autonomously classify the weld seam joints, achieving direct extraction of weld seam feature points. Through testing the extraction of feature points for different types of weld seams, the results show that the algorithm in this paper has an error of less than 0.7 mm in identifying feature points for butt joints, corner joints, T joints, and lap joint, and the identification time of the double characteristic line weld is less than 0.63 s, which not only meets the demand of welding production, but also solves the problem of multi-type welding seam identification and improves the degree of welding intelligence.

To solve the problems of low accuracy and long time-consumption in traditional descriptor algorithms for local registration of circular holes, a three-dimensional point cloud registration algorithm for circular holes by means of constructing pseudo feature quadrilaterals is proposed. Firstly, a circular hole skeleton recognition algorithm based on diameter coefficient weighting is proposed, which introduces a vector angle threshold to achieve diameter feature coefficient weighting and extracts the circular hole skeleton points as key points. Then, a new descriptor is proposed based on the center feature, and a pseudo feature quadrilateral is constructed at the center of the circular hole to achieve rough registration of key points. Next, the ICP algorithm is used to further register the key points, and the obtained transformation matrix is applied to the local circular hole for coarse registration. Finally, ICP is used to achieve precise registration of local circular holes. The experimental results show that the registration error is reduced by more than 10.41% and the registration speed is improved by more than 54.22% with stronger robustness than the traditional algorithm.

At present, high-precision detection of the concentration of many types of gases can be achievedbased on the tunable semiconductor laser absorption spectroscopy (TDLAS) technology. However, in actual engineering applications and mass production of instruments, the performance of some lasers themselves and the limitations of the volume of the detection instrument (larger reflector cells cannot be used), as well as the influence of environmental factors, make it difficult to distinguish low-concentration gases or affect the detection accuracy. To improve this problem, a calculation method using a fixed wave gate and a difference is mainly introduced to achieve effective resolution of low-concentration carbon monoxide, and low-concentration carbon monoxide are measuredunder different temperature conditions. The test results show that the error of detecting 10.01 ppm CO is 2.2% under constant temperature conditions, and the maximum detection error is -1.72 ppm within the temperature difference range of 12 ℃. Under different environmental conditions, the processing method is able to effectively carry out the detection of low-concentration carbon monoxide, and the test results can satisfy the requirements of the actual use of the project, offsetting the impact of device performance to a certain extent. This paper has a positive effect on instrument development, design and production when the batch is large or there are large individual differences in the devices.

Femtosecond laser machining has become a new research direction for complex gear manufacturing. In this paper, the laser incidence angle at each node is measured and calculated by using the three-dimensional model of the tooth surface, and the ablation model of the tooth surface material 18Cr2Ni4WA with femtosecond laser machining is established to study the ellipsoidal ablation spot area, ablation area, and the depth of ablation pit and other tooth surface topographic features. Through the model simulation and experimental results analysis, it is shown that the ablation spot area increases slowly at first and then rapidly with the increase of . And the area of ablation zone increases with the increase of laser power P, and decreases slowly at first and then accelerates exponentially with the increase of . The ablation pit depth increases with the increase of P and decreases approximately linearly with the increase of . The influence of on the tooth surface quality is greater than that of P. When the smaller and P is around 4.9 W, the better tooth surface morphology is achieved, which provides technical support for improving the machining quality of face gear.

Infrared detectors often need to accumulate more infrared signals by extending the integration time, which can make the integration capacitor of the traditional analogue domain readout circuit saturate prematurely, and digital pixel readout circuits can effectively solve this problem. Currently, pulse frequency modulation-based A/D conversion structure is widely used in pixel-level ADC solutions. As an important component of pulse frequency modulation-based A/D conversion units, the performance of comparators directly affects the performance of ADCs. In this paper, a comparator with hysteresis function is designed for digital pixel infrared readout circuits with low noise and low power consumption. The design is based on 0.18 m CMOS process, and verified by simulation to have a good degree of conformity compared with the design objectives.

With the digital development of readout circuits in infrared detector systems, the requirements for clock signals in readout circuits are becoming increasingly stringent to ensure accurate processing of digital signals, including computation, transmission, and storage. In this paper, a high-speed clock signal generation circuit based on a charge pump phase-locked loop structure with excellent comprehensive performance is designed to achieve fast locking and stable output of a 640 MHz low-noise high-speed clock signal under the condition of a 20 MHz reference clock signal from the crystal oscillator. The designed is based on SMIC 0.18m process, and the simulation result shows that total power consumption is less than 5 mW at 1.8 V power supply, the control voltage ripple after the phase-locked loop is kept within 500 V, the locking time is 4s, the phase noise is less than -99 dBc/ Hz@1MHz, and the clock jitter is less than 5 ps.

HgCdTep-on-n structure infrared detector has low dark current and long lifetime, which is the main development direction of high-performance infrared detectors at present. Meanwhile, in order to meet the development needs of future infrared detector miniaturization, this paper mainly studies p-on-n long-wave 10 micron pixel spacing 1280×1024 detector. The p-on-n heterojunction technical route was adopted to study the forming and passivation technology of the small-pitch mesa. The mesa and passivation morphology were evaluated by SEM, the quality of the passivation layer was evaluated by CV test, and the detector was developed. The I-V characteristics of the pn junction of the detector were evaluated by a semiconductor parameter tester at 77K, and the IV characteristic was tested. The performance of the detector is tested by the test of the performance, and the detector with good performance is obtained. This study is of great significance for the preparation of small-pitch long-wave p-on-n infrared detector devices.

The precise segmentation of electrical equipment is a key step in infrared image fault diagnosis. An accurate segmentation method is proposed for infrared images of electrical equipment in complex backgrounds to address the issue of detail loss with mainstream semantic segmentation methods. Firstly, the PSPNet is improved by incorporating UNet network as the main structure to decode the multi-scale pyramid pooling of features extracted by UNet's top layer. Secondly, Convolutional Block Attention Mechanism (CBAM) is integrated into the feature extraction backbone network to incorporate channel and spatial attention mechanisms for gathering image context information from both dimensions, enhancing the network's focus on electrical equipment to improve its anti-interference capability. Finally, the PSPnet-CBAM-Unet network is constructed, and the features output by the CBAM are used as inputs for lower-level feature extraction and skip-connection features in the decoding layer. The effectiveness of this paper's method is tested with the segmentation of three types of devices in infrared images under complex backgrounds including voltage transformers, current transformers, and circuit breakers. Experimental results demonstrate that the proposed method achieves intersection over union and accuracy greater than 92% and 94% respectively, and the accuracy of segmentation is better than that of UNet, PSPNet, and Deeplabv3+ networks, and it is more accurate for the detail segmentation of infrared images of electrical equipment in a complex background.

The non-contact measurement of alcohol concentration has the characteristics of convenience, rapidity, and real-time tracking, which is of great research significance in the field of alcohol production. In this paper, based on the near-infrared absorption spectral characteristics of alcohol solution, a set of rapid alcohol concentration detection system with STM32 microcontroller as the kernel is developed by using a laser diode and a photodetector with a wavelength of 1300 nm. An indium gallium arsenic diode detector is used with the supporting hardware to capture and convert the photoelectric signal, and the infrared absorption of the alcohol solution is reflected by the detection voltage difference. Furthermore, a detection model is established by fitting with the alcohol polynomial, and the temperature correction is deposited into the main control system to realize the alcohol concentration prediction. The experimental results show that the goodness-of-fit of the detection system for the sample group is 0.9996, and the average standard deviation of the test for the validation group is 0.158, which is much lower than that of the traditional alcohol meter of 0.5. The results show that the detection system has the characteristics of high detection accuracy, strong stability and continuous detection compared with the existing infrared detection systems on the market, and can achieve the rapid detection of the concentration without destroying the alcohol sample, which meets the industrial standards and demands in the market, and has important application value for the alcohol industry and other related industries.

To address the issue of image blur due to high-speed relative motion (approaching or moving away) between an infrared imager and a detected target, the principle of blurring caused by information mixing between image pixels as a result of high-speed relative motion is analyzed and derived on the basis of the target’s radiation characteristics and optical Fourier analysis. Firstly, with omnidirectional variable-speed linear motion blur as the main focus, the expansion or contraction of pixel values is added. Secondly, according to the response modeling of traditional infrared imaging systems, a nonlinear mapping relationship is established between the change in pixel grayscale values during exposure time and the imager speed. And A mathematical model of the high-speed motion expansion effect of the imager is developed and its point expansion function is given, which in turn plots the modulation function (MTF) curve. Further, using the theoretical MTF to design filters, the degradation of the test infrared square target image is performed to achieve the simulation of the blurring effect of the infrared image expansion effect. Finally, taking a ship driving on the sea as an example, the imaging effect of the imager during high-speed relative motion is simulated, and the degradation blurring is performed based on the clear actual images to verify the actual expansion effect.

In order to improve the photoelectric conversion efficiency at the receiving end of the laser wireless energy transmission system, and to compare and verify the advantages and disadvantages of different optimization design methods at the receiving end, experimental studies on different arrangements of photoelectric arrays are carried out. In this paper, three types of photovoltaic arrays, a single series layout, a sequential series-parallel combination layout, and a series-parallel combination layout based on the illumination distribution, are developed and experimentally investigated. Under Gaussian distributed spot irradiation, the output characteristics of the sequential series-parallel arrangement and the series-parallel arrangement based on illumination distribution are compared, and the output characteristics of the single series-parallel arrangement under uniform illumination are alsocompared. The results show that the photoelectric efficiencies obtained in different series-parallel arrangements and under different illumination conditions are quite different. The single series arrangement has the highest photoelectric conversion efficiency under uniform illumination, which is 39.14%. And under Gaussian distributed illumination, the photoelectric conversion efficiency of the series-parallel arrangement based on the illumination distribution is 33.3%, which is twice as much as the efficiency of the sequential series-parallel arrangement. The results can provide a reference for the optimal design of the receiving end of the laser wireless energy transmission system.

In view of theproblem that image-based electronic image stabilization cannot adapt to the complex application environment of airborne photoelectric system, including the factors as aircraft maneuvering, photoelectric system rotation and the uncertainty of target scene, the accurately obtainit difficult to compensate. In this paper, the electronic image stabilization technology combining gyro and image processing isd to estimate the jitter pixels of platform, and the image jitter is eliminated by real-time pixel compensationbetween image sequence frames. The stability of the video image of the airborne photoelectric systemimproved without additional hardware. method has been verified. The results show that this method can significantly improve the image stability of the night vision system under the original hardware conditions.

When traditional laser communication is interfered by strong electromagnetic interference, it leads to the failure of electrical auxiliary guidance methods, and the initial pointing cannot be established. In order to solve the problem of abnormal laser communication due to strong electromagnetic interference, a control technology combining servo control technology for all optical capture is proposed. The method is an analytical study of the optical model of all optical guidance, which leads to the all-optical guidance tracking mathematical model, based on which servo control technology is added to improve the fusion of the Gauss Seidel Method algorithm, so as to control the rapid capture, precise pointing and tracking of two independent laser communication terminals. The experimental results show that the designed capture scheme can perform azimuth scanning at 90 °/s throughout the entire cycle and complete capture at an elevation of 20 ° within 55 seconds, which makes the laser communication has broad application prospects for rapid chain building under silent conditions.

The low signal-to-noise ratio and fuzzy morphology of infrared (IR) small targets pose certain challenges in the research of such target segmentation tasks in complex backgrounds. To better separate small targets from clutter backgrounds, an innovative multi-scale cascaded fusion network (MSCFNet) is proposed. Specifically, MSCFNet preserves and utilizes small target information to the maximum extent through the multiple interaction between multi-scale features. At the same time, a feature enhancement module is designed to effectively extract and integrate target information from global semantic and local context, improving the discriminability of targets and complex backgrounds. The experimental results prove that MSCFNet can effectively segment IR small targets in various complex environments and exhibits better performance on two publicly available IR small target segmentation datasets.

In order to improve the accuracy of face detection of infrared thermal imaging cameras, a face detection method based on dual-band image fusion is proposed, which can perform face detection through Yolo-FastestV2 lightweight convolutional neural network after linear fusion of visible light (RGB) images and infrared (IR) images. Compared with the traditional infrared temperature measurement system that requires separate face detection for infrared and visible images, the dual-band face detection method proposed in this paper requires only one detection to obtain the face position in both IR and RGB images, and reduces the mapping error introduced by the traditional method due to the distance change in the mapping process in the coordinate mapping stage. In order to complete the training and testing of dual-band fusion images, a dual-band image dataset containing visible light and infrared, and the dataset is shot by a dual-band camera, which consists of a visible light detector and an infrared detector, and the two sensors can simultaneously capture RGB images and IR images. The experimental results show that 94.35% of the face images in the test set can be correctly detected using dual-band fusion, and the highest detection frame rate can reach 317 FPS.

Aiming at the UAV aerial photography viewpoint target detection spatial scale change is large, the object pixels account for a small proportion, and the algorithm deployment edge computing platform storage space occupies a large proportion of the problem. In this paper, based on the YOLOv8n network structure, an improved aerial photography viewpoint lightweight small target detection method DSF-YOLO-P algorithm is proposed. Firstly, the backbone network C2f module is integrated with FasterNet to form the Faster-C2f lightweight module to ensure that the model achieves network lightweighting and improves the detection speed without affecting the detection accuracy. Then, a new 160×160 prediction head is added and the network channels are reconfigured to improve the accuracy and robustness of the model for small target detection. The improved DSF-YOLO algorithm improves the accuracy by 2.5% and 0.6% on the visible dataset VisDrone2019 and infrared dataset HIT-UAV, respectively, and reduces the number of parameters by 10%. Finally, the DSF-YOLO algorithm is subjected to the dependency graph pruning operation to reduce the redundant parameters of the model without affecting the model performance. The pruned DSF-YOLO-P algorithm achieves the same accuracy and reduces the computational effort and number of parameters by 45% and 26%, respectively, compared with the DSF-YOLO algorithm on the VisDrone2019 dataset. The experimental results fully demonstrate the effectiveness of the DSF-YOLO-P algorithm in detecting small targets in the aerial view of UAVs.

A novel hollow-core anti-resonant fiber based on a dual-tube structure with nested tubes was proposed and numerically studied for near-infrared single-polarization single-mode operation. Unlike conventional anti-resonant structures with polygonal cladding tubes, the proposed fiber consists of two identical thin-walled tubes symmetrically positioned within the outer cladding, each internally nested with a slightly smaller radius tube. The introduction of the nested tubes enhances the confinement loss of higher-order modes, thereby enabling the fiber to achieve single-polarization single-mode operation. Simulation results demonstrate that the fiber exhibits single-polarization characteristics across the wavelength range of 1450 nm to 1700 nm, and single-polarization single-mode operation within the range of 1639 nm to 1672 nm, with a bandwidth of 33 nm. This fiber design holds significant potential for the development of high-performance single-polarization single-mode fiber devices.

To improve the accuracy and speed of identifying node anomalies in fiber optic networks, a node anomaly identification algorithm based on clustering neural networks is proposed in this paper. Firstly, the preclassification of input data is achieved through clustering calculation, which solves the problem of traditional classification and recognition algorithms easily falling into local optima. Then, the test data grouped after preclassification is used as the input layer, and the clustering weights and clustering degrees are used as the weighting coefficients of the hidden layer to improve the recognition of abnormal signals. Experiments are conducted on 64 FBG nodes in a fiber optic network, and the temperature increment, heavy impact and periodic vibration are used to simulate the anomalous signals, respectively. The results of the comparison experiments show that the recognition accuracy of this algorithm is 80.3%, 92.8%, and 91.6% under the condition of aliasing where all three types of abnormal signals exist, which is an improvement of about 20% over the neural network algorithm without preclassification. Therefore, the present algorithm has the optimal test results in the four test cases. For the same data volume test, the speed of this algorithm is only half of that of SVM algorithm, which verifies that this algorithm has better timeliness.

Terahertz (THz) waves have gained remarkable applications in the fields of detection and communication. THz wave non-destructive testing (THz-NDT) technology can penetrate the surface mask layer without damaging the material itself and achieve high-resolution recognition of material characteristics, which has a wide range of application prospects in material testing, especially in the field of coating detection. The research progress of THz-NDT technology in coating materials detection both internationally and domestically in recent years is reviewed, and the current status and trend of its development are summarized and outlooked to provide necessary reference basis and research ideas for its further application.