ObjectiveIn recent years, organic-inorganic hybrid perovskites have attracted the attention of the photovoltaic industry for their excellent photoelectric properties and efficient solar cell processing technology. The current highest efficiency record for single-layer perovskite solar cell device certification is 26.1%, and the highest efficiency record for series structure perovskite solar cell device certification is 29.1%. However, bringing perovskite thin-film photovoltaic technology to the market still faces some problems that need to be solved. At present, the high-efficiency perovskite solar cell devices are small size devices obtained in the laboratory, when the device area is enlarged, the series resistance of perovskite solar cells will increase linearly with the increase of the area (especially the TCO substrate), and eventually cause huge performance loss. Therefore, in order to commercialize perovskite solar cells, a larger area of solar cell series modules must be obtained. The excimer laser wavelength is in the deep ultraviolet band, the perovskite material has high absorption rate and shallow action depth, which is expected to trigger the ablation mechanism and accurately remove the perovskite material by layer, obtain a clean bottom and boundary, and effectively protect the underlying device. In this paper, the processing mechanism of perovskite thin films (MAPbI3) by excimer laser is studied. The chemical composition residue and surface morphology of perovskite thin films treated by excimer laser and fs laser were compared.MethodsDue to the low transparency of the glass substrate aligned molecular laser, excimer laser processing uses the film side irradiation. The effects of excimer laser and fs laser on the processing of perovskite thin films were compared by two irradiation methods: film side and glass side. Field emission scanning electron microscope (FE-SEM), energy dispersive spectrometer (EDS) and white light interference microscopy (WLIM) were used to characterize the surface morphology and residual elements of the samples after laser processing.Results and DiscussionsThe damage threshold of perovskite material irradiated by 248 nm excimer laser was studied. The damage threshold of 248 nm excimer laser irradiation on perovskite materials was 12 mJ/cm2(Fig.5), based on the WLIM 3D graphics and depth data of materials processed with different energy densities and 1-3 pulses. Significant damage begins to occur when the energy density reaches 18 mJ/cm2.The surface processing effect of perovskite thin film was studied by 1-6 pulsed excimer lasers with different energy densities of 20-160 mJ/cm2(Fig.7, Fig.8), Fig.9 is the energy spectrum diagram given by EDS. It can be seen that the intensities of I and Pb in the processed sample are significantly reduced. Due to the gradual exposure of the underlying substrate material, the strength of Sn in the TCO component and Si in the glass component increased significantly, and the processed sample curve was very close to the substrate curve. Atomic number ratio of I and Pb in EDS graph is shown in Fig.10. With the increase of energy density, at low energy density, the atomic numbers of I and Pb almost drop linearly with the increase of energy density, which is consistent with the increase of depth linear shape at low energy. At high energy density, the substrate is gradually exposed, and the atomic number of I and Pb is maintained at an extremely low level, which proves that the excimer laser can effectively remove the perovskite layer. Finally, the perovskite film was processed by 517 nm fs laser to compare the difference between excimer laser and 517 nm fs laser for perovskite film processing. Inside the excimer spot, the substrate is completely exposed and the bottom is clean, with no perovskite layer remaining. The outer grain of the spot is clear, and there is no obvious heat-affected zone and redeposition. The edge of femtosecond laser film side processing area produces obvious melting and material modification. The perovskite layer inside the processing area was effectively removed, but some flake remained near the processing area. The femtosecond laser base side processing area near the edge remains partially melted material. Some flakes are attached to the edges of the membrane.ConclusionsThe processing mechanism of perovskite (MAPbI3) film irradiated by a 248 nm excimer laser was investigated. The surface morphology of the sample after laser processing was characterized by WLIM, and the damage threshold of perovskite thin film was determined based on the surface morphology characteristics and depth curve. On this basis, the influence of different energy density and pulse number on machining depth was investigated by depth curve. The morphology and removal effect of perovskite films were studied by SEM and EDS aligned molecules. It was proved that perovskite materials can be effectively removed by excimer laser. Finally, the perovskite thin film materials processed by excimer laser and fs laser (517 nm) were compared. Excimer laser triggered a unique ablative mechanism on perovskite materials, while fs laser from the substrate surface processing is a combination of ablative mechanism and stripping mechanism. Excimer laser processing of perovskite materials can obtain good boundary and clean substrate, which provides a new technical means for the future processing of perovskite films.

ObjectiveCompared to traditional optical devices, fiber optic temperature sensors have the advantages of low loss, strong electromagnetic interference resistance, and corrosion resistance. Fiber optic Fabry-Perot (FP) sensors have attracted widespread attention due to their simple and compact structure, excellent stability, and manufacturing feasibility. However, there are some issues with the conventional methods used to fabricate the functional structures of fiber optic Fabry-Perot sensors, such as intricate fabrication procedures, low processing efficiency, and severe heat-affected zones. As optical fiber is a thin, transparent, and brittle material, laser as a non-contact processing method and micron-scale focused spot can effectively avoid mechanical damage and achieve diverse processing. Currently, challenges in laser processing include high surface roughness in the fiber core area, poor parallelism, and poor processing quality. Therefore, it is necessary to establish a set of laser process parameters that can process fiber Fabry-Perot cavities with high quality to ensure that the processed cavities meet the performance requirements of the sensors. For this purpose, this study conducted experimental research on femtosecond laser processing of FP cavities.MethodsThis study used a low-repetition-rate infrared femtosecond laser processing system (Fig.1) to conduct experiments on optical fibers and explore the effects of laser parameters on the morphology of the microcavity. The microcavities were observed using a laser confocal microscope and an optical microscope (Fig.2-7), and the roughness of the sidewalls of the processed microcavities was measured (Tab.2). The processed optical fiber is placed in a temperature box, and the response characteristics are obtained using a broadband light source and a spectrometer (Fig.9). Select the optimal cavity length based on the response characteristics, and package the sensor with a cavity length of 80 μm in a metal tube for testing, and obtain the response characteristics after packaging (Fig.11)Results and DiscussionsThe experiment showed that a laser power of 10 mW can avoid the generation of excessive ablation residue and effectively remove the material. The lower scanning speed and scanning interval can avoid the "convex structure" in the microcavity, and the parallelism of the sidewalls can be further improved with the reciprocating scanning and the downward feeding method after each scanning. Finally, a top-down through-cavity with good entrance morphology, sidewall roughness of 2-4 μm, and parallelism between the two sidewalls as high as 87.95° was obtained. Among the five optical fibers with different cavity lengths, the sensor with a cavity length of 80 μm has the best performance of 21.07 pm/℃.The sensor sensitivity was tested by changing different temperatures in a temperature control box. The sensitivities of the three metal tube-packaged sensors with a cavity length of 80 μm were 8 pm/℃, 10.29 pm/℃, and 10.86 pm/℃, respectively. The authors hypothesize that this may be due to the different thermal expansion elongation of the different materials, which in turn leads to different sensor sensitivities. Therefore, the encapsulation material is also an important factor in the performance.ConclusionsThis article used low-frequency infrared femtosecond laser to conduct microgroove processing experiments for fiber-optic Fabry-Perot temperature sensors. The laser finally adopts a reciprocating scanning method. The selected laser power is 10 mW, the scanning speed is 100 μm/s, the scanning interval is 4μm, and the depth direction is scanned 5 times, with each step of 5 μm. Finally, five sensing structures with different cavity lengths and good morphology were processed, with two reflective surfaces close to parallel (up to 87.95°). The roughness of the side walls of the five cavity-length structures is mostly 2-4 μm. The performance of unpackaged optical fiber temperature sensors with different cavity lengths was tested, and it was found that the optical fiber temperature sensor with a cavity length of 80 μm has the best response performance, with a temperature sensitivity of 21.07 pm/℃. Then, optical fibers with a cavity length of 80 μm are used and packaged in stainless steel tubes, copper tubes, and aluminum tubes. The temperature sensitivities of the sensors are 8 pm/℃, 10.29 pm/℃, and 10.86 pm/℃ respectively. Compared with YANG et al[21] and CHEN et al[17], this paper uses laser control to improve the processing quality of the Fabry-Perot cavity of laser-processing fiber temperature sensors, thus further improving the temperature response characteristics of the sensor. The temperature response characteristics are improved by 13.3%. This article tests the response characteristics of the temperature sensor sensor in actual application conditions, and explores the impact of packaging materials on performance. It is found that the thermal expansion coefficient of the packaging material is the main factor affecting the sensor response characteristics. The higher the thermal expansion coefficient, the better the sensor performance. This article provides support for subsequent research on laser processing of cascaded fiber optic temperature sensors to further obtain sensors with more significant performance.

ObjectiveNickel-based superalloy porous components exhibit excellent heat and mass transfer capabilities, making them significantly important in fields such as aerospace. Due to their excellent permeability, porous structures are increasingly preferred for thermal conduction devices, such as high-temperature heat pipes. Using Laser Powder Bed Fusion (L-PBF) to form porous materials enables the customized fabrication of key features such as pore size and porosity. However, the presence of pores significantly reduces the mechanical properties of the materials, which limits the integrated design and manufacturing of the mechanical performance and functional characteristics of porous components. For this purpose, a lattice-reinforced porous structure that integrates functional characteristics and load-bearing capacity is designed in this paper.MethodsTo achieve a synergy between mechanical performance and functional properties in porous components, this study employed L-PBF to fabricate Inconel718 (IN718) lattice-reinforced porous structures. This paper analyzes the effects of laser power, scanning speed, and hatching space on porosity through orthogonal experiments, and establishes the relationship between the porosity of the porous structure and its tensile strength. By comparing the mechanical properties of porous structures and lattice-reinforced structures, the mechanical behavior of both the porous and lattice components is analyzed. Finite element simulations were used to analyze the stress state of the pores and lattice-reinforced structures during loading, investigating the impact of different porosities and lattice-reinforced structures on the mechanical behavior of the porous material.Results and DiscussionsThis paper designs and forms a lattice-reinforced porous structure. The porosity of the L-PBF manufactured IN718 porous structure increases exponentially as the laser energy density decreases, while the tensile strength of the porous structure decreases exponentially with increasing porosity. Tensile tests show that the porous structure primarily exhibits brittle fracture, with unmolten powder particles and irregular pores leading to stress concentration and crack initiation during stretching. Simulation results indicate that the stress in the lattice-reinforced porous structure is primarily borne by the lattice, with increased thickness enhancing structural strength. The mechanical properties of the lattice-reinforced porous structure are positively correlated with the thickness of the lattice, and the reinforcing effect becomes more pronounced with increasing porosity.ConclusionsThe orthogonal test results indicate that among the three parameters (laser power, scanning speed, and hatching space) scanning speed has the most significant effect on porosity. The lattice-reinforced porous structure, which combines minimal surfaces with a porous structure, exhibited a tensile strength of 244 MPa with a lattice thickness of 0.5 mm, demonstrating ductile fracture. As the lattice thickness increased to 0.9 mm, the tensile strength rose to 356 MPa. The mechanical performance of the lattice-reinforced porous structure outperformed that of the porous structure alone. With a porosity of 26%, the tensile strength of the lattice-reinforced porous structure was approximately 200 MPa higher than that of the porous structure, and the elongation after fracture increased by 3.5%. ABAQUS finite element simulations were used to analyze the stress distribution of the porous material under applied loads, revealing significant stress concentration at the pores, which first reached fracture strength, leading to failure. As porosity increased, the load-bearing capacity of the porous material decreased.

Significance Femtosecond laser refers to the pulse duration in the femtosecond scale (10-15 seconds), with ultra-short pulse, long wavelength, ultra-high peak power and nonlinear absorption effect. During processing, the femtosecond laser with high energy density acts on the surface of the material after focusing, and has a nonlinear absorption effect on the substance, which can realize the molding, modification or removal of the material, so as to achieve fine processing. Femtosecond laser processing technology has micro and nano-scale processing resolution and three-dimensional manufacturing capability, which can manufacture microrobots with specific shapes and sizes. The prepared microrobot can perform various tasks at the microscopic scale, has the advantages of small size, light weight and low energy consumption, and can shuttle in various environments, and achieve the effect of targeted therapy without harming the human body. In the future, with the continuous development and improvement of femtosecond laser technology, microrobots are expected to play an important role in more key areas, providing entirely new solutions to solve complex scientific and engineering problems.Progress First of all, the femtosecond laser polymerization processing micro-nano robot technology is based on the two-photon absorption effect, that is, the monomer molecule absorbs the free radical generated by two photons at the same time, and then carries out a complex polymerization reaction to form a polymer solid macromolecule, and finally obtains the three-dimensional micro-nano robot structure through development. According to whether the prepared micro-nano robot can be deformed, it can be divided into hard micro-nano robot and soft micro-nano robot. The rigid micro-nano robot has the advantages of large output force, high speed and high precision. The soft micro-nano robot can change its shape and size according to the needs of the environment and adapt to work in a complex environment. The materials used for femtosecond laser processing of micro-nano robots are mainly responsive hydrogels, photoresist and metals. Hydrogels have the characteristics of good expansibility, strong water absorption, easy water retention, ultra-bionic, etc. The processed micro and nano robots can make regular structure and volume adjustment according to the changes of environmental temperature, pH, light, electric field, magnetic field, etc., and change the gel state and composition, which has high intelligent response characteristics. Due to the excellent strength and hardness of metal materials, it makes microrobots have a strong structure and stable performance, ensuring stable operation in a variety of environments. Photoresist has shown excellent performance in the preparation of microrobots, which can not only achieve fine processing at the micro-nano scale, but also make microrobots highly customizable in structure and function due to their unique photosensitizing properties. The use of photoresist makes the design and manufacturing process of microrobots more flexible, and the structural design of microrobots can be quickly adjusted and optimized according to different application requirements. Metal materials have excellent electrical and thermal conductivity, which is crucial for the energy supply and heat dissipation of microrobots, they can efficiently transmit electrical energy to ensure the power supply of microrobots, while effectively dissipating the heat generated in the movement of microrobots to avoid overheating damage. In addition, with the development of multi-functional 3D microrobots, the application of microrobots has been widely concerned by researchers. Due to their micron-scale size, in the biomedical field, the main applications of microrobots include targeted drug delivery, cell manipulation and minimally invasive surgery. In industry, microrobots are mainly used in the micro-machining of precision devices and the detection of micro-objects.Conclusions and Prospects Because of its high precision, high efficiency and controllability, laser micro-nano machining technology provides a powerful tool for the design and manufacture of medical micro-robots. This technology can achieve precise control of micro-scale materials, and provides a key support for miniaturization, multi-function and intelligence of medical microrobots. Laser micro-nano processing technology, with its unparalleled accuracy and flexibility, is quietly changing the manufacturing and application pattern of micro-robots, not only promoting the rapid development of the field of micro-robots, but also laying a solid road for its wide application in many fields such as medicine, biology, materials science and so on.

ObjectiveNitriding is a common process in the chemical heat treatment of 38CrMoAl steel, which alters the chemical composition and microstructure of the steel surface. It is widely applied to components such as shafts, gears, screws, and hydraulic plungers that operate in harsh environments. By directly coupling laser energy to ammonia molecules through resonant exciting their vibrational modes, the dissociation of active nitrogen atoms from ammonia can be accelerated, reducing heat dissipation during transfer. This enhances the efficient dissociation of ammonia molecules and increases the likelihood of ammonia absorption on the sample surface, leading to a deeper nitrided layer and improved hardness properties. To address the challenges of long treatment times and low efficiency in traditional gas nitriding, lasers were introduced into the gas nitriding process of 38CrMoAl steel. The effect of laser power on the microstructure and hardness of the nitrided samples was analyzed, providing valuable insights for the efficient production of high-quality nitrided layers on 38CrMoAl steel.MethodsThe experimental platform for laser-assisted nitriding (Fig.1(a)) consists primarily of a CO2 infrared laser, a vacuum chamber, vacuum system, a temperature control system, and an ammonia supply system. The experimental material used was tempered and heat-treated 38CrMoAl steels. Using the developed equipment, gas nitriding and laser-assisted nitriding experiments were conducted at laser powers of 50 W, 100 W, 150 W, and 200 W. After the experiments, the cross-sections of the nitrided samples were polished and etched. The surface microstructure was analyzed using an XRD (X-ray diffraction), SEM (Scanning electron microscope), and shape measurement laser microsystem, while the cross-sectional microstructure was examined with an optical microscope. Chemical composition was determined through EDS (Energy-dispersive spectroscopy) and EPMA（Electron probe X-ray micro analyzer） analysis. In addition, the surface hardness and cross-sectional hardness of the nitrided samples were evaluated using a Vickers microhardness tester.Results and DiscussionsAfter gas nitriding and laser-assisted nitriding as varying laser power, nitride particles formed on the surface of 38CrMoAl (Fig.2(a)). As the laser power increased, the nitride particle size grew progressively larger (Fig.2(b)), reaching a maximum size of 865 nm. This increase in particle size also led to higher surface roughness, with a maximum roughness of Sa = 2.92 μm (Fig. 2(c)). EDS analysis was used to study the chemical composition changes on the nitrided surfaces (Fig.3), revealing that higher laser power effectively enhanced the dissociation of ammonia molecules, leading to more active nitrogen atoms being absorbed by the 38CrMoAl. At 200 W, the nitrogen content reached 12.93%, compared to only 6.37% after gas nitriding alone.In addition, after 6 hours of gas nitriding, only a 74.2 μm diffusion layer was observed in the cross-section of the 38CrMoAl, with no visible white compound layer. In contrast, laser-assisted nitriding under the same conditions resulted in both a compound layer and a diffusion layer. The thickness of these layers increased as rising laser power (Fig.4(a)), reaching 232.8 μm for the compound layer and 15.4 μm for the diffusion layer at 200 W. XRD analysis (Fig.1(c)) indicated that the surface of the 38CrMoAl sample underwent a phase transition from α-Fe to γ′-Fe4N, and then to ε-Fe2-3N. EPMA analysis of the 38CrMoAl cross-section (Fig.4(c)-(d)) showed that higher laser power promoted the diffusion of nitrogen atoms along grain boundaries, forming nitrides. The increased laser power also resulted in a denser surface compound layer with enhanced hardness properties (Fig.5). At 200 W, surface hardness reached 1102 HV0.1. Additionally, the higher the laser power, the greater the hardness at equivalent depths below the surface, forming a gradient hardness distribution.ConclusionsIn this study, the nitriding layer of 38CrMoAl was produced using laser-excited ammonia-assisted nitriding technology. The microstructure and hardness of the surface and cross-section of samples treated with gas nitriding and laser-assisted nitriding at different laser powers were compared and analyzed. As nitriding progressed, the surface of the 38CrMoAl plate underwent a phase transition from α-Fe to γ′-Fe4N, and subsequently to ε-Fe2-3N. As increasing laser power, the average size of nitride particles, surface roughness (Sa), and nitrogen proportion all increased. Additionally, the nitrogen content in the nitriding layer rose with higher laser power, which enhanced the decomposition of ammonia and improved the efficiency of ammonia gas utilization.The average surface hardness of the untreated 38CrMoAl substrate was 256 HV0.1. After conventional gas nitriding and laser-assisted nitriding at different laser powers, the surface hardness increased to 788 HV0.1, 843 HV0.1, 936 HV0.1, 1057 HV0.1, and 1102 HV0.1, respectively. The hardness of the nitrided cross section gradually decreased with depth and eventually approached the hardness of the base material.

Significance Since nanoparticle materials have at least one dimension at the nanoscale, they exhibit many unique properties in optics, thermal, electrical, magnetic, mechanical and chemical aspects. Typically, the preparation of nanoparticles relies on chemical or physical vapor deposition, sol-gel method, electrochemical deposition and other conventional techniques. Although these methods perform well in specific applications, they have many limitations, such as high cost, time-consuming, complex equipment, or difficulty in achieving large-area and complex shape growth. As an emerging and efficient technique, laser processing of nanoparticles can achieve efficient and controllable preparation of nanoparticle materials by utilizing the characterizations of local high temperature and high-pressure environment of high-energy laser beams or the rapid heating and cooling of laser processing, as well as regulating factors of laser type, processing parameters, and precursor materials/solutions.Progress First, the basic principles of the interaction between lasers and materials are introduced, including the interaction between matter and light and the redistribution of laser energy during laser-material interaction. The interaction process is analyzed from different time scales and microscopic molecular scales, leading to the control of the reaction progress during the experiment. Then, the latest research progress and characteristics of five common laser processing techniques for nanoparticle are reviewed, including laser-induced transfer, laser-induced hydro-thermal growth, pulsed laser deposition, pulsed laser ablation in liquids and laser in-situ induction and deposition. In particular, the formation rules and the size-influence mechanism of nanoparticles prepared by the five laser processing techniques are summarized. By combining the advantages of various laser processing techniques, it is possible to precisely control the surface properties of materials, resulting in the efficient fabrication of nanostructured materials and devices with multifunction.Conclusions and Prospects In the frontier field of micro-nano manufacturing, laser processing techniques for nanoparticle is showing its unique application potential. By accurately adjusting the processing process, laser processing technique can efficiently realize the reduction, additive and conversion processing of nanoparticles, and then construct various functional micro-nano devices. In the future, with the advancement of science and technology, the research on laser processing of nanoparticles is developing in the following key directions: 1) the interdisciplinary cooperation of optics, chemistry, medicine, robotics and other disciplines is promoting the development of advanced micro-devices; 2) the in-depth study of the interaction mechanism between laser and matter is bringing revolutionary breakthroughs to laser processing technique. Combining computer-aided design and machine learning algorithms, laser processing technique is becoming more and more intelligent and automated. In general, laser processing techniques for nanoparticle will play an increasingly important role in the future manufacturing of electronic devices.

ObjectiveLaser cladding, with the advantages of a small heat-affected zone, dense microstructure, and high bonding strength, is widely used in aerospace, marine, and other fields. However, the intense heating and cooling processes result in uneven strain, which leads to a decline in the mechanical properties of the formed parts. Ultrasonic vibration has shown significant effects in enhancing melt pool flow, reducing temperature gradients, and promoting uniform microstructure distribution, offering potential to reduce and homogenize the strain. Therefore, this paper carried out an ultrasound-assisted laser cutting test on a 316L stainless steel substrate to study the effect of ultrasound on the melting process. Current research on ultrasonic-assisted laser cladding predominantly relies on simulations and post-processing analyses, which do not allow for real-time stress observations. To address this limitation, this paper introduces a method utilizing Digital Image Correlation (DIC) technology to generate speckle patterns appropriate for strain measurement near the melt pool during the ultrasonic-assisted laser cladding process. Corresponding criteria for evaluating speckle quality are established. This method enables real-time strain monitoring, and the effects of ultrasound on strain distribution are analyzed, providing valuable insights for controlling strain during laser cladding.MethodsThe experimental setup for ultrasonic-assisted laser cladding (Fig.1) primarily comprises a fiber-coupled semiconductor laser, a motion control system, a wire feeder, a high-speed video system, and an ultrasonic vibration device. The substrate and wire utilized in these experiments consist of 316 L stainless steel plates. This study builds upon established DIC speckle quality evaluation methods by incorporating the Structural Similarity Index (SSIM) algorithm and the number of speckle autocorrelation peaks to propose an optimized metric for assessing speckle quality. This metric guided the selection of speckles used in the experiments (Fig.6). High-temperature speckles were engraved onto the substrate surface using a laser marking machine, and a high-speed camera was employed to capture images during the ultrasonic-assisted laser cladding process. The captured images were subsequently analyzed using DIC to generate strain contour maps (Fig.8). Additionally, scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) were utilized to compare cladding layers with and without ultrasound, thereby analyzing the mechanisms by which ultrasound affects strain distribution.Results and DiscussionsBased on the simulation results of speckle generation using various methods (Fig.5), the optimized speckle quality evaluation method proposed in this paper demonstrates an advantage in identifying speckle periodicity. This advancement facilitates the preparation of speckles suitable for DIC analysis under high-temperature and high-light-intensity conditions in laser cladding (Fig.6). DIC strain analysis results indicate that, compared to cladding without ultrasound, the strain distribution around the melt pool becomes significantly more uniform with ultrasonic assistance (Fig.8). The standard deviation of strain distribution along a specified line in the cladding direction decreases by up to 82.93%, while in the vertical direction, it decreases by up to 67.47% (Fig.9). Under ultrasonic action, the strain peak at point P2, located 1.5 mm from the cladding layer in a direction perpendicular to the layer, is reduced by 23.91% (Fig.11). When ultrasonic vibration is applied, ripples form on the surface of the molten pool, intensifying the flow within the pool. This phenomenon promotes a more uniform temperature and structural distribution, thereby reducing the occurrence of stress concentrations (Fig.12). SEM and EDS analyses of the precipitated phases in the cladding layer reveal that, following the application of ultrasound, the number of hard granular precipitates rich in Si and Mn is significantly reduced, along with a slight decrease in the corresponding elemental concentrations within the precipitates (Fig.13 and Fig.14). The primary effect of ultrasound is observed in the fragmentation of primary dendrites and the reduction of precipitated phases.ConclusionsBased on the MIG evaluation system, we propose a speckle quality evaluation metric tailored for strain observation during the laser cladding process. This metric considers the structural similarity of patterns and the number of autocorrelation peaks, and it can effectively guide speckle preparation for real-time strain observation using DIC on the laser cladding surface. The application of ultrasonic vibration results in a more uniform strain distribution in the area near the melt pool. Specifically, the standard deviation of strain along the cladding direction decreases by up to 82.93%, while in the vertical direction, it decreases by up to 67.47%. Under the influence of 2500 W ultrasonic vibration, the positive strain amplitude in the analysis area along the cladding direction decreases from 0.0100 to 0.0047, and the negative strain amplitude decreases from -0.0050 to -0.0041. In the vertical direction, the positive strain amplitude decreases from 0.0169 to 0.0094, while the negative strain amplitude decreases from -0.0079 to -0.0012. With the application of 2500 W ultrasonic vibration, the maximum reduction in strain peaks in the x and y directions during the entire cladding process can reach 30.15%. The effect of ultrasonic vibration on reducing strain during laser cladding is significant. Following the application of ultrasound, the number of granular precipitates in the cladding layer is markedly reduced, and the local network-like precipitates are disrupted, which facilitates stress relief and contributes to a more uniform strain distribution. Additionally, noticeable fluctuations occur on the surface of the molten pool, intensifying the flow within the pool. The suppression of granular phase precipitation and disruption of mesh-like structures contribute to stress relief and a more uniform strain distribution.

ObjectiveNowadays, the demodulation precision, speed and stability of existing fiber grating demodulation systems have been greatly improved. However, most FBG demodulation systems are complex in structure, low in integration, poor in engineering adaptability, and the embedded peaking algorithm is complex, so the demodulation needs to be completed by the host computer, which is poor in portability and high in cost. Therefore, the research goal of this paper is to design an FBG demodulation system which is simple in structure, high in integration and can satisfy the large-scale engineering application of FBG.MethodsIn view of the above problems in the current fiber Bragg grating demodulation system, a FPGA high-precision FBG-aware demodulation system based on adaptive peak finding algorithm is designed. Firstly, the sensing mechanism of FBG is studied, and a photoelectric detection module is designed to convert and amplify the weak reflection signal of FBG into a voltage signal. Based on the corresponding relationship between the synchronous trigger signal output by the tunable laser and the scanning wavelength, the FPGA microcontroller realizes the efficient acquisition of the converted FBG reflection signal. Secondly, the Kalman moving mean hybrid filtering algorithm was introduced into the FPGA microcontroller to denoise the original spectral reflection signal, the peak judgment algorithm was used to realize the adaptive multi-peak region discrimination, and the double centroid algorithm was combined to complete the fast and high-precision demodulation of the center wavelength of multiple FBG reflected light.Results and DiscussionsA FPGA high-precision FBG sensing demodulation system based on adaptive peak finding algorithm is built (Fig.5). The Kalman moving mean hybrid filter is used in the FPGA to denoise the collected original spectral data and effectively correct the spectral shape (Fig.6). The experimental results show that under the same conditions, the proposed algorithm has faster demodulation speed than the traditional algorithm, while maintaining better demodulation precision and stability, and consumes less underlying hardware computing resources (Fig.7, Tab.1). When the temperature is 37 ℃, 37.5 ℃ and 38 ℃ respectively, the central wavelength value of the demodulated system in this paper fluctuates within the range of ±3 pm compared with the standard value (Fig.8). When the temperature rises from 25 ℃ to 35 ℃, the correlation coefficient between temperature and wavelength change is above 0.998 5, and the demodulation results of the system have a good linear relationship (Fig.9). Based on the linear experiment, two cooling tests and one heating test were repeated. The temperature measured by the three demodulation experiments is in good agreement with the actual measured temperature without obvious hysteresis and drift (Fig.10), and the repeatability of the demodulation results of the system is good. The multi-grating demodulation of the system is tested experimentally, which still shows good linearity and demodulation accuracy (Fig.11, Tab.2).Conclusion Through theoretical and experimental research, combined with tunable laser, a FPGA high-precision FBG sensing demodulation system based on adaptive peak finding algorithm is designed and built. The system is based on FPGA platform, the photoelectric detection module is designed, and the Kalman-moving mean hybrid filter algorithm is embedded to reduce the noise of the spectral signal, which effectively reduces the influence of noise disturbance on the peak finding accuracy. The peak judgment algorithm and the double centroid algorithm are used to realize the fast and high-precision demodulation of the peak center wavelength of multiple FBG, which effectively improves the demodulation accuracy. To some extent, it solves the shortcomings of the current fiber Bragg grating demodulation system, which is difficult to deploy in the demodulation equipment and requires the help of the host computer for demodulation. The temperature measurement experiment shows that the demodulation accuracy of the central wavelength of the FBG temperature sensor can reach ±3 pm, the fitting linearity of temperature and wavelength change is above 0.998 5, and the system shows excellent stability and repeatability. It provides a useful reference for the portability, integration and engineering of fiber Bragg grating demodulation system.

ObjectiveDistributed fiber optic acoustic sensing (DAS), as a novel vibration sensing technology, leverages single-mode communication fibers to create large-scale, cost-effective sensing arrays. Although this sensing technology is more prone to noise interference compared to traditional strain sensors, it offers a broader response bandwidth and greater durability for long-term deployments. Consequently, it has found widespread applications in various fields, including seismic wave detection, pipeline condition monitoring, perimeter vibration sensing, and more. The DAS system produces a significant amount of data during operation; However, only a fraction of this data contains relevant information. The prevailing approach involves employing machine learning for classification or pattern recognition tasks, thereby maximizing the utilization of the data's value. Since many fiber vibration event recognition methods rely on extracting features from time-frequency domain graphs to accomplish the classification task, and such methods tend to complicate the algorithm due to the incorporation of convolutional neural networks (CNN), this study aims to simplify the process by combining basic data preprocessing with clustering algorithms to categorize vibration signals.MethodsA new method based on time-domain amplitude feature extraction using clustering algorithm for intrusion event recognition is proposed (Fig.2). This method can be used in phase-sensitive optical time-domain reflectometer (Φ-OTDR) to classify the detected vibration signals. Compared with traditional image machine learning algorithms, the signal recognition method proposed in this study requires fewer samples and does not need tedious manual labeling. In this method, firstly, the difference of neighboring data points is calculated to get the maximum value of the difference sequence, and the maximum and envelope values are used to extract key features (Fig.3). Then, the vibration events are classified using hierarchical clustering algorithms. Finally, the effectiveness of the method is verified by evaluation indexes such as V-measure and silhouette coefficient.Results and DiscussionsThe vibration events simulated in the experiment include wind noise, manual knocking and digging. The experimental results show that the clustering accuracy of the three events can be up to 88.68% (Fig.13), and the indexes of homogeneity, completeness and V-measure are higher than 0.7, indicating that the clusters are well defined and the data points in each cluster are similar to each other; The silhouette coefficient is 0.778, and the clusters are well separated, and the number of data points in each cluster is [596, 504, 543] (Fig.14), and the size of each cluster in the clusters is reasonable. The clustering accuracy, homogeneity, completeness, V-measure, adjusted Rand index and adjusted mutual information are all relatively high, indicating that the clustering results match well with the real labels, and the clusters of clusters have a high degree of similarity of the data points within the clusters, and each cluster contains most of the real data points belonging to the class. The effectiveness of the unsupervised learning-based fiber vibration time-domain feature signal recognition method is verified by recognizing three types of events.ConclusionsThis study proposes a feature extraction method based on time-domain amplitude differences to solve the difficulty of poor clustering resolution caused by strong correlation among signal features after conventional time-domain feature extraction of raw data in the Φ-OTDR system. The recognition method based on the proposed feature extraction approach and unsupervised clustering analysis has no requirements with pre-labeling of events as well as large datasets. It has fast calculation speed and effectively addresses the problems of large scale data and much workload for marking data in image recognition machine learning. According to experimental results, the clustering accuracy of the proposed method reaches 88.68%. So it has the function of identifying vibration events caused by intrusion targets. This study provides a novel solution for recognizing the vibration events of Φ-OTDR by machine learning.

In order to verify the feasibility and accuracy of multi-point distributed measurement of remote sensing camera by optical fiber grating sensor under strong vibration environment, the application of Fiber Bragg Grating (FBG) in the field of spatial remote sensors vibration sensing was realized. MethodsFirstly, the finite element simulation model of optical remote sensing camera was builded, modal analysis and vibration response analysis were carried out. The connection process of optical fiber grating sensor and camera in different parts, forms and materials was designed in the system installation environment, and an experimental method of using optical fiber grating sensor to measure the remote sensing camera with single fiber multipoint distribution was proposed. ResultsBy comparing the measurement results of optical fiber grating sensor with those of standard piezoelectric vibration sensor, the maximum error of sinusoidal vibration acceleration measurement is 3.25%, and the maximum error of random vibration root-mean-square acceleration response analysis is 3.14%. The measured values are in good agreement with the accuracy of 0.99. The fitting accuracy of simulation analysis results and measurement results reaches 0.97, which has a good consistency. ConclusionsThe optical fiber grating sensor has high precision and is feasible for the multi-point distribution measurement of the camera in the strong vibration environment, which can meet the requirements of system monitoring, and provides a reference for the application of fiber grating sensing in the high-precision measurement of strong vibration response of space remote sensors.

ObjectiveWith the development of the aerospace field, high temperature resistant materials such as nickel-based alloys and gas film cooling holes are gradually applied to the hot end parts of aero engines, and the manufacturing requirements for the gas film holes of the hot end parts of aero engines are also increasing. Traditional processing methods such as EDM and electrochemistry can no longer meet the manufacturing requirements for machining high-quality microholes on nickel-based alloys. Due to its precision, low damage and cold processing characteristics, femtosecond ultrafast laser is increasingly suitable for the processing of nickel-based alloy microholes, but the problems such as thermal defects and insufficient taper in the processing of large depth to diameter microholes still need to be overcome. To solve the taper problem of nickel-based alloy material deep microhole machining, the femtosecond laser machining of nickel-based alloy without taper single hole experiment was carried out in this paper.MethodsA femtosecond laser numerical control machining system (Fig.1) is adopted to carry out non-taper single-hole machining experiments on nickel-based alloy materials based on the laser processing technology of rotating drilling of inclined workpieces. The influence rules of laser defocusing amount, laser repetition frequency, laser scanning radius and laser optical axis offset on single-hole morphology are investigated by using the control variable method, and then the process is optimized. Three processes of drilling, reaming and repairing are adopted (Tab.3), and an auxiliary device for side-shaft blowing is added (Fig.8).Results and DiscussionsThe effects of laser defocusing amount (Fig.4), laser repetition frequency (Fig.5), laser scanning radius (Fig.6) and laser optical axis offset (Fig.7) on the morphology of single hole are investigated, the optimal process parameters are obtained, and high-quality single hole machining with a taper of 0.04° is realized on nickel-based alloy with 2 mm thickness. On the basis of process optimization, three processes of drilling, reexpanding and hole repair are adopted, and auxiliary technology of side shaft blowing is added, thus deep microhole machining with a taper of 0.14° is realized on 6 mm thick nickel-based alloy (Fig.9).ConclusionsIn order to solve the taper problem of deep microhole machining of nickel-based alloy materials, based on the laser processing technology of rotary drilling of inclined workpiece, the processing method and experimental principle are firstly described. Then, the experiment of single-hole machining without taper is carried out on nickel-based alloy materials. It is found that when the laser defocus quantity gradually changes from positive defocus to negative defocus, the inlet diameter of the microhole is basically unchanged, and the outlet diameter increases significantly. When the laser repetition rate is small, the outlet diameter of the microhole is smaller and the taper is larger. As the laser scanning radius increases, the diameter of the entrance and exit of the hole also increases, but the taper of the hole is basically unchanged. In addition, it is necessary to keep the bias between the laser and the workpiece rotation axis within 10 μm in order to realize non-tapering single-hole machining. Then, on the basis of optimization of process parameters, longitudinal feed and blowing are used to assist. The microhole machining with a large depth-to-diameter ratio of 0.14 has been successfully achieved on a 6 mm thick nickel-based alloy.

ObjectiveDue to its advantages of low cost, high speed, and strong anti-interference capabilities, laser transmission technology has promising applications in both civilian and military fields. However, laser links inevitably pass through atmospheric channels during transmission. The scintillation and absorption effects in the atmosphere can cause laser signal attenuation and angle-of-arrival fluctuations, ultimately affecting the quality of the communication link. In severe cases, this may lead to link interruption and communication failure. Therefore, effectively, accurately, and in real-time measuring the dynamic characteristics of atmospheric coherence length is of great importance for the practical engineering applications of laser transmission.MethodsIn this study, we developed an atmospheric coherence length measurement system based on a six-aperture DIMM (Differential Image Motion Monitor). Both ends of the system use small-aperture optical antennas. At the emitting end, the laser is expanded and the system's focal length is adjusted to ensure the laser exits as parallel light. At the receiving end, a six-aperture mask device is placed in front of the optical antenna. After passing through a 532 nm narrowband filter, the light is focused and imaged onto the focal plane of a CMOS camera within the optical antenna. The spots in the images are then processed using a K-means clustering boundary identification algorithm. The results are calculated on a PC and finally displayed on the upper computer interface.Results and DiscussionsThis study proposes a parameter extraction method for a six-aperture atmospheric turbulence characteristic measurement system based on the K-means clustering algorithm (Fig.2). This method can address issues of spot adhesion and boundary blurring. Compared to the traditional threshold segmentation algorithm, the identification accuracy has been improved from 66.873% to 99.923% (Fig.5). Finally, a six-aperture differential image motion experiment (Fig.6-Fig.7) was conducted to verify the system's feasibility and the algorithm's stability. The atmospheric coherence length values measured using the threshold segmentation algorithm exhibit significant calculation errors under moderate to strong turbulence, demonstrating the stability of the proposed algorithm (Fig.9). Additionally, the turbulence intensity in the Changchun area was measured. The experimental data showed that the trends and values observed throughout the day were consistent with the known patterns of turbulence intensity variation (Fig.10), further confirming the effectiveness and feasibility of the system and method used in this study.ConclusionsThis study constructed an atmospheric coherence length measurement system based on a six-aperture design, which enhances statistical reliability while maintaining system portability. To address issues such as unclear and overlapping speckle boundaries during measurement, a six-aperture K-means clustering algorithm was developed for extracting atmospheric turbulence characteristic parameters. Compared to the traditional threshold segmentation method, this new approach increases recognition accuracy from 66.873% to 99.923%. Through continuous day-long observations over several days, the resulting atmospheric coherence length variation curves align with the expected patterns of turbulence intensity changes. The system and method presented in this study enable real-time, continuous measurement of atmospheric coherence length. The designed upper computer interface allows for real-time display and ease of operation, making it applicable in practical engineering applications.

ObjectiveQuantum dots derived from typical two-dimensional layered materials have attracted widespread attention due to their unique optoelectronic properties. Compared with two-dimensional layered nanomaterials, quantum dots generally have better photochemical stability, higher solubility, and enhanced nonlinear optical (NLO) effects. However, most of the reported NLO performance of quantum dots prepared based on two-dimensional layered materials focused on liquid-phase matrices. Although liquid matrix can quickly restore quantum dots after laser excitation, providing convenience for the study of nonlinear optical properties and mechanisms, which is not conducive to the material and device development of nonlinear optical materials. In addition, prolonged exposure of quantum dot suspensions to air inevitably leads to aggregation and structural damage, resulting in a decrease in their NLO performance. Therefore, if quantum dots can be introduced into the solid matrix, they can effectively prevent aggregation and isolate contact with air, thus effectively solving the above problems. In this paper, the two-dimensional layered tin diselenide (SnSe2) crystal is used as raw material, and SnSe2 quantum dots with uniform size and homogeneous dispersion were synthesized using liquid phase exfoliation method. Subsequently, the resulted SnSe2 quantum dots were introduced into the silica gel glass matrix with good physical and chemical properties and optical transparency by sol-gel wet chemical technology, and obtained SnSe2 quantum dot composite gel glass. And the potential application of the SnSe2 quantum dot composite gel glass in the field of nonlinear optics was explored.MethodsThe morphology, composition, structure and linear optical properties of the SnSe2 quantum dot composite gel glass were systematically characterized by transmission electron microscopy (TEM), field emission scanning electron microscopy (FESEM), Raman spectroscopy and ultraviolet visible absorption spectroscopy (UV/Vis). The nonlinear optical properties of SnSe2 quantum dot composite gel glass under nanosecond and picosecond laser pulses were studied by open-apertures Z-scan technique. In addition, the ultrafast carrier dynamics of SnSe2 quantum dot composite gel glass was explored by pump-probe technique and transient absorption spectroscopy. The NLO parameters and relaxation time of SnSe2 quantum dot composite gel glass were deduced using Eq.(2)-(6) and Eq.(7), respectively.Results and DiscussionsSnSe2 quantum dots with uniform size and homogeneous dispersion were successfully introduced into silica gel glass by sol-gel method (Fig.3(c) and Fig.4), and the resulted composite gel glass with uniform doping and high transmittance (Fig.3(a)). Interestingly, when the laser pulse width changes from picosecond to nanosecond, the NLO absorption of SnSe2 quantum dots composite gel glass changes from saturated absorption (SA) to reverse saturated absorption (RSA) (Fig.6), and the SA performance of SnSe2 quantum dots in gel glass matrix under picosecond and RSA response under nanosecond is improved as compared to SnSe2 quantum dots suspension (Fig.7). By experimentally fitting the obtained kinetic curve with a double exponential decay function, two different time constants of carrier relaxation processes were obtained. The relaxation time of τ1 and τ2 are 4.9-28.66 ps and 288.64-726.28 ps, respectively, in the wavelength of 500-640 nm (Fig.8(d)). The rapid recovery process ( τ1) may be due to electron relaxation in the conduction band, while the slow recovery process (τ2) may come from the recombination of electron hole pairs. The NLO parameters (Tab.1) and relaxation time (Tab.2) of SnSe2 quantum dot composite gel glass are comparable to that of reported low-dimensional materials, confirming the prepared composite gel glass are a promising material for use in photonic devices.ConclusionsSnSe2 quantum dots with uniform size and uniform dispersion were introduced into silica gel glass by sol-gel method, and composite gel glass with uniform doping and high transmittance was prepared. And the morphology, composition, structure, and linear optical properties of the synthesized composite gel glass were systematically characterized. SEM, EDX and Raman spectra results confirmed that SnSe2 quantum dots have been successfully introduced into gel glass matrix. The nonlinear optical effects of SnSe2 quantum dot composite gel glass with ps and ns pulse widths were investigated under 532 nm laser pulses. The results show that when the laser pulse width changes from ps to ns, the nonlinear absorption of SnSe2 quantum dot composite gel glass undergoes a transition from SA to RSA. The mechanisms of SA and RSA were also explained in detail. In addition, compared with SnSe2 quantum dot suspension, the SA and RSA response enhance after incorporating into solid state matrix. The mechanism of enhancing nonlinear optical effects is also explained by the matrix effect. The ultrafast carrier dynamics of SnSe2 quantum dot composite gel glass was further studied by pump probe technique and transient absorption spectroscopy. The calculated relaxation time is equivalent to that of reported low-dimensional materials, which proves that SnSe2 quantum dot composite gel glass shows great potential application prospects in the field of ultrafast optoelectronics. Our work would provide theoretical and material basis for the preparation of novel composite nonlinear optical materials with excellent performance.

Objective The optical tracking system has increasingly stringent requirements on the tracking precision, response time, and anti-interference ability of the servo table. Traditional control methods have shortcomings in terms of tracking precision and disturbance suppression. In order to enhance the control precision and anti-disturbance ability of the optical servo control system, this paper proposes a robust predictive control method with certain parameter self-regulation ability based on the continuous-time model.Methods Firstly, the servo turntable system is modeled, and the influence of disturbances on the system state is analyzed. The linear extended state observer equation is constructed to estimate the uncertain disturbances in the system. Secondly, the derivation process of the turntable speed ring generalized predictive control law from the prediction model, performance indicators to the rolling optimization is given. The linear state model of the system is Taylor expanded to obtain the prediction model, and the system states of all orders are given by the state observer. The reference trajectory is output by the tracking differential. The optimization problem is solved based on the error-based performance index, and the current optimal control law is computed. Furthermore, the prediction domain update formula of the generalized predictive control is designed using the gradient descent idea, achieving self-adjustment, and the stability of the closed-loop control system is analyzed using Lyapunov theory, verifying the feasibility of the system. Finally, the simulation experiments show the improvement in tracking performance and disturbance rejection of the control method.Results and Discussions Firstly, the simulation results of the improved GPC, linear model GPC, and cascade PID control methods are presented (Fig.5), and the tracking accuracy of the three methods is analyzed (Fig.10). The results show that the improved GPC has a 78.72% improvement in control accuracy compared to the cascade PID, and a 59.89% improvement compared to the linear GPC (Tab.2). Secondly, to verify the disturbance rejection capability of the control system, disturbances were added to the system, and the suppression performance of the three methods was compared (Fig.11-Fig.12). The results show that the improved GPC has a 58.95% improvement in the suppression of speed disturbance amplitude compared to the cascade PID (Tab.3); the improved GPC has a 56.91% improvement in the suppression of speed disturbance amplitude compared to the linear model GPC (Tab.4). Finally, the suppression ability of the three methods against sudden load addition was compared (Fig.12), and the tuning effect of the time domain parameters of the improved GPC was given (Fig.13). The robustness of the control method was verified by adjusting the parameters of the controlled object (Fig.14).Conclusions The variable time-domain disturbance-resistant generalized predictive control algorithm is a sophisticated approach that leverages an extended state observer to effectively monitor system disturbances and states. By doing so, it can accurately predict the behavior of the controlled system and make adjustments accordingly. The prediction time-domain parameters are continuously updated using the gradient descent method based on the control error cost function, ensuring that the GPC remains adaptive and responsive to changes in the environment. In order to evaluate the performance of this enhanced GPC, a comprehensive comparison was conducted with cascade PID and linear model GPC, both operating within the same control bandwidth. The results revealed compelling advantages of the improved GPC over its counterparts. Specifically, it demonstrated an impressive 78.72% improvement in tracking accuracy compared to cascade PID, as well as a substantial 59.89% enhancement over linear model GPC. Furthermore, when it comes to speed disturbance suppression, the improved GPC showcased remarkable effectiveness by reducing amplitude by 58.95%, surpassing both cascade PID at 56%. These findings underscore not only the robustness but also the superior performance of this advanced algorithm in handling complex control tasks with high precision and efficiency.

Significance The implementation of the new definitions of international basic units based on fundamental physical constants in 2019 marked the official and comprehensive entry of precision measurement into the quantum era. In 2022, China clearly stated its intention to establish a national modern advanced measurement system centered on quantum metrology, with a focus on researching and establishing quantum measurement standards, exploring quantum metrology technologies based on quantum effects and physical constants, and promoting the upgrading and renewal of the measurement standard system. The atomic lithography grating, a nano-length scale based on quantum effects, has a grating pitch that is traced back to atomic transition frequencies. It can be directly used for instrument calibration without relying on the fixed value of metrological instruments, effectively shortening the traceability chain of nano-length measurement. The "Quantization of Measurement Units" has overturned the traditional hierarchical metrological traceability system. By adopting quantum measurement standards, it significantly improves the accuracy and stability of value reproduction, enabling the reproduction of values at any time, in any place, and by any entity, thereby achieving a flat traceability. Atomic lithography gratings achieve sub-nanometer accuracy with good consistency and can be mass-produced, making them promising for applications in the establishment of nano-scale length measurement systems, quantum precision measurement, instrument calibration, and other fields.Progress First, the preparation principle and technology of atomic lithography quantum gratings are analyzed. A laser wavelength of 425.55 nm is selected, corresponding to the transition energy level of chromium atoms from 7S3 to 7P4, with a silicon substrate. In a vacuum environment, the atomic furnace containing chromium powder is heated to a certain temperature, causing the atoms to erupt and form a chromium metal atomic beam. A pair of laser beams with the same wavelength but opposite propagation directions create a one-dimensional laser standing wave field with an optical intensity period of λ/2. After laser cooling and collimation, the atomic beam is vertically incident into the laser standing wave field. Within this field, the atoms are subjected to a dipole force, causing them to move towards the peaks or troughs of the standing wave. Eventually, they deposit onto the silicon substrate, forming a nanoscale quantum grating with a period equal to half the laser wavelength. The grating pitch can be traced directly to specific natural constants, allowing the grating to serve as a ruler without the need for metrological instrument calibration. Theoretically, the accuracy of atomic lithography gratings can reach the picometer level, similar to the precision of metrological testing instruments equipped with laser interferometers.Subsequently, the research progress in the preparation of atomic lithography gratings, both domestically and internationally, is elaborated upon. As early as 1992, G. Timp and his team successfully utilized atomic lithography technology to produce a sodium grating, albeit an unstable one. Consequently, MCCLELLAND J J and colleagues opted for chromium atoms, successfully fabricating a chromium grating. Since then, numerous researchers have embarked on preparing quantum gratings using various metal atoms. Domestic research into the preparation technology of quantum gratings commenced in 1999, and in 2002, LI Tongbao and his team achieved a milestone by utilizing atomic lithography technology to produce China's first quantum grating. Currently, the preparation technology of atomic lithography gratings is continually optimized and upgraded, with researchers actively exploring specific applications for these gratings. Finally, the applications of atomic lithography gratings in instrument calibration, ultra-precise displacement measurement, grating pitch calibration, and nano-positioning stage calibration are introduced.Conclusions and Prospects Currently, both domestic and international research on the application of atomic lithography gratings is in a vigorous stage of development. The inherent metrological advantage of atomic lithography gratings stems from their ability to be directly traced back to atomic transition frequencies. This paper analyzes the research progress and typical applications of atomic lithography gratings, aiming to provide guidance for future research directions in this field and new insights for the development of key instruments such as photolithography machines in integrated circuits, as well as for the study of metrological traceability systems in China.

Objective Methane (CH4) gas detection plays an important role in many fields, and rapid detection of CH4 gas is of great significance for early warning of accidents. CH4 gas detection methods include electrochemical method, tunable diode absorption spectroscopy (TDLAS), combustion catalytic method, non-dispersive infrared method (NDIR), photoacoustic spectroscopy (PAS), etc. Electrochemical measurement of CH4 gas has high sensitivity properties, but it requires frequent calibration. Combustion catalysis has a fast time response, but produces error in low oxygen. With the rapid development of laser technology and acoustic detection technology, photoacoustic spectroscopy has attracted more and more attention for its fast response, zero background and high sensitivity in trace gas detection. Traditional photoacoustic spectroscopy technology using capacitive microphone as acoustic signal sensor, but the electrical characteristics of the capacitive microphone limits in electromagnetic interference (EMI), high temperature and explosive environmental applications. Fabry-Perot interferometric fiber microphone has the advantages of concentrated sensing area, strong environmental adaptability, easy miniaturization and high sensitivity. In recent years, the all-optical PAS technology based on the Fabry-Perot interferometer has attracted the attention of many researchers. A feasible experimental scheme is proposed for the rapid and safe detection of CH4 gas leakage in industry by using fiber microphone based Fabry-Perot interferometric principle. Now it is enough to be used in the industrial leak detection of CH4 gas, which is contributed to achieve methane gas leakage warning and protect workers safe.Methods Traditional photoacoustic spectroscopy techniques use condenser microphones as photoacoustic signal sensors, but the electrical characteristics of capacitive microphones limit their use in environments such as electromagnetic interference. In this paper, an all-optical photoacoustic spectroscopy device for CH4 gas leakage detection is proposed. The technology is divided into photoacoustic signal generation module and photoacoustic signal detection module. The photoacoustic signal generation module is as follows. The excitation light source generated by the modulated 1653 nm DFB laser enters the photoacoustic cell to generate a photoacoustic signal, the photoacoustic signal is received by the optical fiber microphone, and the photoacoustic signal is transformed from the optical signal to the optical signal through the optical fiber microphone; The photoacoustic signal demodulation module is as follows. The light source generated by the 1 310 nm DFB laser enters the Fabry-Perot (F-P) cavity through the circulator to form an interference signal. Then the self-made optical fiber microphone is used for acquisition, and the interference optical signal is demodulated by the intensity demodulation method based on temperature feedback adjustment, and the advantage of this method is to achieve stable control of the interference signal Q point for a long time and quick response. The final signal is amplified by a lock-in amplifier, collected by the signal acquisition card (NI, USB-6003), and input into the computer to realize the detection of the generated photoacoustic signal.Results and Discussions The CH4 gas in the laboratory is tested using the photoacoustic spectroscopy device, The experimental results show that the detection limit of using the optical fiber optic microphone is 7.47 ppm (1 ppm=1×106). According to the Allan variance results, the detection limit of CH4 gas is 0.23 ppm at an average time of 142 s. Compared with other photoacoustic spectroscopy technologies, the proposed photoacoustic sensing system has the advantages of good stability, fast response speed and simple optical path, and the whole experimental system is simple.Conclusions A cantilever fiber optic microphone is designed through the simulation and optimization of the cantilever beam. The Q-point stabilized intensity demodulation technology reduces the interference of the ambient temperature to the microphone, thereby ensuring the stability of the fiber microphone for long-term operation. In the experiment, the resonance frequency of the optical fiber microphone and the H-shaped photoacoustic cell was matched to realize the double resonance enhancement of the photoacoustic signal, and a set of high-sensitivity all-optical photoacoustic spectroscopy experimental device was built. The acoustic signal generation and detection of the experimental device are based on optical principle and optical fiber structure, which realizes the all-optical and high-sensitivity detection of CH4 gas. The detection limit of the experimental setup for CH4 gas was 7.47 ppm. According to the Allan variance results, the minimum detection limit of the all-optical photoacoustic spectroscopy device is 0.23 ppm under the condition of an average time of 142 s. The photoacoustic sensing system proposed in this experiment has the characteristics of good safety and simple structure. At present, the photoacoustic device can meet the detection level of CH4 gas leakage in the industry.

ObjectiveThe large aperture mobile optical system has bad working conditions, and its support structure needs to ensure the surface shape accuracy, pose accuracy and support stiffness of the mirror after or when experiencing the turbulence and impact of vehicle transportation. Therefore, it is necessary to analyze and design the support structure of the large aperture mobile mirror in detail. Domestic researches on the support structure of mobile mirror mainly focus on mirrors with a diameter of no more than 500 mm and a small mass, and the support structure is simple, which is not suitable for the support of mobile meter-level mirrors. However, the support mode of traditional non-mobile meter-level large aperture mirrors and foreign mobile meter-level mirrors such as Stratospheric Observatory for Infrared Astronomy (SOFIA) usually adopts a set of tangential support mechanism to provide tangential support and tangential positioning at the same time, and the positioning mechanism in the device has a certain deformation due to the force, resulting in reduced positioning accuracy. For a system with a complex optical path, adding an adjusting mechanism to each link of the optical path to compensate for the pose error of the primary mirror will lead to complex system structure, increased cost and tedious installation. Therefore, it is necessary to develop a new tangential support mode of the mirror to improve the pose accuracy of the mobile meter-level mirror.MethodsAiming at the 1.57 m primary mirror of the vehicle optical detection system, this paper proposes the tangential support mode of 6 groups of flexible tangential rod positioning mechanism combined with 16 groups of lever counterweight supporting mechanism. The lever counterweight supporting mechanism carries the weight of the primary mirror, and the flexible tangential rod positioning mechanism realizes the primary mirror positioning, decoupling tangential support and tangential positioning, improving positioning accuracy and enhancing support stiffness. Combined with the axial Whiffletree support mode, the support and positioning of the primary mirror are realized. At the same time, the lever in the lever counterweight support mechanism can rotate around the support point, and there is no displacement constraint on the primary mirror, so there is no displacement coupling between the lever counterweight support mechanism and the flexible tangential rod positioning mechanism, that is, the lateral support force exerted by the lever counterweight support mechanism does not affect the pose accuracy of the primary mirror.Results and DiscussionsThe analysis results show that the new lateral support mode can greatly reduce the sinking of the primary mirror, and prove that the new lateral support mode can effectively improve the pose accuracy of the primary mirror. The maximum thermal deformation Root Meam Square (RMS) of the primary mirror at working ambient temperature is 7.7 nm (Tab.2), and the maximum thermal stress at storage ambient temperature is 10.5 MPa (Tab.3). The thermal analysis results show that the support mode of axial flexible rod and lateral flexible hinge has good pyrolytic coupling ability and can meet the working and storage requirements. The main mirror shape error is 5.7 nm (Tab.4) RMS under gravity load. It can be seen that the support mode can effectively carry the weight of the primary mirror, restrain the generation of additional load, and prevent large surface shape errors. The first 5 order natural frequency of the support system indicates that the support system has good support stiffness. The results of vibration analysis show that the new support mode can be well adapted to road transportation.ConclusionsThe analysis results show that the lateral support method proposed in this paper can reduce the subsidence of the primary mirror from 24.3 μm to 3.6 μm. The maximum integrated surface error of the primary mirror is about 9.6 nm RMS. The lowest natural frequency of the whole mode reaches 54.93 Hz; In random vibration analysis, the maximum displacement response of the support system in three directions of X, Y, Z axis is 0.11 mm, 0.10 mm, 0.36 mm, and the maximum stress response is 12.1 MPa, respectively. The maximum displacement response of the main mirror in the three directions of the X, Y, Z axis is 1.6 μm, 9.7 μm, 12.3 μm respectively, and the maximum stress response is 0.44 MPa. The evaluation results show that the support scheme can provide good surface shape accuracy and pose accuracy for the primary mirror, and can be effectively adapted to vehicle transportation. After vehicle transportation, the measured primary mirror shape accuracy RMS is 0.0237λ (λ=632.8 nm), which verifies that the support scheme has a good effect and proves the accuracy of finite element analysis.

ObjectiveThe early warning of target is realized according to the single difference of infrared radiation scattering characteristics between target and background for traditional space-based optical detection methods. However, due to the interference of many factors such as ultra-long-distance, complex ambient light, earth background clutter, atmospheric transmission attenuation, infrared imaging system diffraction, nonlinear sampling effect, etc., the radiation difference between the target and the background is easy to be confused by the temperature-based infrared imaging technology, which leads to low target detection rate and large false alarm. As a new detection method, infrared polarization detection can provide more target information than traditional infrared detection. Therefore, the exploration of space-based optical detection methods based on multi-dimensional optical information coupling of target radiation and polarization is of great significance to improve the performance of target detection.MethodsThe optical information of infrared radiation and polarization is comprehensively considered. A multi-dimensional imaging feature prediction model for aerodynamic heating targets in orbit under complex sea background is explored. The research results will provide theoretical basis for stable and correct space-based detection of aerodynamic heating targets. Based on the heat transfer and polarization characteristics of the target, the polarization emission and pBRDF model of the aerodynamic heating target are established. The radiation polarization model of the aerodynamic heating target skin is established. Based on Landsat8 remote sensing data, the sea surface temperature (SST) is retrieved. The Cox-Munk pBRDF model and self-polarization model of sea surface are combined to establish the sea surface background radiation polarization model. The second simulation of a satellite signal in the solar spectrum - vector (6SV) model is used to calculate the effect of atmospheric particles on the upward polarization radiation transmission of targets. The physical modulation effect of space-based optical platform is comprehensively considered, and the prediction model of full-chain multi-dimensional optical imaging features of aerodynamic heating target in orbit is established. The imaging characteristics and detectability of the target in orbit at different flight altitudes and detection angles are analyzed by simulation.Results and DiscussionsThe signal-to-clutter ratio (SCR) decreases with the increase of the detection angle at S0 and S1 polarization angles. At S2 polarization angle, the SCR gradually decreases as the detection angle increases from 10° to 30°, and the SCR appears a peak value when the detection angle is 40°. However, the SCR gradually decreases as the detection Angle continues to increase. Meanwhile, the SCR increases gradually with the increase of target flight height at different polarization angles (S0, S1, S2). The increase value of the SCR with the target flight height at S0 polarization angle is smaller than that at S1 and S2 polarization angle(Fig.8). Therefore, the results show that the space-based optical detection method based on infrared polarization information can suppress the sea surface flare and highlight the target in the direction of the strong reflection of the sun incident light from the sea surface in 3-5 μm.ConclusionsIn view of the optical detection requirements for wide area continuous surveillance of aerodynamic heating targets, the detection method based on the space based multi-dimensional optical information of the aerodynamic heating target is studied. An accurate prediction model of the optical radiation polarization imaging feature of the full chain including the aerodynamic heating target-the sea surface-the environmental atmosphere-the optical system-the imaging detector is established. The characteristics of radiation polarization at different detection angles, target flight heights and flight speeds are simulated and analyzed. The results show that in the 3-5 μm band, the target is easier to detect under space-based platforms when the radiation and polarization information are used simultaneously. The results of this paper provide data support for the development of intelligent algorithms for realizing the detection, tracking and identification of targets in the geostationary orbit.

ObjectiveLong-wave infrared has the advantages of good atmospheric transmittance, high temperature sensitivity, and high spatial resolution, making it suitable for various complex environments. Infrared multispectral imaging technology combines the advantages of infrared imaging and multispectral technology, greatly improving the accuracy of target detection. Using an infrared multispectral camera, spatial characteristics, temperature radiation, and spectral characteristics of the target can be obtained. Filter wheel-based multispectral cameras have the advantages of compact structure, small size, high spatial resolution, large field of view, and good flexibility. Domestic and foreign scholars have conducted a lot of research on this. However, these studies have shortcomings such as slow band switching, low frame rate, and low synchronous imaging accuracy, which are not suitable for high-speed imaging applications. This study designs a filter wheel-based infrared multispectral synchronous imaging system for high-speed imaging. To address the synchronization challenges, a phase-locked loop synchronous control method is proposed, and the theoretical accuracy is analyzed and calculated to achieve high-precision synchronous imaging, providing reference for the design of similar instruments.MethodsThe synchronous control circuit is the core of the system, and research is conducted on the synchronous control method and imaging accuracy calculation method. Firstly, to address the challenges of synchronization, a cascaded dual closed-loop synchronization control method is proposed to link the pulse signal of the position sensor with the external trigger signal of the detector, ensuring closed-loop frequency synchronization between spectral segment switching and detector imaging. Subsequently, a method for evaluating synchronization imaging accuracy is proposed, along with error analysis and accuracy calculation. Finally, the complete system is developed, and the synchronous imaging accuracy of the encoder and Hall sensor is experimentally tested, followed by infrared imaging experiments.Results and DiscussionsExperimental results show that six groups of through-hole images with different phases can be collected (Fig.12-Fig.13), and the imaging center position remains consistent, verifying that the system can achieve band switching synchronized with the detector imaging. After registering the collected images at the pixel level, error analysis results show that the experimental errors of the encoder and Hall synchronous control are both less than 3.255°, meeting the design requirements of the system. The accuracy of the encoder synchronous control is 0.107°, and the imaging error is only one pixel, which is 10 times higher in accuracy compared to the Hall method (Tab.1). The cascaded dual closed-loop synchronization control method can achieve high-precision synchronized imaging for the filter wheel infrared multispectral camera. ConclusionsAn analysis of the shortcomings of the existing filter wheel multispectral camera system design is provided, and a filter wheel-type infrared multispectral synchronized imaging design suitable for high-speed imaging is proposed. The electronic architecture of the system is introduced, with a focus on high-precision synchronized imaging technology. To address the synchronization issue, a phase-locked loop synchronous control method is proposed, which utilizes the closed-loop correlation between the pulse signal of the position sensor and the external trigger signal of the detector to ensure precise triggering of imaging under each spectral segment. The synchronization imaging accuracy is quantified for the first time, and an error analysis method for synchronization imaging is proposed to evaluate and calculate the theoretical accuracy. A series of experiments demonstrate the significant effectiveness of the proposed method. The accuracy of the synchronous control based on the encoder is 0.107°, with an imaging error of only 1 pixel, fully meeting the design requirement of 3.255° for the system. High-precision synchronous imaging has been achieved. The system is suitable for high-speed infrared multispectral imaging applications and provides a reference for the design of similar imaging instruments.

ObjectiveFourier Transform Infrared spectroscopy (FTIR), renowned for its high sensitivity and resolution within the infrared spectrum, has become one of the most important spectral characterization methods in fields such as drug development, materials research, and chemical analysis. With continuous advancements in comprehensive detection technologies, FTIR is facing the demand to enhance spectral analysis capabilities while also becoming more lightweight and integrated. Graphene-based photodetectors, with their ultra-broad spectral response range, ultra-fast photo response speed, and micron-scale detector size, exhibit tremendous potential in the development of high-performance integrated FTIR spectrometers. Moreover, traditional FTIR spectrometers usually employ a reference beam to calibrate the optical path difference between the two arms of the interferometer. This reference optical path significantly increases the system's complexity and poses substantial limitations on the miniaturization of the FTIR spectrometer's optical system. The proposed FTIR spectrometer eliminates the need for a reference beam by utilizing a high-precision nano positioning stage. The development of a reference beam-free FTIR spectrometer based on graphene photodetectors is presented.MethodsGraphene devices were prepared as photodetector elements and a high-precision nano-displacement stage was used to replace the reference beam for calibrating the optical path difference in a Michelson interferometer, thereby constructing a reference beam-free FTIR spectrometer based on graphene photodetectors. The graphene was mechanically exfoliated onto fused silica, and gold electrodes were deposited using a masking technique to create the photodetector elements. The spectrometer system employed a data acquisition card with a maximum sampling rate of 50 kS/s and a high-precision nano-displacement stage with a minimum displacement of less than 1 nm, ensuring high-precision sample characterization without a reference beam. Additionally, the photocurrent scanning imaging and power dependency characterization of the graphene photodetector were performed to verify its response to light signals in the 500 nm to 8000 nm wavelength range and the linear dependence of the photoexcitation electrical signal strength on power (Fig.1).Results and DiscussionsThe performance of the constructed FTIR spectrometer was tested and analyzed. Initially, the optimal scanning speed and scanning distance configurations were determined. The spectrometer's spectral testing capabilities were characterized at different scanning speeds and distances. When the scanning speed was in the range of 0.01-0.10 mm/s and the scanning distance was between 1.0-8.0 mm, the developed FTIR spectrometer achieved a spectral resolution better than 5 cm-1 and a signal-to-noise ratio (SNR) exceeding 40 dB. The best performance achieved a spectral resolution of 0.6 cm-1 and an SNR of 40 dB (Fig.2). Further tests demonstrated the FTIR spectrometer's high accuracy under different wavelength excitations (Fig.3). To validate the material characterization performance of the developed reference beam-free FTIR spectrometer, it was used to characterize a self-prepared PDMS film. The spectrometer accurately measured absorption peaks around 2963 cm-1 and 2906 cm-1 (Fig.4), consistent with previously reported literature results.ConclusionsThe study developed a reference beam-free FTIR spectrometer based on graphene photodetectors by utilizing a high-precision nano-displacement stage to replace the complex reference optical path and leveraging the broad spectral response range of graphene photodetectors. Experimental verification demonstrated the excellent spectral characterization performance of the spectrometer. By balancing spectral resolution, SNR, and testing time, we identified the optimal combination of scanning speed and scanning distance, achieving the best performance with a spectral resolution of 0.6 cm-1 and an SNR of 40 dB. The accuracy of the reference beam-free FTIR spectrometer was confirmed under different wavelength excitations. Experimental results showed that the lightweight FTIR spectrometer design offers high resolution, superior SNR, high accuracy, and broad spectral range capabilities. This development significantly enhances the performance of Fourier-transform infrared spectroscopy systems while providing a more portable and precise solution for practical applications.

ObjectiveThe integration of on-chip silicon-based light sources has been a critical challenge in addressing the absence of light sources in silicon-based optoelectronic chips. This integration is essential for enhancing the power output of light sources on silicon-based optoelectronic chips, thereby increasing the chip's speed and capacity. However, traditional integration methods such as end-face coupling suffer from low efficiency due to issues like surface roughness and alignment difficulties, which are difficult to mitigate. Therefore, this paper proposes a method for light source integration using evanescent coupling, which can improve light source coupling efficiency and increase the output power of silicon-based on-chip light sources. Furthermore, traditional silicon-based light source integration typically employs quantum well lasers, whereas this study designs an appropriate quantum dot laser, which offers advantages such as lower threshold current and more stable temperature tolerance. These benefits enhance the overall coupling efficiency and output power of the silicon-based on-chip quantum dot laser.MethodsUsing Lumerical simulation software, we optimized the evanescent coupling waveguide structure and the Bragg grating waveguide structure for an O-band silicon-based on-chip light source. At a wavelength of 1.31 μm, the tapered waveguide structure designed for evanescent coupling achieved a coupling efficiency of over 98% for widths of 0.68 μm, 0.7 μm, and 0.75 μm (Fig.4). The study primarily analyzed the impact of length on coupling efficiency. Additionally, we designed two segments of Bragg gratings to form a resonant cavity, with the paper focusing on the effects of duty cycle, etch depth, and grating length on reflectivity (Fig.7). This structure can achieve reflectivities of 40% and 90% at the three specified waveguide widths, allowing for the amplification and selection of light at a wavelength of 1.31 μm (Fig.8).Results and DiscussionsThrough waveguide mode analysis, a waveguide layer thickness of 220 nm was selected, with waveguide widths of 0.68 μm, 0.7 μm, and 0.75 μm. For these three waveguide widths at a wavelength of 1.31 μm, we designed a tapered waveguide coupler structure with a length of 32 μm, where the width transitions from the waveguide width to 2 μm and then narrows back to the original waveguide width, achieving a coupling efficiency of over 98%. A Bragg grating structure was designed with an etch depth of 100 nm and a duty cycle of 0.75 on both sides, with lengths of 110 μm and 240 μm, achieving reflectivities of 40% and 90%, respectively. These two Bragg gratings form a resonant cavity that amplifies and selects light at a wavelength of 1.31 μm.ConclusionsThis study focuses on silicon-based light sources, with particular emphasis on coupling efficiency, wavelength selection, and integration. Specifically, we designed a 142 μm tapered evanescent coupling structure, achieving a coupling efficiency of over 98%. The Bragg gratings, serving as both mirrors and resonators, are positioned on both sides of the waveguide, with lengths of 110 μm and 240 μm, respectively. These gratings selectively amplify light at a wavelength of 1.31 μm by facilitating oscillation within the cavity. This design not only enhances the emission efficiency of silicon-based light sources but also reduces manufacturing costs and is highly compatible with existing silicon-based photonic manufacturing processes. Compared to previous studies, our work achieves efficient light source integration and single-sided narrowband laser output by optimizing the coupling structure and mirrors.

ObjectiveThe reference beam Laser Doppler Velocimeter (LDV) boasts advantages such as high accuracy, wide range, rapid response, and non-contact measurement. It is extensively employed in the measurement of physical quantities like solid surface velocity, vibration, displacement, as well as in integrated navigation systems. The reference beam type LDV measures sensitive velocity components parallel to the direction of the outgoing light, where the angle between the outgoing light of the LDV and the moving surface constitutes the emission inclination. Evidently, the magnitude of the velocity component to which the LDV is sensitive will hinge on the magnitude of the launch inclination, thereby influencing the system bandwidth. Additionally, the transmission angle also impacts the signal-to-noise ratio of the Doppler signal and the velocity measurement accuracy of the LDV. It can be discerned that the launch angle will have a significant impact on the performance of the LDV. To enable the LDV to select a rational launch angle, the effect of the launch angle on the performance of the LDV is analyzed and discussed in this paper.MethodsThe influence of the launch angle on the performance of LDV is analyzed through theory and simulation. The relationship between the emission inclination angle and the signal frequency is obtained by the principle formula of LDV (Fig.2). Based on the irradiance formula when the laser incident on a surface with different roughness, the signal-to-noise ratio variations corresponding to different emission angles are acquired (Fig.3). Additionally, the impacts of launch inclination on velocity measurement errors were analyzed, including errors caused by velocity measurement resolution (Fig.5), the finite aperture of detector error (Fig.2), principle formula approximation error, finite transit time error and laser divergence angle error, etc. The errors caused by velocity measurement resolution were identified as the main errors (Fig.7).Results and DiscussionsAccording to the relationship between the frequency of the Doppler signal and the transmission angle (Fig.2), to reduce the signal bandwidth of the LDV, a large transmission angle should be selected as far as possible. Meanwhile, the signal-to-noise ratio of the Doppler signal also increases with the growth of the transmission inclination, and this growth trend gradually slows down when the transmission inclination is greater than 60° (Fig.3). Therefore, to enhance the signal-to-noise ratio of the Doppler signal, the transmission angle should be greater than 60°. However, increasing the launch angle will augment the velocity measurement error, especially when the launch angle is greater than 80°, the velocity measurement error will escalate rapidly with the increase of the launch angle (Fig.7). Based on the outcomes of the simulation analysis, the method of setting the launch angle or sampling frequency in segments is proposed for different measuring ranges and measuring accuracy requirements, and the steps of determining different measuring ranges are provided. Compared with unsegmented measurement, segmented measurement can significantly reduce the velocity measurement error of the system (Fig.9), and the equivalence of the segmented setting of the launch angle and the segmented setting of the sampling frequency is elucidated (Tab.1-Tab.2).Conclusions To reduce the system bandwidth and enhance the signal-to-noise ratio of the Doppler signal, a large transmission angle should be selected. However, an excessively large launch angle will give rise to an increased velocity measurement error of the system. When choosing the actual launch angle, a balance should be struck among several factors, and the launch angle is preferably between 60° and 80°. When the range of velocity measurement is overly large, to concurrently meet the requirements of the signal bandwidth and velocity measurement error, the method of setting the transmission angle or sampling frequency in segments can be employed. It holds significant guiding significance for the structural design and practical application of LDV.

ObjectiveIn high-energy physics research and industrial applications, measurement tasks in particle accelerator radiation zones are rendered extremely difficult due to radiation hazards, making direct human measurement infeasible. Additionally, since accelerators are typically installed in tunnels, space constraints and obstructions often cause many measurement target points to be hidden from view. To overcome these challenges, a rod-waving measurement technique based on laser trackers has been proposed to accurately measure the position of hidden points.MethodsThe rod-waving measurement technique utilizes a laser tracker and a specially designed fiducial bar for hidden point measurement to perform spatial coordinate measurements. The fiducial bar for hidden point measurement is equipped with a target sphere. By waving the rod, the laser tracker acquires spatial coordinate data of the target sphere at different positions. Through spherical fitting of these data points, the three-dimensional coordinates of the hidden point can be determined. To address the issue of prolonged computation time associated with traditional spherical fitting algorithms, the algebraic spherical fitting algorithm was proposed. This new algorithm maintains accuracy while significantly reducing measurement time, thereby reducing the radiation exposure to measurement personnel. Subsequently, simulation experiments were conducted to test the impact of changing the length of the fiducial bar for hidden point measurement on fitting results, as well as the effects of varying zenith angles and spherical coverage ranges on the fitting results.Results and DiscussionsThe proposed algebraic spherical fitting algorithm requires only 18.85% of the computation time compared to the Gauss-Newton fitting method (Tab.1). In the simulation experiments, it was found that increasing the reference rod length from 0.5 meters to 3 meters resulted in a root mean square error (RMSE) of the fitting deviation changing by only 0.42 micrometers, indicating almost no impact on the fitting results (Tab.2). Additionally, it was discovered that under both global coverage (the horizontal angle is 0° to 360°) and hemispherical coverage (the horizontal angle is 180° to 360°) conditions, the RMSE of the fitting deviation remained consistently below 30 micrometers when the zenith angle was varied (Tab.4). Finally, practical experiments were conducted at the Beijing High Energy Photo Source laboratory using the AT960 laser tracker to measure four hidden points. The RMS of the fitting deviation for the measured data was 25.53 micrometers, meeting the precision requirement of being within 30 micrometers (Tab.6).ConclusionsThe combination of laser trackers with the rod-waving method has demonstrated significant advantages in measuring hidden points within the radiation areas of particle accelerators. This approach overcomes spatial constraints and line-of-sight obstructions, offering extremely high measurement accuracy and reliability. The proposed algebraic spherical fitting algorithm significantly outperforms the commonly used Gauss-Newton method in terms of computation time, requiring only 18.85% of the latter's time. This not only enhances measurement efficiency but also substantially reduces the duration of radiation exposure for operators. Simulation experiments revealed that the algorithm is highly stable against changes in the fiducial bar length, with the root mean square error (RMSE) of the fitting deviation changing by only 0.42 micrometers. The experiments also showed that whether under global or hemispherical coverage, the RMS of the fitting deviation remained consistently below 30 micrometers when the zenith angle was varied, demonstrating the algorithm's reliability. Ultimately, through field measurements, an RMS of the fitting deviation of 25.53 micrometers was achieved, successfully meeting the precision requirement of being within 30 micrometers. This provides an efficient and reliable solution for precise hidden point measurement in special environments such as particle accelerator radiation areas.

ObjectiveWith the rapid development of photographic and photographic equipment, it has become easier for people to obtain full-color high-definition images and video data in different scenes. Low illumination image enhancement has gradually become one of the most frontier issues in the field of night vision imaging and detection. Exploring low illumination image enhancement methods with high fidelity and high operational efficiency is of great value in military reconnaissance, emergency search and rescue, public safety and other fields. However, achieving real-time full-color low-light images under low-light imaging conditions still poses significant challenges, typically including long exposure time, low image contrast, significant loss of detail, and severe noise contamination. To solve the problem of color image enhancement under low illumination condition, a low illumination image enhancement algorithm based on conditional generative adversarial network (CGAN) is proposed.MethodsCGAN utilizes an adversarial process to build a model, and takes the random noise and the pre-processed low illumination images as the input to the generator, and then generates the generated images which are as close as possible to the normal illumination through the generator network. Then the normal illumination images and the generated images are input into the discriminator at the same time, and the discriminator network is utilized to output the probability value between 0 and 1, and the parameters are updated by the computational error (Fig.1). Secondly, in order to avoid the problem of gradient vanishing due to too deep network structure, the generative network introduces the residual-in-residual dense block (RRDB) module (Fig.4). The RRDB module contains three residual dense block modules (Fig.5), each of which contains five layers of convolutional networks, and the low illuminance image features extracted by each layer of the convolutional network are supplied to the subsequent convolutional layers, allowing the feature signals to be arbitrarily propagated from the shallow to the deep layers. The generative network also introduces a convolutional block attention module (CBAM) (Fig.8), which consists of a cascade of a channel attention module (Fig.6) and a spatial attention module (Fig.7). The channel attention module compresses the spatial information of the low-illumination image by using global maximum pooling and global average pooling, respectively, and feeds the compressed results into the multi-layer perceptron to adaptively adjust the channel weights of the low-illumination image. The spatial attention module adjusts the weights of the spatial dimensions of the low-illumination image using the channel information of the global maximum pooling and global average pooling compressed images. Then, SK-Net, a discriminator network based on selective convolution kernel, is constructed (Fig.9). It enables the discriminator to adaptively adjust its receptive field size according to the input, which is closer to the human eye's judgment of the normal illumination image. SK-Net utilizes convolution kernels of different sizes to convolve the input images to obtain multiple branches. The multi-branch information is fused and compressed into a one-dimensional vector using global average pooling, and then Softmax is used to obtain multiple weight coefficient matrices to weight the convolved multi-branch images, and the output feature images are obtained after summing. Then, the network model is enhanced by designing Prewitt loss function and YUV loss function to enhance the ability of extracting image edge details and eliminating image color distortion, respectively.Results and DiscussionsThe algorithm is tested qualitatively and quantitatively on LOL public dataset. The experimental results show the advantages in low-light image enhancement compared with the current deep learning-based low-light color image enhancement algorithms JED, KinD~~New, Retinex-Net, SNR and URetinex-Net (Fig.10). The algorithm proposed in this paper improves 32.7%, 57.5% and 48.45% in peak signal-to-noise ratio, structural similarity and color difference, respectively(Tab.1). The algorithm is able to better overcome the problem of image noise and color bias interference under the low illumination imaging condition.ConclusionsA conditional generation adversarial network is proposed to solve the problem of image enhancement under low illumination. Firstly, the RRDB module is introduced to optimize the network structure of the generator in order to solve the problem of gradient disappearance in deep layer networks. Secondly, the introduction of CBAM attention mechanism aims to alleviate strong noise interference in low illumination environments by enhancing the attention weight of important features in enhanced images. Additionally the SK-Net network structure is designed so that the receptive field of the discriminator network can be adjusted adaptively, so as to improve the discriminant ability of generating color images. Finally, the loss function including Prewitt edge term, YUV chromaticity term and Content term is designed to solve the problem of edge sharpness degradation and color deviation. Qualitative and quantitative tests show that, compared with the current method based on deep learning algorithm, this method achieves improvements of 2% in SSIM, 26.22% in PSNR and 41.22% in CD respectively. It has excellent performance in noise suppression, color difference elimination and effective information retention.

Objective Infrared image target detection has significant application value in the field of transportation, as it can help people promptly detect targets and respond in special conditions such as strong light at night or in rainy and foggy weather. However, due to the characteristics of infrared images, such as low resolution, lack of color information, poor contrast, and blurred features, existing models do not achieve high average detection accuracy when detecting infrared vehicles and pedestrians. The main issue is the problem of missing detection for overlapping targets and small targets in traffic scenes. Therefore, this paper aims to design an infrared pedestrian and vehicle detection model based on YOLOv8s (You only look once version 8), which is crucial for improving the safety of intelligent driving.Methods YOLOv8s, an advanced object detection model in recent years, is categorized into five distinct versions—n, s, m, l, and x—according to the network's depth and breadth to cater to diverse requirements. YOLOv8s, ensuring a certain level of detection precision with a moderate model size, is chosen as the base model. The manuscript introduces four improvements to the YOLOv8s architecture (Fig.2). Firstly, the network architecture is re-engineered with the incorporation of a small target detection layer to improve detection capabilities for distant pedestrians and vehicles (Fig.3). Secondly, the SPD (space-to-depth) module replaces the original network's 3×3 downsampling convolution in the backbone and neck networks (Fig.4), to safeguard the fine-grained details within the image. Thirdly, a hybrid attention mechanism (Fig.5) is crafted to bolster the network's attentiveness to pedestrians and vehicles. Fourthly, the Focal EIOU loss function is utilized, which not only addresses the deficiencies of the CIOU loss function that may become ineffective under certain circumstances but also mitigates the issue of imbalance between positive and negative samples.Results and DiscussionsThe dataset utilized in this study is the FLIR ADAS (Advanced Driver Assistance System) v2 dataset, which was recently released by Teledyne FLIR in 2022 for the purpose of environmental perception in autonomous driving applications (Fig.1). The main evaluation metrics are mAP (mean Average Precision) and model size, with P (precision) and R (recall) as secondary metrics. Ablation experiments (Tab.1) were used to verify the feasibility of each improvement method introduced, with the improved network showing a 5.3% increase in mAP compared to the initial network. This paper compares the detection effect before and after adding a small object detection layer (Fig.6) and before and after adding an SPD module (Fig.7), compares detection accuracy with different attention mechanisms (Tab.2), and further demonstrates the effectiveness of the hybrid attention mechanism with heat maps (Fig.8-Fig.9). It also compares the detection effect before and after using attention mechanisms, compares the performance with different loss functions (Tab.3), and shows the detection effect before and after changing the loss function (Fig.11). On this basis, the detection performance of different algorithms is compared (Tab.4), and the detection effect before and after the improvement is compared (Fig.12). Through the above experiments, the improved network has shown excellent detection performance.Conclusions This paper presents an improved YOLOv8s-based infrared vehicle and pedestrian object detection algorithm. By adding a small target detection layer, the algorithm enhances its ability to detect small target vehicles and pedestrians. The SPD module is utilized to preserve fine-grained information during downsampling. The designed hybrid attention mechanism enables the network to suppress noise interference and focus more on the targets themselves. The improved loss function enhances the model's learning capabilities. The refined algorithm has demonstrated good detection performance on the test set, showing improved detection capabilities for overlapping targets, small targets, and blurred targets.

Objective In recent years, object tracking has been widely used in smart transportation, drone aerial photography, robot vision and other fields. However, most of these object tracking algorithms are based on RGB modal information, and due to the limited information provided by RGB modality, it is difficult to maintain its tracking robustness in low light, haze occlusion, light change and other environments. With the continuous development of thermal infrared sensor technology, relevant researchers consider that thermal infrared has strong penetration ability and is less affected by light changes, and the thermal infrared mode and RGB modal information are fused for tracking, so as to improve the tracking performance in these environments. At present, deep learning is developing rapidly, and many RGBT tracking algorithms based on deep learning have emerged, but the feature extraction, fusion, and matching methods of these RGBT tracking algorithms are simple, and when facing problems such as deformation, occlusion, and low resolution, the tracking target is lost. Therefore, it is necessary to design a robust RGBT tracking algorithm to deal with the tracking problems such as deformation, occlusion and low resolution.Methods The paper proposes an RGBT progressive fusion visual tracking algorithm with time-domain updated template, SiamDPF, which uses the SiamFC++ algorithm as the baseline network (Fig.1). Firstly, the algorithm uses Dilated Convolution and Transformer to improve the convolution of the last two layers of AlexNet, and proposes a multi-scale dilated attention module (Fig.2). Secondly, a cross-modal progressive fusion module (Fig.3) was proposed by combining cross-attention and gated mechanism. Then, a correlation operation with updated template module (Fig.4) was proposed, which used the target template features in the previous frame to update the online template features interactively. Finally, experiments on GTOT and RGBT234 benchmark datasets show that the tracking performance of the SiamDPF algorithm is more robust than other algorithms.Results and Discussions Based on the above design methods, the success rate (SR) and precision rate (PR) as well as the number of frames per second (FPS) were used as the evaluation indicators for tracking performance. In the process of evaluation experiments, the success rate and accuracy of the proposed algorithm and other algorithms were evaluated on GTOT and RGBT234 datasets, especially compared with the current Siamese algorithms of the same series (Tab.2) and the challenge attributes (Fig.7), which fully verified the superiority of the tracking performance of the proposed algorithm in the face of target deformation, occlusion, low resolution and low light scenes. In the ablation experiment, the experimental evaluation of the modules designed by the proposed algorithm is carried out from the indicators such as parameter quantity, real-time performance (Tab.4), and PR/SR (Tab.3), and the experimental results show that the network parameters of the proposed algorithm are only $ 18.73 \times {10^6} $, and its running speed reaches 68FPS, which verifies the effectiveness of each module designed in this paper, and the running speed also meets the real-time performance. In the qualitative experiments, it can be intuitively seen that the proposed algorithm can maintain its tracking robustness in the face of three video sequence tracking scenarios: target occlusion, target deformation and low resolution, which further verifies the effectiveness of the proposed algorithm.Conclusions An RGBT progressive fusion object tracking algorithm with time-domain updated template is proposed to solve the tracking problems of deformation, occlusion, and low resolution. Based on the SiamFC++ baseline, the convolution of the last two layers of the backbone network is improved by combining Dilated Convolution and Transformer to enhance the representation ability of the target features. Secondly, the progressive fusion module is used to gradually interact with the shallow and deep features of the two modalities, which promotes the efficiency of modal fusion. Finally, the template is used to update the cross-correlation module to obtain reliable target response map. In this paper, quantitative analysis, ablation experiment, and qualitative analysis were carried out on GTOT and RGBT234 datasets, respectively. Among them, the PR/SR of the algorithm on the GTOT dataset reaches 0.916/0.735, and the PR/SR on the RGBT234 dataset is 0.819/0.575, respectively, which verifies the superiority of its tracking performance. In the ablation experiments, the effectiveness of the modules designed by the proposed algorithm and the rapidity of inference are verified. The experiments show that, compared with related algorithms, the proposed algorithm has better robustness in dealing with tracking problems such as deformation, occlusion, and low resolution.

ObjectiveLong-distance infrared target detection technology utilizes infrared detection systems to accurately capture targets, demonstrating significant potential in aerospace fields such as space target detection and battlefield reconnaissance. In practical applications of infrared imaging, numerous engineering challenges arise, such as stray light, detector thermal conduction, and structural radiation. These complex interference factors can cause non-uniform backgrounds in images, affecting image quality and target detection accuracy. Additionally, when the imaging distance is long, the target appears small, with weak signal strength and unclear characteristics, which may result in detection failure due to interference. Therefore, this paper addresses the issues of complex backgrounds interference and weak targets in space infrared images by developing a method for Infrared dim and small target detection based on intelligent suppression method for complex backgrounds. This research provides new solutions for infrared target detection applications in complex backgrounds.MethodsThis paper designs a method for detecting dim and small infrared targets based on intelligent suppression of complex backgrounds. The infrared scene-optimized encoder-decoder background suppression network model is designed to efficiently suppress complex backgrounds (Fig.2). A combination of multiple loss functions is employed to enhance the robustness of background reconstruction (Eq.14). The weak target detection task is accomplished using a threshold segmentation method (Eq.9). The effectiveness of the method is validated through four evaluation metrics: background standard deviation, target signal-to-noise ratio, detection rate, and false alarm rate (Fig.5, Tab.3-Tab.6).Results and DiscussionsThe experimental results were obtained on four infrared weak target datasets. The background suppression results of different detection methods on the test images are shown in Fig.5. The results obtained by the proposed method using the infrared scene-optimized encoder-decoder background suppression model show a significant reduction in background noise, with no false alarm targets, far surpassing the comparative methods. Table 3-Table 6 calculate four evaluation metrics: background standard deviation, target signal-to-noise ratio, detection rate, and false alarm rate. The background standard deviation decreased from 20.1463 to 1.8450, the signal-to-noise ratio increased from 2.2686 to 25.1897, the detection rate reached 0.9986, and the false alarm rate was 0.1824. The proposed method demonstrates superior performance in suppressing complex background interference and target detection compared to other methods.ConclusionsIn the field of infrared long-distance weak target detection, due to complex interference such as stray light, detector heat conduction, and flash blind pixels, the background of infrared images often appears non-uniform. At the same time, the target imaging size is small and lacks obvious shape and texture features, which increases the difficulty of detection and recognition. Traditional feature extraction methods are prone to a large number of false alarms. Deep learning methods have advantages in feature extraction, but they are difficult to train under complex background interference. This paper combines the background reconstruction problem in the field of computer vision with the task of infrared image weak target detection, and proposes an infrared weak target detection method based on complex background intelligent suppression. This method uses the encoder-decoder architecture to design an infrared scene optimized encoding and decoding background suppression network model, introduces a multi-level fusion mechanism and a residual fusion module to achieve multi-scale feature extraction and multi-level feature fusion, and proposes a perceptual consistency loss function to improve the robustness of background reconstruction. Background suppression is effectively achieved through the background residual offset strategy, and finally the weak target detection task is completed by combining global threshold segmentation. Experimental results show that compared with the comparison method, the background standard deviation of this method in background suppression is reduced by up to 43.41%, and the target signal-to-noise ratio is increased to 110.0257. In terms of target detection, the detection rates in the four sets of data all exceeded 95%, demonstrating excellent detection results and strong engineering practicality, providing a new solution for infrared dim target detection tasks under complex backgrounds.

ObjectiveBy leveraging the complementarity between infrared and visible light images, infrared and visible light image fusion technology integrates images obtained from different sensors in the same scene into a fused image that is rich in information, highly reliable, and specifically targeted, providing a comprehensive description and integration of the image information in the scene. The fused image retains both the thermal radiation targets of the infrared image and the detailed texture information of the visible light image. However, existing deep learning-based fusion methods all use convolutional neural networks as the basic framework, such as the encoder structure in the autoencoder method and the generator and discriminator in the generative adversarial network, which all use a large number of stacked convolutional layers to process the input image features. Traditional convolution operations, due to the limitations of the size of the convolution kernel and the scope of its effect, have very limited capabilities in extracting image features, focusing only on the local features of the image, such as the local edges of the thermal radiation target areas in infrared images. They cannot well preserve the global features of the image, including the rich texture background information in visible light images and the contour information of objects or environments in the scene. This one-sidedness of feature extraction leads to blurred background details in the fused image and insufficiently prominent thermal radiation targets. Therefore, there is an urgent need to propose a multimodal fusion method that can extract both global and local features to remedy the aforementioned deficiencies.MethodsA triple multimodal image fusion algorithm based on mixed difference convolution and efficient visual Transformer networks is proposed. The core innovations of this algorithm include: Firstly, at the input end, differential images are introduced to highlight the differences between images through pixel value subtraction, constructing a triple-input network architecture to enhance the discriminability of image features. Secondly, a mixed difference convolution (MDconv) is designed, a variant of traditional convolution that combines edge detection operators and uses the principle of pixel differentiation to enhance the feature learning capability of convolution operations. Furthermore, a dual-branch encoder structure is adopted, which combines a convolutional neural network branch with dense mixed difference convolution and an efficient Transformer branch, to extract both the local details and global background of the image, achieving a comprehensive capture of local and global features. Finally, a multi-dimensional coordinate collaborative attention fusion strategy is employed in the fusion layer to effectively integrate the deep features of the encoded multi-modal images, realizing deep feature fusion.Results and DiscussionsTo verify the fusion performance of the method proposed in this paper, seven representative infrared and visible light image fusion algorithms were selected for comparative experiments with the algorithm proposed in this paper. Subjective and objective evaluations were conducted on the results of the TNO and RoadScence test sets. In terms of subjective evaluation (Fig.16), the CBF method uses cross bilateral filtering operations that require calculating weight eigenvalues, resulting in a significant loss of information. This method lacks the ability to extract multi-source information, leading to rough background texture information and insignificant thermal radiation targets, with an overall poor fusion effect. The ADF method reconstructs images through K-L transformation, and the selection of the maximum pixel value causes the omission of edge pixels, resulting in low overall background texture contrast, with the fused image information biased towards the distribution of the infrared image. The MFST and Swinfusion fusion results based on the Transformer architecture retain the thermal radiation target information to some extent, but there is a certain amount of noise and artifacts, resulting in low target clarity. The Densfuse and Nestfuse methods are both based on simple autoencoders, and the overall background texture and thermal radiation targets of the fused image are preserved, but the overall contrast is low, and the background information is somewhat flattened. The MTDfusion method has an overall unnatural image with a certain degree of noise points. The method proposed in this paper achieves the best fusion effect, highlighting both the thermal radiation targets of the infrared image and the background details of the visible light image. At the same time, the fusion result does not produce noise points and artifact defects. In terms of objective evaluation (Tab.1), compared to other methods, the method proposed in this paper achieves the best values in the four indicators MI, VIF, SD, QAB/F, and the second-best value in the SF indicator. On the TNO dataset, the four indicators increased by 63.25%, 18.86%, 5.21%, and 5.41%, respectively. On the RoadScence dataset, the four indicators increased by 46.44%, 12.65%, 10.03%, and 3.26%, respectively. In summary, the results of subjective and objective evaluations are consistent, overall proving the effectiveness of the method proposed in this paper. At the same time, quantitative analysis of ablation experiments was conducted on the complete network of this paper and four structures (Tab.2): a network structure with basic convolutional layers changed to 3×3 ordinary convolutions; a network structure without a Transformer branch; a CNN branch without dense connections; a two input structure. Finally, the complete network structure of this paper achieved the best objective evaluation results, proving the effectiveness of each structure in this paper for improving fusion quality.ConclusionsA triple multimodal image fusion algorithm based on mixed difference convolution and efficient visual Transformer networks is proposed. Firstly, infrared, visible light, and difference images are input into a dual-branch encoder consisting of joint dense mixed difference convolution and efficient Transformer. Secondly, a fusion strategy based on multi-dimensional coordinate collaborative attention is designed, which assigns weights according to the importance in the feature maps of the three types of images, effectively retaining and integrating deep features. Finally, the deep information is input into the decoder to complete the effective fusion of the images. The mixed difference convolution integrates rich prior gradient information into ordinary convolution operations, enhancing the feature extraction capability of the effective convolution layers; the efficient Transformer utilizes its long-range correlation to integrate the global features of infrared and visible light images; the difference image in the triple network aims to input the prominent information of the image into the network, improving the texture detail information of the fused image from the source, which is more in line with human visual perception. Comparative experiments with advanced infrared and visible light image fusion algorithms show that, in terms of subjective evaluation, the proposed method enhances the richness of background texture details in the fused image, makes the thermal radiation targets more prominent, and aligns with human visual perception. At the same time, the objective evaluation indicators also show significant improvements compared to advanced image fusion methods. The ablation experiments of the network structure demonstrate the effectiveness of the proposed modules. Additionally, this method can be well applied in fields such as security monitoring and multispectral cameras.