With the profound integration of artificial intelligence and photonics technologies, intelligent photonics is evolving into a disruptive technology that looks poised to revolutionize industries and everyday life. The development of intelligent photonics finds applications in diverse fields, including biomedicine, autonomous driving, and virtual and augmented reality. Artificial intelligence (AI) is fueling a new paradigm of photonics research, providing efficient avenues for optimizing photonics design, advancing optical systems and analyzing optical information. Enabled by the maturity of deep learning, silicon-based optoelectronics, optical materials, and quantum information, photonic computing holds great potential to address the challenges faced by Moore's law and the bottlenecks of the von Neumann architecture. Future implementations of photonic computing may meet the demands for high-performance computing in the digital infrastructure of the information era, such as those posed by 5G, big data, cloud computing, and the Internet of Things. In this study, we summarize recent advances in photonic computing, including on-chip integrated optical neural networks based on micro-ring resonators, multimode interferometers, nanobeam resonators, and subwavelength diffractive units and integration of training and computation. We also highlight the progresses in diffractive neural networks enabled by diffractive optical elements and intelligent metasurfaces, as well as the developments in photonic spiking neural networks, reservoir computing, quantum photonic computing, and large-scale optoelectronic computing chips. In terms of computational optics, we review the advances across a broad range of areas, including computational imaging, microscopy, display, fiber-optic sensing, and laser technologies.

Optical computing is a novel computational architecture based on the manipulation of photons or optical fields rather than electrons, utilizing light for information encoding, transmitting, and processing. In recent years, optical computing has leveraged independent information dimensions of photons, such as polarization, frequency, and orbital angular momentum, leading to the emergence of numerous novel architectures. Concurrently, the integration of deep-learning-driven structural design has enabled these architectures to demonstrate exceptional performance in tasks including matrix operation and image processing. This paper begins with the physical foundations of optical computing, systematically summarizes and discusses the principal architectures of free-space diffractive and on-chip integrated optical computing. Furthermore, this paper concludes by highlighting the pressing challenges confronting current developments in optical computing and provides perspectives on future trends in this field.

The development of artificial intelligence has exceeded the capability boundary of the traditional von Neumann architecture computer, and the rise of neural morphology computing has broken through the computational limitations of traditional standard hardware. Optical neural network has become an ideal platform for neural morphology computing with ultra-large bandwidth, ultra-high speed, ultra-low power consumption and natural parallel computing ability. As a high-performance integrated photonic device, integrated microcavity has the nonlinear response, wavelength sensitivity and optical storage ability required to simulate biological neuron mechanism, which strongly promotes the application of wavelength division multiplexing technology in neural networks. In recent years, on-chip optical neural networks based on integrated microcavity structure have attracted extensive attention of researchers. This review systematically introduces the application of integrated microcavity devices in optical neural networks, and classifies different architectures such as optical pulse neural networks, optical convolutional neural networks and optical reserve pool computing. At the same time, the new neural network architecture based on integrated microcavity optical frequency comb is discussed, and the main challenges are analyzed.

With the rapid development of artificial intelligence, traditional electronic neural networks can no longer meet the needs of large-scale data computing, so photonic neural network has emerged. With the advantages of parallel transmission of optical signals and ultra-low energy consumption, photonic neural networks can realize the synchronous processing and high-bandwidth transmission of multi-dimensional information based on optical computing, which demonstrates great potential in improving computing efficiency. Effective training methods are crucial for improving the performance of photonic neural networks, which attracts many teams to conduct research. This study reviews the work of major research teams at home and abroad in the training of photon neural networks, elaborates on the current main training methods, and focuses on analyzing training based on pulse timing dependent plasticity, gradient based training, evolutionary algorithms, hardware perception training, hardware error correction, online learning, and Python training frameworks for photon neural networks. Finally, the principles, advantages, and limitations of each method are analyzed, and prospects for the future development trends in this field are proposed.

Against the backdrop of continuously growing computing power demands and the physical limitations of Moore's Law, optical neural networks have emerged as a promising candidate for next-generation high-efficiency computing platforms due to their advantages in high parallelism, low energy consumption, and high-speed computation. Micro-nano optical neural networks, which deeply integrate micro-nano fabrication technologies with the principles of optical information processing, enable fast and energy-efficient neural network computation within extremely compact dimensions, showing great application potential. This paper first reviews the development of neural networks and the fundamental concepts of optical neural networks, and then systematically summarizes recent research advances in micro-nano optical neural networks based on waveguide propagation and free-space propagation. Finally, the key system components and architectural designs are thoroughly analyzed, and the performance characteristics of optical neural networks and electronic chips are compared across three dimensions: computational power, energy efficiency, and integration density. A systematic evaluation of the differences between cutting-edge optoelectronic hybrid chips and traditional electronic chips is also conducted. Furthermore, the challenges facing future technological development and potential breakthrough paths are proposed.

Facing the challenge of efficiently and accurately solving large-scale combinatorial optimization problems, spatial photonic Ising machines, by leveraging capabilities of space-division multiplexing for large-scale implementation, an intuitive and straightforward fully-connected Ising mapping mechanism, and programmable combinatorial optimization approaches, have emerged as a significant technological path for the deployment and validation of Ising machines, receiving widespread attention from both academia and industry. This paper introduces the photonic Ising mapping principle of spatial photonic Ising machines, the main technical challenges, and future research prospects. It especially focuses on three key technical issues: flexible spatial photonic Ising mapping methods, general-purpose spatial photonic Ising machine architecture, and noise-enhanced photonic Ising optimization methods.

With the explosive growth of information in modern society and the rapid development of artificial intelligence technology, intelligent computing centers are facing unprecedented challenges in terms of communication bandwidth and energy consumption. The traditional high-speed interconnection architecture has been difficult to meet the demand of the continuous growth of data traffic. In this context, silicon photonic technology continues to mature, which has the advantages of high bandwidth density and low power consumption, and is reshaping the interconnection system of modern data centers. By deeply integrating the photoelectric chips and integrating them into one, this technology greatly shortens the electrical connection distance, gradually extends the optical interconnection to the inside of the switch package, and even realizes the low delay and long-distance communication between the computing chips, which greatly expands the boundary of computing power. This paper summarizes the latest research progress of optoelectronic integrated chips for computational optical interconnection, systematically analyzes the technical challenges faced by various key devices and chips, and looks forward to the future development direction of silicon optical engine and its wide application prospect in many fields.

Intelligent optical computing chips have emerged as a promising solution for next-generation artificial intelligence hardware due to their high-speed broadband parallel processing capabilities, low energy-consumption, and low-latency computational characteristics. However, these chips face challenges including accumulated phase errors in complex optical paths and manufacturing process variations, necessitating high-precision, wide-bandwidth characterization and calibration technologies to achieve accurate control and practical implementation. Capitalizing on the performance advantages of photonic devices, optical vector analysis (OVA) techniques enable high-accuracy measurement, ultra-broadband characterization, and multi-dimensional analys of intelligent optical computing chips, thus serving as a crucial enabler for their applications. This paper systematically reviews the system architectures and operational principles of existing OVA technical approaches. By examining the design features and functional requirements of intelligent optical computing chips, we provide an in-depth analysis of the strengths and limitations of different technical routes. Furthermore, we discuss future development directions for OVA techniques and their application prospects in photonic device characterization and optical chip calibration.

Computational ghost imaging (CGI) achieves high-precision image reconstruction by performing second-order correlation operations between modulated optical fields and the corresponding intensity information, overcoming the limitations of traditional "point-to-point" imaging methods. This technique can decouple high-resolution object images from one-dimensional intensity signals, demonstrating high sensitivity and strong anti-interference capabilities. It holds broad application prospects in fields such as medical imaging, microscopic imaging, and LiDAR. This paper provides a detailed overview of the development and applications of traditional CGI, compressed sensing-based CGI, and deep learning-based CGI. It also analyzes the algorithms of each type of CGI and discusses the feasibility of applying large language model to CGI.

Single-pixel imaging, a computational imaging technique utilizing wide-field illumination encoding and single-point detector sampling, offers a novel alternative to conventional imaging methods. However, the limited imaging quality and long imaging time limit the further development of single-pixel imaging in practical applications to some extent. Recent years have witnessed significant advancements in deep learning-driven single-pixel imaging, particularly in enhancing image quality and reconstruction speed. This paper elucidates the fundamental principles of deep learning and single-pixel imaging. We systematically categorize deep learning imaging methods and image-free sensing techniques in single-pixel imaging from a data mapping perspective. Additionally, we examine the advantages and limitations of both deep learning imaging methods and image-free sensing from an application standpoint. Furthermore, we comprehensively analyze the challenges facing deep learning in single-pixel imaging, explore potential solutions, and provide insights for future developments in this field.

The rapid development of the new generation of information technology, such as generative artificial intelligence, large models, and deep learning, has led to explosive growth in global data traffic, which puts forward higher requirements for computing power and energy consumption. Brain-inspired computing is committed to using the brain's structure, function and low-power information processing mechanism for reference to develop new information processing modes, computing models, algorithms and intelligent systems to effectively alleviate the current pressure on computing power and energy consumption. Among them, pulse neural network has many advantages, such as sparse coding, low power consumption, outstanding spatio-temporal information processing ability, and biological rationality. Optical pulse neural network further integrates the advantages of pulse neural network and photonics, such as high speed, large bandwidth, low energy consumption, and strong parallel processing ability, and has become a hot research topic. This paper reviews the work of major research teams at home and abroad in the modeling of photonic pulse neurons, device development and dynamic characteristics research, the model architecture of photonic pulse neural networks, integrated chips and other aspects, and looks forward to the challenges and future development directions.

The thermo-optic effect enables precise and dynamic modulation of refractive index distributions through controllable temperature gradients in optical materials. Capitalizing on its broadband transparency, polarization independence, and non-mechanical tuning capability, this technology overcomes limitations of conventional optical modulation methods and facilitates the development of high-stability, low-power intelligent optical systems. This study systematically reviews fundamental principles, key technologies, and recent progress in thermally tunable optical devices, emphasizing the applications and achievements of two thermal modulation schemes, laser-driven and electrical-driven in tunable-focus lenses, wavefront shaping, and beam shaping. We present a comprehensive comparison of their performance characteristics and application scenarios. Additionally, this study discusses potential applications of thermally tunable optical technology in adaptive optics correction, high-resolution microscopic imaging, smart wearable devices, and precision optical fabrication, proposing theoretical frameworks and technical roadmaps for next-generation intelligent photonic devices.

In the intelligent era, optical perception serves as a fundamental modality for information acquisition and processing, playing a pivotal role in enabling machines to interpret the physical world. Leveraging advanced camera systems, sensors, and data processing technologies, optical systems transform rich real-world scenes into digital signals. These signals are then subjected to feature extraction via artificial intelligence algorithms to facilitate decision-making. With the proliferation of visual tasks, sensing systems have undergone continuous advancements in comprehensive performance. Among these innovations, coded optical imaging and optical pre-sensing computing, rooted in optical encoding technologies, have emerged as transformative tools in optical perception systems. These techniques not only enhance the quality of signal acquisition but also optimize system architectures, thereby boosting operational efficiency and reducing computational power consumption. This paper begins by outlining the workflow of optical perception, then delves into the revolutionary roles of optical encoding and optical computing in advancing perception technologies. It focuses on the research progress of coded optical imaging and optical pre-sensing computing, and concludes with prospects for future development opportunities and challenges in this dynamic field.

Rapid growth in data storage and processing driven by training and inference of large-scale artificial intelligence models necessitates development of novel optical non-volatile memory materials and devices, which offer a promising solution for enhancing computational efficiency while reducing energy consumption in neural networks. Phase-change materials (PCM)-based photonic devices exhibit several advantages in big data processing with high clock frequency, large bandwidth, picosecond latency, and high energy efficiency, making it a key enabler for neuromorphic photonic computing. This review focuses on the recent advancements in optoelectronic PCM for neuromorphic computing. These PCM can be classified into several categories based on their crystallization mechanisms. We provide an in-depth discussion of their bonding mechanisms, optical properties, and performance tuning strategies. Additionally, we review the progress on PCM in photonic waveguide devices for multi-bit storage, bio-inspired synaptic behavior, neuromorphic computing, and hybrid photonic-electronic waveguide technologies. Finally, this review outlines the opportunities and challenges for the research on optoelectronic PCM.

Multimode fibers demonstrate significant potential in short-distance optical communications, microscopic endoscopy, and optical power transmission. However, modal dispersion causes interference and crosstalk in spatially multiplexed optical signals during transmission, affecting demodulation performance. To address this challenge, researchers have proposed various demodulation techniques, including phase conjugation and transmission matrix methods. Yet these approaches have not fully met the demands of the information age for high fidelity, interference resistance, and high-speed transmission. In multimode fiber optical information transmission systems, deep neural networks have proven effective in overcoming multiple scattering issues, enabling precise information transmission. This paper summarizes the research progress of multimode optical information transmission technologies based on deep learning, including high fidelity, high speed, anti disturbance, multi-dimensional and physical prior enhanced optical information transmission, and discusses the optimization of key parameters of the system. At the same time, the opportunities and challenges of this technology in the future are prospected.

Conventional imaging systems based on lenses (groups) face inherent limitations such as cost, hardware size, and multifunctional applications. Lensless computational optical imaging system is a type of compact and lightweight minimalist multifunctional sensing architecture, which has been shown to be scientifically significant in a variety of fields, including biomicrography, multimodal imaging, and data security. This paper provides a concise overview of the fundamental principles, functional expansion, and intelligent reconstruction technologies underlying lensless imaging. By leveraging an"optical encoding+computational decoding"framework, this paradigm enables high-resolution, multimodal, and high-dimensional imaging, even under extreme conditions. The employment of sophisticated artificial intelligence algorithms has been demonstrated to result in a substantial enhancement of the quality of reconstructed images. This paper also discusses the potential for integrating lensless imaging and quantum intelligence technology, and anticipates its extensive applications in future endeavors, including high-security and multifunctional imaging.

Optical edge detection technology has the advantages of high-speed parallel computing and high-throughput processing, and has attracted more and more attention in recent years. However, how to achieve dynamically adjustable optical edge detection is still a challenging problem. Based on this, a scheme of optical edge detection and fast switching in multiple directions is proposed. A fast tunable wave plate was fabricated based on ferroelectric liquid crystals, and a nematic liquid crystal q-plate was fabricated using photopatterning alignment technology. The experimental results show that the combination of the above two liquid crystal elements can achieve one-dimensional edge detection with high quality, and realize fast switching in two orthogonal directions as low as 35 μs by flipping the polarity of the electric field at low voltage. Based on ferroelectric liquid crystals, this scheme provides a flexible dynamic manipulation method for edge detection, which is expected to promote the application of liquid crystal elements in optical computing, optical image processing and other fields.

To meet the development requirements of high-capacity fiber optic communication systems, this study employs an inverse design method to develop and implement a 0° perfect vertical grating coupler (PVGC) for multi-core fiber communication on a standard 220 nm silicon-on-insulator platform. The adjoint optimization-based inverse design approach effectively addresses multiparameter optimization challenges, demonstrating advantages of high efficiency, strong robustness, and excellent process compatibility for the optimized device. Analysis results show that the designed PVGC achieves a simulated coupling efficiency of -2.98 dB (with a 3 dB bandwidth of 50 nm) and a measured peak coupling efficiency of -3.89 dB (with a 3 dB bandwidth of 43 nm) at 1550 nm wavelength. Notably, this research has successfully developed a PVGC array for seven-core fibers, exhibiting an average peak coupling efficiency of -4.45 dB with channel uniformity errors controlled within 0.12 dB. These findings provide a high-performance and highly reliable photonic integrated solution for next-generation space-division multiplexing networks.

As a novel optoelectronic information material, metasurfaces face a primary scientific challenge: maximizing information capacity while minimizing cost. To address this, this paper proposes a phase modulation method that combines geometric phase and propagation phase using minimalist metasurfaces composed of only two nanostructures. This design achieves holographic decoupling across three wavelength channels, enabling low-crosstalk tri-channel display performance despite limited degrees of freedom. Furthermore, we present a multi-key three-wavelength encryption scheme that integrates conventional image encryption algorithm. This approach significantly enhances information security and reliability. The designed dual-structure metasurfaces offer advantages of simple manufacturing, flexible design capability, and high information storage density, providing new solutions for image display, information encryption, anti-counterfeiting, and high-density optical storage applications.

This paper proposes a deep learning-based demodulation method for all-fiber Mach-Zehnder interferometer (MZI). By leveraging convolutional neural network (CNNs) to establish a nonlinear mapping model between transmission spectral curvature and applied curvature, breaking through the dynamic range limitation caused by spectral saturation in traditional demodulation methods. Two intensity-modulated fiber sensors (two-path type and in-line type) are fabricated, demonstrating original curvature demodulation ranges of 0.05747?0.10449 m-1 and 0.02612?0.49106 m?1, respectively. To achieve extended dynamic range demodulation, a Gramian angular field (GAF) encoding technique is introduced to transform one-dimensional spectral signals into two-dimensional images. The CNN regression architecture implements a neural network structure with progressively decreasing neuron counts in fully connected layers, replacing conventional classification output layers to establish nonlinear spectral-curvature mapping. Experimental results demonstrate that under constant maximum sensitivity conditions, both sensor types achieve expanded curvature measurement range of 0?1.5672 m?1, has been increased to 33 times and 3 times the original level, respectively. Validation across four network architectures (ResNet, GoogleNet, etc.) confirm the universality of this proposed method in overcoming traditional spectral limitations in fiber sensing, establishing a novel methodological framework for deep learning-enhanced fiber optic sensing technology.

In conventional infrared device systems, the von Neumann architecture, which integrates sensors, processors, and memory, faces limitations due to high power consumption and high latency. Drawing inspiration from mammalian bipolar cells, this paper designs a metal-semiconductor-metal (MSM) structured infrared sensing-computing integrated device utilizing black phosphorus-indium arsenide-black phosphorus (BP-InAs-BP) as the semiconductor material. This device achieves symmetric positive/negative photoresponses and amplitude modulation across both infrared and visible light spectra through bias adjustment, enabling integrates perception and computation at the detector level. Extensive fittings of physical models facilitate the establishment of a physical simulation model for this detector, allowing microscopic-level analysis of its design, including theoretical derivation of energy band structures and micro-scale charge distributions. This significantly advances the development of BP-based infrared detector applications. In convolutional neural network tests based on this design, classification accuracy for digits 0?9 classification tasks in MNIST dataset with scale of 16 pixel×16 pixel exceeded 92%, highlighting the superior performance of this proposed infrared sensing-computing device. The simulation methodology presented in this study provides a novel design framework and theoretical analysis approach for BP material applications, offering a practical solution for neuromorphic visual perception.

To address the low accuracy of PCBA defect detection caused by the lack of 3D morphological information, an MGT-Fusion defect detection method incorporating both RGB texture and depth image features is proposed. The proposed method enhances traditional RGB texture image-based defect detection by integrating depth images to capture richer spatial and morphological details. Gate fusion module (GFM) and Transformer encoder fusion module (TFM) are designed to effectively fuse features from the two modalities. The GFM employs a dual-gated attention mechanism to perform shallow fusion and extract complementary features, while the TFM leverages a self-attention mechanism to capture global correlations and achieve deep fusion. To support the method, high-precision automatic optical inspection equipment based on a structured light phase-shift fringe technique is developed, enabling the acquisition of both depth and RGB images for constructing a comprehensive PCBA defect dataset. Experimental results demonstrate that the proposed method achieves a mean average precision of 99.89% on the dataset. Furthermore, comparative and ablation experiments are conducted to assess the individual contributions of the GFM and TFM, confirming the effectiveness and advancement of the overall approach. This method offers a valuable reference for improving surface defect detection in PCBA applications.

Based on the laser-induced breakdown spectroscopy assisted with laser-induced fluorescence (LIBS-LIF) technology, this paper proposes a feature equilibrium method to fuse the feature data of homologous laser-induced breakdown spectroscopy (LIBS) to analyze the lead content in kelp with high precision. First, LIBS-LIF technology is employed for univariate quantitative analysis of lead in kelp, with a detection limit of 0.085 mg/kg, which is well below the limit set by national standard, and the sensitivity meets the detection requirements. Then, the LIBS-LIF spectra are interpolated using cubic spline interpolation, while the peak fragment selection for the LIBS spectra is performed using a genetic algorithm. This process resulted in a feature-equilibrium spectrum with matching feature dimensions. Finally, the proposed method is validated using three machine learning algorithms: ridge regression (RR), random forest regression (RFR), and support vector regression (SVR). Results show that the feature equilibrium method significantly improved the prediction accuracy and generalization ability of the model, among which the SVR model achieves the best quantitative performance, with the coefficient of determination, root mean square error, and average relative error for prediction set are 0.957, 0.251 mg/kg, and 7.36%, respectively. It is proved that the proposed method offers high quantitative accuracy, which provides a new approach for achieving high-sensitivity and high-precision detection of lead in kelp.

Complex interactions between neurons can be simulated using multi-layer networks, such as cross-layer coupling between neurons in different brain regions. This study employs numerical calculations to investigate the synchronization characteristics of multi-layer optical pulse neural networks based on vertical-cavity surface-emitting lasers with saturable absorber (VCSEL-SA). Considering the limited signal transmission speed in neural systems, we comprehensively evaluate the impact of intra-layer and inter-layer delays on network synchronization. Our findings reveal that different coupling delays effectively induce transitions in network synchronization patterns. Furthermore, we examine the influence of key VCSEL-SA parameters on synchronization stability and demonstrate the robustness of neuronal synchronization against parameter mismatches between different layers. Finally, we validate the universality of our conclusions through a three-layer photonic neuron network. This work presents a systematic and in-depth investigation of synchronization characteristics in multi-layer networks composed of photonic neurons. The results provide valuable insights for practical applications of brain-inspired optical neural networks.

To reduce the impact of atmospheric turbulence on free space optical communication (FSOC) links and improve the performance of systems, an adaptive collaborative stochastic parallel gradient descent (AC-SPGD) optimization algorithm is proposed. In the iterative process of the AC-SPGD algorithm, the genetic algorithm (GA) is first used to find potential optimal parameter combinations, and then the particle swarm optimization (PSO) algorithm is used to quickly converge to the global optimal solution. Finally, the stochastic parallel gradient descent (SPGD) algorithm is used to make fine parameter adjustments, avoiding the problem of easy local extreme value convergence and slow iteration speed for SPGD. Simulation results show that the AC-SPGD algorithm can compensate for the wavefront error caused by strong turbulence in real time, achieving a good correction effect, and has faster convergence speed and higher correction accuracy than the traditional fixed gain optimization algorithm.

Fiber-optic plasma sensors continuously collect substantial real-time data during detection processes in biomedical applications, aquatic environment monitoring, and food safety assessments. However, the relatively small variations in output spectra, combined with anomalies and noise from environmental factors or human interference, make it challenging to rapidly extract valid data, thus affecting detection accuracy. To address this, we propose an efficient hybrid intelligent processing method using the Robust iterative reweighted penalized least squares (RirPLS) algorithm to tackle issues including slow data processing, multiple outliers, significant noise, and baseline drift. This algorithm effectively eliminates outliers by continuously updating weights and fitting valid data. Additionally, we implement a two-level improved complete ensemble empirical mode decomposition with adaptive noise (2L-ICEEMDAN) for denoising, preventing information loss in high-frequency intrinsic mode functions. Simulation experiments demonstrate that this method improves the signal-to-noise ratio to 18.0539 dB. In a biological antigen-antibody detection case study, after processing extensive complex data collected from fiber-optic plasma sensors, the noise intensity decreased by 0.5018, correlation dimension reduced by 0.1797, and amplitude-aware permutation entropy decreased by 0.6932. The results indicate that the proposed method demonstrates excellent performance and broad applicability in processing complex data.

Owing to the chaotic time delay signature (TDS) of chaotic light, there is a "double-peak" phenomenon during the acquisition process of the chaotic Brillouin dynamic grating (BDG) system, which results in inaccurate measurements of the gain and reflection spectra of chaotic BDG systems under temperature variations. This research analyzes the principle behind the appearance of the double-peak in gain and reflection spectra, introduces the ratio of double-peak powers, and investigates the impact of TDS-induced sideband BDG systems on the gain and reflection spectra. The results indicate that the emergence of TDS significantly interferes with the sensing measurements of BDG systems, thereby degrading the measurement accuracy. Experimental results show that by altering the feedback cavity length and feedback strength of the chaotic light feedback loop, the number of sideband BDGs present in the optical fiber can be changed. The relationship between the feedback strength and the ratio of double-peak powers follows a quadratic polynomial rule, and the ratio of double-peak power also alters the influence range of TDS on the measurements. When the feedback strength increases from 0.01 to 0.09, the TDS decreases from 0.441 to 0.095, reducing the influence range of TDS on the gain spectrum by half. Further, the sensing error of the reflection spectrum is reduced to 30 MHz in the temperature range of 35?50 ℃, ensuring the accuracy of chaotic BDG system measurements.

In this study, we design, fabricate, and thoroughly test a fiber-optic biomimetic microelectromechanical system (MEMS) membrane-type acoustic. A silicon-based coupled-bridge double-diaphragm biomimetic membrane is developed by simulating the auditory organs of parasitic flies. The vibrations of the membrane are detected employing a Fabry-Pérot interferometer composed of optical fibers, thereby enabling the measurement of acoustic signals. The results demonstrate that the fabricated biomimetic membrane exhibits two distinct vibration modes. The packaged sensor demonstrates two resonant frequencies at approximately 810 and 1600 Hz, with minimum detectable sound pressures of 2.76 and 2.17 mPa/Hz1/2, respectively. In addition, the sensor exhibits a figure-eight directional response pattern. Within an incident angle range of 0°?60°, the amplitude of the sensor varies linearly with the angle. Thus, the results indicate that the proposed sensor holds potential for use in miniaturized soundsource target localization systems.

The use of conical pick in hard rock cutting processes often leads to severe wear issues due to high-temperature friction. Therefore, enhancing the wear-resistance of the cone of conical pick and studying the thermal distribution and wear patterns during the cutting process are crucial. We employ laser cladding technology to deposit a NiCr-Cr3C2 wear-resistant coating on the pick substrate. This study analyzes the influence of process parameters on the properties of coating and investigates the cutting heat and wear conditions. The results indicate that coating hardness is positively correlated with laser power and powder feed rate but negatively correlated with scanning speed. The optimal process parameters are laser power of 2100 W, scanning speed of 10 mm/s, and powder feed rate of 37.5 g/min. During the cutting process, temperature changes can be divided into three stages: rapid heating, slow heating, and thermal equilibrium. The coated picks tend to experience more localized heat concentration at the interface between the coating and substrate compared to uncoated picks. In the cutting process, uncoated picks primarily exhibit adhesive wear, whereas coated picks mainly show abrasive wear. The wear rate of the picks is positively correlated with the heating rate. The wear resistance of spiral coated picks is superior to that of linear ones, reducing wear by 50.3% compared to uncoated picks.

Aluminum-copper alloy is commonly used in aerospace lightweight structures because it is lightweight and possesses high strength. Laser welding has a high energy density and a small heat-affected zone, making it widely applicable in welding aluminum-copper alloys. However, due to the inherent characteristics of aluminum-copper alloys, problems such as porosity and cracks are prone to occur when using ordinary single-light-source laser welding. This study employs an infrared/blue light composite laser to weld 2A14T6/2A12T4 aluminum-copper alloy and studies the impact of process parameters on weld formation and joint microstructure. The results indicate that as welding heat input increases, weld seam width increases, while stability during welding decreases. When heat input decreases, porosity at the weld seam is reduced, but fusion on both sides of the weld seam becomes insufficient. The best weld forming effect is achieved when the infrared light power is 4500 W, the blue light power is 600 W, and the welding speed is 30 mm/s. The formation of the weld seam starts at the edge, gradually advances toward the center, and finally results in coarse columnar and fine equiaxed crystals. During welding, due to solidification imbalance, a large amount of strip-shaped θ-Al2Cu precipitates at the grain boundaries.

Compared with graphene (Gr) and tungsten disulfide (WS2), the Gr/WS2 heterojunction exhibits a greater modulation depth, owing to its strong interlayer coupling effect, and demonstrates excellent nonlinear optical properties. In this study, a Gr/WS2 composite material nano-film is prepared via the liquid-phase exfoliation method and coated onto the surface of an etched fiber via photodeposition method, thus forming an all-fiber saturable absorber device with advantages such as a high damage threshold, large modulation region, and simple structure. The proposed device is applied to an erbium-doped fiber continuous-wave laser, and traditional soliton mode-locking with a central wavelength of 1531.34 nm is achieved. Compared with single materials, the Gr/WS2 heterojunction under the same experimental conditions enables a narrow pulse width (5.6 ps) and higher output power (13.61 mW). These results indicate that the Gr/WS2 heterojunction is a promising candidate for pulse laser applications and provides a solid foundation for the development of high-performance ultrafast photonic devices.

To ensure the stable operation of unmanned aerial vehicle water depth detection LiDAR under different working conditions, a LiDAR heat dissipation structure designed by combining optical, mechanical, and thermal integration analysis is proposed. The design helps achieve temperature control under different working conditions. The heat dissipation structure is designed such that it fully integrates the environmental conditions during the operation of the drone. Firstl, a thermal analysis model for the laser radar system is established using Icepak. Then, Ansys is used to perform thermal mechanical coupling analysis on the optical structure. Next, the optical system structure deformation is imported into Zemax to calculate the modulation transfer function (MTF) curve of the system through the optical mechanical interface Sigfit. Finally, the heat dissipation structure is further optimized based on the imaging quality. The experimental analysis results indicate that the temperature of each module remains within the allowable range under different operating conditions. After optimizing the heat dissipation structure, it is observed that the MTF value of the optical system is greater than 0.285 at an ambient temperature of 40 ℃, and the MTF of the optical system is greater than 0.25 at normal operating temperatures, indicating good imaging performance of the system. The results of the field experiments verify the accuracy of the optical mechanical thermal integration analysis and also indicated that the receiving optical system can achieve echo reception. In summary, the heat dissipation structure designed in this article can integrated in unmanned aerial vehicle water depth measurement LiDAR systems to ensure the stable operation of the system.

A suitable denoising method is explored to reduce the influence of noise on atmospheric Rayleigh lidar echo signal. Combined with simulated and measured echo signals, the denoising effects of moving average, Hanning window sliding-window, wavelet transform (WT), ensemble empirical mode decomposition (EEMD), WT-EEMD-LOWESS, and EEMD-VMD-IMWOA methods are compared with respect to their influence on photon number profile, atmospheric fluctuation information, and temperature retrieval accuracy. The analysis results show that the temperature retrieval results processed by the Hanning window sliding-window and the EEMD-VMD-IMWOA methods have high accuracy and effectively retain the atmospheric fluctuation information, surpassing the other methods. However, EEMD-VMD-IMWOA method has better robustness, and the Hanning window sliding-window method is not suitable for processing high signal-to-noise ratio signals. If the signal-to-noise ratio of the measured signal is in the range of 0?200, the Hanning window sliding-window method can be selected for denoising; otherwise, EEMD-VMD-IMWOA method should be preferred to improve retrieval accuracy.

To address the challenges of cumbersome operation and spatiotemporal discontinuities in traditional laboratory-based water quality testing methods, this study proposes a total nitrogen inversion method based on hyperspectral imaging. Taking the Shiwuli River in the Chaohu Basin as the research object, near ground hyperspectral data serve as the data source. The hyperspectral data of the water samples are resampled to match the resolution of unmanned aerial vehicle (UAV) hyperspectral data. The random frog (RF) algorithm is used to extract the characteristic bands of the total nitrogen mass concentration in the water samples. The particle swarm optimization (PSO)-backpropagation neural network (BPNN) algorithm is then used to construct a total nitrogen inversion model, enabling the inversion of total nitrogen mass concentration in water bodies using UAV hyperspectral images. These results indicate that the feature bands 465.1, 495.2, 756.2, 830.1 nm, and 847.7 nm, extracted using the RF algorithm, align with the sensitive band range of total nitrogen. The established PSO-BPNN inversion model has a prediction coefficient of determination (R2) of 0.862 and a root mean square error (RMSE) of 0.405 mg/L for the training set, while the test set yields a prediction R2 of 0.711 and RMSE of 0.640 mg/L. The RMSE of the test set is significantly reduced compared with those by the BPNN and partial least squares models. Applying this model to UAV hyperspectral imaging enables rapid inversion of the spatial distribution characteristics of total nitrogen, with the relative deviation between the inversion values at verification points and the measured values remaining below 5.50%. These findings demonstrate that the model exhibits a certain degree of generalization and strong practical applicability.

NOx is a major pollutant in exhaust gas and primarily comprises NO and NO2, with NO being the main component. To meet the stringent requirements for high-precision detection of NO concentration, this study proposes an NO concentration inversion method based on ultraviolet (UV) differential spectroscopy. This method employs an empirical wavelet transform (EWT) to decompose the UV spectrum of NO and applies an adaptive Savitzky-Golay (ASG) filter to process the decomposed signal. The filtered signal is reconstructed using the inverse EWT (IEWT). Finally, a long short-term memory (LSTM) neural network performs the inversion to determine the NO concentration. The spectral signal of NO is first processed via the EWT-ASG method and subsequently analyzed using the LSTM neural network. Results demonstrate that the predicted NO concentration has maximum and minimum errors of approximately 6% and 0.01%, respectively, when compared to the true values. Moreover, the root mean square error of the inversion accuracy improves by 35.86% compared to the unfiltered state. Thus, the proposed method provides strong technical support for the concentration inversion of NO and other components of exhaust gas.

Rare earth elements (REEs) play a crucial role in modern technology owing to their unique physical and chemical properties. However, their distribution and content in the environment have been continuously altered by human activities and industrial development. On-site, sensitive, and fast analysis of multiple REEs is essential, but remains a challenging task. This study introduces a flexible superhydrophobic film (FSF) combined with laser-induced breakdown spectroscopy (LIBS) for the high-sensitivity and rapid detection of multiple REEs in aqueous solutions. By employing a flexible substrate film with micron-sized pockets as a protective layer for fragile hydrophobic nanostructures, the FSF exhibits enhanced hydrophobicity and unique self-cleaning properties. These features render it more convenient and flexible in both preparation and application. Furthermore, microwave-assisted droplet evaporation is achieved on hydrophilic dot arrays of the FSF, thereby enabling efficient element-targeted enrichment. This is seamlessly integrated with the instantaneous and high-throughput analysis provided by LIBS. The proposed method demonstrates the ability to detect six REEs in aqueous solutions simultaneously with high sensitivity and high speed within 25 min. The limits of detection of REEs are obtained at the μg/L level, which is several orders of magnitude lower than previously reported LIBS techniques. The FSF-LIBS approach offers a potential tool for on-site elemental analysis, energy utilization, geochemical cycles monitoring, and environmental monitoring.

To address the difficulty and low accuracy of the non-destructive detection of thermal growth oxide (TGO) thickness, a detection method combining terahertz time-domain spectroscopy and a deep learning model is proposed. By establishing a simulation model of the thermal barrier coating, the terahertz time-domain spectral data of the simulation and physical sample are obtained, and the deep learning model is optimized to improve the accuracy of TGO thickness detection. When processing the simulation data, the average determination coefficient of the model is 0.934, whereas when processing the physical sample data, the average determination coefficient is 0.857, indicating a high degree of fitting. For the detection results of TGO thickness in the range of 3?10 μm, the mean relative error of the model is less than 10%, indicating a high detection accuracy. This method effectively captures the complex relationship between TGO thickness and terahertz time-domain spectral signals, and provides technical support for the life assessment and failure prediction of thermal barrier coatings.