Traditional machine vision systems are constrained to capture specific wavelength ranges of light, such as visible light, near-infrared, or ultraviolet, suitable for basic image capture and analysis tasks, but with limitations in dealing with complex optical effects and color analysis. As sensor and coating techniques continue to advance, the emergence of multispectral cameras has become apparent. In response to the escalating demands for sophisticated detection and analysis, machine vision systems are progressively integrating multiple spectral domains. A machine vision system for multispectral cameras based on Zemax was designed, with working bands of 400 nm to 700 nm, 700 nm to 800 nm, and 800 nm to 1 000 nm, a focal length of 12 mm, a relative aperture of 1 : 2.2, a half field angle of 20.6 °, a total length less than 115 mm, distortion less than 0.8%, and telecentricity lower than 1°. The design results indicate that the system achieves great imaging quality over a working distance of 500 nm to 1 500 mm through a set of focusing adjustments and meets tolerance processing requirements.

A novel adaptive dimming method for clear ground scanning imaging of airborne electro-optical (EO) system was proposed, which could solve the problem that the image quality of the EO system deteriorated when the scanning scenes changed rapidly. Based on the traditional automatic detection and dimming technology, the three-level adjustment strategy was adopted to reduce the contrast difference between frames and lines of image brightness of reconnaissance videos by the adaptive dimming method of on-board imaging sensor and the contrast correction algorithm of the video processing board. After being processed by ground intelligence processing unit, a continuous image sequence of ground scanning with uniform and consistent image contrast was obtained. The test results show that after processing scene images with large differences by this method, the image contrast, average gradient and information entropy increases, which effectively improves image quality and overall image contrast and clarity, and lays a foundation for subsequent intelligence information processing such as target detection, tracking and stitching based on image sequences.

The liquid phase diffusion coefficient is an important basic data for studying mass transfer process, calculating mass transfer rate, and conducting chemical engineering design and development. Given the limitations of the existing methods of measuring liquid phase diffusion coefficient in terms of measurement accuracy and scope of application, a new method based on liquid-core cylindrical lens to rapidly measure the liquid phase diffusion coefficient by equivalent concentration thin layer move was proposed. By taking liquid-core cylindrical lens with spatial concentration measurement capability as diffusion cell and imaging element, the said method only needed to note down the time-dependent variation of a thin layer with a certain fixed concentration (fixed image width) in the experimental images, and the liquid phase diffusion coefficient could be calculated based on Fick's second law. At room temperature (25 ℃), the diffusion coefficient of potassium chloride aqueous solution was measured as 1.804 9×10?5 cm2/s by using this method. Meanwhile, the influence of selection of thin layers with different concentrations on the measurement results was studied. It was found that the calculated liquid phase diffusion coefficient was relatively stable when the concentration of the selected thin layers was less than 0.16 mol/L, and increased with the increasing concentration of the selected thin layer when the concentration was equal or greater than 0.17 mol/L. When the diffusion coefficient was measured by this method, thin layers of multiple concentration could be selected simultaneously in one experiment to calculate the liquid phase diffusion coefficient, which had the characteristics of simple experimental operation, short measurement time and accurate measurement results.

In order to improve the capability of semi-physical simulation applied to the aerial remote sensing test scenes, the problems of insufficient fidelity and low precision were solved. An optoelectronic simulation detection and recognition technology platform based on miniature physical model was designed and implemented. Application scenarios were reproduced by constructing a miniature sand table model, and the remote sensing data acquisition and processing were realized based on the flight simulation turntable equipped with a truss robot. The experimental results show that the designed semi-physical simulation platform can simulate remote sensing test data by acquiring various photoelectric sensor data, laser radar can obtain the target orientation and distance information for three-dimensional reconstruction, and visible light camera and infrared camera can obtain the target two-dimensional image information for detection and recognition. Through demonstration and verification, the semi-physical simulation platform can provide effective support for aerial remote sensing test.

Metalenses and multi-diffraction-lenses (MDL) play a crucial role in focusing imaging applications. However, metalenses have their own limitations in terms of large aperture and processing, and MDL are unable to achieve truly high transmittance in terms of transmittance. Aiming at the above problems, a single-layer ring band discretized planar lens was designed by replacing each ring band of a MDL with multiple unit structures of a metalens, and the ring band discretisation of a MDL was successfully realised by using Si, a high refractive index material, as the phase modulation unit and the substrate. In addition, the structural properties and optical performances of the ring-band discretized planar lens, metalens and MDL were compared and analyzed. The results of simulation show that the ring-band discretized planar lens not only achieves beam focusing, but also has a high transmittance of 76%, which is 25.3% higher than that of the MDL. The maximum depth-to-width ratio of the structure is 8.9 : 1, which is 50.5% lower than that of the metalens, and it has certain advantages in batch processing. The band discretized planar lens has the advantages of both metalens and MDL, and shows a broad application prospect in optical system integration, infrared imaging, optical sensing, and other important fields.

Compact optical beam splitters are important for enhancing the performance of integrated photonic system or developing new applications. A compact beam splitter based on a tapered multi-mode interference structure was proposed according to the self-imaging theory. Firstly, the optical performance of the beam splitter was simulated by time-domain finite difference method, and the optimized structure parameters were obtained after maximizing the transmission efficiency: the tapered waveguide length was 20 μm, the multi-mode interference waveguide length was 3.8 μm, and the gap width was 0.50 μm. Secondly, the beam splitter with the optimized structure parameters was simulated and analyzed. The results show that in the wavelength range from 1 480 nm to 1 610 nm, each output has a low loss of more than ?3.46 dB while the energy difference between the two splitting paths is less than 0.1%. Finally, the designed device structure was processed and experimentally tested. The results show that, at the wavelength of 1 581 nm, the loss of the two channels of the beam splitter reaches the minimum values, which are ?2.31 dB and ?4.83 dB, respectively. The device has a simple structure and has major advantages in terms of integration, device fabrication and low cost, which has promising applications in integrated photonic systems.

Aiming at the research of pollutant diffusion prediction in the field of water environment, it is difficult to achieve the real-time measurement of concentration field in large area of water. A multi-channel fluorescence concentration measurement system was designed, and the system hierarchy architecture of sensor, acquisition device and upper computer was proposed. A photoelectric concentration measurement sensor based on the principle of laser induced fluorescence (LIF) was developed, the LED excitation light source modulated by the frequency was adopted by transmitting part, and the filter set and photocell were adopted by receiving part. The isolation reflective cover with a bamboo hat structure style and the signal processing circuit were innovatively designed, which could extract weak fluorescent electrical signals from water bodies and avoid the influence of natural light sources and interfering light sources on electrical signals. After laboratory tests, the measurement range of fluorescence substance concentration in the system is 0.001 mg/L to 0.040 mg/L, the resolution is 0.001 mg/L, and the measurement error is not exceeding 5%, which verifies the system running stably and reliably for a long time. The system has been applied in the project referring to the research on environmental pollution prediction of nuclear power plants, which lays a foundation for the correctness and reliability of the experimental results in physical model test of environmental pollution in nuclear power plant, and has good promotion and application values.

A multi-scale dense residual and global attention combined image denoising network was proposed to address the issues of texture detail loss and excessive smoothness in reconstructed images caused by the lack of intrinsic connection between spatial features and denoising tasks in current low-dose computed tomography (LDCT) image denoising methods. The multi-scale dense residual blocks were introduced to extract multi-scale feature information from images, and the global attention mechanism (GAM) was used to focus on cross dimensional information between different channels of the model, while adding skip connections to further expand the range of global interactive features, and finally the multi-scale feature loss function was used to enhance image texture details and avoid the problem of image smoothness. After experimental verification, the proposed algorithm achieves 35.183 8 dB and 0.960 5 in peak signal-to-noise ratio (PSNR) and structural similarity index method (SSIM), respectively, which effectively preserves image details while removing noise, outperforming other algorithms.

Traffic sign detection is the key in the driving process of intelligent vehicles, which is of great significance to vehicle analysis of road conditions. Aiming at the problems of too many parameters and low accuracy of existing traffic sign detection algorithms, a lightweight traffic sign detection algorithm based on improved real-time detection transformer (RT-DETR) was proposed. Firstly, the ResNet network in the backbone network of the model was replaced by VanillaNet network to reduce the number of layers and parameters. Secondly, the bi-directional feature pyramid network (BiFPN) was used to replace the path aggregation network (PAN) structure in the feature fusion module of RT-DETR to increase the fusion ability of the model and extract more abundant feature information. Finally, the global attention mechanism (GAM) was added to the feature fusion module to enhance the model perception of global information and improve the detection performance of multiple targets and obscured targets. The proposed algorithm was tested on traffic sign datasets. The mean average precision (mAP) value of the improved RT-DETR algorithm reaches 87.7%, 3.6% higher than that of the original algorithm, and the parameters are reduced by 21%, meeting the deployment needs of intelligent vehicle equipment, which proves the effectiveness of the improved algorithm.

Non-uniformity is a common inherent defect in infrared imaging systems, mainly manifesting as streaks and granular fixed-pattern noise that severely degrade image quality. Current scene-based non-uniformity correction methods generally rely on moving scenes, and stationary scenes are easily assumed to be non-uniform, resulting in incorrect correction and ghosting artifacts. To solve the problem, the scene assumption was discarded to correct the large-scale strike non-uniformity based on one-dimensional guided filtering combined with progressive correction and energy blocking mechanism from the characteristics of non-uniformity. On the temporal low-pass image, based on the scale awareness and convergence properties of the rolling guidance filter, the small-scale scatter non-uniformity was corrected by solving the parameters of the linear correction model under the minimum mean square error. To verify the robustness of the algorithm, simulations and real experiments were performed on motionless scenes, and through subjective and objective evaluations, it was verified that this algorithm had the advantages of small non-uniformity correction residuals, strong ability to inhibit ghosting, no over-correction, no dependence on motion scenes, and had the ability to correct both video streams and single frame images.

For random noise presenting in phase-sensitive optical time domain reflectometer (Φ-OTDR) signals, a disturbance localization method based on differential Savitzky-Golay non-local means (SG-NLM) algorithm was proposed to reduce the noise in non-disturbed areas. The differential Savitzky-Golay (SG) smoothing filtering was used to achieve the preliminary localization of external disturbances. The Φ-OTDR amplitude signal noise could be further reduced by using the non-local means (NLM) algorithm, which was suitable for dealing with random noise included in two-dimensional signals. The experimental results show that the proposed method can maximally filter out random noise and achieve better localization effect. Compared with the traditional NLM algorithm and the amplitude variance localization method, the signal-to-noise ratio (SNR) is increased by 15.14 dB, and the spatial resolution is improved by 4.05 m.

To address the disparity calculation and 3D reconstruction problems for small textured objects, a PSMNet-ECSA algorithm based on a dual three-pooling attention mechanism was proposed. By embedding two dimensions of channel and spatial attention mechanisms into the backbone of the residual network, each dimension was fused using average pooling, maximum pooling, and mixed pooling techniques to merge feature dimensions, which prevented overfitting to some extent, thereby enhancing the network information extraction and generalization capabilities. Under the conditions of consistent experimental environments and datasets, an analysis was conducted through experiments on SceneFlow, KITTI2015, and real-world scenes. Compared to the original PSMNet algorithm, the proposed algorithm achieves a 10% improvement in metrics such as mean absolute errors and threshold errors. Applying this algorithm to reconstruct three-dimensional point cloud models of abalone, the average relative errors in distance measurements such as length, width, and breathing holes are within 3%, which can automatically measure and record the growth status of small marine organisms, and has promising practical application values.

With the continuous development of camouflage technology, the spectral similarity between the camouflage target and the background is getting higher and higher, which brings challenges to the recognition task. Most of the existing band selection methods focus on the information content of the band or the overall separability of each band image, so it is difficult to select the feature band combination that can distinguish the similar spectrum effectively. Therefore, a hyperspectral image band selection method for camouflage target recognition was proposed. The spectral difference index model was constructed to quantitatively describe the spectral difference between camouflage target and similar background in each band, and then guided the selection of feature bands. Firstly, the spectral gradient angle was introduced to explore the local morphological features of the spectrum. Then, the amplitude differences between spectra were measured by Fréchet distance and normalized to eliminate the effect of scale changes. Finally, Pearson correlation coefficient was used to strengthen spectral difference and band independence. The experimental results on the camouflage vehicle data set and the San Diego airport public data set show that the proposed method is better than the comparison method in the camouflage target recognition task.

As an important part of the field of image processing, target tracking is widely used in intelligent video surveillance, military reconnaissance and other fields. However, in the face of the target deformation, occlusion, and other complex application scenarios, the relevant filtering algorithms follow the wrong targets, slowly drift to the background and lack of redetection mechanism when the target reappears after occlusion due to the deficiency of strategies for target and background discrimination and occlusion state judgment, which leads to a substantial decline of tracking performance in practical engineering. According to the above problems, the improved design was carried out. Firstly, in normal tracking, the network optimizer was used to update the multi-layer deep feature extraction network, and the loss function was optimized to improve the discrimination ability of target and background. Secondly, the multiple detection and anti-occlusion optimization mechanism was used to determine the tracker state update mechanism. Finally, the integrated detection, tracking and identification based on deep learning was designed to realize the automatic capture of typical targets before tracking and the recapture positioning of typical targets when the targets reappeared after occlusion. In the experimental analysis, the performance was verified from the aspects of tracking accuracy, visual quantitative loss and algorithm speed. The measured data shows that the adopted method performs well in the above aspects, which is better than that of the ECO algorithm before improvement.

An optical flow estimation network suitable for edge GPU devices was proposed, aiming to solve the problem that dense optical flow estimation was difficult to deploy on embedded systems due to huge quantity of computation. Firstly, to fully exploit the GPU resources, an efficient feature extraction network was designed to reduce memory access costs. Secondly, by adopting a flat-shaped iterative update module to estimate the optical flow, the size of the model was further reduced, and the utilization of GPU bandwidth was improved. Experimental results on different datasets show that the proposed model has efficient inference capability and excellent flow estimation performance. In particular, compared with the advanced lightweight models, the proposed model reduces the error by 12.8% with only 0.54 Mb parameters, and improves the inference speed by 22.2%, demonstrating the satisfactory performance on embedded development boards.

In the colorimetric temperature measurement method, the traditional assignment of complex refractive index assumes a constant refractive index for soot at a designated wavelength, employing either empirical formulas or empirical values for assignment. However, with the study of the mechanism of soot deepens to a smaller scale, this assignment method will lead to a large error in the calculation results of colorimetric thermometry. The relationship between the complex refractive index and soot particle size was established by bridging the physical correlation between the optical band gap and particle size with the relationship between the optical band gap and complex refractive index derived through linear interpolation. The influence of the scale effect of soot particles on the complex refractive index in the colorimetric thermometry was explored through this relationship, and the complex refractive index assignment method was optimized by empirical formulas and experimental values, respectively. The optimized calculated temperature of soot particles was compared with the temperature data measured by laser-induced fluorescence to evaluate the optimization effect of the two methods. The experimental results indicate that the temperature measurement results based on the traditional assignment method of complex refractive index exhibit a relative error of 20% to 50% compared to the experimental results, with the deviation falling within the range of 300 K to 1 000 K. Among the two proposed optimization methods, the approach based on empirical formulas only marginally reduces the relative error by about 5%. In contrast, the optimization method relying on experimental values significantly enhances the accuracy of flame temperature measurements, reducing the relative error by approximately 40%.

For the needs of high precision, high repeatability and fast real-time ellipsometry measurement, a stable ellipsometry measurement technique based on double elastic ellipsometry parameters was studied. The principle of the elastic modulation ellipsometry parameter measurement system was introduced, and a stable ellipsometry parameter measurement system was designed. The multi-rate processing method (downsampling method) could achieve the effect of narrowband low-pass filtering while reducing the utilization of field programmable gate array (FPGA) hardware resources. The CIC and FIR filters used in downsampling were designed, the digital phase-lock technique was used to demodulate the double-elastic modulated optical carrier signal, and the initial ellipsometry parameters were obtained. The experimental system was designed and the calibrated Si-based SiO2 standard sample was tested for 2 000 times. The experimental results show that when the sampling time is set to 20 ms, the repeatability accuracy of amplitude ratio and phase difference is better than 0.001°, and the repeatability accuracy of film thickness of the measured sample is 0.001 nm, which verifies that the design has better measurement repeatability and higher measurement accuracy.

With the wide application of microlens, the rapid detection of optical properties of microlens is a key problem to be solved urgently by users and processors. Hartmann wavefront sensor was used to detect the wavefront of microlens and to quickly characterize its optical properties. In order to verify the feasibility of this method, the overall scheme of measuring microlens wavefront with Hartmann sensor was designed, the measurement experiment system was set up, and the measurement error source was analyzed. Combined with the experimental scheme, the centroid extraction algorithm and the Zernike polynomial wavefront reconstruction algorithm were studied to ensure the centroid extraction accuracy and wavefront reconstruction accuracy of the spot, and the effect of aperture diffraction of the lens to be tested on the wavefront detection accuracy was analyzed. The wavefront error and primary aberration information of a 200 μm convex lens were obtained, which provided a new idea for the rapid detection of optical properties of microlenses.

Aiming at the demands of high-speed and high-precision detection of pollution defects in integrated circuit chips, a high-precision and short-wave infrared polarization micro-imaging system based on the combination of short-wave infrared micro-imaging technology and polarization measurement technology was designed and developed. The detection light source transmitted through the sample, and the polarized light of the sample was collected by the microscope and incident on the polarization analysis device composed of wave plate and polarization detector to complete the measurement of the polarized image of the sample. Then the polarized image fusion technology was used to fuse the measured images to complete the feature extraction of the polarized image of the sample, thus achieving defect detection of integrated circuit chip contamination. The edge and center of the chip were tested with Op-Amp integrated chip. The results show that the defect characteristics tested by this system are clear and the quantity is complete, which can provide important basis to ensure the accuracy of integrated circuit chip contamination defect detection.

Indirect time of flight (iTOF) camera has a wide application prospect in the field of three-dimensional environmental perception. According to the imaging principle of iTOF camera, its imaging quality is closely related to the exposure time. When the exposure time is too large causing the camera to work in the nonlinear area, the depth information of the solution will also introduce additional biases and thereby affecting the accuracy of the measurement results. In order to further improve the application accuracy of time-of-flight camera, according to the optical imaging mechanism of time-of-flight camera, a method was put forward to measure the performance parameters of time-of-flight camera. Through experiments and calculations, the global system gain and other performance parameters of time-of-flight depth camera were indirectly obtained, then the corresponding curves between distance and camera output gray value could be obtained by substituting them into the optical imaging model of time-of-flight camera, and the necessity of measuring related performance parameters was verified by experimental results. The relative error between modeling and experimental results can be within 20%, and the average relative error is 0.16%. These performance parameters and simulation models are used to guide the integration time selection of iTOF cameras when used in different scenarios, which can effectively solve the problem of reducing distance measurement accuracy due to the introduction of non-linear errors by improper use.

In order to obtain the distribution of delay amplitude of the elastic modulator across the entire aperture, the theoretical and simulation analysis of its delay distribution law was conducted, and on this basis, an experimental measurement system was built for verification experiments. The measurement system generated modulation signals through an optical path with polarization devices, and then used a digital phase-locked device to accurately match the received signal with the local reference signal in terms of frequency and phase, thereby achieving signal demodulation. Finally, the demodulated signal was transmitted to the upper computer to calculate the relevant phase delay parameters. Simultaneously, a precise two-dimensional displacement platform was used to achieve full aperture movement of the elastic optical modulator, thereby enabling measurement of all positions. At the end of the experiment, the upper computer saved the phase delay parameters at each coordinate and imported the data into the drawing software to achieve intuitive visualization of the phase delay amount, and the simulation software was used for theoretical simulation comparison. According to the comparison between experimental results and theoretical values, the relative error of the measurement was 0.13%, and the two-dimensional distribution of the delay of the elastic light modulator across the entire optical aperture was obtained, which was consistent with theory and simulation. A precise calibration method for the entire aperture of the elastic light modulator was presented, which provided references for large aperture, high-precision polarization modulation, and detection.

Laser weapons are the important optoelectronic payload for the new generation of combat aircraft concept. In a mobile environment, the far-field focus spot of laser weapons is affected due to external factors such as atmospheric turbulence and vibration impact, which leads to the sharp decrease of energy concentration, and the defocused spot is in urgent need to be repaired in real time. Combined with the optical residual characteristics of low-altitude tactical laser weapons, the system of nonlinear equations between the mirror adjustments of adaptive optics wavefront corrector and the primary aberration Seidel coefficient was constructed, the strong correlative adjustment variables were eliminated by means of correlation coefficient matrix method, and the low-order adaptive optical correction system using a unfocused off-aperture telescope secondary mirror as a wavefront corrector was proposed. The numerical simulation results show that after a finite number of translation adjustments of the secondary mirror, the root-mean-square value of the wavefront error can be quickly reduced to below the diffraction limit, which meets the requirements of laser weapon systems for optical residual correction.

In order to scientifically evaluate the protection ability of the laser angle deception interference system, it is necessary to consider the important link of laser warning to guide the interference. Firstly, according to the principle of scattering detection, the warning model of laser scattering warning device with six windows was established. Secondly, according to the relationship between the attack direction of laser-guided weapons and the direction of laser indication, as well as the protection ability of diffuse reflectors, a calculation model of protection coverage under warning guidance was established. On this basis, the results of warning orientation were analyzed, and the protection ability of laser deception interference system guided by warning was evaluated. The results show that when the warning-guided interference laser irradiates three fixed diffuse reflectors with uniform distribution, the protection coverage is 78.1%. If the direction of the diffuse reflector can be adjusted according to the warning results, only one diffuse reflector will be used, and the protection coverage can also be increased to 98.2%. This study can provide a theoretical basis for the use and evaluation of protection ability of laser angle deception interference system.

Supercooled water in the cloud is a key factor of weather modification, and the supercooled water detection technology and equipment employed on unmanned aerial vehicle (UAV) platforms are of great significance for weather modification. A compact polarization LiDAR operating at 1 064 nm wavelength was introduced, which could identify water clouds by measuring the depolarization ratio of the scattered echo signals, and the detection of supercooled water clouds was realized by combining the height of the zero degree isotherm. The main technical parameters and composition of the LiDAR system were introduced, and its miniaturized, lightweight and high-reliability design could be mounted on the UAV to detect the spatial distribution of supercooled water. An improved rotating waveplate method was used to accurately calibrate the depolarization ratio of the LiDAR, and its accurate measurement was achieved, which was the key to effectively detect supercooled water. The observation experiments with high temporal and spatial resolution were carried out on the ground to simulate and verify the detection ability of the LiDAR on the UAV platform. The results show that the proposed LiDAR has the ability to detect supercooled water in the cloud on the UAV platform, and can provide important supercooled water distribution information for weather modification.

LiDAR has advantages such as high accuracy, strong anti-interference ability, small size, and light weight, which has important application values in sports recognition and evaluation scenarios: the accuracy of basic sports movements is crucial for scoring, and promoting standardization of athlete movements, which is of great significance to improve the athlete movements, especially to directly improve the scoring rate. The intelligent scoring system not only can score the performance of athletes to reduce controversies over subjective scoring items such as diving and gymnastics, but also can improve their competitive levels by providing feedback on the quality of their movements. An intelligent automatic scoring system based on LiDAR point cloud was proposed, which used human body object detection network, human body key point recognition network, action classification network, and dynamic time warping sequence action similarity evaluation algorithm to determine the difference between sequence actions and standard actions and score them. The experimental results show that the system has the characteristics of automation, intelligence, and real-time, which has certain reference significance for the construction of autonomous training evaluation systems in the field of sports.

With the development of optoelectronic technology, the importance of color charge coupled device (CCD) detectors in various applications is continuously increasing. Therefore, analysis of damage properties under laser irradiation is key to enhancing their reliability. A nanosecond laser with a wavelength of 532 nm, a pulse width of 10 ns, and a repetition frequency of 1 Hz was used to experimentally study the color CCD detectors, and the damage properties were analyzed under varying peak power densities. The microstructure and depth of the damaged regions on the color CCD detectors were observed using metallographic microscopy, scanning electron microscopy (SEM), and a 3D surface profiler, thereby determining the internal damage locations. The results indicate at a power density of 2.675×105 W/cm2, the damage is mainly concentrated in the color separation filter layer, leading to the appearance of white spot damage. When the power density increases to 3.534×106 W/cm2, the aluminum light-shielding film melts, causing white line damage. Further increasing to 4.526×106 W/cm2, the damage penetrates into the N-type photosensitive region, leading to gray-white screen damage. At the maximum power density of 5.926×106 W/cm2, the N-type silicon substrate is damaged, causing the color CCD detector to completely fail.

A single-cavity multi-comb fiber laser based on multi-dimensional pulse multiplexing transmission was proposed by combining wavelength multiplexing, polarization multiplexing and direction multiplexing. Polarization-maintaining fibers and inline polarizers were placed on different branches of bidirectional fiber lasers to realize polarization multiplexing mechanism based on birefringence effect and spectral filtering effect based on Lyot. Dual-comb, tri-comb and quad-comb generation could be realized by adjusting the pump powers, laser polarization states and intracavity loss of lasers. Based on the single-cavity tri-comb laser, the envelope of the beat frequency signals of obtained interferogram was extracted by using the Hilbert-transform algorithm, and the absolute distance measurement of 2 km single-mode fiber was realized according to the optical vernier effect. The ranging results coincide with the results by using standard optical time-domain reflectometer, which verifies the good coherence between the optical combs. This scheme provides a low-complexity multi-comb light source, which has the potential to be used in multi-comb precision measurement field.

Highly nonlinear chalcogenide fibers are widely used in the applications of Brillouin fiber sensing and fiber lasing. A stimulated Brillouin scattering (SBS) dual-parameter sensing method based on a highly birefringent chalcogenide polymer composite fiber (As2Se3-PMMA) was introduced, a highly nonlinear elliptical-core As2Se3-PMMA tapered fiber was designed and fabricated, and the SBS characteristics of the fiber were measured using a Brillouin optical time-domain analysis (BOTDA) system. Due to different Brillouin frequency shifts of temperature and strain in the fast and slow axes of the elliptical-core As2Se3-PMMA tapered fiber, the dual-parameter measurement of temperature and strain of the sensor was achieved. The temperature sensitivities in the fast and slow axes of the elliptical-core chalcogenide tapered fiber are ?2.772 MHz/°C and ?2.605 MHz/°C, respectively, and strain sensitivities are ?21.9 kHz/με and ?16.3 kHz/με, respectively. By establishing a matrix based on the different temperatures and strain responses of the fast and slow axes, the temperature and strain resolution of the chalcogenide optical fiber sensor are 0.9 °C and 28 με, respectively.