This PDF file contains the front matter associated with SPIE Proceedings Volume 13278, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

Large-scale two-dimensional gratings are typically produced using reactive ion beam scanning etching. However, this method is limited by uneven etching depth, which can negatively impact the performance of large-aperture gratings. To address this issue, we propose optimizing the scanning strategy during reactive etching to improve uniformity. Based on the specific beam distribution, we have established a row spacing calculation model using the convolution algorithm. In the actual reaction etching experiment, we found that a row spacing of only 16 mm resulted in a uniformity within the etching range of 600 mm that was better than 6%. This improvement enhances the diffraction uniformity of the grating. Simulation and experiment were used to verify the influence of row spacing on the uniformity of surface grooves during etching. This method was then used to significantly improve the uniformity of grating etching depth after processing. Therefore, the proposed optimization method of reactive etching scanning strategy has a good industrial application prospect for large-scale two-dimensional grating, wafer etching, and large-scale integrated circuit manufacturing.

With the continuous improvement of optical performance requirements, weaponry, space observation, laser fusion, extreme ultraviolet lithography, and other fields of optical components of increasing precision requirements, the number of requirements is increasingly large, so the processing of optical components faces serious challenges. Pulsed Ion Beam Shaping is a non-contact optical component machining technology. Pulsed Ion Beam Shaping has the advantages of atomic-level machining accuracy and no edge effect. The ability of pulsed ion beam reshaping mainly depends on factors such as the stability of the removal function, the dynamic performance of the machine's motion axes, and the size of the removal function. At present, due to the difficulty of further improving the dynamic performance of the machine tool, a single beam diameter, and removal efficiency of the removal function for processing, trimming efficiency is not high, and can only be balanced according to the needs of trimming efficiency and accuracy. Thus, we proposed a pulsed ion beam reshaping processing method based on a controlled removal function to achieve the optimal beam diameter selected by the removal function according to the different reshaping face shapes, which improves the reshaping capability of the pulsed ion beam while reducing the additional material removal layer to ensure high processing efficiency. The main research on the shaping method based on the controlled time-varying removal function is carried out, the shaping model based on the controlled time-varying removal function and the residence time solution model are analyzed, the global optimization algorithm based on the error distribution of the surface shape and the removal function library, and the optimal combination of the removal function under the constraints is established, and the theory of the shaping of the controlled time-varying removal function is perfected, and the iterative genetic algorithms, variation and crossover design are used to find the optimal beam diameter according to different shapes of the surface, to improve the processing efficiency. The genetic algorithm is used to iterate the genetic algorithm at different processing points, and the mutation and crossover design is used to find the optimal solutions at different processing points, and the processing is carried out after the optimal solutions are obtained to get the corresponding results, and the two variables of the beam diameter and time are matched during the processing, so as to achieve the maximum improvement of the processing efficiency and the accuracy of the shape trimming. The simulation results show that the controlled time-varying pulsed ion beam reshaping method reduces the residence time by 30 % compared with the conventional ion beam reshaping, the efficiency is 19% higher than that of traditional processing and the facet accuracy reaches 5 nm level.

In nighttime infrared scenarios, data generation plays a pivotal role. However, the limited availability of nighttime infrared image datasets often constrains the model's generalization ability and accuracy. To mitigate this challenge, this study proposes a novel method for generating infrared image data by integrating adversarial training techniques with foreground enhancement methods. A foreground enhancement algorithm is employed to process visible light images, thereby enhancing the sensitivity of visible light images to targets during the CYCLEGAN model generation process. This approach effectively improves the quality of the generated nighttime infrared images and enhances the performance of target detection in such images, resulting in notable enhancements. Experimental results on publicly available infrared image datasets demonstrate that training with augmented data can significantly improve the performance of target detection models, providing a new solution for scenarios with limited nighttime infrared image data.

We report a design of double-layer MoSi superconducting microstrip single-photon detector(SMSPD) at 1550 nm. The proposed structure consists of a top anti-reflection dielectric layer, a optical cavity and a double microstrip layer. We optimize the thickness of the anti-reflection layer to enhance the light absorption through the Finite-Difference Time-Domain(FDTD) method. The simulation result indicates that the optimized device structure has an light absorption rate exceeds 99.9%.This paper provides a new design structure for subsequent high-efficiency SMSPDs.

The Hall-effect metasurface described in this paper uses a combination of Pancharatnam-Berry ( PB ) phase and geometric phase to achieve independent phase control of different spin states. According to the established vector diffraction rigorous coupled wave model, the genetic algorithm is used to optimize the phase distribution of two different shaping effects. Then, different shaping functions are integrated into one device through the arrangement of metasurface microstructure units. By manipulating the polarization chirality of the incident light, the focusing and wavefront shaping of different spin-state photons can be realized at the same time, and two coaxially distributed composite spots can be obtained to achieve the purpose of multi-dimensional shaping of far-field spin-state photons. By this method, the central beam with better flat-top effect and the annular spot with larger radius can be obtained. The designed diffraction efficiency is greater than 90% and the spot uniformity is less than 15%. The welded products have both the smooth surface of semiconductor laser welding and the high aspect ratio of fiber laser welding.

The face-centered cubic (Fcc) structured TiC_x films with different C content were prepared by reactive magnetron sputtering deposition. The influence of carbon content on the TiC_x crystal structure and dielectric properties were studied. All the films are fcc structured with (111) and (200) preferential orientation. Additionally, a tunable dual epsilon-near-zero characteristic is exhibited in visible and near infrared region. With C content increasing, the screened plasma frequency decreases. The calculated electronic states shows that atom vacancy is the main defect type underlying the optical response, while Ti antisites, Ti interstitials, Ti dumbbells or Ti interstitials may be the main factors influencing the crystal structure.

Large angle beam splitter grating is an important optical element but the current design methods are mostly based on the scalar diffraction theory based on Thin Element Approximation approximation, which is difficult to evaluate and calculate well when large angle beam splitting is involved. In this work, a method combining finite difference time domain method with adjoint optimization is proposed. Each iteration only needs two electromagnetic simulations to obtain the gradient distribution of the grating structure region. Using this method, a 1×5 beam splitting grating with a splitting angle of 10 degrees is designed. The final uniformity error is 3.7%, and the total diffraction efficiency is 86.6%. For the two-dimensional splitter , a 3×5 beam splitting grating with a diffraction full angle of 82.4×14 degrees is designed. Under the condition of large angle beam splitting, good uniformity error can still be maintained, and the final uniformity error reaches 23.1%.

Surface plasmon polaritons can manipulate and transmit light fields at the nanoscale, offering a wide range of applications in areas such as optoelectronic detection, optical filtering, novel light sources, and biosensing. This study investigated the transmission enhancement characteristics of conventional nanohole arrays through finite-difference time-domain (FDTD) simulations. Additionally, T-shaped metal nanoholes are designed based on the distinct spectral transmission properties of rectangular holes under varying polarized light conditions. Research has demonstrated that compared with their rectangular counterparts, T-shaped metal nanoholes exhibit polarization selectivity and sharper transmission spectra. Furthermore, this study investigated the impact of different components within the structure of T-shaped metal nanoholes on light field modulation by analyzing the electric field distribution. Finally, the influence of various parameters, such as the period, film thickness, and hole size, on the transmission spectrum of T-shaped metal nanoholes is investigated.

In order to effectively address the issues of signal distortion and attenuation resulting from dispersion effects in modern-day communication system. A new dispersion-compensating concentric core photonic crystal fiber (PCF) is presented and analyzed, using finite element method (FEM). The simulation results indicate that by maintaining the structural parameters of this PCF at a large hole diameter of 1.60μm, a small hole diameter of 0.50μm, a central hole diameter of 0.80μm, and a lattice constant of 2.00μm constant, adjusting the refractive index of the index-matching fluid from 1.417 to 1.423 can result in negative dispersion values ranging from -2049.3 to -9893.6ps/nm/km. This refractive index tuning allows the proposed PCF to effectively compensate dispersion within the 1.4-1.7μm wavelength range. The proposed PCF maintains low CL throughout the compensation process, ranging from 10^-8 dB/m to 10^-6 dB/m. Moreover, the proposed PCF demonstrates a high linearity relationship of 0.99426 between the refractive index of the index-matching fluid and the phase-matched wavelength. This PCF holds significant prospect for modern large-scale high-speed communication systems.

Plasma machining technology is an advanced optical manufacturing technology developed in recent years, which can quickly mitigate or remove surface/subsurface damage caused by conventional optical machining methods, as well as the advantages of efficient, high-precision and high-resolution trimming of optical surface shapes. In this paper, based on atmospheric inductively coupled plasma (ICP), we analyze the effects of changes in reactive gas flow, power, and residence time on the removal function during processing, as well as experiments on the long-time stability of the plasma jet. Finally, selected appropriate machining parameters and performed trimming machining on the element to make the surface shape converge quickly and reduce the low-frequency error of the shape.

Metalenses based on all-dielectric metasurfaces plays an important role in microscopic imaging, machine vision and holographic imaging. Chromatic aberration correction is an important factor that imaging devices. In this work, we propose an transmission achromatic metalens that can operate over a broad band of wavelengths. Based on the principle of phase compensation, the finite-difference time-domain method is used to calculate the wavefront of light for the design of the nano-units in the metalens with eliminating chromatic aberration. In the whole incident spectrum of visible light from 425 nm to 625 nm, The maximum focal shift of the achromatic metalens is about 7.9 %.And the average efficiency of the metalens with a numerical aperture of 0.6 is about 42 % in the wavelength band.

Convolutional Neural Networks (CNNs) have witnessed increasingly widespread applications in the field of computer vision with the development of artificial intelligence. The Depthwise Separable Convolutional Neural Network, owing to its characteristics such as fewer parameters, high computational efficiency, and good generalization capabilities, holds significant advantages for deployment on resource-constrained embedded terminals. Presently, numerous accelerator solutions focus on accelerating larger network models like VGG and ResNet. However, when running Depthwise Separable Convolutional Neural Networks on these accelerators, issues such as slow execution speed and low utilization of computational resources persist. In this study,we adopts the advanced Deep Separable Convolutional Neural Network, ShuffleNetV2, as the foundational architecture for investigation. A novel hardware optimization approach is proposed for the implementation of standard convolution and depthwise convolution, aiming to enhance the utilization efficiency of hardware computational resources. The proposed hardware accelerator is implemented on Xilinx xczu9eg FPGA at a clock frequency of 150 MHz, achieves a maximum frame rate of 675.7 fps. Simultaneously, it exhibits a power consumption of only 7.3 W, resulting in a power efficiency of 13.45 GOPS/W. In comparison to GPU platforms, the proposed design demonstrates a 12% improvement in execution speed, while simultaneously achieving a power efficiency that is 20.4 times than that of GPU platform. Compared to similar hardware accelerators proposed in recent years, the resource consumption of this design is comparatively smaller, and it holds a certain advantage in terms of speed and power consumption.

High-quality remote sensing images can be obtained by utilizing hyperspectral imaging technology. The task of detecting features can be realized by analyzing the rich spectral information contained in hyperspectral remote sensing images(HRSI), and categorizing features is an important research direction of hyperspectral technology in the field of remote sensing. However, due to spectral variations, noise interference, and spectral mixing among different features, it is challenging to extract the category information of feature targets from HRSI. In recent years, deep learning has performed well in image classification tasks and can effectively extract deep features of targets. Therefore, we propose an intelligent classification method for HRSI based on hyperspectral imaging technology by combining convolutional neural network and attention mechanism to extract the spatial and spectral features of HRSI, which realizes the accurate classification of feature targets in HRSI. Experiments have shown that our method can achieve superior classification performance compared with existing methods.

Aerosol extinction hygroscopic growth is one of the important parameters to characterize the hygroscopic properties of aerosols, as well as explore the environmental effects of aerosols and evaluate air quality. In this paper, the variation characteristics of aerosol extinction factor, atmospheric visibility (VIS) and relative humidity (RH) were analyzed based on the measurements in Hefei during winter (from December 2021 to January 2022). It is shown that the aerosol extinction coefficient (VIS) is positively (negatively) correlated with RH, especially in high-RH environments where the extinction coefficient (VIS) is extremely high (extremely low). Based on four commonly used empirical parameterization schemes of aerosol extinction hygroscopic growth factor, an empirical model of aerosol extinction hygroscopic growth factor for winter in Hefei is presented. There is no significant difference in the parameterization results of the four models, which are able to express the variation characteristics of aerosol extinction hygroscopic growth with RH, but the simulations of the four models are higher than the observations in high-RH environment.

For a single optical sparse aperture imaging system, because of the sub-mirrors’ dispersion and sparsity, the modulation transfer function (MTF) is unlikely to satisfy all frequencies due to inadequate sampling of the discrete sub-apertures. Thus, a baseline adjustable optical sparse aperture imaging system is proposed by expanding the sub-aperture array. Sufficient information from separate frequency regions can be maintained and fused. An improved Wiener filter algorithm based on the system's transfer function and noise features is designed for image restoration. Both the simulation and experiment prove that the proposed method can achieve a satisfactory MTF level and a clear reconstruction effect. The average peak signal to noise ratio is raised from 22.53 dB of degraded images to 29.17 dB of the restored images. The structural similarity index of the results is increased from 0.65 to 0.92. And compared with several conventional algorithms, scores of multiple evaluation indexes of the proposed method is the highest.

To mitigate the inter symbol interference caused by various linear and nonlinear effects in bandwidth limited short reach link, we give a review of the advanced digital signal processing with pulse amplitude modulation format.

In recent years, visible light communications (VLC) have been developing rapidly in many applications. Although VLC can be applied in many scenarios, a key barrier for its widely deployment is the line-of-sight (LOS) transmission nature since the direct link can be easily blocked by obstacles. To address the link blockage issue in VLC, we propose an omni-digital reconfigurable intelligent surfaces (Omni-DRIS) enhancement VLC system architecture, named Omni-DRIS system, which makes use of the reflection and refraction natures of Omni-DRIS. By deploying Omni-DRIS in one room, we first propose the joint use of LEDs in two rooms to combat blockages. To study the bit error rate (BER) performance of Omni-DRIS system, we derive mathematical expressions and use Monte Carlo simulations to validate the analytical results. Based on the simulation results, we find that when the system’s links are free from blockages, the Omni-DRIS system achieves an approximate one-orders-of-magnitude improvement in BER performance compared to the OneRIS system and an approximate three-orders-of-magnitude improvement compared to the NoRIS system. When system has random blockages, the Omni-DRIS system achieves an approximate two-orders-of-magnitude improvement in BER performance compared to the OneRIS system. Our findings reveal that the use of Omni-DRIS can significantly improve system BER performance if the room has random blockages. In the future, Omni-DRIS can be better controlled using intelligent algorithms to further optimize the system performances. Our first attempt in applying OmniDRIS to the scenario where one room’s LED is totally blocked is of great importance for the future large-scale deployment of Omni-DRIS assisted VLC system.

Anomaly detection based on template image registration is one of the methods used to detect anomalies of objects with similar structures. However, the imaging device of the Electronic Multiple Units (EMU) train is a line-scan camera, and the geometric transformation law between its image and the area-scan image is different. The conventional image registration method of the area-scan image cannot accurately align the line-scan image. Moreover, the line-scan image of the EMU train collected in uncontrollable environmental scenes exhibits significant grayscale changes, and the registered images cannot detect differences by directly comparing grayscale. To address these two challenges, this paper proposes a two-stage anomaly detection method based on line-scan image registration and edge comparison. In the registration stage, a coarse-to-fine line-scan image registration method was designed. First, the feature-based registration method was used to achieve coarse position of the EMU train template image in the target image. Then, the direct registration method based on the line-scan image geometric transformation model achieved precise geometric alignment. In the anomaly detection stage, edge information is extracted from the template image and the registered target image, and anomaly detection of EMU trains is achieved through edge comparison, edge expansion, and edge connection. The experimental results on the line-scan image of EMU trains show that the two-stage method proposed in this paper can achieve the registration of line-scan images of EMU trains, and on this basis, achieve detection and segmentation of abnormal areas of EMU trains.

We propose a hardware-efficient, low-cost integrated communication and vibration sensing method that relies solely on existing commercial coherent optical communication systems. The scheme utilizes dynamic frequency offset estimation to adapt to commercial 100kHz ECL, while employing sliding averaging and nearest-neighbor interpolation to greatly reduce algorithm complexity, making it conducive for integration into real-time platform. We demonstrate our scheme in a dual-polarization DSCM coherent optical communication system and discuss the performance in terms of communication and sensing capabilities under different conditions.

Multifunctional integration of electronic equipment is a main development trend in the future, wherein the integrated signal generation enables a key part. Therefore, it is of great significance to develop the generation of anti-jamming joint radar-communication (JRC) signal. Here, a photonics-assisted generation scheme of millimeter-wave (MMW) anti-jamming JRC signal is proposed. A large-bandwidth MMW dual-band agile JRC signal is generated based on a photonic MMW up-conversion and frequency permutation techniques. Meanwhile, thanks to the dual-band photonic radar de-chirping combined with coherent fusion method and low-cost communication self-coherent reception, the high resolution radar detection and communication with large amount of information are realized simultaneously. In the photonics-assisted JRC simulation system in W-band, a dual-band agile stepped-linear frequency modulation JRC signal covering 81-93 GHz is generated. Moreover, through a dual-band coherent fusion processing, the dual-band signals occupying with only a bandwidth of 2 GHz are successfully fused into an equivalent ultra-wideband signal with a bandwidth of 12 GHz, enabling a radar ranging with a resolution of 1.26 cm. Using a low-cost self-coherent reception, an anti-jamming wireless communication with factorial 10 is demonstrated, which can achieve up to 21.8 bits quantity of information.

Multispectral imaging systems are widely used in many security-sensitive areas, so it is crucial to study multispectral adversarial attack methods to assess the robustness of their detection systems. However, current research has focused on single-spectrum pedestrian detectors, and multispectral pedestrian detection adversarial attacks for aerial photography views have not been fully investigated. In this paper, we propose a new multispectral aerial photography adversarial attack. We establish an adversarial patch generation framework for attacking the aerial photography pedestrian detection system, design an adversarial patch with a barcode-like structure, and use an SPA-oriented optimization algorithm to study the effects of the shape, position and angle of the adversarial patch on the attack effect. Moreover, our designed adversarial patches have the same structure under visible light and infrared conditions, which is convenient for the subsequent physical transferability study. The experimental results show that the generated adversary patches produce good attack effects on two aerial multispectral detectors, and the effectiveness of this method in visible and infrared attacks is 72.46% and 44.22%, respectively.

Port storage tank safety accidents involve large-scale and multi-dimensional spatial problems, making it challenging to conduct traditional experimental research and simulation. This paper utilizes three-dimensional scene modeling and rendering for port storage tanks and their surrounding affected areas. The driving actions of each model structure are analyzed, and their corresponding subordinate relationships are configured. Numerical calculations are used to simulate the three-dimensional dynamic evolution process of safety accidents in the storage tanks. Additionally, a combination of spatial floating naked-eye enhanced stereoscopic display and real-time human-machine interactive technology is proposed. The constructed three-dimensional simulation scene is directly projected in the air, allowing for the intuitive representation of the processes of leakage, dispersion, ignition, propagation, and destruction of port storage tanks. This approach enables immersive and interactive stereoscopic perception, facilitating comprehensive understanding. It also provides technical support for the analysis of port storage tank safety accidents, guiding emergency response, and conducting emergency drills.

In this paper, we study the sequences pattern effects on a new imaging architecture for unambiguous ranging by combining the advantages of space coding based single-pixel imaging technology and spread spectrum time coding based scanning imaging technology. We firstly derive the time-space united correlation nonlinear detection model based on single photon detection.The depth image is restored by convex optimization inversion algorithm.Then we introduce the arm probability of steady response model to the signal-to-noise ratio model. The relationship of SNR and the 1-bit ratio in bitstream with different dead time by Monte Carlo simulation is studied. The simulation test results show that with the ratio of randomly distributed 1-bits in transmitted sequences pattern increased, the system SNR gets better first and then gets worse. Best pattern of transmitted bit stream according to different dead time leads to the best SNR. Theory model is almost consistent with Monte Carlo simulation. Theoretical model and simulation test both prove that, compared to the conventional space coding based single-pixel imaging technology, this approach enhances scene reconstruction quality with depth accuracy improvements of 9 using 0.18 1-bit ratio sequences with 80ns deadtime. The proposed imaging architecture may provide a new path for improved non-scanning lidar systems.

Flexible solar technology based on perovskite and ultra-thin silicon substrate can be applied to integrated photovoltaic systems, aerospace, drones, airships, etc. Structural designs with high light absorption are important for photovoltaics because flat ultra-thin silicon is less absorbent. At present, the optical management of ultra-thin Tandem batteries has not been reported much. In this study, the bottom cell is composed of an ultra-thin silicon substrate of 5 microns, and the top cell is composed of a 200 nanometer perovskite layer, which is integrated with silver plasmon nanorings. The light absorption enhancement of the top perovskite layer and the bottom monocrystalline silicon layer is designed by finite element analysis.

Monte Carlo simulation offers significant advantages in the prediction of second harmonic generation polarization photon propagation behavior. The existing Monte Carlo (MC) simulation models, which primarily focus on sphere scattering particles, neglect the modeling of collagen fibers, which is the primary medium to generate the second harmonic signals. Additionally, the impact of structural changes in cylindrical collagen fibers on the nonlinear susceptibility ratio has not been sufficiently explored. In this paper, we propose an MC simulation of sphere-cylinder scattering mediums based on the DSMP. The MC simulation is divided into a three-layer structure consisting of sphere-cylinder-sphere configurations, based on the optical process of second harmonic generation. Furthermore, we analyze the impact of model parameter variations on the second-order nonlinear susceptibility. The results indicate that the augmentation of the second-order susceptibility ratio is observed when increasing the radius of sphere scattering models or decreasing their thickness. The increase in the radius of the cylinder scattering model and variation of orientation leads to an increase in the second-order susceptibility ratio R. Particularly, the orientation has a significant impact on the R value. This study provides valuable insights for the diagnosis of tissue abnormalities.

A temporal convolution system for the short-time Fourier transformation (STFT) of an electrical signal based on a bidirectional chirped fiber Bragg grating (CFBG) is proposed and experimentally demonstrated. In this system, the electrical signal to be analyzed is applied to an electro-optical modulator to simultaneously modulate the temporal waveform and the spectrum of a time-stretched optical pulse, which is generated by a mode-locked laser and dispersed by a CFBG. The modulated optical signal is filtered to be several parts, added with separate time delays and sent to the other port of the same CFBG. Thus the optical signal is temporally recompressed and the spectrum of the electrical signal is able to be mapped into the time domain. The bidirectional CFBG realizes exactly complementary dispersion value for the optical pulse propagating in two opposite directions, which guarantees an optimal frequency resolution of the STFT system. An STFT experiment for a microwave signal with four different frequencies at 5 GHz, 10 GHz, 15 GHz and 20 GHz has been demonstrated.

Antimony sesquisulfide Sb₂S₃, an emerging low-loss phase change material, has attracted great interest for its unique properties, enabling its huge potential applications in programmable integrated optics. A reconfigurable mode converter is proposed and demonstrated numerically assisted by a rectangle Sb₂S₃ inlaid in a slab 4H-SiC waveguide. A threedimension finite-difference time-domain (3D FDTD) method is employed to simulate and optimize the proposed structure. The TM0-to-TM1 mode conversion is realized with transmittances (T) of 0.91 and mode purity (MP) of 93% at the wavelength of 1550 nm in the crystalline Sb₂S₃ state. When the Sb₂S₃ is switched to the amorphous state, the mode-conversion effect disappears, and the incident TM0 mode propagates unimpededly with T > 0.99 and MP > 97.64% within the waveband from 1500 nm to 1600 nm. The nonvolatile reconfigurable mode converter can contribute to programmable photonic integrated circuits and neuromorphic optical computing.

A nanoscale high reference material is a physical reference material with a specific value, mainly used for the transfer and calibration of relevant nanometric instruments. this paper develops a probe scanning atomic force microscopes (AFM) system for step height topography characterization. Firstly, the step standard template is proposed, and the computation time is reduced by iterative approach. Second, the principle of AFM and the roughness measurement method are investigated. Finally, the same step height is compared and measured using AFM. The results show that the maximum standard deviation of the AFM is around 0.015.

The objective of this study is to address the issue of data imbalance by augmenting the milling tool breakage dataset using Auxiliary Classifier Generative Adversarial Networks (ACGAN). The research team developed an ACGAN architecture capable of producing samples labeled with various states of tool breakage. To assess the fidelity of the ACGAN-generated data, this study employed evaluation metrics such as the Kullback-Leibler divergence, Euclidean distance, and the Pearson correlation coefficient, comparing the generated samples against actual samples. The findings indicate a high degree of similarity in data distribution between the synthetic and real samples, suggesting the effectiveness of the generated data for training purposes. This research introduces a cost-effective and efficient approach for data augmentation, significantly enhancing the capabilities of milling tool condition monitoring systems.

Long-period fiber grating (LPFG) based temperature sensors have a wide range of applications in biological and chemical sensing. However, conventional LPFGs exhibit low sensitivity because of the low thermo-optic coefficient of silica. In this paper, a novel liquid-core LPFG device is proposed. The sensor is fabricated by using femtosecond laser to periodically inscribe long-period grating onto the silica material near the air core of the hollow-core fiber (HCF). Liquid with a high refractive index (RI) is filled into the HCF to form a liquid-core waveguide, and transmission peaks resulting from the grating resonance are observed because of the coupling between the fundamental mode and cladding modes. Experimental results show that the temperature sensitivity of the LPFG is highly dependent on the RI of the filled liquid. When liquid with a refractive index of 1.476 is filled in the fiber core, the temperature sensitivity is approximately 6.34 nm/°C in the range of 27-40°C. The device has great potential for applications in areas such as medicine, and chemical industry.

The Wolter microscope, characterized by its high spatial resolution, large geometric solid angle, and broadband achromatic correction, serves as an ideal initial configuration for the next generation of X-ray imaging diagnostic techniques. Nevertheless, the development of small-aperture Wolter mirrors encounters significant challenges, which primarily hinder their widespread application. This article presents a design approach for micro-imaging small-aperture Wolter microscopes, considering both optical design principles and mirror preparation techniques. It reveals the intricate trade-off relationships among spatial resolution, collection efficiency, and the feasibility of optical processing. By effectively integrating optical principles with manufacturing practices, a staged development roadmap for small-aperture Wolter mirrors is proposed. Through technological convergence, the electroforming nickel preparation process can be gradually mastered. This article provides technical insights that can guide the design and development of high-quality hard X-ray Wolter microscopes.

In order to solve the problems of large surface figure errors in large value, high spatial frequency and multi-aberration modes which are beyond the dynamic range of the interferometer, and the problems of slow iterative convergence and high non-convergence rate of existing adaptive compensation optimization algorithm, an adaptive wavefront interferometry utilizing convolutional neural network(CNN) for large surface figure error is proposed, based on the existing adaptive compensation interference detection methods. This paper first introduces the aberration regulation principle of spatial light modulator (SLM), and sets up a convolutional neural network. Then, SLM is controlled to generate Zernike aberrations with different coefficients. Combined with Zygo Verifire interferometer, the corresponding far-field light intensity is collected to compose a labeled data set to train CNN. Finally, a large surface figure error coefficient prediction experiment is carried out with the trained CNN, and the aberration compensation is performed according to the prediction coefficient to verify the effectiveness of the method. The experimental results show that the dense fringes can be transformed into resolvable fringes with this method, and the resolvable probability of full-diameter fringes after compensation is 66.7%. This method is able to greatly improve the performance of adaptive compensation detection, thereby meeting the demand for high dynamic range interference detection technology in the ultra-precision optical surface manufacturing process.

Detecting adversarial examples is an important defense against adversarial attacks. Existing supervised learning detectors perform well for known attacks but deteriorate when detecting unseen instances. To mitigate the sensitivity with training instances, we propose a detector based on the output inconsistency between the protected model and a designed dual model to detect unseen attacks. A test image with different predicted labels on the protected model and the dual model is taken as adversarial. To detect highly transferable adversarial examples and defense adaptive ensemble attacks against the proposed detector, an orthogonal knowledge distillation is employed to train the dual model. The distillation suppresses the transferability across the protected and dual model, therefore forcing them to output different labels for strong adversarial examples. Experimental results on CIFAR-10 and ImageNet show that our method detects various adversarial examples effectively. Compared with state-of-the-art methods, our method achieves at least 6.2% higher average detection accuracy in the cross-attack test. Our method is robust to the popular transferability-enhanced methods, with a minor accuracy decrease by up to 4% in the robustness test.

A pre-trained Transformer network is proposed for the application in temporal photonic compressive sampling, which can address the neck-strangling issues in classical compressive sampling algorithms when using random or orthogonal measurement matrices. The Transformer network is pre-trained to accommodate a diverse array of needs, and specific application requirements can be addressed by fine-tuning the network parameters to learn prior information. In this paper, we preliminarily validated the algorithm model through simulation to address the waveform measurement performance of linear frequency modulated (LFM) signals. Using a photonic compressive sampling architecture with an average sampling rate of only 40 MSa/s, the Transformer accurately realized waveform reconstruction of LFM signals with a frequency range from 0.1 to 50 GHz and an instantaneous bandwidth as large as 10 GHz under strong interference. A frequency identification error of less than 0.3 GHz was achieved, corresponding to a compression ratio of 1500:1.

Laser scanning confocal microscopy cannot distinguish fluorescence signals of different labels, and there are overlapping and interference of different fluorescence signals in the acquired images. In order to solve this problem, a laser scanning confocal microscopy spectral imaging system based on filters was designed, to realize spectral screening and detection of different fluorescence signals. Spectral screening of the optical signals from the laser scanning confocal microscopy was performed using dichroic filters and detected through the blue-violet channel, the green channel, the yellow channel and the red-orange channel, respectively. The spectral imaging system operates in the wavelength range of 400nm≤λ≤720nm. The green channel and the yellow channel jointly achieve signal detection in the 475nm≤λ≤625nm wavelength range. In addition, the green channel and the yellow channel can further screen the signal spectrum through the continuously variable filters. The minimum spectral screening resolution in the 475nm≤λ≤625nm wavelength range is better than 5nm. Spectral calibration light source is white LED, and the spectral analysis was performed by a fiber optic spectrometer. The spectral calibration results were tested using a low-pressure mercury lamp, and the experimental results verified the accuracy of the spectral calibration results. The spectral imaging system proposed in this paper, has the advantages of high spectral flexibility and high optical efficiency, meeting the requirements of engineering applications.

Low-power signal acquisition is critical in smart sensing applications, such as multi-channel EEG systems. SAR ADC is a perfect candidate for power-efficient A/D conversion because of its amplifier-free structure. This paper presents the circuit design of a 10-bit low-power SAR ADC for biomedical signal acquisition. Synchronous SAR logic is adopted for its robustness, and a fully dynamic comparator is utilized to reduce ADC power consumption. Unit capacitance is designed to 10fF in this ADC to guarantee the DAC matching requirement. The transistor-level simulation results of the SAR ADC in 40nm CMOS show that with a 500KS/s sampling frequency and a Nyquist input frequency, the designed ADC achieves an SNDR of 61.04dB and an SFDR of 70.41dB. The power consumption of the SAR ADC is only 9.36μW, corresponding to a state-of-the-art FoM of 20.28fJ/conv-step.

Metal halide perovskite solar cells have achieved impressive development in power conversion efficiencies (PCEs) in both single-junction and tandem devices, with efficiency up to 26.14% and 33.9%, respectively. However, the unsatisfied long-term stability of functional layers, the self-degradation process and ion migration become challenges to realized practical applications. Thus, the practical and effective strategy that simultaneously improves the efficiency and stability of perovskite solar cells are highly desired. The outstanding perovskite solar cells with PCEs beyond 25% are mostly fabricated with Spiro-Ome TAD. Comparatively, the new star hole transport layers (HTL) of self-assembled monolayers (SAMs) attract much attention due to their distinct benefits in transparency, diversity, stability, sensitivity, selectivity, and surface passivation ability. However, the carrier dynamics and mechanisms of perovskite solar cells based on SAMs are still obscure and confusing. Herein, we proposed to prepare perovskite solar cells with MEO-4PACz SAMs as hole transport layers, and studied its carrier dynamics and effects on devices performance. Moreover, the joint synergistic effects of SAMs and PEDOT:PSS will be conducted systematically in this work.

We comprehensively review the possible four modulation schemes for flexible transceivers. In addition, we classify the double sideband and single side band method and the corresponding equalization techniques which are embedded in transceiver.

In clinical practice, failure to promptly diagnose acute limb ischemia(ALI) can result in muscle necrosis and severe impairment of limb function, significantly impacting the patient’s life and health. The objective of this study is to investigate methods for non-invasive real-time monitoring of muscle ischemia severity to aid clinicians in diag- nostic decision-making. This study employed multispectral photoacoustic imaging to assess a rabbit model of lower limb ischemia. Initially, ischemia was induced in one limb of New Zealand white rabbits by applying a tourniquet for 2 hours to occlude the arterial blood flow. Subsequently, the tibialis anterior muscle was subjected to multispectral photoacoustic measurements using a photoacoustic imaging system at 0, 1, and 2 hours of compression. The acquired photoacoustic spectral data were then analyzed to determine the muscle oxygen saturation(SmO₂). Photoacoustic functional imaging quantified muscle oxygenation by evaluating the percentage of oxygenated hemoglobin and myo- globin relative to the total hemoglobin and myoglobin content. Finally, venous blood gas analysis was performed at corresponding time points to ascertain intravascular oxygen saturation levels. The results of blood gas analysis re- vealed venous oxygen saturation in the lower limbs to be 73.1%, 62.7%, and 38.8%, respectively, indicating localized ischemia and hypoxia in the lower limbs of the animal model. Photoacoustic imaging yielded skeletal muscle oxygen saturation values of 78%, 46%, and 38% at the respective time points. As ischemia duration increased, skeletal muscle hypoxia intensified, leading to a reduction in muscle oxygen saturation values, consistent with physiological expecta- tions. When the tourniquet is released and blood supply is restored, SmO₂ rapidly returns to baseline levels, indicating that photoacoustic imaging is sensitive to changes in muscle oxygenation. Consequently, multispectral photoacoustic imaging enables real-time measurement of SmO₂ during acute limb ischemia. This validates the capability of this non-invasive technique to provide localized, real-time assessment of skeletal muscle ischemia, which holds significant importance for investigating numerous clinical conditions.

Carbon fiber-reinforced polymer (CFRP) is a multiphase material consisting of fibers, interfaces, and matrix. Due to their excellent mechanical properties, they are widely used in the energy, military, and aerospace sectors. However, due to the anisotropic and non-homogeneous nature of the material, tool wear inevitably occurs during machining. In order to ensure the quality of material machining and to control tool costs, tool condition monitoring has become an integral part of machining. By monitoring the tool condition in machining, predictive maintenance can be achieved, and early warning of tool failure can be achieved, thus drastically reducing downtime and saving costs in terms of time and labor. On this basis, this paper proposes a novel physics-guided neural network approach for tool wear prediction. Firstly, the fusion of physical and data information is achieved through cross-physical data modeling. Second, a multi-channel 1D-CNN convolutional neural network is utilized to reduce the complexity of local feature extraction. In addition, a loss function considering physical subject factors is proposed to quantify the physical inconsistency. Experiments of the proposed model are carried out on carbon fiber reinforced ceramic matrix composites to validate the performance of the model in terms of MAE and RMSE.

The presence of random scattering layers diffuses incident field information of objects, which interferes with the imaging process of objects, resulting in the geometry of objects not being accurately imaged. In this paper, we propose and experimentally demonstrate an approach for computational imaging of moving hidden objects through random scattering layers based on speckle cross-correlation method. Theoretical analysis denotes that imaging of moving objects is achieved by the speckle cross-correlation function and the conventional Fienup-type iterative phase-retrieval algorithm. The proposed results may have applications in imaging through grove and biological tissues.

Multi-wavelength photoacoustic imaging (PAI) has garnered significant attention due to its excellent capabilities in molecular and functional imaging. Researchers in the PAI community are consistently in need of reliable multi-wavelength imaging platforms. For clinical applications, PAI machines that are mobile, compact, and rapidly tunable are required. We have designed a stable and cost-effective PAI platform which consists of an optical parametric oscillator (OPO), a data acquisition system (DAQ), and various imaging probes. The stability of the OPO has been enhanced through the implementation of air-floating springs and mirror frame designs, alongside open-loop wavelength control. Effective monitoring of energy using built-in energy meters has improved the accuracy of PA spectral measurements. Integration with digital boards has effectively enhanced the noise resistance of DAQ and reduced its physical size. The platform can achieve a repetition rate of 10 Hz, swift wavelength tuning within the range of 680 to 950 nm (with a resolution of 1nm), and single-pulse energy greater than 80 mJ. The spectral range covers the absorption features of important chromophores such as hemoglobin, fat, and indocyanine green. The DAQ system can record PA data with 80 MHz sampling rate, 14-bit resolution, and 128/256/512 channels. The platform is equipped with linear array probes and semi-circular array probes to meet the requirements of both animal and human imaging. The semi-circular array probe utilizes a polydimethylsiloxane (PDMS) membrane with good light transmission to form a water bag for ultrasound coupling. This membrane is flexible and can conform well to different tissue shapes. Using this platform, we have conducted experiments including blood oxygen measurement, imaging of arm muscles and fat. In these experiments, we demonstrated accurate blood oxygen analyses and high-contrast muscle and fat imaging.

All-dielectric metalens based on the transmission phase was designed, which can uniformly shape a beam with a uniform energy distribution. The problems and related fixes for maintaining beam homogeneity and broad divergence angle simultaneously at 940 nm are explained. In order to solve the problem of wavefront shaping, based on the iterative design principle of complex surfaces such as freeform surfaces, a design scheme to shape the incident wavefront into 80° divergence angle wavefront was proposed. An effective, polarization-independent silicon nanopillar element is built based on the effective dielectric theory, and instead of using the conventional step size selection method, the size of each nanopillar is determined by the phase diameter function, covering the phase tuning from 0 to 2π.

Knowledge Graphs (KGs) are essential in various application areas. However, their quality is often compromised due to a large number of errors. Existing KG error detection methods have limitations in versatility and practicality. They mainly rely on supervision information such as entity types or error labels, which is not always easy to obtain in real scenarios. To address this issue, the paper proposes SymNet , an innovative framework for efficiently detecting errors in KG. SymNet utilises a triple embedding strategy, treating each triple as a node, and constructs a dynamic triple network through relational symmetric triples. To overcome the challenges posed by the special data characteristics and label scarcity of KG, a multi-layer information integration design is introduced. The BiLSTM module captures local-level intra-triplet translation information, while the graph attention network collects global-level inter-triplet contextual information. This multi-layer information integration strategy enables SymNet to more comprehensively understand the associated information in the knowledge graph, resulting in a significant improvement in error detection accuracy. The experimental results demonstrate that SymNet outperforms current state-of-the-art error detection algorithms in terms of both accuracy and efficiency when tested on two real-world knowledge graphs. Our research offers novel concepts and efficient solutions for addressing error detection issues in knowledge graphs, providing robust backing for enhancing the quality of knowledge graphs in practical scenarios.

In this paper, we propose a fiber Bragg grating (FBG) multiplexing method based on mode diversity, which achieves FBG multiplexed sensing by utilizing spatial mode dimensionality. The mode diversity FBG multiplexing system based on few mode fiber (FMF) circulator and photonic lantern is constructed, and the LP₀₁ and LP₁₁ temperature sensing multiplexing are experimentally verified. The results show that the proposed technique is capable of achieving multichannel high-resolution temperature measurements, with temperature resolutions better than 0.5 °C for both LP₀₁ and LP₁₁ spatial modes, and detection sensitivities of 15.8 pm/ °C and 16.2 pm/ °C, respectively.

Polarization imaging and computational polarimetric methods based on backscattering Mueller matrix measurement have shown great potential as a powerful information probing tool in the field of in vivo biomedical detection. However, for the backscattering polarization measurement system, the calculation and analysis of the Mueller matrix derived parameters are often related to the photon coordinate system, which can produce distinct responses with different selection and transformation of coordinate. Here, for the backscattering polarization measurement system with nearly the normal incidence, we first explore the representation of Mueller matrix elements in right-handed-nonunitary and non-right-handed-unitary systems, and further deduce and prove the photon coordinate system transformation invariants in the Mueller matrix decoding methods prevalently used in backscattering tissue polarimetry. Finally, we validate the theoretical derivation results experimentally, which can be helpful for providing a crucial criterion of parameters selection for backscattering Mueller matrix imaging under different photon coordinate systems.

As a pair of "wise eyes" for autonomous vehicles to perceive the external environment, Lidar (Light detecting and Ranging) plays a crucial role in detecting target characteristics in driving scenarios. To ensure the accuracy and reliability of Lidar, precise measurement of target reflectance under θ/θ reflection conditions is an indispensable step. Determining the reflectance value of targets under θ/θ reflection conditions is a critical part of completing Lidar calibration and traceability. When designing the θ/θ optical path, a significant challenge lies in achieving almost perfect overlap between the lighting and detecting paths while ensuring system compactness and measurement accuracy. Fiber optic spectrometers, known for their fast and accurate measurement capabilities, can be directly applied to measure target reflectance. Therefore, combining a compact θ/θ reflection optical path with a fiber optic spectrometer is key to achieving small-angle reflectance measurements for Lidar, marking an important step towards improving the calibration and traceability chain. For Lidar under θ/θ reflection conditions, various "N+1" lighting/detecting combined optical paths based on fiber bundles have been designed. Simulation analysis of these designs has been conducted using ray-tracing methods, comparing the uniformity and optical flux efficiency of the tested samples. The results indicate that when N=6, the uniformity and incident flux efficiency are optimal. Based on the simulation results, a "6+1" lighting/detecting fiber optic spectrometer has been developed, and actual measurements have been performed on a standard diffuser. The measurement data shows that the angular accuracy under 0/0 and 45/45 conditions is better than 0.1°, and the optimal relative error of the reflection measurement results in the 905nm laser wavelength was less than 0.5%. This meets the requirements for on-site measurements and is significant for further improving the Lidar measurement traceability chain.

The emergence of adversarial examples has confirmed the significant vulnerabilities of deep learning models. By introducing subtle perturbations, these examples can easily cause a drastic decline or even complete failure in the performance of well-trained models. Recent studies have indicated that these perturbations pose not only theoretical threats but also substantial risks and impacts in real-world scenarios. This study focuses on the issue of physical adversarial attacks against object detection models, providing a clear and precise definition of the concept. From multiple perspectives of target detection systems, including faces, pedestrians, vehicles, and traffic signboards, we delved deeply into and summarized a series of physical adversarial attack methods and their characteristics against object detection network models in recent years. Finally, we discussed the severe challenges faced by physical adversarial attacks, particularly the limitations of adversarial training and its inadequacies in practical applications. Based on current research progress, we envision possible future development directions and vast application prospects in this field, aiming to provide valuable references and insights for enhancing the security and robustness of deep learning models.

We present a reflection quantitative phase measurement system via transport of intensity equation (TIE). The proposed system includes an illumination collimation module and a microscopic imaging module. LED illumination is used in illumination module to avoid the effect of speckle noise. In imaging module, the 4f imaging system is formed by a long working distance objective and a lens. The constructed system was calibrated and the actual magnification of the system was 5x for 10x objective imaging. By moving the camera, two images at different defocused distances can be recorded and used for solving TIE to retrieve the phase of tested sample. Finally, the characterization of microlenses demonstrate the effectiveness of the proposed system.

Compared to traditional underwater cameras, lidar can capture more dimensional information about targets, thereby offering substantial advantages in underwater target detection. The Single-Slit Streak Tube Imaging Lidar (SS-STIL) is a high temporal resolution device designed for 3D precision measurement. It operates on the principle of time-of-flight, recording the 3D information of target as multiple high-precision 2D streak images. These images are then used to reconstruct the target's 3D information through advanced reconstruction algorithms. Existing researches on the imaging quality of Streak Tube Imaging Lidar (STIL) often fall short in thoroughly investigating the impact of water turbidity on imaging quality and particularly lack quantitative measurements of underwater imaging environments. To address the aforementioned issues, we first performed theoretical calculations and simulations of the SS-STIL for imaging targets in both air and underwater environments. Based on these simulation results, we determined the parameters for the main modules of the actual imaging system. We measured the water's attenuation coefficient in the experimental setting using a photometer, quantified five levels of underwater turbidity, and conducted experiments with our SS-STIL under these five different conditions. At an imaging distance of 4.5m and a water attenuation coefficient of 0.51m^-1 , our SS-STIL system achieved an imaging resolution of 1cm and a spatial resolution of 3cm, which is superior to other existing STIL systems.

Space debris affects the safety of Earth orbit and the detection of space debris is becoming increasingly important. Space-based detection has the advantages of not being affected by weather and being close to each other. A high-sensitivity optical system for space debris detection is designed, which has a field of view of 1° × 1° , a wavelength range of 450nm-900nm, a aperture of 150mm, a signal-to-noise ratio of 5, and can detect 12-magnitude debris, it can also provide early warning for space debris smaller than 1 cm approaching 100km. The results of image quality evaluation, tolerance analysis, temperature adaptability analysis and ghost image analysis show that the system has a speckle diameter of 6.8μm, distortion less than 0.01% and high capability concentration. The results of tolerance analysis show that the lens yield is higher than 90% if the RMS radius of the system is greater than 0.0058 mm. The results of temperature adaptability analysis show that the defocus of the system is 0.004mm from atmospheric pressure to vacuum in the range of -20°C-50°C, and the system has good adaptability to temperature environment. The results of ghost image analysis show that the system ghost illuminance is less than 1E-15w/mm² , and has no effect on imaging. The results show that the designed space debris detection optical system has the characteristics of high sensitivity and large detection range, and meets requirements of space debris detection optical system.

The scattering effects of random media severely limit the imaging capability of optical detection and ranging systems through clouds, fog, rain, turbid water, dust, biological tissues, and other medias. The method of precisely simulating the process of light transmission to invert object images is gradually becoming the mainstream approach for imaging through random media. The diffusion equation method, which involves solving the point spread function of light diffusion in random media, is a commonly used approach to simulate the transmission of light in random media. However, the diffusion equation method struggles to describe the behavior of light diffusing into objects within random media and returning directly. Based on the diffusion equation method, we have proposed a point spread function to describe the behavior of light diffusing into objects within random media and returning. Through the application of Wiener filtering, successful imaging of objects submerged in random media has been achieved. The imaging method we have proposed may have potential applications in fields such as medicine, search and rescue operations, exploration, and autonomous driving, where objects are typically submerged in random media.

This study aims to achieve fast and accurate identification of tool breakage in multi-tooth milling cutters using methods such as thresholding, clustering, and neural networks. A multi-level thresholding strategy combining fixed thresholds and dynamic thresholds was designed to enable rapid and accurate response in tool breakage identification. The fuzzy c-means clustering algorithm (FCM) and one-dimensional convolutional Softmax classifier (1D-CNN Softmax) were employed to identify the tool breakage states, distinguishing between normal cutting, single-tooth breakage, and doubletooth breakage. Experimental results demonstrate that this method exhibits fast response and high accuracy in classifying the breakage states of the three-tooth milling cutter, achieving an accuracy rate of 98.6%. This research provides a rapid and accurate technique for tool breakage identification in the field of multi-tooth milling cutter tools.

A liquid core fiber Bragg grating (FBG) sensor is proposed for curvature and temperature sensing. The sensor isfabricated by inscribing two FBGs with different periods on both sides of the hollow-core fiber(HCF) using femtosecondlaser direct writing technique, and filling the hollow core with liquid. Due to the overlap of propagation modes formedby liquid fibre cores and the grating, the reflection spectrum of FBG exhibits two well-defined resonance peaks. Bendinginfluences the fibre gratings on both sides of the air core differently, causing the intensity of the reflection peaks of thegratings to change. In the small curvature range, Bragg grating close to and far from the direction of bendingarestretched and compressed respectively, thus the resonance peaks show opposite trends, which can be used to determinethe direction of bending. As the curvature further increases, bending loss is the dominating factor that affects thereflectivity, and the peak intensity exhibit a decrease response, with sensitivity of -2.15468 dB/m^-1and -1.93504 dB/m^-1 . The temperature sensitivity of the FBG is improved due to the presence of the liquid with high thermo-optic coefficient, and blue shift response is observed when temperature is increased, with sensitivities of 86.67 pm/℃and 66.61pm/℃, respectively.

In this paper, a comprehensive evaluation of a long period fiber grating sensor (LPFGs) based on the transmission intensity at a fixed wavelength is presented. Firstly, the application of spiral long-period fiber grating sensor (H-LPFGs)and tapered long-period fiber grating sensor (T-LPFGs) based on transmission intensity at fixed wavelength is studied, that is, the sensor of torsion, temperature, strain and liquid concentration. The transverse pressure and temperature are then evaluated using this method, and the optical signal is converted into an electrical signal using photoelectric conversion to prepare a complete LPFGs. The temperature sensor has a voltage-temperature sensitivity of -18.7mV/ ℃for a measuring range of 50-150℃ with an accuracy of 0.1℃, and the strain sensor has a voltage-strain sensitivity of 0.248 mV/με for a measuring range of 0-1800με with an accuracy of 1 με. Compared with other long - period fiber grating sensors, the accuracy is improved and the measurement range is wider. The demodulation system of the LPFG sensor based on the transmission intensity ratio measurement method has the advantages of simple operation, wide application range, and low price, so it has a very good application prospect in the LPFG sensor.

In order to realize fast and effective alignment of reflective optical systems, a computer-aided alignment method based on CGH(computer-genereted hologram) is proposed. The method is based on the CGH that realizes zero compensation, phase modulates the spherical light wave emitted from the interferometer, makes it perpendicularly incident on the optical element to be aligned, and converts the misalignment information brought back by the reflected light into Zernike coefficients, which is used to guide the alignment work. This paper is based on an infrared coaxial reflective system to carry out a comparative design study of CGH applied to alignment. Due to the use of different areas of a CGH to align multiple optical elements, different optical elements need CGH to provide a large difference in the degree of optical focus, the design and processing of the CGH is more difficult. The design found that the minimum stripe width of the planar CGH is 4μm, and the method of single point diamond turning (SPDT)can not achieve its machining accuracy, while the minimum stripe width of the curved CGH is 57μm, and it can be processed by SPDT.

An optical multi-path wideband self-interference (SI) cancellation system with adaptive feedback function is proposed and demonstrated. With the help of wavelength-division multiplexing, multiple independent reference channels are constructed to mitigate the multipath effect. The particle swarm optimization (PSO) algorithm is used to adaptively adjust the operating parameters of the corresponding optical devices to optimize the cancellation performance. Experimental results show that a cancellation depth exceeding 30dB across a 200-MHz instantaneous bandwidth is achieved. A 16QAM signal with a symbol rate of 10 MBaud is successfully recovered with the assist of the proposed cancellation system.

Poisson distribution model is the basis of data analysis for GM-APD lidar, but it is only applicable to the mirror reflected target under ideal conditions, and cannot accurately describe the photon triggering process of actual GM-APD lidar detection. For the actual target with rough surface, the negative binomial distribution with M as the parameter can describe the photon distribution model more accurately. In order to solve this problem, this paper compares and analyzes Poisson distribution triggering model and the negative binomial distribution triggering model that conforms to the triggering situation of the echo signal of the target with rough surface, considering the differences in the triggering probability under different noise and signal levels. The results show that the trigger probability curve corresponding to the trigger model based on negative binomial distribution is lower in peak value, wider in bottom value and fatter overall than that of the trigger model based on Poisson distribution, and the difference between the two is more prominent under the conditions of low noise level and high signal level. This study has guiding significance for the signal extraction research based on different surface echoes.