Nanosecond pulsed visible laser is widely used in remote sensing, coherent radar system, precision machining, laser cleaning, and liquid dye laser pumping and so on. All-solid-state laser is favored for its compact structure, small volume, low-cost, long life and so on. In recent years, nanosecond visible all-solid-state lasers have been developing rapidly. In this paper, the state of the art of nanosecond pulsed visible solid-state lasers generated by second-order nonlinear frequency conversion of near-infrared light output from Nd3+, Yb3+ and other rare-earth ion-doped crystals pumped by conventional laser diodes is briefly described. The technical characteristics of several nanosecond pulse visible all-solid-state Raman lasers with abundant outgoing wavelength bands are introduced from the perspective of nonlinear frequency conversion, focusing on Raman frequency shifting, Raman frequency mixing, and diamond Raman, with an overview of their performance characteristics and technical bottlenecks, and concluding with a summary and outlook.

In this paper, the generation of stage shaped pulses in thulium fiber lasers based on nonlinear polarization rotation mode locking is investigated. By introducing a segment of SMF-28 fiber with a length of 100 m and 350 m respectively before and after the nonlinear polarization rotation mode locking device to enhance intracavity nonlinearity and accumulate negative dispersion, a dual-wavelength (wavelengths of 2025 nm and 2034 nm, respectively) step-shaped pulse with a base width of 50 ns, a repetition frequency of 615.6 kHz, and a signal-to-noise ratio of 55 dB is obtained, and it is found that carefully adjusting the polarization controller and changing the pump power can finely adjust the shape of step shaped pulses. Through the study, it is found that the stepped pulse is formed by the combination of two rectangular pulses, and the front and rear pumps have different influences on the two rectangular pulses, and the energy can be realized by changing the polarization state between the front and rear two rectangular pulses. The experiment provides a good experimental platform for studying the pulse dynamics and pulse shaping of passive mode-locked fiber lasers.

In this paper, based on the analysis of the operation characteristics of laser diode end-pumped laser crystal, a thermal model of laser diode end-pumped Tm∶YAG crystal rod is established. The temperature field, thermal stress field and end-pumped crystal rod are numerically calculated by finite element method using heat conduction theory. The effects of laser beam with different laser energy distribution (circular Gaussian spot, elliptical Gaussian spot, circular flat top spot, square flat top spot), pump spot radius and Tm3+ doping concentration on the temperature field distribution and end surface shape variables of laser rod are analyzed, and plotted in three-dimensional distribution diagrams. The results show that under the stable state, if the laser diode pump power is 30 W, and the pump spot radius is 400 m, the maximum temperature rise of the pump surface of the Tm∶YAG crystal rod with the doping concentration of 3.5at.% is 124.55 ℃, and the maximum stress of the pump surface is 209 MPa along the crystal z axis. The maximum heat shape variable of the pump surface is 0.888 m. This study provides theoretical guidance for the design of Tm∶YAG laser.

In order to achieve high-quality and high-efficiency cutting of carbon fiber reinforced composites, water-jet guided laser processing is used for the CFRP microgroove processing. The influence of the laser power, scanning speed and scanning path overlap rate on the width of the heat-affected zone and the material removal rate are investigated by the orthogonal and one-factor experimental methods and optimized with this goal in mind. The results show that the laser power and the overlap rate of the scanning path have a significant effect on the width of the heat-affected zone and the material removal rate. When the laser power is 35 W, the scanning speed is 4 mm/s and the scanning path overlap rate is 40%, the width of the heat-affected zone is 184.484 m, and the material removal rate is 0.068 mm3/s, which can obtain smaller width of the heat-affected zone and higher processing efficiency.

In this paper, a laser guidance hardware-in-the-loop simulation system is proposed, which consists of a software simulation subsystem, a hardware-in-the-loop simulation subsystem, and a visual simulation subsystem. The software simulation subsystem establishes the kinematics and dynamics model of the guided aircraft and the motion model of the target based on Simulink, which can complete the digital simulation inner-loop attitude control and outer-loop guidance of the guided vehicle. The hardware-in-the-loop simulation subsystem is controlled by an industrial computer to control the laser guidance unit and the target simulation unit respectively, to realize the laser guidance for guided aircraft guidance. An improved fitting algorithm based on Gaussian spot is applied to improve the detection accuracy of the four-quadrant detector. Simulink communicates with LabView via UDP communication protocols. The three-dimensional physical model of the guided aircraft and the target is built with AC3D, and the co-simulation of the guided aircraft and target is achieved based on FlightGear, and the view simulation subsystem is constructed. Simulation experiments show that the system is not only capable of completing the study of the flight attitude control and guidance law of the guided vehicle, but also realizing the three-dimensional visual display in real time.

In order to enhance the modeling capability of global features in deep learning-based 3D point cloud classification models and improve their generalization performance, a point cloud classification model based on the fusion of graph neural network and attention mechanism is proposed on the basis of PointNet. Firstly, the extracted features are used to make the model pay more attention to the global context information, suppress the noise information, reduce the redundant parameters, and enhance the modelling ability of the global features by increasing the channel attention module and the spatial attention module, respectively. Secondly, different K-values nearest neighbor searches are performed within multiple scales of sphere radius to construct the input features for encoding, which not only reduces the scale of the graph and training overhead but also enables the model to learn features at different levels. Finally, neighborhood information is aggregated and node features are updated through graph convolutional neural networks. The output features of different graph convolutional neural network layers are summed up to fuse multi-level features and improve classification accuracy. The proposed model is trained and tested on the public dataset ModelNet40, achieving an overall classification accuracy of 88.6%, which outperforms the commonly used 3DShapeNets, VoxNet, ECC, and PointNet models, demonstrating its superiority in point cloud classification.

Nowadays, Unmanned Aerial Vehicles (UAV) is increasingly being used in groups, and the number of UAV in cluster is increasing, yet the endurance of UAVs has been the reason that limits its application. In this paper, a cluster strategy of simultaneous charging for laser-powered UAV cluster is proposed to solve the problems of short endurance and low charging efficiency of UAV cluster. The initial position of the cluster charging UAVs are derived through the formula of circle flocking, then optimized by using the Improved Artificial Potential Field (IAPF) combining with the Particle Swarm Optimization (PSO) algorithm and finally the results are verified by MATLAB simulation at last. The results show that in the case of laser beams of 1 and 7 beams, the highest efficiency of clustering is 92.6% and 91.9%, with an average increase of 12.64% and 10.41% in efficiency over the pre-optimization period, and a decrease of 7.06% and 6.27% in spot OD, which gives a better effect of clustering. The cluster charging strategy can address the scheduling problem of the traditional charging method when there are many numbers of UAV swarms, which can be used in the field of UAVs laser charging.

The precise measurement of the disease parameters is affected by the distance between the underwater camera array and the water-involved parts of structures such as bridges and dams, and the usual image stitching methods depend on the salient feature points. For the above, a method is proposed to measure the object distance and splice the image array based on the image by an underwater camera array and a laser. According to the position of laser spot in the image, the formulas of laser ranging are derived respectively when underwater camera array plane is parallel to the observed surface, when there is a rotation angle and a pitching angle between underwater camera array plane and the observed surface, and the image array is corrected and stitched after the size of the overlapped region is calculated. The experimental results show that the ranging error is less than 2 mm and the relative error is less than 1.5% in the three cases, and correction and stitching of image array are realized well. The method has a great application prospect in underwater disease detection of infrastructure.

This study aims to address the remote measurement requirements for multiple alkane gas leaks in petrochemical plant areas, taking into account the effect of water vapor spectra on alkenes spectra as most of these petrochemical storage tanks are located at the sea side. To eliminate the influence of water vapor and carbon dioxide in the air, the absorption peak of propane at an infrared wavelength of 3364 nm is selected. By using an ICL laser, simultaneous measurement of both propane and n-butane gases in this band is achieved. Furthermore, an open light range method is proposed where a reflective film is placed at a distance of 100 meters and the returned light is gathered through a lens onto a detector, thus enabling remote telemetry analysis of alkane gases at a distance of 100 meters. This paper presents data on the absorption signals and harmonic signals of the two gases at 100 meters. Based on the analysis of harmonic signals and noise, the detection limit for the gas to be tested is calculated to be less than 100 ppm·m.

In this paper, a three-dimensional noise model is applied to analyze the noise characteristics of a gaze-type mid-wave infrared detection system, and the suppression effects of information processing methods on various types of noise components are investigated. The image data are continuously collected and analyzed at different integration times for different temperatures of surface source blackbodies, and it is found that when the integration time and the incident energy of the blackbody increase, each noise component decreases rapidly and then tends to be constant in a nonlinear form, and each noise component presents its own unique image pattern. It is also found that spatial noise is the main component of system noise in the original image without any information processing. After comprehensive application of spatial and temporal noise suppression methods, the spatial noise is almost completely suppressed, the temporal noise is reduced by about half, and the transient pixel noise becomes the main component of noise.

In the product structure design, along the existing products or by virtue of the existing experience is a common phenomenon, infrared thermal imaging camera continuous zoom mechanism optical axis jump overrun has been the industry's problem, and the size of its jump directly affects the system's performance indicators. In this paper, Taking the guide rod two-component linkage continuous zoom mechanism as an example, the factors affecting the runout of the optical axis of the continuous zoom mechanism are firstly analyzed from the perspective of assembly, and then a clear, reasonable and correct assembly dimension chain model is constructed based on the principle of dimension chain by analyzing the associated dimensions. Then, through the transformation of optical machine parameters, the tolerance of the closed ring in the dimensional chain is determined. Finally, based on the principle of tolerance design, the tolerance of each component ring in the dimensional chain is assigned, and the limit deviation of the closed ring is accounted for by means of tolerance analysis, so as to ensure that the tolerance design of each component ring is correct and error-free.

This paper focuses on the technology of mercury cadmium telluride (MCT) vertical liquid phase epitaxy, in which a temperature field optimization method and a growth method are proposed to improve the material quality and process stability. The progress in the preparation of mid-wave, long-wave, and very long-wave MCT detector components is based on double-hetero structure materials using methods such as tabletop junction device processing.

Aiming at the problems of low contrast and easy loss of small defects in the traditional phase-locked thermal imaging defect feature extraction algorithm, a defect detection algorithm based on the combination of robust principal component analysis (RPCA) and FFT is proposed, and the RPCA model is solved by the inexact augmented Lagrange multiplier method (IALM). The original infrared thermal wave sequence vector is transformed into a two-dimensional matrix, and the data is decomposed into two parts by RPCA. The low-rank matrix that approximates the extraction of the non-uniform background, and the sparse matrix that reflects the defective information, and the magnitude and phase maps of the non-uniform background are obtained by using the FFT on the obtained sparse matrix, which is aimed at the problem that IALM needs to artificially introduce the initial value to solve the RPCA model, which affects the optimization results. Tyrannosaurus optimization algorithm (TROA) is used to construct the fitness function by selecting the signal-to-heterodyne gain and the background suppression factor, and to optimize the initial equilibrium parameters and the penalty factor. The experimental results show that the image obtained by this algorithm has outstanding contrast, obvious information of small defects, and better objective evaluation indexes than other algorithms, in which the entropy value has been greatly reduced, effectively suppressing the non-uniform background of the heat wave image.

A zoom infrared imaging temperature measurement method is proposed in the context of a fixed-mounted zoom temperature measurement thermal imaging camera suitable for use in extreme weather conditions, such as high winds and high temperatures, in order to address the temperature measurement problems of a continuous zoom thermal imaging camera. Based on the engineering temperature measurement method of fixed focus thermal imagers provided in the article, the focal length dimension is expanded to construct a polynomial model for temperature measurement of zoom thermal imagers, and the inversion parameters are calculated through optimization methods to obtain temperature measurement results at different focal lengths. Furthermore, a data calibration method for zoom temperature measurement is provided. A self-developed zoom thermal imager is calibrated based on high-precision blackbody data, and the temperature measurement accuracy at different focal lengths is analyzed. Observation experiments are conducted in both laboratory and outdoor environments. The experimental results show that high-precision temperature measurement results can be obtained with different focal lengths, reaching the ±2 ℃ accuracy required by the national standard. This achievement can be promoted and applied to various types of zoom thermal imagers, improving the temperature measurement accuracy for long-distance targets such as high-voltage lines.

The safe and stable operation of electrical equipment in hydropower plants is critical. In order to achieve the automatic early warning of equipment faults, through infrared features extraction and gray-associated analysis, it is proposed to establish an early warning model of equipment failure with principal component analysis (PCA) and density-based clustering algorithm (DBSCAN). Firstly, the missing data are made up through the data pre-processing, the abnormal data are eliminated, and the principal component analysis is performed to reduce the dimensionality and extract the new principal component features. Secondly, the new principal components are used to construct the feature sample set by DBSCAN algorithm, establish a gray association model, calculate the gray associated coefficient, and then fail to warn the degree of change point of the gray association coefficient carry out the fault early warning through the change degree of the grey correlation coefficient of the mutation points. The experimental results show that the proposed method can effectively extract infrared characteristics and achieve equipment fault warning under the abnormal state of the equipment, and the fault warning accuracy rate reaches 97.88%.

Electro-optic system equipment is usually composed of multiple components, so the deployment of software for each component and loading and upgrading of program have become a key technology. Due to the many shortcomings of traditional loading methods in terms of loading methods and loading efficiency, it is crucial to develop an efficient and convenient remote loading technology. In response to the above issues, this article designs and develops a remote loading technology, and successfully applies this technology to infrared electro-optic systems. The remote loading function is mainly implemented through the computer and remote loading board. The computer is responsible for remotely sending the upgrade file to the loading board. After successfully receiving the data, the loading board completes the program upgrade of the loaded components. To increase the reliability of the loading function, a backup boot function is designed to enable secondary loading of the program in case of loading failure.

Based on the dispersion vector analysis method of Buchdahl model, this paper designed a multi-wavelength achromatic laser emission lens that can be used for laser ranging, which has low cost, small size, good beam quality and resistance to laser damage at peak power. Firstly, the dispersion vector of commonly used domestic high-intensity laser lens materials was calculated by Buchdahl dispersion coefficient equation. Then, light tracing was performed on the optical system and the scale factor was calculated to calculate the Buchdahl dispersion vector of the non-focal system. Finally, the optimization was guided by this result. The color difference of the optimized system at 0.707 aperture of 1064 nm, 1572 nm and 1550 nm was only 2.49×10-6 D and 4.43×10-6 D, respectively. The color difference between 1550 nm and 1530 nm is only 3.04×10-6 D, the maximum color difference in the band range is only 4.43×10-6 D, and the maximum shift difference is only 0.0005 D. MTF and dot plot of each field of view fully reach the diffraction limit, and the polychromatic difference of the system is effectively corrected. The research results make the dispersion vector analysis method of Buchdahl model successfully applied to the focus-free optical system in engineering design, and provide a new idea for the design of focus-free optical system.

In recent years, convolutional neural networks have made remarkable progress in the field of hyperspectral image classification, but they can only perform regular grid operations on images, and cannot adaptively perform feature aggregation. Therefore, a segmented forest-based multi-scale convolutional neural network hyperspectral image classification method is proposed in this paper, which consists of four steps. Firstly, principal component analysis is used for dimensionality reduction, and a multi-scale segmented forest is constructed according to the spatial information of images to establish the relationship between the subtrees. Then, a U-net model architecture based on graph convolutional network is proposed to establish the transformation of graph structural features between multiple scales by pooling and unpooling. The network uses a graph convolutional neural network to perform adaptive feature aggregation and fuses multi-scale features by layer hopping connection between encoder and decoder. Finally, the semi-supervised classification of nodes is carried out through SoftMax. The experiment is verified on the public hyperspectral dataset, all of which achieves good classification accuracy, demonstrating the effectiveness of the method.

To address the limitations of traditional target detection and recognition methods in complex battlefield environments, a deep convolutional neural network (CNN)-based method for extracting image features and localizing targets is proposed in this paper. Traditional shape and texture features as well as the hotspot information in infrared images are comprehensively considered, and the accuracy of target detection and identification is improved through the training of large-scale labeled datasets and the optimization of the back propagation algorithm. Compared with traditional methods, the method can automatically learn the feature representations in images without relying on manually designed features and classifiers. In order to verify the effectiveness of the algorithm, this paper selects the Hathi Hi3559AV100 as the core processing chip to design the hardware platform, and by porting the algorithm to this platform, the collected data samples are analyzed and tested, and the experimental results show that the system exhibits relatively stable performance in complex background environments, and is able to reliably perform target detection and recognition.

Infrared spot center location is the key technology of infrared optical measurement. Aiming at the problems of low infrared spot resolution, poor contrast, blurred target edge and low accuracy of spot location, an infrared spot location algorithm with high precision and small size is proposed in this paper. For infrared image pre-processing, the image is pre-processed by single filter and combined filter, after which the sub-pixel level coordinates where the spot center is located are determined by the IR spot center-of-mass and form-center methods. The results show that the error of this algorithm is less than 0.035 pixels compared with other algorithms in the infrared spot image with noise pollution, which ensures the high-precision positioning of the infrared light plate.

Aiming at the problem of high peak-to-average power ratio of asymmetrically clipped optical orthogonal frequency division multiplexing system applied in visible light communication, a joint scheme combining Hadamard precoding with normalized -law companding is proposed to reduce peak-to-average power ratio on the basis of the study of coding technology and distortion technology. The simulation results of the joint scheme demonstrate an improvement of 5.8 dB in peak-to-average power ratio suppression performance compared to Hadamard precoding alone. Additionally, compared to normalized -law companding, it still exhibits superior performance in terms of error rate, achieving a 2 dB optimization.

In order to meet the requirements of ultra high speed and long-distance transmission in wavelength division multiplexing systems, it is necessary to minimize the damage caused by nonlinear effects such as four-wave mixing that affect spectral efficiency and system performance. In this paper, a method is proposed to suppress the four-wave mixing effect in wavelength division multiplexing optical networks based on the mechanism of four-wave mixing. This method determines the individual and combined effects of the first, second, and third optimization priority parameters (such as effective area, channel spacing, and fiber input power) on the four wave mixing effect with or without pre-chirp, and performs numerical calculations on the system bit error rate to analyze system performance. In 16 channel system, as the effective area increases, input power decreases, and channel spacing increases, the four-wave mixing effect in the fiber optic is reduced, and the four-wave mixing effect decreases even lower under pre-chirp conditions. When the pre-chirp is 800ps/nm, the optimal combination of three parameters results in a minimum bit error rate of 8.16×10-23 in the channel compared to fiber optic communication systems without parameter optimization and pre chirp, the performance of the system is improved by 132.8 dB. Finally, this work indicates that the proposed parameter optimization strategy based on pre-chirp is an ideal solution for optimizing the performance of dense wavelength division multiplexing systems.