最新刊期
卷 41 ， 期 2 ， 2026
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Lattice structure based on temperature control in polymer- stabilized blue phase liquid crystals AI导读
摘要：Blue phase liquid crystals（BPLCs） possess several extraordinary optical properties， such as fast response time （sub-milliseconds）， Bragg reflection in visible range， optical isotropic property. Meanwhile， polymer-stabilized blue phases LC（PSBPLCs） expanded the temperature range of BPLCs， which makes PSBPLCs have a good application in electro-optical devices. However， photoisomerization perturbs the arrangement of liquid crystal molecules， which induces defects in lattice structures. Herein， we introduce temperature control in photo polymerization. Pure liquid crystal samples and unpolymerized samples of BPLC are tested to find out the relationship between temperature and lattice constants. Based on temperature properties， lattice structures are observed at three different temperature region. Experimental results indicate that the lattice constants of blue phase Ⅰ （BPⅠ） change discretely （about 40 nm） after introducing liquid crystal polymer. Therefore， polymerizing in addition to mid-temperature region could avoid distortion of lattice structures and broadening of spectrum.关键词：polymer-stabilized blue phase liquid crystals;lattice structure;temperature control339|301|2更新时间：2026-09-12
Material Physics
- 摘要：This study investigates the morphological structure， polarization mechanism， and fluorescence regulation effect of Polymer Dispersed Liquid Crystal（PDLC） composite materials doped with Carbon Quantum Dots（CQDs） nanomaterials. Based on the micro-morphological structure of PDLC films， the influence of this structure on liquid crystal droplet size is analyzed. Using dielectric measurement data and Cole-Cole plots， the CQDs-induced non-Debye relaxation polarization process of the composite film under different frequencies is studied， while the effects of CQDs doping on relaxation time and relaxation distribution parameter are also analyzed. Furthermore， the impacts of these two factors on the electro-optical properties of the films are explored. Under the synergistic regulation of CQDs mass fraction and exposure intensity， the effects of PDLC’s dielectric properties and scattering performance on the film’s fluorescence performance are investigated. The experimental results show that： when the CQDs doping amount is 0.2% （mass fraction）， compared with undoped PDLC， the relaxation time of the PDLC composite film decreases by 56%， and the threshold voltage and saturation voltage decrease by 19.9% and 11.8% respectively， and the on-state and off-state switching response times decrease from 32 ms and 50 ms to 10 ms respectively； when the exposure intensity decreases from 30 mW/cm2 to 1 mW/cm2， the peak fluorescence intensity of PDLC increases by approximately 130%. This work effectively realizes the synergistic improvement of electro-optical properties and fluorescent characteristics of PDLC， providing valuable insights for the development of PDLC composite materials.关键词：polymer-dispersed liquid crystal;carbon quantum dot;dielectric properties;fluorescent392|438|2更新时间：2026-09-12
- 摘要：Cross type liquid crystal polarization volume grating（PVG）-based two-dimensional pupil expansion waveguides have emerged as a research hotspot in AR displays due to their compact structure and fabrication simplicity （no dynamic adjustment of interference angles is required during holographic exposure）； however， their field of view（FOV） for AR applications is constrained by traditional dual-waveguide stacking approaches. To enhance the FOV， this work proposes an optical waveguide architecture based on a single-layer high-refractive-index glass substrate （n=1.9） coated with left-handed and right-handed PVGs on both surfaces， where the horizontal azimuthal angles of the reciprocal lattice vectors for the input-coupler and output-coupler gratings are 0° and 60°， respectively. Incident light is split into two separate total internal reflection paths via PVG polarization selectivity； partial out-coupling occurs when the combined reciprocal lattice vector provided by the input and output gratings sums to zero， forming a complete two-dimensional expanded exit pupil. Based on the grating diffraction equation and substrate refractive index， we derived the required PVG period for the central FOV aligned with the waveguide normal direction. In addition， the input-coupling mechanism for vertically incident 532 nm collimated light is validated via COMSOL simulations. The fabricated prototype （28 mm × 22 mm output grating） achieved a 32.8° （horizontal） × 18.7° （vertical） FOV （37.8° diagonal）， representing a 26% improvement in the diagonal FOV over prior work. This design eliminates traditional bonding processes by direct dual-side PVG fabrication， circumventing stringent requirements for index-matching adhesives/substrates， significantly simplifying fabrication process and reducing costs. The high-refractive-index substrate further increases FOV while enhancing image quality， providing a promising high-performance solution for AR near-eye displays.关键词：liquid crystal polarization volume grating;field of view;two-dimensional exit pupil expansion;waveguide-based AR display;liquid crystal optics828|592|0更新时间：2026-09-12
- 摘要：Spectral Domain Optical Coherence Tomography（SD-OCT） systems have been extensively studied for biological tissue measurement， featuring non-invasiveness， micron-level resolution， and real-time detection capabilities. To expand their application scope to the measurement of industrial products with multilayer structures， it is necessary to optimize the performance of SD-OCT systems. Firstly， this study optimizes the parameters of the sample arm field lens using ZEMAX. Aberrations are reduced by adjusting the lens curvature， focal length， and optical path layout， and a matched scanning lens is designed to expand the detection range. Secondly， the spectrometer parameters are optimized： appropriate lenses and gratings are selected， and the optical path layout and device tilt angle are adjusted to improve spectral resolution and imaging depth. Finally， MATLAB is used to establish a simulation model for the system’s axial resolution， which supports tomographic imaging simulation experiments of multilayer sample structures. The results show that the comprehensive performance of the optimized SD-OCT system is significantly improved. The lateral resolution is enhanced to 20 µm， while a large scanning range of 34 mm×34 mm is achieved. The imaging depth is increased to 2.44 mm， enabling effective penetration of multilayer industrial structures. The axial resolution is verified by simulation to be 6.40 µm， with the simulation results highly consistent with the theoretical calculation values. Additionally， the tomographic imaging simulation of multilayer sample structures is successfully realized. The proposed optimization design method for the SD-OCT system provides valuable reference and promotion for its applications in other fields.关键词：ZEMAX;spectral-domain optical coherence tomography;resolution;imaging depth;spectrometer282|242|0更新时间：2026-09-12
Device Physics and Device Preparation
- 摘要：Surface defects on liquid crystal displays（LCDs） impair appearance and reliability， presenting challenges such as wide-scale variations， complex backgrounds， and difficulty in detecting small targets. This paper proposes FFD-YOLO， an LCD defect detection algorithm based on the lightweight YOLOv8n framework. The algorithm uses the FasterNet backbone to enhance feature extraction. It designs the Feature Pyramid Shared Convolution（FPSC） module， which uses multi-expansion-rate convolutions and shared convolutional mechanisms to enhance multi-scale feature modeling. Additionally， it proposes the Multi-Scale Adaptive Convolution Module（MACM）， which employs dynamic convolution weights and multi-scale convolution kernels to enhance the representation of small objects and stability in complex backgrounds. Experimental results demonstrate that on the self-built industrial-grade LCD-NET dataset， FFD-YOLO achieves 5.0%， 4.7%， and 3.2% improvements in Precision， Recall， and mAP50， respectively， compared to baseline models， with a 6.4% boost in accuracy for detecting small stain-type objects. These results demonstrate that FFD-YOLO significantly enhances LCD defect detection performance while maintaining lightweight efficiency， offering an effective and reliable solution for industrial vision inspection systems.关键词：liquid crystal display;defect detection;feature pyramid shared convolutions;multi-scale adaptive convolutions;YOLOv8344|417|1更新时间：2026-09-12
- 摘要：Object detection in foggy and dynamic environments remains challenging， especially for small objects whose features are easily degraded by low visibility. Existing methods often suffer from insufficient robustness and limited real-time performance. This study aims to develop a high-efficiency and highly robust detection framework tailored for foggy small-object detection. We propose HS-MambaDet， a hybrid state-space detection network built upon the Mamba-Transformer architecture. A hidden-state compression-enhanced backbone（HSM-SSD） is designed to reduce computational overhead while preserving global dependency modeling. Additionally， a Multi-scale Frequency-Spatial Fusion（MFSF） module—consisting of a high-frequency perception branch and a spatial dependency modeling branch—is introduced to enhance fine-grained details and long-range spatial relations for small objects. The overall framework integrates CNN， Transformer， and Mamba components to jointly capture local textures， global context， and efficient sequential dependencies. Extensive experiments on the RTTS and CityScapes datasets demonstrate that HS-MambaDet outperforms mainstream SOTA models in both accuracy and efficiency. On RTTS， the complete model achieves 87.3% precision， 73.1% recall， 81.2% mAP@0.5， and 51.0% mAP@（0.5~0.95）， exceeding the best comparison baselines by up to 3.8%， 3.9%， 3.6%， and 3.3%， respectively. The inference time remains low at 0.26 s， ensuring real-time capability. In small-object scenarios， HS-MambaDet improves mAP@0.5 by up to 4.4%， and cross-domain tests under varying fog intensities further verify its superior generalization and robustness. By combining hidden state compression with multi-scale frequency-spatial fusion， HS-MambaDet effectively enhances fine-detail perception and spatial dependency modeling under foggy conditions. The proposed framework achieves strong performance in accuracy， robustness， and inference speed， offering a practical and efficient solution for real-time object detection in adverse weather and dynamic environments.关键词：object detection;mamba;high-frequency perception;fusion hidden state compression193|408|0更新时间：2026-09-12
- 摘要：To address the inter-rater and intra-rater variability， as well as the time-consuming nature of manual scoring for the “three-step command” in the Mini-Mental State Examination（MMSE）， an object detection method based on MLC-YOLO is proposed to achieve automatic and precise detection of this action. First， multi-level classification object detection is performed on key human body parts and paper， and a same-class maximum confidence selection strategy is adopted to simplify post-processing and ensure the uniqueness of targets of the same class. Second， an adaptive spatial-channel decoupling module is designed to achieve efficient downsampling. Then， Ghost convolution and wavelet transform are introduced into the C3 module to enhance the efficiency and capability of multi-scale feature extraction. Finally， for the small object detection layer， spatial and channel collaborative attention is introduced to improve precision and recall in complex scenes.Experimental results indicate that the mAP95 of the proposed method reaches 61.8%， an increase of 5.6% compared to the original model， while the parameter count is reduced by 51.52%. It proves the effectiveness of the method， offering a new approach for object detection problems involving unique targets and multi-level classification， and providing an effective object detection method for the automatic scoring of the “three-step command” in MMSE.关键词：deep learning;object detection;multilevel classification;lightweight network239|331|0更新时间：2026-09-12
- 摘要：To address the insufficient feature extraction of existing detection models for tiny targets， this paper proposes a lightweight small object detection model named PRSU-YOLO. First， a Pinwheel-shaped Convolutional Adaptive Module is designed to enhance the directional extraction capability of subtle features. Second， a Reparameterized Spatial-Channel Convolution Module is constructed to optimize multi-scale representation through dynamic feature reconstruction. Third， a Small Object Detection Branch is embedded in the neck network to establish an enhancement pathway for high-resolution detail features. Finally， a Scale-based Dynamic Intersection over Union loss function is introduced， enabling the model to adaptively adjust the bounding-box regression strategy. With only a 14.6 GFLOPS increase in computational complexity， the proposed model achieves an mAP@0.5 of 37.4% on the VisDrone2019， representing a significant improvement of 4.4% over the baseline. On the TinyPerson， it attained a precision of 34.5%， which is an increase of 3.5% compared to the baseline. The experimental results demonstrate that the model significantly enhances detection capability while effectively controlling computational cost， providing an effective solution for small object detection tasks in UAV-based ground observation scenarios.关键词：computer vision;unmanned aerial vehicle imagery;small object detection;feature enhancement345|2649|1更新时间：2026-09-12
- 摘要：To achieve efficient and accurate defect detection for wind turbine blades， this study develops a UAV-based intelligent inspection system and investigates its core algorithms， including multi-stage defect screening， representative frame selection， and deep learning-based defect recognition. First， based on image features such as structural integrity and texture patterns， an improved EfficientNetV2-S network is proposed for preliminary damage screening （accuracy 87.5%）. Next， Gaussian Mixture Clustering is employed to select representative damaged frames from video streams， effectively reducing data redundancy. Then， a YOLO-v11 network integrated with Transformer modules and dynamic anchor strategies is developed for precise defect localization and classification. Additionally， a comprehensive dataset containing 15 000 annotated UAV images of various defects （e.g.， cracks， delamination） is established for algorithm validation.Experimental results demonstrate that the system achieves 88.4% recall and 71.5% precision in single-frame detection， while video-level analysis improves to 95.0% recall and 86.4% precision. The method outperforms existing approaches in detecting multiple defect types under complex environmental conditions.The proposed system meets the requirements for non-contact inspection， real-time processing， high accuracy， and strong anti-interference capability， providing an effective solution for intelligent wind turbine maintenance.关键词：surface defects of wind turbine blades;UAV tour-inspection;Gaussian mixture model;YOLO-v11;efficientNetV2547|389|0更新时间：2026-09-12
- 摘要：Existing image super-resolution algorithms face issues such as high computational cost， limited perception of local high-frequency textures， and insufficient extraction of key information. To address these challenges， this paper proposes a lightweight image super-resolution reconstruction network based on high-frequency fusion attention. First， a Dual-Path Enhancement module is designed within the network architecture to explicitly decouple features into parallel high-frequency and low-frequency branches. The low-frequency branch captures smooth structures， while the high-frequency branch employs learnable high-pass filters to accurately extract edges and textures. Second， a spatial refinement module is introduced， utilizing directionally separated convolutions to enhance local features and generate queries rich in spatial information. Finally， an efficient asymmetric frequency-aware attention mechanism is designed to achieve effective interaction between local and global information. This mechanism uses the local information output from the spatial refinement module as a query to dynamically retrieve complementary information across the entire image， effectively overcoming the limitation of local windows on long-range dependencies. Experiments on the Urban100 dataset demonstrate that at ×4 magnification scales， the proposed algorithm achieves an average PSNR improvement of 0.53 dB and an average SSIM improvement of 0.014 7 compared to other models， while running at only 14% of SwinIR's computational time. Regarding visual quality in image reconstruction， the model generates sharper edges and more realistic textures， fully validating its effectiveness and advanced capabilities in image reconstruction while maintaining lightweight efficiency.关键词：image processing;super-resolution reconstruction;attention mechanism;feature fusion;Lightweight326|206|0更新时间：2026-09-12
- 摘要：In fire monitoring， fire sources are often occluded by buildings or equipment， making traditional methods based on flame brightness or texture features ineffective. To address this challenge， this paper proposes a smoke-based fire source estimation method. A lightweight TANet is first used to predict pixel-level smoke concentration in video frames， generating a continuous and stable concentration field for subsequent motion analysis. Farneback dense optical flow is then applied to extract smoke motion features， leveraging the directional consistency and spatial smoothness of smoke across consecutive frames to obtain a global dense velocity field. To cope with complex smoke morphology， varying illumination， and optical flow noise， the traditional RANSAC algorithm is improved with multi-level consistency filtering and density-based clustering， using the convergence point of reversed smoke motion as the estimated fire source. Moreover， to handle high occlusion scenarios where lower smoke motion structures may be missing， a multi-line optical flow joint backtracking strategy is introduced to collect flow information at multiple levels， enhancing the stability and robustness of source estimation. Experimental results demonstrate that the proposed method achieves a normalized error below 2% under low to moderate occlusion and remains stable around 3% under high occlusion after optimization， indicating reliable fire source localization in complex visibility environment.关键词：smoke density estimation;dense optical flow;improved RANSAC;fire source localization;occluded scenes314|193|0更新时间：2026-09-12
- 摘要：Cell segmentation is a fundamental task in biomedical image analysis， holding significant application value in pathological diagnosis and cell biology. Accurate nuclear segmentation is crucial for disease screening， tumor microenvironment analysis， and treatment planning. However， the high morphological heterogeneity and frequent overlapping of cells in tissue images， coupled with noise and uneven illumination， significantly increase the difficulty of automated segmentation. Despite advances in traditional and deep learning methods， they still face notable limitations under complex conditions such as cell overlap and blurred boundaries. To address these challenges， this study proposes NucleiViT， a Vision Transformer（ViT）-based model designed for efficient and accurate cell detection and segmentation. The core innovation of the proposed model lies in the integration of a sliding-window dilated attention mechanism， which enables feature interaction among different task branches within a shared module， thereby maximizing the advantages of multi-task learning.Additionally， a joint boundary-aware loss function is introduced to enhance sensitivity to cell boundary delineation.Experimental results demonstrate that NucleiViT outperforms existing state-of-the-art methods in terms of segmentation accuracy and computational efficiency， exhibiting superior robustness in densely packed and overlapping cellular regions. This approach provides an efficient and reliable solution for clinical pathological image analysis.关键词：cell segmentation;multi-task joint optimization;boundary-aware loss;neural network258|282|0更新时间：2026-09-12
Image Processing
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