Monitoring the finger rehabilitation process is critical for preventing accidental injuries and achieving superior personalized therapeutic outcomes. However， current research focusing specifically on monitoring this process remains limited. To address this gap， this study aims to accurately detect finger joint movement angles and assess contact forces during rehabilitation training. The primary objective is to develop a solution that enables comprehensive monitoring of both non-grip and grip rehabilitation exercises.

This work proposes a novel optical Fiber Bragg Grating （FBG） finger exoskeleton rehabilitation device. The core of the device consists of two specialized FBG fingerstalls： a monitoring fingerstall for tracking movement and a tactile fingerstall for measuring force， both integrated with advanced FBG sensor units. To validate the design， a mathematical model of the rehabilitation device was established， and its rationality was rigorously analyzed. The experimental methodology included extensive calibration and testing： the FBG sensor units underwent sensitivity， connectivity， fit， and creep resistance tests； the monitoring fingerstall was calibrated for motion angle and its angular velocity sensing performance was compared against an IMU sensor using Bland-Altman analysis on two volunteers； and the tactile fingerstall was calibrated for force， with its sensory capabilities further analyzed using arc-length， linear， and BP neural network methods. Finally， volunteers conducted grip rehabilitation training experiments to assess contact force by measuring six different objects.

The experimental results comprehensively demonstrated the high performance of the proposed system. The FBG sensor units exhibited excellent connectivity， with an average sensitivity exceeding 0.911 98 nm/N， an average fit greater than 0.995 5， and outstanding creep resistance （maximum wavelength shift of 0.9 pm）. The monitoring fingerstall achieved an average angular sensitivity greater than 9.5 pm/（°）. Critically， its performance in sensing finger angular velocity was nearly identical to that of a commercial IMU sensor， and it successfully recognized six different hand gestures. For the tactile fingerstall， sensitivity measurements at symmetric points were nearly identical， with average sensitivity at points 1/2， 3/4， and 5/6 being higher than 19.5 pm/N， 13.45 pm/N， and 9.1 pm/N， respectively. The arc-length and linear analysis methods yielded low errors （8.45% and 8.38%）， confirming model reliability， while BP neural network analysis resulted in an extremely low mean squared error of 0.000 003 309 6. The grip experiments revealed that object volume had a more significant impact on the tactile fingerstall's readings than object mass.

This study successfully developed and validated a sophisticated FBG-based finger exoskeleton rehabilitation device. The system demonstrated high accuracy， reliability， and multi-functionality in monitoring finger joint angles， assessing contact forces， and recognizing complex hand gestures. The findings confirm that the proposed device and its underlying mathematical model are effective for comprehensive finger rehabilitation monitoring. Consequently， this work provides substantial theoretical and technical support for the practical implementation of FBG intelligent sensing technology in clinical finger rehabilitation applications， paving the way for more data-driven and personalized therapeutic interventions.

All-time laser ranging to space debris is a critical direction in the development of future laser ranging systems. During Earth's shadow periods， however， debris objects receive no solar illumination and are invisible to optical cameras. In these conditions， telescope pointing corrections cannot rely on imaging and must instead depend on echo signal strength. Because debris moves rapidly and orbit prediction errors remain， even targets briefly captured within the field of view quickly drift out. This prevents stable tracking and reduces ranging success rates.

To address this challenge， this study introduces a novel approach by incorporating a fiber array into the laser ranging system. The array consists of a central fiber surrounded by six uniformly arranged multimode fibers. Each fiber is divided into two outputs： one reference fiber of equal length and one guiding fiber of varying lengths. The fourteen outputs are fused into a single fiber， which is then connected to a Superconducting Nanowire Single-Photon Detector （SNSPD）. By measuring the relative delays among echo signals， the incident fiber can be identified， enabling calculation of the target's offset direction in the field of view and guiding the telescope for tracking.

Experiments were conducted using the 1.2 m coaxial laser ranging system at Yunnan Observatory during May-June 2025. Cooperative targets with small orbit prediction errors were first selected as observation objects to verify the effectiveness of the fiber array method. After validation， the fiber array method was applied to space debris ranging experiments during Earth's shadow periods. The results demonstrate that the fiber array can effectively enhance the tracking capability of the ranging system for invisible targets. For cooperative targets equipped with retro-reflectors， the system efficiently extracted echo signals and accurately identified the incident fiber， thereby deriving the offset direction and achieving stable and reliable ranging. For non-cooperative debris， the current system shows certain limitations. As shown in Fig. 6（d）， debris with NORAD ID 28480 produced strong echoes that allowed identification of the incident fiber as F5. In contrast， debris with NORAD IDs 22220 and 21393， shown in Figs. 6（e）-（f）， yielded more dispersed echoes， making it difficult to accurately invert the incident fiber from time-delay information. This phenomenon mainly arises from temporal overlap of signals received by adjacent fibers. Specifically， debris generally exhibits a size effect， where differences in reflection depth across the surface broaden echo signals in the time domain. At the same time， the low surface reflectivity of most debris weakens the echoes and increases their susceptibility to background noise. Together， these factors cause temporal overlap among the multichannel data， which appears in the residual plots as clearly dispersed echo patterns and prevents reliable fiber identification based on inherent time delays.

To improve performance for non-cooperative targets， fiber parameters require targeted optimization. First， the delay difference between reference and guiding fibers should be increased so that the minimum channel separation exceeds three times the ranging precision， thereby reducing signal overlap. Second， the spatial arrangement of the array should be optimized to maximize the average channel delay difference， further suppressing inter-channel interference. Additionally， the current multimode fibers use a core diameter of 62.5 µm and cladding diameter of 125 µm， with the core occupying only 25% of the cross-sectional area. Under weak signals， this may limit detection efficiency. Future designs could reduce cladding thickness to increase area utilization while maintaining low crosstalk， thereby improving overall system performance. In summary， the proposed fiber array method provides a new solution for high-precision tracking of invisible targets and constitutes an important technical step toward all-time space debris laser ranging. With further optimization of fiber parameters， the method offers strong engineering potential and prospects for practical application.

This study establishes a high-precision measurement methodology for determining the polarization vector and the circular-to-linear birefringence ratio of Spun Fiber， addressing the critical need for accurate parameter decoupling in Fiber Optical Current Transformers （FOCT）. By leveraging the fixed-point principle within the framework of quaternion polarization optics， the proposed method identifies the intrinsic rotation axis of the fiber on the Poincaré sphere， thereby circumventing the systematic errors induced by polarization axis misalignment and the complexities of prior axis calibration. The research focuses on the 1 310 nm wavelength band to provide a robust physical basis for optimizing FOCT sensitivity and stability through the precise quantification of residual linear birefringence and inherent circular birefringence. The methodology is centered on the application of quaternion polarization optics， where any polarization-dependent system is characterized by a Jones-Mueller Quaternion （JMQ） whose vector part defines the polarization vector W.

Utilizing an experimental configuration comprising an eight-wavelength discrete light source （1 284~1 318 nm）， an electric tunable Polarization Controller （PC） with 0.01 V resolution， and a high-accuracy Polarization Analyzer （PA）， the measurement process exploits the invariance of the output State of Polarization （SOP） when the input SOP is aligned with the fiber's polarization vector. During the direction-finding phase， continuous wavelength scanning is performed while the PC iteratively adjusts the input SOP； the polarization vector's orientation is confirmed when the output SOP trajectory on the Poincaré sphere converges to a stationary “fixed point”， minimizing the rotation radius to near zero. Following the identification of this vector， the magnitude of the birefringence is determined by shifting the fixed point to the Poincaré sphere's pole and moving the SOP to the equator via an output PC. By recording the angular displacement \mathrm{\Delta }\varphi of the SOP along the equatorial plane during subsequent wavelength scans， the total beat length L_p is calculated using the variational phase-shift formula L_p=(L\cdot \mathrm{\Delta }\lambda )/({\lambda }_{\mathrm{0}}\cdot (\mathrm{\Delta }\delta /\mathrm{2}\mathrm{\pi }))， where L is the fiber length and \mathrm{\Delta }\delta is the phase change derived from the trajectory arc. The circular beat length （L_c） and linear beat length （L_l） are then extracted by projecting the synthesized polarization vector onto the Z-axis and the equatorial plane， respectively， enabling a direct physical separation of the two birefringence components. Empirical validation was conducted using Fibercore SHB1250 elliptical polarization-maintaining Spun Fiber across multiple specimens with lengths of 1.20 m， 0.80 m， 0.53 m， and 0.46 m to ensure consistency and repeatability. For the 1.2 m sample， the polarization vector was precisely located at Stokes coordinates.

Across the experimental trials， the average circular beat length was measured at \mathrm{93.347}\mathrm{m}\mathrm{m} and the average linear beat length at \mathrm{497.451}\mathrm{m}\mathrm{m}， yielding a circular-to-linear birefringence ratio of \mathrm{5.33}. These results are highly congruent with the manufacturer's specified circular beat length range of 63~125 mm， verifying the accuracy of the fixed-point detection. Comprehensive error analysis indicates a systematic error of approximately \mathrm{0.063}， primarily stemming from the discrete resolution of the polarization controller and the intrinsic measurement uncertainty of the analyzer. The random error was restricted to \mathrm{0.002}， demonstrating the method's superior immunity to environmental perturbations， such as thermal drift and mechanical vibration， facilitated by the trajectory-fitting approach.

The fixed-point principle offers a physically intuitive and mathematically rigorous solution for the characterization of complex optical fibers， providing a significant advantage over Mueller matrix decomposition by eliminating the need for coordinate-dependent calibration. This methodology facilitates the high-precision decoupling of birefringence components， which is essential for suppressing zero-point drift and improving the long-term reliability of FOCT systems in high-voltage sensing applications. The ability to directly output the polarization vector and beat length parameters under varying environmental conditions establishes this approach as a candidate for the standardized testing of complex polarization-maintaining components. Furthermore， the robust performance of the fixed-point identification under broad wavelength scanning demonstrates its potential for broader applications in the standardized detection of non-uniform birefringence in advanced optical waveguides.

In the modern electronic countermeasure environment with strong electromagnetic interference， traditional wireless communication methods such as radio frequency， Wi-Fi， and millimeter waves face challenges like unstable connections and vulnerability to interference or detection. These issues hinder their ability to satisfy the high requirements for stealth and strong anti-interference needed by rear combat formation Unmanned Aerial Vehicles （UAVs）. Ultraviolet Communication （UVC）， with its non-line-of-sight transmission capability， low detectability， and high anti-interference performance in the “solar-blind”band， has become an important means of new-generation covert communication.

In this paper， a joint optimized clustering method （MOPSO-DE） integrating Multi-Objective Particle Swarm Optimization （MOPSO） and Differential Evolution （DE） is proposed to solve the problems of uneven node energy consumption and limited data transmission of traditional clustering algorithms in order to meet the demands for efficient and cooperative communication of formation UAVs in combat missions under UV covert communication.In this paper， we first develop models for UAV inter-copter Ultraviolet （UV） non-line-of-sight communication links and UAV energy consumption. We then propose four key metrics for selecting cluster heads： the node's remaining energy， stability of the UV link's Received Signal Strength Indicator （RSSI）， the node's normalized distance to the leader UAV， and consistency in speed. For the above multi-objective optimization problem， this paper introduces the multi-objective particle swarm optimization algorithm into the cluster head election process， constructs the Pareto optimal solution set by the non-dominated sorting mechanism， and filters out the global optimal cluster head set by combining with the congestion distance strategy， and at the same time introduces the differential evolution mechanism to prevent the algorithm from falling into the local optimum. In the data transmission phase， a communication framework based on the three-layer structure of ‘leader-cluster leader-member’ is designed， and an intra-cluster fast reconfiguration mechanism is proposed to cope with the failure of the cluster leader nodes due to energy depletion， deterioration of the link， or positional deviation. By constructing a single-objective weight function to achieve rapid cluster head replacement without global reclustering， the dynamic adaptive capability and control overhead of the network are effectively improved.

In the simulation validation this paper deploys 100 UAV nodes in a 1 000 m×1 000 m area based on Matlab platform， and comprehensively compares the network performance of MOPSO-DE with five algorithms： LEACH， WCA， LEACH-X， DSWCA， and CNNUC， under different simulation rounds. The results show that MOPSO-DE has significant advantages in terms of node energy consumption， network survival time and data transmission volume. Compared with the LEACH algorithm， WCA algorithm， LEACH-X algorithm， DSWCA algorithm and CNNUC algorithm， the MOPSO-DE algorithm respectively reduced the average energy consumption by 25.8%， 23.6%， 17.1%， 15.3% and 11.1%， and increased the data transmission volume by 91.6%， 54.42%， 29.3%， 12.8% and 9.6%. The article further simulates and analyses the effects of different node numbers， different packet lengths and different node movement speeds on the performance of the algorithm， and the results show that the increase of node density increases the overall communication load of the system； the increase of packet length improves the amount of data transmission per unit of time， but it also significantly accelerates the energy consumption of the nodes； and the effects of different movement speeds on the performance of the network show a ‘pre-favorable， post-favorable’ effect. The impact of different mobile speeds on the network performance shows the stage characteristics of ‘favorable in the early stage and unfavorable in the later stage’.

In summary， the MOPSO-DE multi-objective clustering algorithm proposed in this paper effectively integrates multi-factor optimization strategies and intelligent optimization methods， and achieves significant results in improving clustering efficiency， extending network life cycle， reducing energy consumption and enhancing link robustness， which provides theoretical support and algorithmic basis for the efficient deployment of UAV swarm self-organized communication networks in the future under the complex battlefield environment.

Calcium ions and dissolved oxygen are critical physiological indicators： Calcium ions regulate nerve conduction， muscle contraction， and cardiac function， while dissolved oxygen reflects tissue oxygenation status via blood oxygen partial pressure. Both parameters are implicated in diverse physiological and pathological processes， including respiratory， cardiovascular， and renal disorders.While traditional electrochemical methods can simultaneously detect calcium ions and dissolved oxygen， they have limitations such as the need for blood sampling， high maintenance requirements for electrodes， and long detection cycles. Existing multi-parameter sensing technologies based on fiber optics still have shortcomings in terms of stability and biocompatibility， limiting their application in long-term in vivo monitoring. In contrast， hydrogels， as a novel sensing material， offer the potential for monitoring physiological parameters within the body due to their excellent biocompatibility. Changes in calcium metabolism and oxygenation status interact in many physiological and pathological processes. Therefore， developing a technology capable of simultaneously detecting calcium ions and dissolved oxygen concentrations is crucial for comprehensively understanding an organism's physiological state and disease progression.

In response to the above problems， a hydrogel-based dual-parameter fibre-optic fluorescence sensing probe was proposed in this paper based on the principles of fluorescence enhancement and fluorescence quenching. The probe aims to provide an innovative clinical tool for early disease diagnosis， condition monitoring， and treatment evaluation. The probe was made of 80% w/v and 40% w/v polyethylene glycol diacrylate as core and cladding materials， respectively， and the core was doped with calcein and dichlorotris （1，10-phenanthroline） ruthenium （Ⅱ）， which acted as fluorescent indicators for calcium ions and dissolved oxygen， respectively. The preparation process of the dual-parameter sensing probe was optimized， and the sensitivity and accuracy of the probe for the dual-parameter detection were optimal when the concentration of calcein was 0.05 mg/ml， the concentration of dichlorotris （1，10-phenanthroline） ruthenium （Ⅱ） was 2.5 mg/ml， and the pre-soaking time of the probe was 4 h. The double peaks of the probe were obvious in the extreme concentration of the dual-parameter detection.

The sensing performance of the probe was investigated experimentally and the results showed that in the range of 0~2 mmol/L for calcium ions， the sensitivity of the system was 0.340 2 （mmol/L）^-1 with an R² of 0.997 8， and the lower limit of detection reached 0.095 mmol/L with a response time of 11 min. In the range of 0~41.6 mg/L for dissolved oxygen， the sensitivity of the system reached 0.020 03 （mg/L）^-1 with an R² of 0.996 82， and the lower limit of detection was 0.44 mg/L， with a response time of 12 min. The stability of this sensing probe was 99.31% and 99.4%， respectively， over a period of 3 days. The reproducibility of the probe reached 98.47% and 99.48%， respectively， after five sets of replicated experiments. Within the physiological temperature range， the probe has reliable detection performance. Compared with other substances in the blood， the probe has high selectivity for calcium ions and dissolved oxygen.

The probe takes advantage of the good biocompatibility and flexibility of hydrogel to overcome the rigidity limitations of traditional fiber optic materials and is more suitable for long-term monitoring in vivo. At the same time， the probe can also realize electromagnetic radiation-free detection， featuring a simple preparation process， low cost and high integration. By simultaneously detecting calcium ions and dissolved oxygen concentration， the probe can provide more comprehensive physiological data， overcoming the shortcomings of single-parameter detection. Based on this， the prepared dual parametric hydrogel fiber-optic probe for calcium ions and dissolved oxygen has the advantages of implantability， high sensitivity， and real-time online monitoring， which provides an innovative solution for biomedical detection. Especially in clinical scenarios， it is valuable for early prevention and real-time monitoring of diseases， accurate diagnostic decision-making， treatment planning and efficacy assessment， and other key aspects.

Pulse diagnosis， as a core diagnostic method in traditional Chinese medicine， has long faced challenges such as strong subjectivity and difficulty in standardization. To address these issues， this paper proposes a pulse diagnosis sensing system based on a tri-channel U-shaped Micro-Nano Fiber （MNF） sensor. The system can synchronously acquire pulse signals from the cun， guan， and chi positions at different pressing depths （floating， middle， and sinking）， comprehensively capture subtle variations in pulse waveforms， and integrate deep learning models to achieve accurate pulse recognition， thereby enhancing the objectivity and digitization of traditional Chinese medicine diagnosis.

To construct a synchronous signal acquisition system， three well matched U-shaped MNFs were embedded in a Polydimethylsiloxane （PDMS） film and fixed on a Polycarbonate （PC） substrate to form sensing units， which were arranged on the cun， guan， and chi parts respectively. MNF was prepared using flame heating cone method， with a diameter of about 3 μm and a bending radius of 2 mm. Its structural parameters were optimized through COMSOL simulation to achieve a balance between evanescent field effect and mechanical strength， ensuring both adaptation to radial artery pulsation and stable and accurate detection. The PDMS package thickness was optimized through ANSYS finite element simulation and ultimately determined to be 100 μm to achieve the best balance between mechanical strength and sensitivity. Three sensing units are integrated into a customized 3D printed silicone platform， which has good biocompatibility and can adapt to individual physiological differences. In the experimental section， a tri-channel Photoelectric Detection （PD） module was used to convert the optical signals into electrical signals， and the dynamic waveforms were displayed in real time on an oscilloscope. To simulate the three pulse conditions of “floating， middle， and sinking” in traditional Chinese medicine， an airbag cuff loading system was designed to apply different levels of pressure to the pulse positions， thereby enabling signal acquisition. Through signal processing， multiple physiological parameters including Rise Wave Transit Time （RWTT）， Upstroke Time （UT）， Pulse Transit Time （PTT）， Left Ventricular Ejection Time （LVET）， etc. are extracted. In terms of intelligent recognition， a dataset containing 1 260 annotated pulse wave data was constructed， covering seven typical pulse types： normal pulse， forceful pulse， rapid pulse， flooding pulse， slippery pulse， tense pulse， and thready pulse. A deep learning model combining one-dimensional Convolutional Neural Network （1D-CNN） and Bidirectional Gated Recurrent Unit （BiGRU） is proposed， where CNN is used for local feature extraction and BiGRU is used to capture temporal dependencies， thereby achieving efficient classification of multi-channel pulse signals.

Mechanical performance tests show that the sensitivity of the three channel MNF sensor in the pressure range of 0~7 kPa is 11.49% kPa^-1、11.64% kPa^-1 and 11.13% kPa^-1， respectively. The response time and recovery time are 22.7 ms/9.4 ms、31.2 ms/10.6 ms and 29.8 ms/8.5 ms， demonstrated favorable sensitivity and a high level of consistency. After 600 cycles of loading unloading， the signal remained stable， verifying the reliability of the packaging structure. Compared with existing flexible pulse sensors， this device has advantages in sensitivity， channel count， and response speed.In the pulse diagnosis experiment， the system can simultaneously obtain pulse signals from the cun， guan， and chi parts， and clearly characterize the waveform differences at different compression depths. As the pressure increases from shallow to deep， the pulse wave gradually flattens， UT shortens， and the rate of rise accelerates， indicating physiological changes in reduced vascular wall compliance. Through waveform analysis， objective parameters of seven common pulse patterns were successfully extracted， which were highly consistent with clinical manifestations in volunteer experiments. For example， diarrhea patients showed thready pulse， while cold patients showed tense pulse， demonstrating the effectiveness of the system in capturing pulse patterns. In the intelligent recognition experiment， the deep learning model achieved 91.92% and 92.01% accuracy in classifying left and right hand pulse signals， respectively， when only using intermediate data； When integrating data from floating， medium， and sinking phases， the accuracy significantly improves to 96.61% （left hand） and 96.07% （right hand）. Compared with existing classification methods based on manual features or single networks， this method exhibits higher classification performance on similar data scales， demonstrating the superiority of combining 1D-CNN and BiGRU in complex pulse recognition.

The flexible pulse diagnosis and perception system based on three channel U-shaped MNF proposed in this study can achieve synchronous acquisition of pulse waves from multiple parts and depths of compression， and combine deep learning to achieve high-precision pulse recognition. This method effectively improves the objectivity and standardization level of traditional Chinese medicine pulse diagnosis， and has broad application prospects in remote medical care， digital diagnosis and treatment， and chronic disease management.

Vehicle-to-Vehicle Visible Light Communication （V2V-VLC） offers significant advantages in intelligent transportation systems， such as leveraging existing vehicular lighting， spectrum license exemption， low latency， and resistance to multipath interference. By transmitting critical information like vehicle position and driving instructions via LED light sources， V2V-VLC facilitates collaborative decision-making among vehicles. However， the open nature of optical transmissions exposes V2V-VLC to interception risks， leading to privacy breaches and traffic hazards. Current security measures primarily rely on higher-layer encryption and authentication， which fail to address vulnerabilities caused by physical-layer signal exposure. Physical Layer Security （PLS） complements existing methods by providing transparent protection across multiple data types. Yet， PLS research in VLC has been predominantly limited to static indoor environments， which are unsuitable for dynamic road scenarios characterized by high-speed mobility， variable topologies， and wide-beam light coverage. Particularly at intersections， extensive signal exposure and unpredictable eavesdropper locations exacerbate security risks.

This study proposes an Artificial Noise （AN） interference mechanism based on traffic infrastructure collaboration to minimize eavesdroppers' Signal-to-Interference-plus-Noise Ratio （SINR） under worst-case conditions， thereby enhancing V2V-VLC security. To improve the physical-layer security of V2V-VLC in dynamic road environments， a collaborative AN jamming scheme coordinated by traffic infrastructure was introduced. As illustrated in Fig. 2， the source vehicle （S） transmits confidential data to the target vehicle （D） via LED headlights， while the traffic signal （J） concurrently emits controlled artificial noise. The jamming signals are optimized to avoid interference with legitimate vehicles while disrupting eavesdroppers' reception. An optimization problem is formulated to minimize the worst-case SINR of eavesdroppers while maintaining the quality of legitimate communication. The concave-convex procedure （CCP） is employed to derive the optimal solution， ensuring effective suppression of eavesdropper signals and improving the system's secrecy rate without degrading communication performance. Simulations validate the proposed scheme's effectiveness in enhancing V2V-VLC security. Vehicle parameters are configured as follows： the source vehicle's LED headlights are mounted 0.8 m above the ground and spaced 1.6 m apart， while the receiver's photodetector （PD） is installed on the target vehicle's rear bumper， 0.8 m above the ground， with a field of view （FOV） of 60°. Detailed parameters are listed in Table 2. Results indicate that eavesdroppers' SINR is highest near the source vehicle's LED and decreases with distance due to path loss （Fig. 3）. Localized SINR peaks occur near traffic signals due to the directional characteristics of their LEDs. The maximum SINR of eavesdroppers reaches-7.07 dB， 8.77 dB lower than the reference scheme （Table 3）. Compared to traditional spatial jamming methods， the proposed scheme achieves superior SINR suppression across all regions. For near-field eavesdroppers， SINR is reduced by 11.25 dB and 10.61 dB in Scenarios 1 and 2， respectively. For far-field eavesdroppers， reductions of 17.12 dB and 17.41 dB are observed （Fig. 4）. Furthermore， the system's average secrecy capacity improves by 0.68 bits/s/Hz （Fig. 5）. These findings confirm that minimizing the worst-case SINR effectively disrupts eavesdropper reception while preserving legitimate communication quality.

In summary， this study addresses physical-layer security challenges in V2V-VLC systems under dynamic road conditions. An infrastructure-assisted AN interference scheme is proposed to mitigate interception risks， particularly in high-risk areas like intersections. By optimizing interference generation from both source vehicles and traffic signals， the scheme achieves multidimensional suppression of eavesdropper signals while maintaining legitimate communication quality. Simulations show that the proposed method suppresses eavesdroppers' worst-case SINR to-7.07 dB， 8.77 dB lower than traditional spatial jamming methods， with maximum SINR reductions of 11.25 dB and 17.41 dB for near-field and far-field eavesdroppers， respectively. The scheme also improves system secrecy capacity by 0.68 bits/s/Hz.

Compared to conventional methods， this approach offers superior physical-layer security for V2V-VLC systems in complex and dynamic road environments. Key innovations include： 1） Addressing security challenges in V2V-VLC systems with unknown and randomly distributed eavesdroppers at intersections. 2） Proposing a joint jamming scheme utilizing traffic infrastructure as a cooperative interference source， extending the spatial dimension of security. 3） Formulating and solving an optimization problem to minimize the worst-case SINR of eavesdroppers without interfering with legitimate users. This study provides theoretical and technical solutions for enhancing V2V-VLC security， laying a foundation for future research.

In space-based gravitational wave detection systems， ultra-long laser interferometry arms are established between three communication satellites to detect the gravitational wave， requiring the space telescopes to perform both laser transmission and reception functions. However， the extreme weakness of gravitational wave signals imposes stringent requirements on optical path stability and backward stray light suppression in these telescopes， that is， the backward stray light must be less than 10^-10 of the output laser. The primary source of backward stray light from space telescopes arises from surface scattering. Even in ultra-smooth optical elements （roughness less than 1 nm）， the surface scattering inevitably occurs during laser transmission， generating backward-scattered light that affects the gravitational wave detection. Therefore， precise characterization of ultra-smooth optical surfaces is critical for analyzing and suppressing telescope backward stray light levels. However， conventional non-contact measurement methods face significant challenges， including complex instrument structure， high experimental cost， and insufficient accuracy， making it difficult to achieve rapid and high-precision measurement of ultra-smooth optical surface. Consequently， there is an urgent need to develop a surface quality assessment method for ultra-smooth optical elements to meet the extraordinary precision requirements of gravitational wave space telescopes.

For high-precision scattering measurement of ultra-smooth optical elements， Li B C's team proposed a method based on multi-channel cavity ring down technique. This method obtains the surface scattering rate by analyzing multi-channel ring-down signals， offering advantages including absolute measurement， immunity to laser source intensity fluctuation， and exceptional measurement accuracy. This work further combines the cavity ring-down-based scattering measurement technique with the Generalized Beckmann-Kirchhoff （GBK） scalar scattering model， to establish a method for estimating the surface parameters of ultra-smooth optical elements. In this method， the GBK scattering model is used to establish the scattering rate database of the optical elements， and the scattering rate of the ultra-smooth optical elements at different solid angles is measured based on the cavity ring down technique. Then， the scattering rate distribution measured by cavity ring down method is numerically fitted with the scattering rate database to obtain the surface parameters of the ultra-smooth optical elements， which provides a reference for the scattering characteristics analysis and stray light suppression of the ultra-smooth optical elements of space telescopes.

The scattering rate distribution of ultra-smooth optical element measured by the cavity ring down technique was simulated and calculated， with the measured data numerically fitted through our proposed surface parameter estimation approach. As shown in Figure 3， the fitting achieves remarkable precision with an error of 0.16% and a goodness of fit of 0.999 8. Further evaluation of the method's performance across different parameters demonstrates that within the applicable range of GBK theory， the method can achieve an error of below 4% （typically<0.5%）， while the goodness of fit is greater than 0.96 （often>0.999）. In addition， to address measurement errors induced by scattering overlap during bidirectional beam propagation in standing-wave cavity ring-down experiments， we analyzed key influencing factors. By integrating these findings with our predictive model， we quantified scattering overlap effects at various measurement angles and derived an optimized angular selection scheme to minimize interference.

This work proposes a novel method for estimating surface parameters of ultra-smooth optical elements by combining GBK scalar scattering theory with cavity ring-down scattering measurement techniques. The simulation analysis shows that， within the scope of application of GBK scalar theory， the relative error of this method is less than 4%， and the fitting degree is greater than 0.96， confirming its high accuracy for rapid and precise measurement of surface quality parameters. In addition， aiming at the error caused by the scattering overlap of the back and forth propagation of the beam in the scattering measurement experiment of the standing wave cavity ring down method， we propose a measurement angle selection scheme considering both the signal-to-noise ratio and the measurement accuracy， so as to reduce the influence of the scattering overlap of the forward and backward propagation on the scattering measurement and subsequent surface quality estimation.

The objective of this study is to develop a high-precision optical interferometric phase demodulation method that does not require preset phase shifts and can accurately recover phase information from only three interferograms. The proposed method is designed to overcome the inherent limitations of conventional random phase-shifting algorithms， which typically suffer from large errors， instability， and restricted accuracy improvements， particularly when the number of interference fringes is limited. At the same time， the method ensures robustness under varying fringe types， Signal-to-Noise Ratios （SNR）， and fringe numbers. In this work， a novel three-frame random phase-shifting phase demodulation algorithm is proposed， referred to as the VU&LEF method， which integrates the VU algorithm with ellipse-fitting correction. First， the VU algorithm is applied to three randomly phase-shifted interferograms to extract two orthogonal components of the interferometric signal. In practice， due to noise interference and the limited number of frames， these components are not perfectly orthogonal， and their distribution often forms an ellipse rather than an ideal circle. To correct this deviation， ellipse-fitting is introduced to estimate the ellipse coefficients. A subsequent normalization procedure maps the ellipse into a circle， thereby restoring strict orthogonality between the two components and significantly reducing systematic errors in the subsequent phase calculation. This optimization enables high-precision phase recovery even with only three interferograms， effectively overcoming the main drawbacks of the conventional VU method. To evaluate the performance of the proposed approach， extensive numerical simulations and experimental validations were conducted under diverse conditions. The results demonstrate that the VU&LEF method consistently outperforms the conventional VU algorithm， achieving significantly improved phase accuracy. Even under low-SNR conditions， the proposed method maintains stable phase recovery， whereas the traditional approach exhibits severe degradation. To further assess computational efficiency， the processing time of the VU&LEF method was compared with that of the conventional VU method and the AIA algorithm. Although the ellipse-fitting step introduces a slight additional computational cost， the proposed method remains more efficient than the AIA algorithm while simultaneously offering superior phase accuracy. Furthermore， the robustness of the proposed method was verified using representative open， closed， and complex fringe patterns with significant background fluctuations. The results clearly indicate that the method effectively corrected elliptical distributions into ideal circular forms， thereby reducing measurement errors. In contrast， the conventional VU method exhibited large phase errors and severe distortions in regions with strong background interference， whereas the VU&LEF method substantially mitigated these errors through ellipse correction. Additional tests under varying frame numbers confirmed that the proposed method yields stable and reliable phase recovery with significantly reduced error fluctuations. These findings further indicate that phase correction is indispensable when performing demodulation with only three interferograms， highlighting the wide applicability of the method in practical interferometric measurements. From an application perspective， requiring only three interferograms greatly reduces data acquisition time compared with conventional multi-frame methods. The additional computational cost introduced by ellipse-fitting correction is negligible compared with the measurement time saved. This combination of high efficiency and accuracy makes the method particularly suitable for real-time and high-speed measurement scenarios， such as dynamic surface profiling， deformation monitoring， in-situ industrial inspection， and other precision optical metrology tasks. In conclusion， the proposed three-frame random phase-shifting phase demodulation method with ellipse-fitting correction does not require the phase shifts of interferograms to be predefined or equally spaced， and thus constitutes a true random phase-shifting technique. It inherits the advantage of the VU method in not requiring prior calibration of phase steps， while enabling high-precision phase recovery from minimal data. Moreover， it demonstrates strong robustness against noise， fringe variability， and complex background interference. Both simulation and experimental results verify that the proposed method outperforms conventional approaches in terms of accuracy， stability， and computational efficiency.

Binocular stereo vision has become a mainstream three-dimensional imaging solution in medical imaging analysis and industrial inspection because of its low cost， passive sensing and real-time processing capability. However， conventional stereo matching algorithms exhibit intrinsic limitations when confronting highly reflective and weakly textured objects such as polished ceramics， metals or water-like surfaces. Specular highlights saturate the sensor and obscure surface micro-structure， while the lack of natural texture causes a reduction in reliable feature points， resulting in mismatches， depth discontinuities and holes in reconstructed point clouds. Existing methods can be broadly categorized into active feature augmentation， which relies on projected patterns， added particles or structured light， and passive algorithmic optimization， which refines feature detection or incorporates learning-based priors. Active schemes increase system complexity， cost and sensitivity to environmental illumination， whereas passive schemes remain vulnerable to strong glare and texture scarcity， and often depend on prior knowledge of material properties. Polarization imaging， which is sensitive to surface reflection mechanisms and material characteristics， offers a promising avenue， yet current polarization-stereo fusion strategies still suffer from reflection-model inaccuracies， normal ambiguity， dependence on refractive index， and error accumulation during normal integration. These limitations motivate the present work， which aims to develop a polarization-augmented binocular stereo framework that alleviates specular interference and texture deficiency without introducing additional active devices.

The proposed method constructs a polarization-fused binocular stereo vision pipeline that combines physically based reflection decomposition with multimodal feature enhancement and conventional triangulation. A stereo pair of polarization-sensitive cameras acquires synchronized images at four polarization angles （0°， 45°， 90° and 135°）， and the system is calibrated using a polarization chessboard and Zhang's method to obtain intrinsic and extrinsic parameters and to ensure accurate epipolar rectification. At the image-formation level， the observed intensity at each polarization angle is modeled as a superposition of specular and diffuse components. Multi-angle intensities are assembled into an observation matrix and approximated as low rank via singular value decomposition， which factorizes the data into coefficient and reflection matrices. An unknown transformation matrix is then optimized by minimizing the Pearson correlation between specular and diffuse estimates， yielding a separation in which diffuse components retain texture while specular energy is strongly attenuated. In parallel， Stokes-parameter analysis of the four polarization channels provides the degree and angle of polarization， from which azimuth and zenith angles of surface normals are inferred under an assumed refractive index. Intensity-based SURF descriptors are extracted on the glare-suppressed diffuse image， polarization-normal descriptors are computed from the normal map， and both standardized descriptors are concatenated into a joint feature representation that guides stereo matching， disparity computation and depth estimation.

Extensive experiments on highly reflective ceramic and metallic targets demonstrate that the proposed polarization-enhanced stereo framework markedly improves both feature richness and three-dimensional reconstruction quality. Using a dual MER2-502-79U3M polarization camera system with precise baseline control and synchronized acquisition of four-angle polarization sequences， the authors first examine feature detection on ceramic samples with strong specular highlights. SURF-based analysis shows that， before enhancement， only a few dozen stable feature points are available in each region， whereas after specular-diffuse separation and polarization-based feature augmentation， the number of keypoints increases by roughly an order of magnitude and feature coverage improves correspondingly. Gray-level histograms confirm that the proportion of pixels in high-intensity highlight ranges drops sharply after processing， indicating effective suppression of saturated regions and recovery of latent texture. Depth profiles along selected cross-sections reveal that original stereo reconstructions exhibit pronounced oscillations and artificial depressions in glare-dominated areas， while the enhanced method yields smooth， continuous depth curves that closely approximate the true object geometry. Quantitative distance measurements between reconstructed and physically measured point pairs show absolute errors below 5 mm and relative errors below about 0.5. On metallic heater targets， the method fills the vast majority of point cloud holes， elevates surface completeness to almost full coverage， reduces highlight-dominated pixel proportions， and increases local feature density by several tens of percent in difficult regions.

Comparative evaluation against three representative baselines further highlights the advantages and limitations of the proposed approach. Traditional binocular stereo vision， although computationally efficient， exhibits the lowest structural similarity because it relies solely on intensity texture and is easily disrupted by glare and weak-patterned surfaces. Pure polarization-based three-dimensional imaging improves detail recovery by exploiting polarization-derived normals， yet its accuracy is constrained by unknown or spatially varying refractive indices and by error accumulation during normal integration. A hybrid method that fuses polarization with deep neural networks achieves higher reconstruction quality by resolving angular ambiguities， but requires substantial computational resources and long inference times， which challenge real-time deployment. In contrast， the proposed specular–diffuse separation and polarization-augmented stereo matching strategy achieves the highest structural similarity index while maintaining moderate runtime， thereby realizing a favorable balance between precision and efficiency without resorting to additional active projection hardware. Overall， the work shows that incorporating polarization cues at the level of reflection modeling and feature description can effectively overcome the dual challenges of high specular reflection and texture scarcity， significantly reducing point cloud hole rates and enhancing geometric fidelity. The method therefore offers a promising solution for high-precision， passive three-dimensional measurement of highly reflective， weakly textured objects in industrial inspection and related application scenarios.

The high penetrating power， high resolution， and non-invasive nature of X-rays make them widely applicable in medical examinations， industrial flaw detection， and security screening. While X-ray Computed Tomography （CT） technology is extensively used and relatively mature， its significant radiation exposure， lengthy data acquisition time， and intricate mechanical structure hinder broader adoption. Furthermore， CT can not achieve three-dimensional imaging for certain flat samples. Computed Laminography （CL）， a derivative of CT technology， effectively addresses this limitation. However， prolonged exposure times and complex operational procedures still result in extended imaging durations and reduced efficiency， preventing in-situ observation. Consequently， there is an urgent need to develop a new technology capable of achieving high-efficiency， high-quality three-dimensional imaging of flat samples. Unlike visible light imaging， X-ray transmission methods yield images of relatively poor quality relative to the demands of stereoscopic imaging algorithms. Stereo imaging is a promising approach， as it requires only two images captured from different angles to achieve depth reconstruction. This method holds potential for enabling high spatio-temporal resolution stereoscopic imaging when combined with synchrotron radiation sources. Existing denoising algorithms prove ineffective for these images， failing to achieve satisfactory depth information recovery. Consequently， stereoscopic imaging techniques—well-established in the visible light domain—have not been successfully applied to X-ray imaging. This study focuses on the lack of dedicated denoising algorithms in the field of X-ray stereo vision imaging， and proposes a Gray Value Remapping Denoising Method （GVRM）. This method employs the NlMD function from the OpenCV open-source library during the preprocessing stage to perform initial noise reduction. It then enhances edge features using the Laplacian operator and further processes anomalous pixels with the NlMD function after adjusting its parameters. Subsequently， in the feature segmentation stage， a radial attenuation model is established based on Lambert-Beer law， and the Otsu's method is applied to achieve adaptive threshold segmentation， which supports multilevel gray-scale quantization. Finally， a normalized inter-correlation algorithm is used in the stereo matching stage to calculate the parallax field and generate high-precision parallax maps. In order to evaluate the advantages and disadvantages of the algorithms， the simulation of embedded wire in the pre-slab material and the nano-resolution low-latency imaging experiments at the synchrotron nanowire station were carried out， and the parallax calculations were performed on the images obtained by using the different processing methods， and the results were evaluated by using the standard parallax. Additionally， due to beam instability in synchrotron radiation experiments， which can cause spot jitter or brightness fluctuations， the jitter of the beam and various components may also shift the position of X-rays incident on the sample. This results in inconsistent projection positions at different angles， leading to image misalignment. Consequently， image registration is required. A simulated plate-like material embedded with filamentous objects was projected at two distinct angles， with added noise. The depth information of the filamentous objects was recovered， achieving a peak Signal-to-noise Ratio （SNR） of 17.28 dB and a Structural Similarity Index （SSI） of 0.92， outperforming depth information obtained from existing denoising algorithms. The experiments based on the BL18B at the Shanghai Synchrotron Radiation Facility show that the algorithm enables the parallax map to reach a peak signal-to-noise ratio of 40.54 dB， a root-mean-square error of 2.4， and a structural similarity index of 98.25%. The method breaks through the performance bottleneck of existing algorithms in complex noise scenarios， and achieves nano-resolution stereo vision imaging using synchrotron radiation， which significantly reduces the noise level of the parallax map of X-ray stereo vision imaging， improves the pixel-point parallax accuracy， and provides an effective solution for high spatial-resolution three-dimensional imaging of nano-scale samples. The success of this algorithm verifies the feasibility of the image noise reduction strategy based on the laws of physics in the field of X-ray stereo imaging， and provides a new paradigm for high-precision X-ray stereo vision imaging.

Infrared sensors capture the thermal radiation emitted from the surface of objects to create images. However， infrared images are prone to noise interference and are limited in capturing fine details and texture information. In contrast， visible sensors capture reflected light from the surface of objects， providing rich details and texture information that align with human visual perception. However， the quality of visible images can be unstable due to factors such as lighting conditions. Therefore， effectively fusing infrared and visible images to generate high-quality images is an important research direction.

Currently， image fusion methods for infrared and visible images can be broadly categorised into traditional fusion strategies and deep learning-based fusion strategies. Traditional methods struggle to effectively integrate the feature information from the two modalities， especially in complex fusion scenarios. Deep learning-based fusion methods， such as ResNet and DenseNet， can better handle multi-modal information， but they involve complex training processes and high computational overhead. To address these challenges， this paper proposes an infrared and visible image fusion method that combines CNN-Mamba feature extraction with an information selection mechanism. The aim is to overcome the shortcomings of traditional methods in complex scenarios while simplifying the training process of deep learning-based fusion strategies. In the image encoding phase， we introduce the Spatial Context Aware Module （SCAM） for the first time in the field of image fusion， which integrates contextual feature information and allocates attention weights adaptively. Then， we design the CNN-Mamba Feature Extract （CMFE） module， which integrates CNN and Mamba networks to perform deep feature extraction on both modalities of the image.In the fusion phase， considering the information differences between infrared and visible images in the same scene， we propose a novel adaptive weight selection fusion layer to retain more source information. Specifically， we calculate the normalised mutual information between the two modalities and set a threshold. When the normalised mutual information exceeds the threshold， the fusion weights are set to be equal to each other. When the mutual information is below the threshold， we assign higher weights to the modality with higher information entropy to preserve more source information. Finally， in the decoding phase， we utilise a decoder consisting of four CNN modules to progressively reduce the number of channels and reconstruct the fused image.

During the experimental phase， ablation studies validate the rationality of the proposed network structure and loss function design. When compared with seven advanced fusion methods， our method demonstrates excellent performance on the MSRS， Road-Scene， TNO， and M3FD public datasets， ranking first in at least three objective metrics. Moreover， the EN and CC values achieve the highest in all tests. This indicates that the proposed method effectively facilitates cross-modal information interaction and preserves the original information as much as possible. Additionally， pedestrian and vehicle detection experiments were conducted on the Road-Scene dataset， and the results show that our method achieved the optimal performance in both pedestrian and vehicle detection， further validating its practical application advantages.

In conclusion， the proposed method demonstrates significant advantages in both image fusion quality and subsequent applications.

In the field of high-resolution optical remote sensing image analysis， target detection and recognition technologies serve as foundational pillars for critical applications such as environmental monitoring， urban planning， agricultural yield estimation， and military reconnaissance. In recent years， deep learning algorithms have revolutionized this domain by leveraging their exceptional ability to extract intricate features： they efficiently capture both the geometric characteristics of ground objects （e.g.， shape contours， spatial distribution patterns， and structural details） and spectral attributes （e.g.， band reflectance variations， spectral curve uniqueness across land covers）. This capability has driven transformative breakthroughs in target detection for high-resolution optical remote sensing images， enabling more accurate and automated identification of diverse ground targets compared to traditional handcrafted-feature-based methods.

However， the practical application of deep learning in multispectral remote sensing image analysis is hindered by inherent challenges stemming from data acquisition and scene complexity. First， multispectral images exhibit significant variability in both image quality and spatial resolution due to differences in sensor platforms—ranging from satellite-borne sensors （with meter to sub-meter resolution） to Unmanned Aerial Vehicle （UAV） cameras and airborne imaging systems. These hardware discrepancies lead to inconsistencies in Signal-to-Noise Ratio （SNR）， radiometric accuracy， and spatial granularity， directly impacting the reliability of feature extraction. Second， remote sensing scenes are inherently complex and variable： factors such as topographic relief， cloud cover， atmospheric scattering， and heterogeneous land cover （e.g.， intermingled vegetation， buildings， and water bodies） create a cluttered background that obscures small targets （e.g.， small vehicles， individual buildings， or vegetation patches）. These challenges are particularly acute for small target detection in heterogeneous scenes， where traditional deep learning models often suffer from high false detection rates， missed detections， or degraded localization accuracy due to insufficient discriminative features or severe background interference.

To address these issues， this study focuses on the SuperYOLO model—a state-of-the-art object detection architecture optimized for remote sensing scenarios—and conducts a systematic investigation to evaluate its performance in small target detection across diverse multispectral datasets. The research specifically analyzes two core factors influencing detection capability： the characteristics of scene datasets and the quality/resolution of multispectral images. For dataset analysis， the study employs two types of experimental data： independent datasets （VEDAI and HRRSD） and a mixed dataset. The VEDAI dataset specializes in small ground targets （primarily vehicles） with consistent data distribution and high annotation precision， while the HRRSD dataset covers multiple small target categories （e.g.， buildings， roads， and small vegetation clusters） in complex urban and natural scenes. In contrast， the mixed dataset is constructed by fusing multispectral data from different sensors， geographic regions， and acquisition times， simulating the high data heterogeneity encountered in real-world remote sensing applications.

The research findings yield three key insights. First， dataset homogeneity significantly impacts SuperYOLO's detection accuracy： the model achieves notably higher performance on independent datasets （VEDAI and HRRSD） compared to the mixed dataset. This is attributed to the consistent data distribution and reduced background variability in independent datasets， which minimize the model's sensitivity to irrelevant background features； in contrast， the mixed dataset's diverse land cover types and spectral variations increase the difficulty of distinguishing small targets from cluttered backgrounds， leading to reduced detection stability. Second， instrument noise emerges as a critical limiting factor for SuperYOLO's precision. When 5% and 10% instrument noise （simulating real-world sensor electronic noise， radiometric distortion， and atmospheric interference） are introduced into multispectral images， the model's mean Average Precision （AP） at an Intersection-over-Union （IoU） threshold of 0.5 （mAP@0.5） plummets sharply from the original 0.813 to 0.420 and 0.058， respectively. This degradation occurs because noise disrupts the integrity of spectral features in multispectral bands， interfering with the multi-branch network architecture of SuperYOLO—specifically impairing its ability to extract and fuse discriminative spectral and geometric features for small targets. Notably， integrating a Denoising Convolutional Neural Network （DCNN） into the data preprocessing module effectively mitigates this issue： the DCNN's adaptive noise-filtering capability restores spectral feature integrity， leading to a significant recovery of SuperYOLO's detection accuracy. Third， the spatial resolution of multispectral images exerts a relatively minor impact on SuperYOLO's performance. After downsampling the original images to simulate lower-resolution scenarios， the model's mAP@0.5 decreases from 0.813 to 0.741 （for mild downsampling） and 0.464 （for severe downsampling）. This resilience is attributed to SuperYOLO's feature pyramid structure， which enables multi-scale feature fusion to compensate for partial resolution loss—though severe downsampling still causes accuracy degradation due to the loss of fine-grained details in small targets.

In conclusion， instrument noise significantly impairs the extraction and fusion of spectral features in SuperYOLO's multi-branch architecture， ultimately limiting its detection accuracy and practical utility for small targets in multispectral remote sensing images. Enhancing optical image quality （e.g.， optimizing sensor hardware to reduce inherent noise） and incorporating denoising algorithms （such as DCNN） into the SuperYOLO framework can substantially improve the detection capability， accuracy， and applicability of small target detection algorithms in multispectral remote sensing imagery. This study emphasizes that maximizing the potential of deep learning models in remote sensing applications requires prioritizing solutions to sensor-induced noise and optimizing image preprocessing strategies. The proposed integration of denoising techniques with advanced detection architectures like SuperYOLO not only provides a more practical technical solution for small target detection in multispectral remote sensing but also points to a promising direction for future research （e.g.， multi-modal data fusion detection， real-time remote sensing image processing） and engineering applications （e.g.， UAV inspection systems， satellite remote sensing monitoring platforms） in this field.

With the wide application of intelligent devices and the development of artificial intelligence technology， significant value has been demonstrated by nighttime image enhancement in multiple fields such as visual Internet of Things， autonomous driving， medical imaging， and object detection. Nighttime image enhancement is defined as the process of increasing the brightness， enhancing the contrast and restoring the details of images under insufficient lighting conditions.To address the issues of texture detail loss and noise amplification in existing nighttime image enhancement methods， a nighttime image enhancement method based on a multi-scale fusion network （MSFNet） is proposed in this paper.

Firstly， in order to effectively extract the texture detail features of the dark area of the image， a dark area feature extraction module is introduced at the front end of the backbone network. The global features of the image are extracted through convolution to generate channel attention weights， enhancing the channel features related to the detail information. Secondly， multi-branch network modules are designed in the backbone network， and specific sub-networks are designed for different scales， so that rich multi-scale information can be perceived during the enhancement process and the loss of detail reconstruction at a single scale is avoided. Finally， a dynamic weight allocation mechanism is proposed in this paper， through which the multi-scale features of each sub-network are adaptively fused to enhance the feature expression ability and further improve the image enhancement effect.

On the three paired datasets of LOLv1， LOLv2-real， and LOLv2-syn， the PSNR values of MSFNet were higher than those of Retinexformer by 1.37， 0.72， and 0.50， respectively. The SSIM values on the LOLv1 and LOLv2-syn datasets were also higher than those of Retinexformer by 0.012 and 0.007， respectively. On the three unpaired datasets of LIME， MEF， and NPE， the optimal NIQE values were achieved by MSFNet， with values as low as 3.74， 3.91， and 3.36， respectively. The brightness of the enhanced images was significantly improved， and the details were rendered more clearly. Furthermore， on the publicly available nighttime face detection dataset DARKFACE， higher face detection accuracy was achieved for the enhanced images. The AP values under the three IoU thresholds of 0.5， 0.6， and 0.7 were recorded as 0.264， 0.107， and 0.014， respectively. More detail information was extracted from the images enhanced by MSFNet， thereby enhancing the discrimination between the target and the background and demonstrating the effectiveness of this method in downstream tasks.

MSFNet is proposed to address the the issues of texture detail loss and noise amplification commonly encountered during enhancement. First， a dark area feature extraction module is introduced at the front end of the backbone； channel attention weights are generated via convolutions on image features， and channel features related to fine details are emphasized， thereby enabling effective extraction of low-light texture details. Second， multi-branch network modules are designed， in which three sub-networks in parallel extract image features at different scales， allowing the recovery of global structure and local details in nighttime images. Finally， a dynamic weight allocation mechanism is proposed， by which multi-scale features from the sub-networks are adaptively fused； the feature representation capability is strengthened， and image enhancement performance is further improved. MSFNet is shown by the experimental results to outperform existing methods on multiple datasets. In comparative experiments on night face detection， a significant improvement in night target detection accuracy is achieved by MSFNet， thereby verifying its practical value in downstream visual tasks.

The accurate measurement of explosion point coordinates is a critical technology for assessing weapon strike efficiency and damage effects in modern warfare. Traditional optical and acoustic methods often suffer from environmental interference， high equipment costs， or a lack of real-time capability. Furthermore， emerging deep learning models require vast amounts of classified training data and high-performance computing resources， which limits their deployment on resource-constrained airborne embedded platforms. To address these challenges， this study proposes an infrared image localization method that integrates ground cooperative markers. The primary objective is to leverage the strong penetration and anti-occlusion advantages of long-wave infrared imaging to achieve high-precision， real-time explosion point detection within a lightweight framework， thereby eliminating the heavy data dependency characteristic of deep learning approaches.

The methodology utilized a pinhole imaging model and a specialized set of ground cooperative markers—including cross， L-shape， straight line， and dot—to establish a stable spatial coordinate system. To facilitate marker identification on an airborne embedded platform， an enhanced convex hull detection algorithm was developed. This algorithm introduced an adjacency constraint to merge points within a 2-pixel threshold， preventing the misidentification of a “cross” caused by imaging fluctuations. Additionally， a maximum distance threshold of 18 pixels was established to distinguish between “cross” and “L-shape” markers when they exhibited similar vertex counts. Characteristic points， such as the centroid of the “cross” and the right-angle vertex of the “L-shape”， were calculated using image moments and geometric side-length comparisons to serve as the origin and axis points for the coordinate system. To maintain stability during Unmanned Aerial Vehicle （UAV） rotation， a coordinate projection transformation model using vector normalization was applied. Finally， a homography matrix was solved using singular value decomposition to map detected explosion point pixels to their actual physical world coordinates.

Experimental validation was conducted through both simulations and practical tests at a height of 13 meters， utilizing an infrared camera mounted on a rotary base to simulate airborne flight dynamics. In a dataset of 1，666 frames capturing various angles and interference conditions， the enhanced convex hull detection algorithm achieved a comprehensive recognition accuracy of 98.2% for the cooperative markers. This performance significantly outperformed that of traditional algorithms， which struggled particularly with the “L-shape” markers， leading to a much lower coordinate system establishment success rate of 46.3%. In simulation-based detection， the enhanced method achieved a mean absolute error of approximately 0.01 meters with a relative error of only 0.14%. Practical experiments using fireworks to simulate explosion points demonstrated that the system could maintain high precision even under the influence of wind fields and thermal fluctuations. The average measured distance for the simulated explosion point was 1 636.6 mm against a reference of 1 631.0 mm， resulting in a measurement accuracy of 0.056 meters and a relative error of 0.3%. These results indicate a substantial improvement in precision over both traditional methods and the YOLOv8 model， the latter of which was constrained by the small target nature of the markers and limited training data.

The research successfully demonstrates that a refined traditional image processing approach can effectively alleviate the data-dependency issues of deep learning in range-based applications. By combining specialized ground markers with an enhanced convex hull detection mechanism， the method enables reliable UAV localization and stable spatial coordinate construction in a simple， embedded-friendly model. The experimental outcomes confirm that the system is robust against camera rotation and background interference， providing a reliable technical foundation for the accurate measurement of explosion positions. While the current resolution of infrared cameras and the assumption of a two-dimensional terrain pose limitations， future research will focus on improving accuracy with higher-resolution sensors and testing the algorithm's stability on a dedicated fixed-wing UAV platform to account for real-world flight jitters and complex terrain undulations.

The rapid proliferation of modern electronic devices has made Electromagnetic Interference （EMI） a crucial challenge， impacting the reliability of optical systems and human health. Transparent optical windows， ubiquitous in aerospace， medical imaging， defense， and communication systems， require materials exhibiting simultaneously high optical transparency， robust EMI shielding， and minimal higher-order diffraction energy. While conventional periodic metal meshes provide effective EMI shielding， their regular structures generate orthogonal array diffraction patterns that compromise the resolution and performance of imaging systems. Alternative transparent EMI shielding materials， including Transparent Conductive Oxides （TCOs）， carbon-based materials， two-dimensional transition metal carbides/nitrides （MXenes）， conductive polymers， and metal nanowires， typically exhibit limitations in simultaneously achieving high optical transparency and robust broadband EMI Shielding Effectiveness （SE）. To overcome these limitations， this study introduces a fabrication technique for structurally randomized metal meshes based on a random crack-template method， integrated with comprehensive theoretical modeling. This strategy optimizes optical and electromagnetic performance while suppressing and homogenizing higher-order diffraction energy.

Fabrication of the structurally randomized metal meshes commenced with the synthesis of an acrylic resin crack-template precursor featuring tunable physical properties. This precursor was spin-coated onto a quartz substrate to generate a random crack template （~6.5 µm depth）. Copper was subsequently deposited into the crack grooves via electron-beam-assisted evaporation. Finally， the crack template was removed using an organic solvent. Scanning Electron Microscopy （SEM） and energy dispersive X-Ray Spectroscopy （EDX） analyses confirmed the formation of a random copper mesh characterized by uniform metal line thickness （~300 nm） and high connectivity. A multiphysics-coupled framework integrating structural parameters， optical performance， and electromagnetic performance was developed to evaluate the random copper mesh. Optical performance was assessed through transmittance （T_{\mathrm{o}\mathrm{p}\mathrm{t}}） and total reflectance （R_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}）， incorporating the effects of metal line coverage， surface roughness （modeled using the Beckmann scattering formulation）， and multipath interference. Diffraction analysis employs Fraunhofer theory to quantify the suppression of higher-order diffraction energy by structural randomization. The electromagnetic shielding model establishes quantitative correlations between geometric parameters， electrical properties （sheet resistance R_{\mathrm{s}}）， and total shielding effectiveness （\mathrm{S}{\mathrm{E}}_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}=\mathrm{S}{\mathrm{E}}_{\mathrm{R}}+\mathrm{S}{\mathrm{E}}_{\mathrm{A}}+\mathrm{S}{\mathrm{E}}_{\mathrm{M}}）.

Experimental results demonstrate an average visible-light transmittance （380~760 nm） of 85.5% with a 7.6% loss relative to the quartz substrate， and an average reflectivity of 10.3% is tested at a 6° incidence. Bidirectional Reflectance/Transmittance Distribution Function （BRDF/BTDF） measurements indicated that the random copper mesh optical window exhibited predominantly specular reflection， with scattered light intensity as low as 10^-4 1/sr， demonstrating excellent optical uniformity. The He-Ne laser diffraction testing revealed a uniform diffraction pattern. The fabricated random copper mesh demonstrated favorable electrical conductivity， exhibiting a sheet resistance of 14.38 Ω/sq. This measured value slightly exceeded the theoretical prediction （11.69 Ω/sq）， primarily due to imperfect connectivity within the mesh structure， manifested as isolated metal lines. Furthermore， the random copper mesh achieved an average EMI SE of 24.56 dB across the 1~18 GHz frequency band， as independently verified by Vector Network Analyzer （VNA） measurements and CST Microwave Studio simulations. The inherent structural randomness of the crack-template method effectively suppressed the orthogonal array stray light. Critically， the developed coupled optical-electromagnetic performance model establishes quantitative relationships between structural parameters and functional metrics， facilitating scalable industrial production. This innovation successfully resolves the inherent trade-off between EMI shielding and optical transparency， offering significant promise for precision imaging applications in environments with high EMI. Future investigations will explore advanced materials （such as silver and graphene） and hybrid architectures to extend performance across broader electromagnetic spectral ranges.

With the increasingly serious problem of commodity forgery， it is of great significance to develop new anti-counterfeiting technologies. Photonic crystals have shown broad application prospects in the field of anti-counterfeiting due to their unique optical properties， especially structural color effects. However， traditional photonic crystal materials generally have problems such as poor mechanical properties and insufficient thermal stability， which limit their practical application.

In this study， a flexible composite film of photonic crystals based on epoxy resin （EP） and thermoplastic polyurethane （TPU） interpenetrating network structure was innovatively proposed. Monodisperse silica colloidal microspheres were prepared by the improved Stöber method， and photonic crystal arrays with opal structure were assembled by the vertical deposition method. On this basis， TPU/EP-based photonic crystal structural color films were prepared by a filling and curing process using TPU and EP mixture as flexible substrates. The optical properties and mechanical properties of the composite films were systematically studied by adjusting the ratio of TPU to EP.

The experimental results show that when the mass ratio is 1∶1， the film exhibits excellent structural color rendering and flexibility， with a clear reflectance peak that shifts little after multiple bending. Furthermore， through optical and mechanical tests， it is confirmed that the film exhibits dynamic structural color change at different angles and has excellent durability and environmental stability. By combining the iridescence effect of photonic crystals with fluorescence recognition technology， the film exhibits iridescence under natural light and displays specific fluorescence patterns under ultraviolet light excitation， achieving dual anti-counterfeiting through the combination of “iridescence + fluorescence”.

The practical application tests show that the film can effectively adhere to the surface of the shoes， and the anti-counterfeiting label is difficult to replicate. It can be quickly verified and has good practicability and market potential. It also provides an innovative solution for dynamic anti-counterfeiting certification of footwear products.

Gallium nitride （GaN） is a cornerstone of third-generation wide-bandgap semiconductor technology， valued for its exceptional electronic mobility， thermal stability， chemical inertness， and robustness under extreme operating conditions. These qualities have driven its adoption in high-power electronics， high-frequency devices， and ultraviolet optoelectronics. However， a persistent challenge in GaN-based photonics is achieving efficient and stable red emission. Conventional InGaN-based red Light-Emitting Diodes （LEDs） suffer from efficiency droop and phase separation issues inherent to the high indium （In） content required for red wavelengths. As an alternative， doping GaN with rare-earth （RE） ions—which exhibit sharp， stable intra-4f shell emissions largely independent of the host matrix—offers a promising route to high-color-purity red light. Among these， trivalent europium ions （Eu³⁺） are renowned for their intense red emission lines around 620 nm. To enhance the optical activation and emission efficiency of such RE centers， co-doping with a sensitizer ion is a widely employed strategy. Samarium ions （Sm³⁺） have demonstrated effective sensitization of Eu³⁺ luminescence in various host materials through resonant energy transfer. Nevertheless， the specific system of Sm³⁺/Eu³⁺ co-doped GaN is still in its infancy. This study aims to systematically investigate the structural modifications and luminescence characteristics—particularly the energy transfer dynamics—in GaN thin films co-doped with Sm³⁺ and Eu³⁺ ions， thereby evaluating its potential as a novel red-emitting material platform compatible with established GaN semiconductor processes.

This research employed ion implantation as the doping technique to introduce Sm³⁺ and Eu³⁺ ions into high-quality GaN thin films grown on c-plane sapphire substrates via metal-organic chemical vapor deposition （MOCVD）. A series of samples were prepared： including Sm³⁺ single-doped samples with varying doses （1×10¹⁴， 5×10¹⁴， 1×10¹⁵ at/cm²）， and Sm³⁺/Eu³⁺ co-doped samples with a fixed Sm³⁺ dose （5×10¹⁴ at/cm²） and varying Eu doses （1×10¹⁴， 5×10¹⁴， 1×10¹⁵ at/cm²）. Post-implantation thermal annealing at 1 000 ℃ in an ammonia atmosphere was conducted to recover implantation-induced lattice damage and to optically activate the RE ions. The structural integrity and stress state of the doped films were comprehensively assessed using High-resolution X-ray Diffraction （HRXRD） and Raman spectroscopy. The optical emission properties were investigated using Cathodoluminescence （CL） spectroscopy.

HRXRD rocking curve analysis of the GaN （0002） plane revealed that the Full Width at Half Maximum （FWHM） increased with higher Sm³⁺ concentrations in single-doped samples， indicating a degradation of crystalline quality due to implantation-induced defects. In co-doped samples with a fixed Sm³⁺ dose， the FWHM exhibited a complex， non-monotonic trend as the Eu³⁺ dose increased， showing a slight initial decrease followed by a rise at higher doses. This suggests that the incorporation of a low dose of Eu³⁺ may induce minor lattice distortions that slightly improve local ordering， while higher doses exacerbate defect formation. Lattice constant analysis from 2θ/ω scans and Raman shift measurements of the E₂^high phonon mode provided consistent evidence of stress evolution. Sm³⁺ single-doping introduced tensile stress， whereas the introduction of Eu³⁺ in co-doped samples led to a significant increase in compressive stress， which intensified with higher Eu³⁺ doses.

Cathodoluminescence spectroscopy provided clear evidence of efficient energy transfer from Sm³⁺ （sensitizer） to Eu³⁺ （activator）. In Sm³⁺ single-doped samples， characteristic emission peaks were observed from Sm³⁺ at approximately 577 nm （⁴G_5/2 → ⁶H_5/2）， 618 nm （⁴G_5/2 → ⁶H_7/2）， and 666 nm （⁴G_5/2 → ⁶H_7/2）. In the co-doped samples， the Sm³⁺ emission intensity， particularly the 577 nm peak， was significantly quenched， while the characteristic Eu³⁺ emission lines， especially the dominant red emission at ~620 nm （⁵D₀ → ⁷F₂）， were strongly enhanced with increasing Eu³⁺ concentration. This anti-correlation provides clear evidence for a non-radiative energy transfer process. The primary energy transfer pathway was identified as ⁴G_5/2 （Sm³⁺） + ⁷F₀ （Eu³⁺） → ⁶H_5/2 （Sm³⁺） + ⁵D₀ （Eu³⁺）， favored by the near-perfect resonance （energy mismatch of only ~74 meV） between the Sm³⁺⁴G_5/2 → ⁶H_5/2 transition and the Eu³⁺⁷F₀→⁵D₀ absorption.

Further quantitative analysis based on Dexter's theory was conducted to elucidate the dominant interaction mechanism. By analyzing the dependence of the Sm³⁺ emission quenching ratio on the total RE concentration through fitting， it was determined that energy transfer occurs primarily via electric dipole-dipole and electric dipole-quadrupole interactions. Critical energy transfer distances （R_c） were calculated to lie between 1.66 nm and 2.24 nm for the studied concentration range， confirming the feasibility of efficient resonant transfer at these doping levels.

The impact of co-doping on the emitted light color was evaluated using the Commission Internationale de l'Éclairage （CIE） chromaticity coordinates. As the Eu³⁺ dose increased from 1×10¹⁴ to 1×10¹⁵ at/cm²， the CIE coordinates systematically shifted from （0.527 5， 0.369 5） in the yellowish-red region to （0.606 3， 0.353 2） in the deep red region. Correspondingly， the calculated Correlated Color Temperature （CCT） decreased from 1 580 K to 730 K. This demonstrates that by adjusting the concentration of Eu³⁺ relative to the fixed proportion of the Sm³⁺ sensitizer， a clear and controllable tuning of the emission color toward saturated red can be achieved.

In conclusion， this study successfully fabricated and characterized Sm³⁺/Eu³⁺ co-doped GaN thin films via ion implantation. Although the doping process introduces compressive stress and affects the crystalline quality， it enables efficient resonant energy transfer from Sm³⁺ to Eu³⁺. This transfer effectively pumps the Eu³⁺⁵D₀ level， resulting in intensified red emission around 620 nm. Most notably， the emission color can be tuned from yellowish-red to deep red by varying the Eu³⁺ concentration， providing a direct means to achieve targeted red chromaticity. These findings offer foundational experimental insights and a material processing framework for developing GaN-based red-emitting devices utilizing the Sm³⁺/Eu³⁺ co-doping scheme， presenting a potentially advantageous alternative to high-In-content InGaN for red micro-LEDs and other advanced optoelectronic applications. Future work may focus on optimizing implantation and annealing parameters to mitigate structural damage and further enhance the luminescence efficiency of this co-doped system.

Erbium glass lasers offer advantages such as compact size， high efficiency， and excellent beam quality. Additionally， the 1 535 nm wavelength falls within the atmospheric transmission window， exhibits strong penetration capability， and lies in the eye-safe range， significantly reducing operational risks. These lasers are widely used in laser ranging systems and also hold important applications in fields such as lidar and military medicine. However， the weak stability， hydrophilicity， and high thermal expansion coefficient of erbium glass often lead to excessive stress in thin-film devices， resulting in poor adhesion—a critical technical bottleneck limiting the development of erbium glass lasers. Research on reducing residual stress and improving thin-film adhesion on erbium glass substrates remains scarce. To address this， this study employs the control variable method combined with magnetron sputtering technology to optimize three key process parameters—argon （Ar） flow rate， target （TG） power， and inductively coupled plasma （ICP） power—thereby reducing thin-film stress and enhancing the adhesion of erbium glass thin-film devices.

First， the optical constants of the erbium glass substrate were determined using a Cary7000 spectrophotometer. Based on optical thin-film design theory， Ta₂O₅ and SiO₂ were selected as coating materials for the near-infrared band. The initial film structure was designed as Sub |（0.5LH0.5L）^S| Air， with the reflection bandwidth formula confirming a bandwidth of 355.2 nm at the central wavelength of 1 535 nm， meeting the specifications. The reflectivity formula indicates that， for a given ratio of high-to-low refractive indices， reflectivity increases with the number of film periods （S）. To achieve a reflectivity of 99.9%， at least 12 periods were required， resulting in the finalized structure： Sub|（0.5LH0.5L）¹²|Air. The film design was globally optimized using Macleod software， yielding the following configuration： Sub| 1.06H 0.76L 0.97H 0.86L 1.16H 1.11L 0.93H 0.92L H 1.08L H 0.93L 0.96H 1.06L 1.03H 0.94L 0.93H 1.02L 1.08H 1.02L 0.87H 0.96L 0.75H 0.49L |Air. Theoretical spectra showed an average reflectivity of 0.076% in the 800~1 000 nm band and 99.92% in the 1 500~1 600 nm band. Based on the robustness principle and considering deposition process constraints， manufacturability was improved by introducing film thickness error analysis. Subsequently， a beam-splitting film sample was prepared following standard deposition processes.

After deposition， adhesion testing was performed using 3M tape pressed onto the film surface and rapidly peeled off vertically five times， revealing delamination. Comprehensive analysis indicated that film delamination was primarily caused by thin-film stress， which consists of intrinsic stress and thermal stress. To address this issue， the study focused on optimizing deposition parameters and temperature variations during coating. By analyzing the influence of deposition parameters on single-layer film stress using the Stoney formula and thermal stress equations， the following trends were observed：The stress of Ta₂O₅ and SiO₂ single-layer films decreased with increasing Ar flow rate. The stress of Ta₂O₅ and SiO₂ single-layer films initially decreased and then gradually increased with rising TG power. The stress of Ta₂O₅ and SiO₂ single-layer films increased with higher ICP power. A low-stress film preparation process was identified， and the curvature radii of the samples were measured before and after deposition using an ET-4000A step profiler. The calculated multilayer film stress was -105 MPa， and subsequent adhesion tests showed no delamination.

The final beam-splitting film prepared on the erbium glass substrate exhibited an average reflectivity of 0.16% in the 800~1 000 nm band and 99.87% in the 15 00~1 600 nm band， meeting the specifications. The slightly higher reflectivity in the 800~1 000 nm band compared to the design spectrum was attributed to surface roughness-induced interfacial scattering in the multilayer film. This was further analyzed using a Zygo laser interferometer to measure surface roughness. After undergoing environmental tests in accordance with specifications， the film showed no defects such as cracking， delamination， or bubbles. As laser energy increases， the thermo-mechanical coupling interaction between the laser and the film raises the risk of film failure due to residual stress. Further reducing film stress remains a key focus for future research.

At present， most optical chaotic logic devices can only complete a single logic function， and can not meet the requirements of more complex logic functions for optical signal packet switching nodes in chaotic networks， so the related research of optical chaotic logic devices has shifted from basic logic to complex sequential logic. Optical chaotic latches and flip-flops， serving as the basic building blocks and memory units of optical chaotic sequential logic devices， can be integrated with combinational logic devices such as adders， subtractors， and multipliers to enable the realization of more advanced modules， including shift registers， counters， and random-access memories. However， previous studies have predominantly focused on clock-free single-trigger flip-flops with identical triggering conditions， exhibiting limited confidentiality and weak noise immunity. Current research on implementing flip-flops using chaotic lasers remains relatively scarce.

Based on optical injection cascaded VCSELs chaotic system， electro-optic modulation theory and polarization bistability， we successfully implement high-speed reconfigurable multi-triggered optical chaotic D-latch and D-flip-flop operations under clock synchronization. Firstly， we numerically investigate the evolution of dynamic states for both polarization component （x-PC and y-PC） in the strongly injected VCSEL₂ with respect to the optical injection amplitude and applied electric field， as well as the polarization bistability as a function of the optical injection amplitude. We observe that the polarization switching behavior of VCSEL₂ can be induced by alternating the applied electric field between 11 kV/mm and 25 kV/mm. When E₀=11 kV/mm and E_inj exceeds 4.7， the y-PC is completely suppressed by the x-PC， and the x-PC assumes absolute dominance. If E₀=25 kV/mm and E_inj also exceeds 4.7， y-PC dominates， while the x-PC is completely suppressed. And we propose a novel mean-value comparison mechanism to demodulate the x-PC and y-PC from VCSEL₂ for the acquisition of logic outputs. Then we demonstrate the implementations of reconfigurable multi-triggered optical chaotic D-latches and D-flip-flops that are synchronized by a clock signal and response for as short as 10 ps bit time. By calculating the dependence of the success probability of D-latches and D-flip-flops on bit time and optical injection strength， we discover that when the optical injection strength k_x （k_y） is less than 9 ns⁻¹， the success probability of the D-latches is below 1， indicating the occurrence of bit errors in the logic output. And under the premise of a success probability of 1， increasing the optical injection strength can effectively reduce the lower limit of bit time. When the optical injection strength exceeds 43 ns^-1， the lower limit of bit time can be stably reduced to 3 ps， achieving a computational speed of up to 333.3 Gb/s. Finally， by evaluating the impact of spontaneous emission noise on the reliability of D-latches and D-flip-flops， we demonstrate that the success probability remains unity even at a noise strength as high as 1.6×10¹⁰. Even the noise strength increases to 5×10¹⁰， the success probability remain above 0.98. These results demonstrate that the D-latches and D-flip-flops exhibit outstanding characteristics including high-speed operation， strong noise immunity， and flexible reconfigurability， which hold significant application and reference value in chaotic secure communication networks.

In adaptive Optics （AO） systems， wavefront aberrations are commonly represented by Zernike polynomials， whose coefficients describe distortions such as tilt， defocus， coma， and spherical aberration. Accurate estimation of Zernike coefficients is critical for high-quality imaging and effective wavefront correction. Due to the symmetry of the Fourier transform， Zernike coefficients with opposite signs can produce identical Point Spread Function （PSF） images， causing ambiguity in coefficient prediction. The Phase Diversity （PD） method mitigates this issue by simultaneously capturing focal and defocused intensity images， introducing additional phase information to improve reconstruction accuracy.

Based on PD theory， this study proposes a MobileViT-based wavefront reconstruction method. The approach uses a pair of focal and defocused intensity images as input and automatically extracts image features to achieve non-iterative phase retrieval. The reconstruction performance of MobileViT is systematically evaluated under varying turbulence intensities and compared with CNN， ResNet-50， and EfficientNet-B0 models， providing a reference for practical applications.

Experimental results demonstrate that MobileViT consistently outperforms the other models across all turbulence conditions. Although CNN has the fewest parameters and fastest inference， its reconstruction accuracy is the lowest. Under weak turbulence， all four models achieve reasonable wavefront reconstruction； as turbulence intensity increases， CNN's accuracy deteriorates， EfficientNet-B0 performs slightly worse than ResNet-50， and MobileViT maintains the highest accuracy， with the smallest residual RMS and largest Strehl Ratio （SR）， indicating stronger robustness under strong turbulence. Mean Absolute Error （MAE） comparisons of predicted versus true Zernike coefficients further show that MobileViT is least affected by turbulence， achieving superior precision and stability.

Considering reconstruction accuracy， model size， training time， and inference speed， MobileViT achieves an optimal balance between lightweight design and high precision. This method demonstrates strong robustness under varying turbulence conditions， improving wavefront correction efficiency while reducing hardware dependency. These findings highlight the potential of combining deep learning with phase diversity for practical sensorless AO systems and suggest promising applications in fields such as astronomical imaging and laser communications. Future work will involve building an experimental platform to further validate the proposed approach.

This study tackles the key challenges in designing integrated， lightweight， and high-resolution remote sensing satellites， with a focus on miniaturization， structural stability， and thermal control. Its primary objective is to propose and validate a novel satellite-borne integrated scheme that incorporates an optical camera， a laser communication terminal， and star trackers onto a unified platform. The design aims to achieve sub-meter resolution imaging and high-speed laser data transmission while maintaining high optical axis stability and thermal performance under complex on-orbit conditions.

A holistic， multi-disciplinary design and optimization approach was adopted. The imaging payload centers on an improved Cassegrain system. A key innovation is the square primary mirror （190 mm× 190 mm）， providing an equivalent circular aperture of 215 mm. This design increases the photon collection area by approximately 27% within the same envelope compared to a conventional circular mirror， directly enhancing the signal-to-noise ratio. Coupled with a 3.2 µm pixel pitch detector and a 1 300 mm focal length， the system is designed to deliver better than 1-meter ground sampling distance from a 400 km orbit. For the laser communication terminal， a reflective off-axis afocal beam expander was developed. This system features multiplexed optical paths， allowing the transmit， receive， fine tracking， and self-calibration subsystems to share key components such as the fast steering mirror and beam expander optics. This integration drastically reduces the terminal's volume， resulting in a compact assembly with a 60 mm effective aperture capable of supporting a 4× beam expansion ratio for high-rate data transmission. Structurally， the integration cornerstone is a Z-shaped precision base made of aluminum-based silicon carbide （SiC/Al）. This material was selected for its superior specific stiffness and high thermal conductivity， which are critical for maintaining dimensional stability under thermal and mechanical loads. All core payloads such as the optical camera， laser terminal telescope， and star sensor are mounted on this unified reference structure， which not only ensures inherent alignment stability among the payloads but also simplifies the system architecture. To counteract the detrimental effects of temperature fluctuations on optical alignment and image quality， a sophisticated “active compensation + passive isolation” thermal management strategy was implemented. The active system employs a network of five distributed heaters on the Z-base， governed by a discrete Proportional-Integral （PI） control algorithm with finely tuned parameters （proportional gain=0.8， integral gain=0.05）， maintaining the base temperature within 20±0.5 ℃. Passive measures include applying high-conductivity graphene films to critical structures like the camera truss to homogenize temperature gradients and using L-shaped thermal isolation brackets to decouple sensitive optical paths from external thermal disturbances. We established a rigorous analytical framework to guide and validate the design. This involved developing linear mathematical models for multi-payload line-of-sight stability and system wavefront aberration， which directly link mechanical and thermal deformations to optical performance metrics. These models feed into a coupled optical-mechanical-thermal multi-disciplinary analysis and optimization workflow. This integrated simulation platform enables the iterative assessment and refinement of system performance under a comprehensive set of environments， including ground gravity-thermal coupling and extreme on-orbit thermal cases spanning the satellite's full orbital beta angle range. The final design underwent extensive verification through thermal balance testing in a space environment simulator and thermal-optical MTF testing， culminating in on-orbit performance evaluation after launch.

The proposed integrated design demonstrates significant improvements in compactness， stability， and performance. The optical system has a total length of 330 mm with an equivalent aperture of 215 mm， enabling sub-meter resolution. The laser terminal beam expander has a 60 mm effective aperture， supporting 4× beam expansion/reduction for data transmission up to 10 Gbps. The Z-shaped SiC/Al base ensures high structural rigidity. Under a coupled gravity and 4 ℃ thermal load， the maximum optical axis deviation between payloads is ≤3.24″， and mirror displacements are ≤5 µm with tilts ≤5″. The system wavefront error remains ≤0.06λ （RMS， λ=632.8 nm）. In extreme on-orbit thermal cases， the peak-to-peak optical axis variation is ≤6.77″. The thermal control system maintains the camera and base within 19.5~20.5 ℃， with gradients <1 ℃ and stability ≤±0.3 ℃. Thermal balance and thermal-optical tests confirm that the MTF remains >0.1 under controlled conditions. On-orbit validation shows that the satellite maintains a dynamic MTF >0.08 at the Nyquist frequency （156 lp/mm） after one year in orbit， with no significant degradation. High-resolution panchromatic images have been successfully downlinked via the laser terminal， confirming stable imaging and data transmission performance.

In conclusion， this work successfully develops and validates a highly integrated， miniaturized satellite design that effectively combines high-resolution optical imaging and high-speed laser communication within a compact 42 kg platform. The innovative use of a common Z-shaped precision base， advanced thermal management， and multi-disciplinary optimization results in a system with superior structural stability， thermal precision， and optical performance compared to existing designs. The integration scheme not only reduces volume and mass but also enhances payload synergy， as demonstrated by using the optical camera for coarse tracking in the laser communication link. The proposed approach provides a feasible and efficient technical pathway for future lightweight， high-performance multi-payload remote sensing satellites， balancing rigorous performance requirements with stringent size and mass constraints.

Raman microscopy， a label-free detection technique that identifies targets through the intrinsic vibrational spectra of samples， is highly significant in biomedicine， especially for early disease diagnosis. Its ability to avoid exogenous labels offers distinct advantages when studying biological tissues. However， biological tissues and complex materials have strong optical scattering properties， which disrupt light propagation by randomizing their paths. This severely limits the detection depth of traditional Raman techniques—often restricting imaging to superficial layers， rarely exceeding 100 μm in most biological tissues—and impairs signal contrast， as scattered light mix with target signals， resulting in blurred or noisy outputs. To tackle these critical limitations， this study presents an innovative wavefront shaping method based on spectrally resolved variance optimization.​

The core of this method is establishing a sophisticated wavefront feedback mechanism that combines Raman spectroscopy with speckle variance analysis. The detailed process is as follows： First， the experimental sample is carefully designed to simulate in vivo conditions， consisting of Raman-active particles embedded in a scattering medium. This medium is engineered to replicate the forward scattering characteristics of biological tissues， such as those in epithelial layers or soft tissues， ensuring the results are relevant to real-world applications. The Raman particles， chosen for their biological significance， show a unique and distinct Raman peak at 1 200 cm⁻¹， a wavelength range less prone to autofluorescence interference in biological samples. Using an imaging spectrometer with high spatial and spectral resolution， the system simultaneously captures both the spatial distribution （2D speckle patterns） and spectral signatures of Raman signals. A custom-developed algorithm then filters these data to isolate speckle signals specifically at the 1 200 cm⁻¹ peak. This targeted selection minimizes interference from background noise in non-target wavebands—like autofluorescence from cellular components or stray light—thus extracting speckle contrast parameters suitable for quantitative analysis.​

Next， an iterative optimization algorithm is used to build a closed-loop feedback system， a key innovation of this approach. In each iteration， the Spatial Light Modulator （SLM）， a device with high phase modulation precision， dynamically adjusts the wavefront of the input light. This adjustment causes changes in the excited speckles， rearranging their distribution within the scattering medium. At the same time， the system monitors Raman speckle variance in real time， with maximizing variance as the main objective. By continuously refining the phase modulation depth of the SLM—adjusting individual pixels to counteract wavefront distortions from the scattering medium—the input light is gradually guided to focus on a single Raman particle in the medium. The optimization process goes through 3 000 iterations， a number determined by preliminary tests to balance convergence speed and accuracy， ensuring stable and reliable focusing of excitation energy on the target particle. A critical point for this enhancement is the strict linear relationship between the intensity of spontaneous Raman signals and the excitation light intensity： when the focused excitation light reaches maximum intensity， the Raman signal is proportionally amplified to its peak level. Additionally， to address potential focal deformation caused by light absorption by Raman particles—a common issue in high-intensity focusing—a pair of galvanometers rotate the incident light at small angles （within the optical memory effect range）. Using the memory effect principle， where speckles translate as a whole without changing their distribution under small-angle rotations， the system obtains clear， undistorted focused spots from the translated speckle patterns.​

Experimental results confirm the effectiveness of this method　it achieves a 6-fold enhancement of Raman signals in scattering media， outperforming many existing techniques. The spatial resolution reaches the micrometer scale， allowing precise targeting of individual particles—essential for distinguishing between cellular or subcellular structures in biological samples. Furthermore， the method enables non-invasive focusing on single Raman particles with high specificity： background noise outside the 1200 cm⁻¹ peak is enhanced by only~30%， showing effective suppression of non-target signals. This specificity comes from the spectral filtering step， which limits optimization to the target Raman peak.​

In summary， this method provides a new technical solution for enhancing weak Raman signals in scattering environments， addressing long-standing challenges in deep-tissue imaging. Its ability to support label-free imaging in deep biological tissues， along with high signal enhancement and precise spatial focusing， makes it a valuable tool in biomedicine. Potential applications include early diagnosis of diseases like cancer—where subtle Raman spectral changes precede morphological alterations—and sensitive detection of biomarkers， such as proteins or metabolites， in their native tissue environments. By overcoming limitations in depth and signal strength， this approach advances the practical use of Raman microscopy in clinical and research settings.

The Surface Plasmon Polariton （SPP） vortices confined to a metal-dielectric interface， are characterized by the near-field enhancement and orbital angular momentum. They find broad applications in particle manipulation， super-resolution imaging， biochemical sensing， and so on. Recent advances in Plasmonic Vortex Lenses （PVLs） for SPP vortex generation have progressively simplified the excitation source requirements—evolving from complex vector beams inherently carrying orbital angular momentum to scalar fields employing circularly or linearly polarized light. Although positive elliptically polarized light， as a fundamental polarization state of scalar fields， has been utilized for PVL excitation， the existing schemes necessitate the incorporation of additional transmission phases—achieved through spiral contour designs—to generate arbitrary-order SPP vortices under positive elliptically polarized light. The SPPs generated by the meta-atoms arranged at the end of the spiral have more attenuation because of the more optical paths they have to travel than those generated by the slit pairs at the front， which ultimately leads to an uneven field distribution of the SPP vortex. Furthermore， an issue arises as the size of the PVL increases with the radius of the spiral contour， presenting a challenge for miniaturizing the PVLs. Furthermore， these devices exhibit inherent polarization sensitivity， which substantially restricts the application scenarios of both SPP vortices and PVLs.

To address the aforementioned challenges， this work proposes a geometric phase-based PVL. The lens comprises composite meta-atoms arranged along a circular contour， with each meta-atom integrating four rectangular slits. By systematically controlling the rotation angles of slits within meta-atoms， the proposed PVL not only generates SPP vortices sensitive or insensitive to the amplitude ratio and handedness of incident positive elliptically polarized light but also enables flexible manipulation of the Topological Charge （TC） of the SPP vortices. Specifically， the rotation angles of slits are adjusted through four parameters， namely a， b， c， and d， to construct different configurations of PVL1 and PVL2. Under illumination by the positive elliptically polarized light of arbitrary handedness and amplitude ratio， PVL1 generates SPP vortices with a TC of l=\mathrm{1}-\mathrm{2}a, and the intensity of the vortex field varies with changes in the incident light's handedness and amplitude ratio. When illuminated by circularly polarized light of opposite handedness， PVL2 produces SPP vortices with TCs of l_{\mathrm{L}\mathrm{C}\mathrm{P}}=\mathrm{2}c-\mathrm{1} and l_{\mathrm{R}\mathrm{C}\mathrm{P}}=\mathrm{1}-\mathrm{2}a. Notably， under the condition a+c-\mathrm{1}=\mathrm{0}\mathrm{a}\mathrm{n}\mathrm{d}b+d=\mathrm{\pi }/\mathrm{4}， PVL2 generates SPP vortices with l=\mathrm{1}-\mathrm{2}a for any positive elliptically polarized incident light， and the vortex field intensity remains invariant to changes in the incident polarization's handedness or amplitude ratio. By modulating the slit rotation parameter a， the TC of SPP vortices generated by both PVL1 and PVL2 can be tuned. To demonstrate the properties of PVL1 and PVL2， eight structures witha of 0.5，1，1.5 and 2 respectively were designed. Finite-difference Time-domain （FDTD） simulations reveal that PVL1-1 and PVL2-1 both generate SPP focal spots with l=\mathrm{0}. When ais 1， 1.5 and 2 respectively， both PVL1 and PVL2 produce SPP vortices with TC of -1， -2 and -3 respectively. Crucially， the intensity of the SPP field generated by PVL1 varies with the amplitude ratio and handedness of the incident positive elliptically polarized light， confirming its polarization-sensitive nature. In contrast， the SPP field intensity from PVL2 remains unchanged under identical variations， validating its polarization-insensitive functionality.

Leveraging these functionalities， the proposed PVLs can be deployed in optical tweezers， optical field manipulation， and optical switching systems， thereby expanding the application domains and practical scenarios for plasmonic vortex lenses. Beyond this extended applicability， the device architecture offers inherent advantages in simplified fabrication workflows and miniaturization. This stems from its exclusive use of single-shaped， uniformly sized slit structures and the absence of introduced transmission phases. These features collectively facilitate the streamlined design and on-chip integration of advanced photonic devices.