A lossy mode resonance (LMR)-based fiber optic humidity sensor coated with polymeric humidity-sensitive materials is proposed. Titanium dioxide (TiO2) is deposited on the multimode fiber core using the layer-by-layer assembled (LBL) technique to excite LMR effects. The refractive index sensitivity of the LMR sensor is systematically investigated through both theoretical analysis and experimental validation. Two distinct humidity sensors are fabricated by employing polyvinyl alcohol (PVA) and polyimide (PI) as the functional sensing layers, respectively, where humidity detection is achieved by monitoring the resonance wavelength shift in the absorption spectrum. Experimental results demonstrate that both sensors exhibit effective humidity-sensing capabilities with comparable performance characteristics. Notably, the PVA-based sensor achieves a maximum sensitivity of 2.94 nm/%RH (relative humidity) within the 60% RH—90% RH range. Owing to their high sensitivity, excellent reversibility, and outstanding repeatability, the proposed sensors exhibit significant potential for practical applications in humidity monitoring.

In order to realize the efficient processing of optical information and the high integration of optical devices, according to the principle of resonant coupling between the ring cavity and the waveguide of a photonic crystal (PC), in this paper, we design a novel four-channel filter consisting of a point-defect resonator, a triangular ring resonator and a line defect waveguide and a reflective heterojunction based on a two-dimensional triangular lattice photonic crystal. The band maps of intact photonic crystals and line defect waveguides are obtained by plane wave expansion method (PWEM). The calculated results show that the reflected heterojunction has obvious effect on improving the filtering efficiency. Using the finite-difference time-domain method (FDTD), the filtering efficiency of the four-channel filter at 1.456 m, 1.493 m, 1.422 m and 1.476 m is simulated. The simulation results show that the filtering efficiency of the four-channel filter is above 99.84%, realizing the high efficiency filter output by adjusting parameters, such as the center medium column radius of the resonator. Moreover, the maximum insertion loss of each channel of the filter is only 0.009 dB, full width at half maximum less than 7.2 nm, and quality factor greater than 207. The four-channel filter designed in this paper achieves narrowband filtering and good performance parameters, with good applicability and generalizability in optical communication applications.

To address the application requirements of wideband, high polarization conversion ratio (PCR) and miniaturized polarization converters, a dual-wideband and high PCR transmissive polarization converter based on chiral metasurface is designed in this paper. The proposed polarization converter consists of a three-layer structure:open rings at the top and bottom layers and a dielectric layer. The simulation results show that the metasurface uniformly achieves linear polarization conversion in the frequency ranges of 6.21—7.05 GHz and 8.74—9.73 GHz with the PCR value all exceeding 90%, and the corresponding relative bandwidths are 12.70% and 10.72%, respectively. The mechanism of high PCR and dual-band polarization conversion is revealed by analyzing the surface currents at the resonant frequencies. In addition, the influence of geometric parameters, thickness of the dielectric layer, polarization mode and polarization angle of the incident wave on the polarization manipulated characteristics of the dual-band are also studied. The proposed chiral dual-band polarization conversion metasurface possesses the advantages of simple structure and polarization insensitivity, which has potential application values in microwave communication, microscopic imaging and stealth technology.

This study presents a high-performance refractive index sensor based on surface plasmon resonance (SPR) in a photonic crystal fiber (PCF). By exciting SPR in the near-infrared band (1.3—2.3 m), the proposed sensor extends the refractive index detection upper limit for similar SPR-PCF sensors while enabling high-refractive-index sensing. The sensor′s optical loss, SPR characteristics and refractive index sensitivity are systematically investigated using the finite element method (FEM), and its structure is optimized for enhanced performance. Within the refractive index range of 1.44—1.58, the sensor achieves a remarkable sensitivity of 9 100 nm/RIU and a resolution of 1.099×10-5 RIU. These advancements make the sensor highly promising for applications in environmental monitoring, particularly in crude oil leakage detection.

Weld surface defect recognition plays a vital role in the welding process and quality control. The classical two-dimensional principal component analysis (2DPCA) algorithm using the F norm metric in weld defect recognition suffers from the problems of being sensitive to abnormal deviation values and noise, poor robustness, and not being able to effectively reduce the reconstruction error while the projection distance is maximum. Aiming at the above problems, this paper uses a joint-norm metric, a two-dimensional principal component analysis algorithm called L1-2DPCA-R1 is proposed by adding L1 and R1 norm to the function model, and the iterative solution method of the algorithm is listed. This algorithm reduces the reconstruction error of the image, has better reconstruction performance, suppresses the influence of abnormal deviation values and noise, improves the robustness, and maintains the advantage of classification rate. Experiments show that the algorithm can accurately detect various weld defect types, with better resistance to large noise, better robustness, and smaller reconstruction error than other principal component analysis (PCA) algorithms.

Aiming at the lack of digital inheritance and innovation technology of blue calico patterns of Chinese intangible cultural heritage, this paper proposes a method for generating blue calico patterns based on the improved stable diffusion (SD) model, which realizing the active generation of single and multiple blue calico patterns through text-to-image and image-to-image technology. For the problem that the blue calico pattern datasets is less and it is difficult to train a large model, a low-rank adaptation algorithm (LoRA) fine-tuning network with blue calico characteristics is trained by combining the stable diffusion model and the LoRA fine-tuning network. Aiming at the problem that the output of the stable diffusion model is random, the stable diffusion model is combined with the discriminative network to judge the generated images and filter out the texture images that are consistent with the characteristics of blue calico. The experimental results show that the new blue calico patterns with semantic information and picture characteristics can be generated by the key prompt words or pictures.

Cracks pose one of the most safety hazard to concrete building structures. A lightweight crack segmentation algorithm with improved DeepLabV3+ is proposed for efficiently segmenting concrete cracks and assessing their hazards in a timely manner. Firstly, MobileNetV3 is used as the lightweight backbone to significantly reduce the number of model parameters. Secondly, the attention-based intrascale feature interaction (AIFI) module is used to model the global information, and the normalization-based attention module (NAM) is introduced to facilitate the interaction of multi-level crack feature information. In addition, the mixed model of both self-attention and convolution is introduced after extracting the low-level high-resolution features, which captures the detailed features more efficiently; and finally, the C2f-SCConv module is designed to decode the fused high- and low-level feature streams, reducing computational redundancy and improving the perception of multi-scale features. Experimental results on the public crack datasets Concrete3k and Asphalt3k show that the number of parameters of the proposed model is reduced by 88.1% compared with that of the DeepLabV3+ model, the pixel accuracy is improved by 0.02%, the mean intersection over union (mIoU) reaches 86.21%, and the average frame rate is 47.91 frames per second. It means that the proposed methods reduce complexity of the model while improve segmentation efficiency to the cracks significantly.

Aiming at the problems of low detection accuracy, slow detection rate and single detection category of wheelset tread defects, an improved lightweight YOLOv7-tiny tread damage detection method is proposed. In this method, the lightweight MobileNetV3 network is used to replace the backbone network of YOLOv7-tiny, and the parameter number and calculation amount of the model are reduced. Embedding BiFormer attention mechanism into the backbone network can strengthen the features of the learning target region and improve the detection accuracy of the model. The centralized feature pyramid (CFP) is used to enhance the feature′s in-layer adjustment ability, capture the global long distance dependence and local critical information of tread defects. Wise intersection over union (WIoU) loss function is employed to accelerate the convergence rate of border regression loss and enhance the robustness of the model. GSconv decoupled head (GSDH) is introduced into the YOLOv7-tiny detection header to decouple separated feature channels from classification and regression tasks, effectively improving the parallel computation rate and detection accuracy of the model. The experimental results show that the improved YOLOv7-tiny network parameter number and computation amount are reduced by 11.7% and 21.2% respectively, the detection precision is increased by 7.9%, the recall rate is increased by 10.5%, the mean average precision is increased by 10.1%, and the frame per second is increased by 7.4 frames/s, which realizes lightweight and has better detection performance. The improved method has a wide application prospect in wheelset tread damage detection.

In response to the issues of poor surface accuracy, excessive cutting allowances in subtractive processing, material waste, and long manufacturing times in wire arc additive manufacturing (WAAM), this paper proposes a deviation identification and removal method using a high-speed laser profiler to measure the deposited surface of the workpiece. A hybrid additive-subtractive manufacturing system based on dual robots for arc welding and milling is constructed in this study. The system employs a high-speed laser profiler for three-dimensional inspection of the deposited surface. Based on the 3D measurement data, a rapid identification and removal method for surface deviations is proposed. This method encompasses workflow design, 3D measurement, noise reduction and reconstruction of point cloud data, identification of convex and concave deviations, and path planning and programming for additive-subtractive processes to remove the identified deviations. Experimental results demonstrate that the proposed algorithm can efficiently and accurately identify the types and geometric shapes of surface deviations, providing essential basis for deviation removal, and significantly improving the forming accuracy of the arc additive-subtractive manufacturing process.

In addressing the challenges posed by the protracted nature of the process, its suboptimal efficiency, and the substantial labor costs incurred in the fabrication of shield segments, a novel automatic plastering robot hardware system has been conceptualized. In order to ensure the fulfillment of the quality inspection requirements for precast concrete following the initial plastering, a human-computer interaction software system has been developed. The system is founded on the YOLOv5 feature detection algorithm. The integration of the automatic plastering robot with the feature detection human-computer interface enables the automation of the shield segment production process. Empirical evidence demonstrates the efficacy of the YOLOv5 feature detection algorithm in accurately detecting defects in concrete surface features after initial plastering, while the robot exhibits high positioning precision. The system's comprehensive alignment with practical production demands ensures enhanced efficiency.

In an arrayed waveguide gratings (AWG) based optical interconnected data center, each node may receive multiple signals carried by arbitrary wavelengths, which may result into channel collision if the node is unable to separate the signals. A triple-channel reception scheme that allows each node to receive an arbitrary set of three wavelengths simultaneously (i. e. , collision-avoid) is proposed. The polarization difference between any two signals is 60°. At the reception, the combined signals are split to three polarization beam splitters (PBSs) followed by three photodetectors (PDs). Each PBS filters out one signal and the PD followed detects the other two signals. The three signals could be recovered from the detected results by arithmetic operation in electrical domain or digital domain. The proposed scheme requires no wavelength-tunable receivers or redundant receivers and enables a single node to receive signals of any set of three wavelengths simultaneously. The simulation results verify the feasibility of the proposed scheme.

Point cloud registration plays a crucial role in the field of computer vision, covering multiple areas such as 3D reconstruction, target recognition, and robot navigation. With the advancement of sensor technology, the acquisition of dense point cloud data has become easier, and the geometric details of models are more precise. However, the processing of dense point clouds faces many challenges, such as high computational complexity and low efficiency. To address this, this paper proposes a dense point cloud registration algorithm based on distance tramsform. The algorithm first projects the source point cloud and the target point cloud through distance tramsform technology to obtain distance tramsform maps from different perspectives. Then, taking the difference between the two maps as the objective function, the gradient descent algorithm is used to optimize the tramsform relationship between point clouds. After multiple iterations, accurate alignment is achieved. Experiments on the Stanford 3D Scanning dataset show that this algorithm has high precision and fast speed when processing dense point clouds. Registering hundreds of thousands of point clouds only takes about 2 seconds, and the error is less than 0.3° and 0.5 mm. It has broad application prospects.

To address the challenges of inaccurate lesion region localization and blurred segmentation boundaries in colorectal polyp images, this paper proposes a colorectal polyp segmentation algorithm that integrates Transformer and dual graph convolutional network (dual-GCN) techniques. First, the Transformer encoder is utilized to parse multi-scale feature information within the images, establishing long-distance dependencies between pixels. Second, a dual graph convolution semantic detail injection (DGSDI) module is designed to integrate the spatial and structural information of deep features, enhancing boundary representation. Third, a local pyramid attention (LPA) module is employed to calculate global and local attention weights on shallow features, precisely localizing the lesion regions while suppressing irrelevant information. Finally, a dynamic feature fusion (DFF) module is introduced to adaptively aggregate multi-scale features, improving the handling of pathological images with large scale variations and irregular shapes. Experimental results on four public datasets, Kvasir, CVC-ClinicDB, CVC-ColonDB, and ETIS, show that the Dice coefficients are 0.924, 0.943, 0.817 and 0.813, respectively, and the mean intersection over union (mIoU) scores are 0.871, 0.896, 0.733 and 0.732, respectively, verifying the effectiveness of the proposed algorithm.