The third-generation semiconductor material silicon carbide offers significant application potential and strategic value due to its superior properties, including large bandwidth, high thermal conductivity and robust chemical stability. Wafer scribing represents a critical process in silicon carbide power device manufacturing, yet conventional diamond knife-wheel dicing methods remain dominant in this workflow. However, the high hardness and brittleness of silicon carbide has brought great challenges to the traditional processing technology, resulting in high processing cost, low yield, etc. Consequently, there is an urgent need for advanced dicing technologies to overcome these limitations. In this paper, the existing scribing methods for silicon carbide wafers at home and abroad are systematically reviewed, the principles and progress of knife-wheel scribing, laser ablation, laser thermal cracking, water-guided laser dicing, water-jet-assisted laser dicing, and laser internal modification dicing are comprehensively introduced. Strengths and weaknesses of these methods are rigorously compared and analyzed, followed by a discussion of future development trends in silicon carbide wafer dicing technology.

At present, laser weapons have been progressively integrated into practical applications, and are emerging as critical factors influencing future battlefield configurations. As operational scenarios and weapon systems grow in complexity, the significance of digitalized weapon equipment development continues to escalate. Effectiveness simulation, constituting a vital component of weapon digitalization, has garnered significant attention. In this paper, the research status of laser weapon modeling and simulation are summarized, the digital modeling framework and effectiveness evaluation methods of laser weapon are categorized and synthesized, and the challenges faced by current laser weapon modeling and simulation are analyzed.

Phase-changed direct-cooling method is a novel efficient heat management scheme for fiber lasers. The cold-plate, as a critical component of the phase-change direct cooling system, significantly influences the temperature stability of the laser device. In this paper, three cold-plates are designed initially for the phase-changed direct-cooling system of a 2 kW fiber laser. Differences among the designs primarily arise from variations in cold plate fabrication methods. Afterwards, numerical simulations coupling fluid-heat-solid based on computational fluid dynamics are carried out to investigate the heat transfer performance of the cold-plates. The results indicate that the cold-plate, which incorporates finned ribs in certain areas of a single-layer channel, achieving a peak surface temperature of 27.13 ℃ and an average temperature of 24.56 ℃. Its average heat transfer coefficient is 78% higher than that of the designed perforated serial-parallel channel configuration, demonstrating superior thermal performance. This design effectively reduces the operating temperatures of key fiber laser components. Moreover, the mass of the cold-plate is reduced by 47.64% compared to the parallel and series channel design, contributing to the lightweight design objectives for high-power fiber laser products.

With Cr4+∶YAG as the Q-switch and LDA end-pumped Nd∶YAG crystal, the passively Q-switched laser is composed. The 1.06 m pulse laser output of 25 mJ、~6 ns and 24 mJ、~15 ns are obtained in 2 different designs of flat-flat cavity (output couple reflectivity R=35%) and flat-concave cavity (output couple reflectivity R=23%) respectively. The experiment results are in good agreement with the theoretical data of passively Q-switched laser output. In another experiment, the pulse interval accuracy of the 20 Hz passively Q-switched laser in the temperature range of -40 ℃~+50 ℃ is measured. In this temperature range, the pulse interval accuracy is less than ±2.8 s. Finally, according to the above experimental results, in order to meet the requirements of laser designator for the use of laser sources, some optimization ideas for the laser design areproposed.

The integration of wind speed and direction information into wind turbines operation to achieve attitude correction and pitch control is critical for enhancing energy efficiency and ensuring safe production, so that the LiDAR is widely used in reconstructing the wind field ahead of wind turbines. However, the installation position of nacelle-mounted LiDAR inherently affects the variability and accuracy of wind speed and direction measurement algorithms. In terms of nacelle LiDAR, the wind speed and direction measured in front of LiDAR are not the wind speed and direction of hub, while system noise and outliers further exacerbate measurement discrepancies. To resolve these problems, a linear model of vertical wind shear is proposed to establish relationships between high plane wind speed, low plane wind speed and hub wind speed. Subsequently, the sliding window filtering is adopted to tackle the data of hub wind speed and direction, significantly improving their precision. Finally, the measured error of wind speed and direction are experimentally demonstrated, and can be reduced to 0.5m/s and 10° respectively, the R2 between measured and theoretical value is close to zero. This research provides technical guidance and novel idea for the nacelle LiDAR development in wind electrical industry.

Single-photon ranging technology breaks through the limitations of traditional laser ranging systems, demonstrating broad application prospects in weak signal detection scenarios. However, its high sensitivity restricts effective deployment in complex environments characterized by strong background noise. In response to the needs of this technology in practical ranging situations, the evaluation criteria for ranging capability were analyzed are analyzed based on the principles of single-photon ranging. A methodology is proposed to improve ranging performance by filtering noise photoelectrons to reduce false alarms, with corresponding photoelectron counting thresholds and judgment criteria established. Through experimental verification, the effectiveness of filtering through photoelectron counting thresholds in noise reduction is determined, which can effectively improve the resolution of target signals for the detectors within a certain range. This theoretical framework can be integrated with overall design and algorithm optimization of detection systems, offering substantial potential for advancing single-photon ranging technology.

With the continuous development of water transportation and shipping, the detection and tracking of ships traveling on the river have become increasingly critical. While the image-based ship detection and tracking methods have matured, their inability to directly obtain 3D dimensions and spatial positions due to the lack of depth information remains a limitation. The point cloud data generated by LiDAR, on the other hand, naturally carries precise geometric and distance information, demonstrating significant potential for enhancing ship detection and tracking capabilities. Current 3D point cloud object detection approaches can be categorized into methods based on classical algorithms and those leveraging deep learning. However, classical point cloud algorithms applied to ship detection suffer from poor generalization and difficulty in distinguishing adjacent ship point clouds. To address these challenges, an improved algorithm of PV-RCNN++ based on focal sparse convolution is proposed for ship detection in river channels. The improved algorithm not only effectively differentiates ship point clouds in various situations, but also improves the recognition ability of distant ships, achieving an 11.56% increase in detection accuracy in practical scenarios compared to classical methods. On this basis, a multi-target ship matching and tracking method based on the degree of correlation between ship positions and 3D dimensions is proposed, in which the ICP alignment is used to calculate the ship speed and predict the ship position. Experimental validation results demonstrate that the proposed tracking method exhibits stable performance and achieves accurate ship matching between consecutive data frames.

To investigate the influence of different process parameters on the quality of entrance and exit holes, and etching depth during water-jet guided laser processing of silicon carbide (SiC) materials, multiple sets of experiments are designed by varying three critical parameters: laser power, scanning speed, and processing passes. Post-processing specimen morphologies are observed and measured using white light interferometry, with data analyzed via Origin software. The experimental results show that the increase of laser power during the processing of holes is helpful to reduce the hole taper and improve the roundness, and meanwhile, it has less effect on the hole diameter due to the rapid decrease of thermal conductivity and high melting point of SiC material at high temperature. While the scanning speed has little change on the entrance and exit diameters of the processed microholes, it has a greater effect on the exit roundness. With the increase of laser power, the decrease of scanning speed and the increase of processing times during the etching process, the etching depth of the material increases significantly. Compared to conventional laser techniques, water-jet guided lasers demonstrate superior performance in SiC processing by minimizing defects such as thermal damage and recast layers. This study provides valuable insights into optimizing process parameters for water-jet guided laser machining of SiC materials.

To address the insufficient utilization of features and the significant semantic discrepancy between encoder and decoder features in existing point cloud semantic segmentation networks, a point cloud semantic segmentation method based on cross-layer attention feature fusion is proposed in this paper. Initially, a local feature encoding module is designed to refine neighboring points coordinates through polar coordinate encoding and offset updates, and the differences in relative semantic features are calculated to enhance the richness of neighborhood features, allowing the network to learn the local details of objects of different shapes and sizes. Then, an adaptive feature aggregation module is introduced to enhance the network's perception of local regional features, ensuring comprehensive utilization of neighborhood feature information. Finally, a cross-layer attention fusion network is incorporated to mitigate the semantic discrepancy between encoder and decoder layers. Experimental validations are conducted on the large-scale indoor scene point cloud dataset S3DIS and complex outdoor scene point cloud dataset Semantic3D. On the S3DIS dataset, Area 5 achieved a mean Intersection over Union of 65.2%, and a mean accuracy of 75.1%, showing improvements of 2.8% and 3.7%, respectively, compared to RandLA-Net. For the Semantic3D dataset, the mean Intersection over Union is 76.7%, and the overall accuracy is 94.6%, marking increases of 4.9% and 0.4% over RandLA-Net. The results substantiate the method's capability to extract distinctive features from complex point clouds and achieve precise segmentation across diverse scene categories.

Conventional titanium alloys are prone to ignition under conditions of high temperature, pressure, and high-speed airflow, resulting in rapid spread and causing "titanium fire" failures, which significantly limit their application in aero-engines. This study aims to prevent "titanium fire" accidents by preparing a Ti-Cu burn-resistant alloy layer on the surface of Ti6Al4V titanium alloy using laser alloying technology. The morphology of the Ti-Cu burn-resistant alloyed layer was analyzed by optical microscope and SEM, and the micro-hardness of the Ti-Cu burn-resistant alloyed layer was analyzed by Vickers micro-hardness instrument, and finally the burn-resistant behavior of the modified layer was studied by high temperature friction wear testing machine. A continuous dense alloying layer can be formed on the surface of the titanium alloy by the laser alloying technology, The thickness of the Ti-Cu burn-resistant alloyed layer is about 220 m; The hardness of the modified layer is gradient changes with the depth of alloyed layer, The highest hardness of the Ti-Cu burn-resistant alloyed layer reached 616 HV which is 1.61 times compared with that of the Ti6Al4V titanium alloy. At 600 ℃, The friction coefficient of the Ti6Al4V alloy substrate (0.47) and the Ti-Cu burn-resistant alloyed layer (0.46) are roughly same. At 600 ℃, The main wear mode of both the Ti6Al4V titanium alloy and the Ti-Cu burn-resistant alloyed layer is a mixture of adhesion and abrasion. The wear width of Ti6Al4V titanium alloy is 2.14 times compared with that of Ti-Cu burn-resistant alloyed layer, and the wear volume and wear rate of Ti6Al4V titanium alloy are 5.5 times compared with those of the modified layer.

In this paper, the development progress of multi-band infrared detector applications is introduced, with a focus on the readout circuit design tailored for infrared devices capable of simultaneous multi-band signal detection. The image input level is identified as one of the critical modules in the design of the readout circuit for multi-band infrared detectors. A Buffered Direct Injection (BDI) type pixel input level circuit structure is adopted, characterized by high stability and low equivalent input impedance, which effectively enhances the injection efficiency of the long-wave detector and provides a stable operating conditions for optoelectronic detection components. The full circuit design and simulation are completed using a CMOS 0.18 m 5 V standard process. The results demonstrate that the circuit operates normally at a cryogenic temperature of 70 K, with a 40 m pitch incorporating four-band pixel input stages covering short, medium, long, and extended wavelengths. The integral signal output of the input stage achieves a linearity of 99.7%, while the noise level remains below 0.3 mV.

To address the limitations of conventional four-terminal beam MEMS infrared thermopile sensors, which suffer from inefficient surface utilization leading to low fill factor and degraded performance, an improved four-end beam structure is proposed. This enhanced design optimizes chip architecture by vertically arranging thermocouple strips along the square edges of the chip and implementing a layered configuration of absorber and thermocouple layers, thereby effectively increasing the absorption area of the thermopile and enhancing the duty cycle of the device. Comparative analyses of temperature and voltage distributions between the two structures are conducted via COMSOL simulations. The test results show that the key performance parameters of the novel four-terminal beam structure chip, such as response rate and detection rate, are greatly improved compared with the traditional counterpart, with experimental results aligning closely with simulation predictions.

The operating temperature of infrared detectors can be elevated to reduce cooling requirements, thereby minimizing the size of cryocoolers and Dewar structures and enabling detector miniaturization. In this paper, the development of high operation temperature (HOT) infrared detector in North China Research Institute of Electro-optic is introduced. Through key technology research on Type-II superlattice material design, chip fabrication process, and miniaturized cooler, a 640×512 Sb-based Type-II superlattice detector with pixel pitch of 15 micrometers is developed, with a working temperature of 140 K, a quantum efficiency of 61%, an effective pixel rate of 99.8% or more, and a NETD of less than 20 mK. The detector performance is comparable to that of an HgCdTe detector at 80 K, and with satisfactory imaging results. t

In this paper, an atmospherical attenuation calibration model for external infrared imaging, is established based on the recalibration of atmosphrerical transmittance with a standard blackbody and an infrared imager. Initially, the infrared camera undergoes calibration in a laboratory setting. Then, the least squares fitting of measured results is employed to determine the responsivity of the infrared imager and the fixed bias of the sensor, which arises from dark current. After that, a calibration model of atmopherical attenuation is formulated. When using infrared imager for external experiment, the output of display and blackbody radiation brightness are obtained by varyinging the distance of testing, as well as the temperature of blackbody. Through incorporating the relative spectral responsibility coefficient of detector, the corrected value of atmospherical transmittance at a certain position can be calculated. Notably, the deviation value between corrected atmospherical transmittance and simulation is less than 2%.

In this study, the issue of potential photosensitive absorption interference induced by conventional red-light sources in the optical detection of photopolymer dark reactions is investigated. To address this issue, infrared light is employed as the coherent light source for holographic monitoring of dark reactions, significantly reducing the extraneous photosensitive absorption interference generated by the light source during the detection process. Utilizing Irgacure 785/PMMA photopolymer as the material under investigation, absorption spectral analysis along with comparative experiments on refractive index variations under green light (sensitizing light), red light, and infrared light are conducted. Results indicate that infrared light elicits minimal refractive index changes in the material, making it more suitable as a detection light source. Infrared digital holographic technique is applied to detect refractive index changes during the dark reaction of the photopolymer material, yielding visualized grating phase distributions and temporal curves of grating peak refractive index variations. The findings demonstrate that the application of infrared light sources can effectively mitigate extraneous photoreaction interference triggered by detection light sources during dark reaction holographic measurements, thereby enhancing the accuracy of refractive index change detection in photopolymer materials.

The operating distance is a critical parameter for evaluating the performance of infrared thermal imaging cameras, influenced by factors such as atmospheric transmission characteristics, target radiation characteristics, detector performance and optical system design. Given the highly complex and variable environmental optical radiation characteristics across diverse meteorological conditions, a method for calculating operating range under different weather conditions is proposed. A calculation formula model is derived through formula derivation, enabling the calculation of operating range under varying meteorological conditions via table lookup and interpolation techniques. This approach simplifies the process and allows measured data to be converted and extrapolated, thereby providing significant assistance in verifying compliance with performance criteria.

To address challenges in optoelectronic tracking system testing, such as limited test scenarios, difficulties in constructing test environments, and insufficient performance evaluation, a dynamic-base optoelectronic tracking algorithm performance verification system is designed in this paper. The architecture and operating principle of this algorithm verification system are elaborated, and various FPGA functional modules in the system are connected using the VPX architecture and work together, with the ability to process two high frame rate videos. On the premise of ensuring compatibility design, it provides a modular and scalable information processing hardware platform for the development of optoelectronic tracking software. Experimental results demonstrate that the verification system can simulate common carrier disturbance environments and target movements while executing algorithm processing. The system can be developed through secondary development and engineering porting, and various testing scenarios can be set up to achieve performance testing and verification of optoelectronic tracking algorithms, thereby saving costs and improving operational efficiency.

In this paper, the structural design and assembly process of optical system with variable F-number switching mechanism are studied. The variable F-number design of the infrared optical system is achieved by alternately switching between two sets of optical lens groups. In assembly process, the reference optical lens group is positioned through mechanical frock, while the variable F-number lens assembly is adjusted based on the field of view (FOV) of the reference assembly. Both focal plane and optical axis alignments are achieved through respective adjustments, ensuring system-wide consistency in focal plane alignment and optical axis collinearity. The proposed switching mechanism features a compact structure, short switching stroke, rapid switching speed, and convenient assembly-alignment processes. It satisfies stringent requirements for optical axis stability and focal plane consistency.

Spectral holography, as a holographic imaging technology, can simultaneously record the spatial and spectral information of objects and holds promising application prospects. In the optical path of spectral holography recording, a mirror driven by a high-precision displacement stage is utilized to control the phase-shift of the reference beam and to record the corresponding white-light hologram. This is a crucial step in achieving the reconstruction of monochrome holograms, and subsequently the reconstruction of an object's spatial and spectral information. To realize high-quality reconstruction of the original information, the effects of different displacement parameters of the moving mirror on the reconstructed image quality are thoroughly analyzed. Firstly, the impact of the displacement step number on the reconstructed image quality is studied, and the optimal displacement step number under different reconstruction wavelength conditions is determined. Under the condition of the above displacement step, the influence of displacement symmetry on the reconstructed image quality is analyzed, and a parameter for displacement asymmetry is proposed, along with its relationship to reconstruction quality. Finally, the influence of displacement accuracy on the reconstructed image quality under different reconstruction wavelength conditions is analyzed under the optimal conditions of the aforementioned two parameters. The results show that considering the holographic recording speed, the amount of recorded hologram data, and the reconstructed image quality, the larger the number of displacement steps, the smaller the asymmetry, the higher the displacement accuracy and the higher the reconstructed image quality. These findings provide robust supports for the selection of electronically controlled displacement stages and the precise control of displacement parameters in experiments.

To meet the requirements for detecting and tracking space debris and to enhance the analytical and predictive capabilities regarding space debris, an optical system has been designed that integrates visible, infrared, and laser paths with a common aperture. The basic specifications are as follows: For the visible spectrum: 400 to 850 nm, field of view of 2° × 2°, focal length of 366.7 mm, and entrance pupil diameter of 100 mm; For the infrared spectrum: 7.5 to 9.5 m, field of view of 2° × 2°, focal length of 190 mm, and entrance pupil diameter of 95 mm; For the laser spectrum: 1064.4 ± 0.5 nm, field of view of 0.6 mrad, focal length of 370 mm, and entrance pupil diameter of 100 mm. The three optical paths share an off-axis three-mirror structure. By analyzing the design of the free-form surface shape, the first image surface exhibits good imaging quality and coaxial symmetry. The wedge plate is placed backward in front of the first image plane for the laser and infrared channels to correct the aberration caused by the beam splitter, allowing the front groups of the two channels to achieve improved imaging. Consequently, the relay system exhibits coaxial characteristics and can be independently tested and adjusted. Through comprehensive analysis and optimization of aberrations in each optical path, the visible light system achieves an MTF value of 0.44 at the characteristic spatial frequency of 200 lp/mm. The infrared system demonstrates near-diffraction-limited performance with over 80% energy encircled within a 2×2 pixel area, while the laser system achieves >93% energy concentration within a 30 m region. All channels deliver high-quality imaging performance. This system significantly reduces alignment and assembly complexity and allows modular replacement of relay components as needed, demonstrating broad application potential in space debris detection and orbit determination missions.

In response to the limitations of conventional single-modal object detection algorithms in complex scenarios, an infrared and visible light fusion object detection algorithm based on YOLOv7 is proposed in this paper. Firstly, a dual-branch backbone network is constructed to facilitate the extraction of feature information from both modal images. Secondly, a dual-modal feature fusion module is designed to optimally fuse features from the two different modalities, achieving effective information complementarities. Furthermore, a novel feature extraction fusion network is developed to extract and comprehensively integrate multi-scale feature layer information, enhancing inter-layer information flow and improving multi-scale detection capability. Experimental results demonstrate that the improved algorithm in this paper outperforms the original algorithm in both infrared and visible light modal scenarios, significantly enhancing detection performance in complex scenarios.

To address the issue of the lack of targeted detection of infrared dim small targets by existing deep learning network structures, a method for recognizing infrared dim small targets based on an improved YOLOv8, named UT-YOLOv8 (YOLOv8 enhanced with UniRepLK Block and Triplet Attention) is proposed. In this method, a triplet attention mechanism is introduced in the detection head at the output end of the feature fusion network. Additionally, new small target detection layers and detection heads are added within the feature fusion network, and large kernel convolutions are incorporated within the spatial pyramid pooling layer of the feature extraction network. These enhancements are tailored to the imaging characteristics of infrared dim small targets. Validated on real infrared image data, the experimental results indicate that the UT-YOLOv8 algorithm, while maintaining high detection speed, has effectively enhanced the network's precision in recognizing infrared weak small targets, achieving a mean Average Precision mAP@0.5 of 95.9%.

Panoramic segmentation represents a significant advancement in the field of computer vision, integrating the techniques of semantic and instance segmentation. Its applications are numerous and diverse, including but not limited to medicine, autonomous vehicles, and unmanned aircraft. At present, image panorama segmentation technology based on deep learning has become a mainstream approach and has been widely adopted in numerous fields. In this paper, a review of the research outcomes pertaining to deep learning in the domain of panoramic image segmentation is reviewed at first, and the fundamental processing techniques and associated concepts pertinent to panoramic segmentation are introduced, along with a discussion of the commonly utilized panoramic graphic segmentation datasets and evaluation metrics. Subsequently, a deep learning image panoramic segmentation model is outlined based on a network structure for classification. Furthermore, illustrative examples of their typical applications in medicine, autonomous driving, and unmanned aerial vehicles are given. Finally, existing limitations and challenges are identified, and future research directions for panoptic image segmentation are prospected.

To address the challenges of drone detection under background clutter or occlusion, a correlation filter algorithm based on PSR discrimination was designed and implemented to achieve fast and robust drone tracking. In this paper, the KCF algorithm is used to detect drone in real time. The PSR detection confidence is used to estimate whether the drone is occluded or encountering a cluttered background, and the KCF algorithm parameters are adjusted accordingly to achieve more robust tracking. Experimental results show that the proposed algorithm effectively reduces the impact of background clutter and occlusion, can run in real time on a computer, and has high practical value for engineering applications.

To conduct long-term, stable, and real-time strain field monitoring of wind turbine blade structures, a fiber Bragg grating (FBG) sensor based on carbon fiber reinforced polymer (CFRP) packaging is designed, and a sensing system based on multiple FBGs is built. The FBG encapsulation is implemented using a CFRP-sealed aluminum foil structure. Simulation analyses are conducted to determine the stress field distribution across the blade at varying angles, providing a basis for FBG placement. In the static strain calibration experiment, the strain sensitivity at the 1 cm position of the FBG test site is 5.3 pm/kg, with the stress field exhibiting a radial distribution centered at the stress point. In real-time operational tests of the blade, FBG1 and FBG2 have the largest range of strain changes, with maximum and minimum strain values of 482 , -472 , and 462 , -476 , respectively. The effect of vertical strain on blades is much weaker than axial strain. The test data has a threshold range, which verifies that the system can evaluate blade operational status through threshold settings. This structural design, characterized by high sensitivity and superior tensile resistance, demonstrates promising application potential.