This paper introduces a novel plasmon refractive index nanosensor structure based on Fano resonance. The structure comprises a metal–insulator–metal (MIM) waveguide with an inverted rectangular cavity and a circle minus a small internal circle plus a rectangular cavity (CMSICPRC). This study employs the finite element method (FEM) to analyze the sensing characteristics of the structure. The results demonstrate that the geometrical parameters of specific structures exert a considerable influence on the sensing characteristics. Simulated experimental data show that the maximum sensitivity of this structure is 3240 nm/RIU, with a figure of merit (FOM) of 52.25. Additionally, the sensor can be used in biology, for example, to detect the concentration of hemoglobin in blood. The sensitivity of the sensor in this application, according to our calculations, can be 0.82 nm∙g/L.

Aiming to address the issues of missing detailed information, the blurring of significant target information, and poor visual effects in current image fusion algorithms, this paper proposes an infrared and visible-light image fusion algorithm based on discrete wavelet transform and convolutional neural networks. Our backbone network is an autoencoder. A DWT layer is embedded in the encoder to optimize frequency-domain feature extraction and prevent information loss, and a bottleneck residual block and a coordinate attention mechanism are introduced to enhance the ability to capture and characterize the low- and high-frequency feature information; an IDWT layer is embedded in the decoder to achieve the feature reconstruction of the fused frequencies; the fusion strategy adopts the $l_1-norm$ fusion strategy to integrate the encoder’s output frequency mapping features; a weighted loss containing pixel loss, gradient loss, and structural loss is constructed for optimizing network training. DWT decomposes the image into sub-bands at different scales, including low-frequency sub-bands and high-frequency sub-bands. The low-frequency sub-bands contain the structural information of the image, which corresponds to the important target information, while the high-frequency sub-bands contain the detail information, such as edge and texture information. Through IDWT, the low-frequency sub-bands that contain important target information are synthesized with the high-frequency sub-bands that enhance the details, ensuring that the important target information and texture details are clearly visible in the reconstructed image. The whole process is able to reconstruct the information of different frequency sub-bands back into the image non-destructively, so that the fused image appears natural and harmonious visually. Experimental results on public datasets show that the fusion algorithm performs well according to both subjective and objective evaluation criteria and that the fused image is clearer and contains more scene information, which verifies the effectiveness of the algorithm, and the results of the generalization experiments also show that our network has good generalization ability.

This paper presents an algorithm for infrared and visible image fusion using significance detection and Convolutional Neural Networks with the aim of integrating discriminatory features and improving the overall quality of visual perception. Firstly, a global contrast-based significance detection algorithm is applied to the infrared image, so that salient features can be extracted, highlighting high brightness values and suppressing low brightness values and image noise. Secondly, a special loss function is designed for infrared images to guide the extraction and reconstruction of features in the network, based on the principle of salience detection, while the more mainstream gradient loss is used as the loss function for visible images in the network. Afterwards, a modified residual network is applied to complete the extraction of features and image reconstruction. Extensive qualitative and quantitative experiments have shown that fused images are sharper and contain more information about the scene, and the fused results look more like high-quality visible images. The generalization experiments also demonstrate that the proposed model has the ability to generalize well, independent of the limitations of the sensor. Overall, the algorithm proposed in this paper performs better compared to other state-of-the-art methods.

This paper introduces a refractive index sensor based on Fano resonance, utilizing a metal–insulator–metal (MIM) waveguide structure with an Anchor-like cavity. This study utilizes the finite element method (FEM) for analyzing the propagation characteristics of the structure. The evaluation concentrated on assessing how the refractive index and the structure’s geometric parameters affect its sensing characteristics. The designed structure demonstrates optimum performance, achieving a maximum sensitivity of 2440 nm/RIU and an FOM of 63. Given its high sensitivity, this nanoscale refractive index sensor is ideal for detecting hemoglobin concentrations in blood, and the sensor’s sensitivity is 0.6 nm·g/L, aiding in clinical prevention and treatment.

Based on the characteristics of plasmonic waveguides and resonators, we propose a refractive index (RI) sensor that couples a gear ring with a metal–insulator–metal (MIM) waveguide. Using the finite element method (FEM), we conduct extensive spectral analysis of the sensor’s properties in the near-infrared spectrum. Furthermore, we investigate the structural parameters affecting the refractive index sensing characteristics. This study reveals that the complexity of the ring cavity edge can significantly enhance the sensitivity of the nanosensor. Optimal structural performance parameters are selected when the number of gears is six, resulting in a sensitivity of 3102 nm/RIU and a Figure of Merit (FOM) of 57.4 for the sensing characteristics of the gear ring. It possesses the advantages of small size and high sensitivity. This nanoscale sensor design demonstrates high sensitivity in the field of industrial material temperature detection.