Abstract:In this paper, a terahertz slotted waveguide array antenna is designed based on photonic crystal, which can realize efficient radiation of terahertz waves. The electromagnetic wave is fed from the rectangular waveguide at the bottom of the antenna, coupled to photonic crystal waveguide through photonic crystal cavity, and radiated outward through slots at the top layer of antenna. The simulation results show that the antenna achieves a peak gain of 13.45 dBi at 360 GHz, a half-power beam width of 10.9°, and a side lobe level of −13.9 dB. The antenna based on photonic crystal has the advantages of low profile, low loss, and high radiation efficiency, which can be applied to terahertz wireless communication systems.

Abstract:This paper presents a biosensor utilizing a whispering gallery mode (WGM) resonator characterized by azimuthal symmetry and crescent-shaped coatings of silver. The study investigates the impact of the coupling gap on the extinction ratio and Q-factor of the setup. The resonator is coated with silver in crescent shapes, ranging from 40 nm to 65 nm in thickness. Coupling is achieved with a silica waveguide, simulating the tapered fiber coupling method. Notably, the resonator exhibits a maximum sensitivity of 220 nm/RIU when coated with 55-nm-thick silver in conjunction with a 4-nm-thick layer of thiol-tethered deoxyribonucleic acid (DNA). This biosensor holds promise for biomolecule detection applications.

Abstract:By doping with Mg atoms, the bandgap of Mg-doped NiO thin films can be adjusted larger. By using NiO and MgO as sputtering targets, Mg-doped NiO thin films were deposited using radio-frequency magnetron co-sputtering method in pure argon and pure oxygen gas, respectively. The crystal structure, morphological characteristics, composition and optical properties of the obtained films were compared by X-ray diffraction (XRD), scanning electron microscope (SEM), energy dispersive spectrometer (EDS) and ultraviolet (UV)-visible spectrophotometer. The properties of the thin films deposited in different sputtering gases are quite different. For the films deposited in pure argon gas, it is a polycrystalline thin film with (200) preferred orientation, while the film deposited in pure oxygen has no preferred orientation. The grain size, molar ratio of Mg to Ni atoms and optical bandgap are larger for the films deposited in pure argon gas than those deposited in oxygen gas.

Abstract:Di-calcium magnesium silicate (Ca2MgSi2O7) doped with various concentrations (1.0 mol%, 2.0 mol%, 2.5 mol%, and 3.0 mol%) of dysprosium (III) was prepared using a high-temperature technique named as solid state reaction method. The sample with 2.5 mol% of dysprosium (III) underwent X-ray diffraction (XRD) characterization to confirm the proper phase formation in the sample. Observed XRD pattern matched significantly with crystallographic open database (Card No.96-210-6180) with a significantly high figure of merit (0.84). Photoluminescence (PL) excitation and emission spectra were also recorded. PL excitation spectrum of Ca2MgSi2O7 doped with 2.5 mol% of dysprosium (III) exhibited a most prominent peak at 395 nm, therefore, the emission spectra of the samples were monitored at 395 nm excitation. The emission spectra exhibited prominent peaks centered at 483 nm (blue), 577 nm (yellow), and 664 nm (orange red) due to the transitions 4F9/2®6H15/2, 4F9/2®6H13/2, and 4F9/2 ®6H11/2, respectively. The Commission Internationale de L’Eclairage (CIE) of this emission spectra was found at (0.304, 0.340) which lies in the white light region. Keeping the objective to evaluate the emitted white light for its suitability in light-emitting diode (LED) application, color rendering index (CRI) and color correlated temperature (CCT) were also calculated. Radiation life time was estimated using Judd–Ofelt analysis.

Abstract:Fiber Bragg grating (FBG) sensors are extensively used in various sensing applications due to their high sensitivity. However, they are inherently sensitive to both strain and temperature, with a cross-sensitivity problem, making it impossible to simultaneously monitor these two parameters using the Bragg wavelength shifts of a single uniform FBG. In this study, we bend the FBG pigtail to cause bending loss. The peak power of the FBG is used as the second characterization quantity. Our experimental results show that the Bragg wavelength sensitivities to strain (Kε) and temperature (KT) are 0.17 pm/με and 16.5 pm/°C, respectively. Additionally, the peak power sensitivities to strain (Pε) and temperature (PT) are −0.002 02 dBm/με and −0.06 dBm/°C, respectively. The linear correlation coefficients for these measurements are all above 0.996. In this way, it is possible to simultaneously measure both strain and temperature using a single uniform FBG.

Abstract:Although deep learning methods have been widely applied in slam visual odometry (VO) over the past decade with impressive improvements, the accuracy remains limited in complex dynamic environments. In this paper, a composite mask-based generative adversarial network (CMGAN) is introduced to predict camera motion and binocular depth maps. Specifically, a perceptual generator is constructed to obtain the corresponding parallax map and optical flow between two neighboring frames. Then, an iterative pose improvement strategy is proposed to improve the accuracy of pose estimation. Finally, a composite mask is embedded in the discriminator to sense structural deformation in the synthesized virtual image, thereby increasing the overall structural constraints of the network model, improving the accuracy of camera pose estimation, and reducing drift issues in the VO. Detailed quantitative and qualitative evaluations on the KITTI dataset show that the proposed framework outperforms existing conventional, supervised learning and unsupervised depth VO methods, providing better results in both pose estimation and depth estimation.

Abstract:The phenomenon of attenuation and scattering of light propagating in water leads to such problems as color deviation and blur in underwater imaging. These problems bring great challenges to the subsequent feature matching, target recognition and other tasks. Therefore, this paper proposes an underwater image enhancement method by double compensation with comparative adjustment or edge reinforcement. The experiments have found that the proposed method has good underwater color image quality evaluation (UCIQE) value, underwater image quality measures (UIQM) value, and the number of feature matching points. This demonstrates that the proposed method has good color correction ability for underwater images with different attenuation levels, where the processed images have more details suitable for feature matching.

Abstract:Aiming at the problem of low detection accuracy due to the different scale sizes of apple leaf disease spots and their similarity to the background, this paper proposes a multi-scale lightweight network (MSL-Net). Firstly, a multiplexed aggregated feature extraction network is proposed using residual bottleneck block (RES-Bottleneck) and middle partial-convolution (MP-Conv) to capture multi-scale spatial features and enhance focus on disease features for better differentiation between disease targets and background information. Secondly, a lightweight feature fusion network is designed using scale-fuse concatenation (SF-Cat) and triple-scale sequence feature fusion (TSSF) module to merge multi-scale feature maps comprehensively. Depthwise convolution (DWConv) and GhostNet lighten the network, while the cross stage partial bottleneck with 3 convolutions ghost-normalization attention module (C3-GN) reduces missed detections by suppressing irrelevant background information. Finally, soft non-maximum suppression (Soft-NMS) is used in the post-processing stage to improve the problem of misdetection of dense disease sites. The results show that the MSL-Net improves mean average precision at intersection over union of 0.5 (mAP@0.5) by 2.0% over the baseline you only look once version 5s (YOLOv5s) and reduces parameters by 44%, reducing computation by 27%, outperforming other state-of-the-art (SOTA) models overall. This method also shows excellent performance compared to the latest research.

Abstract:Image coloring is an inherently uncertain and multimodal problem. By inputting a grayscale image into a coloring network, visually plausible colored photos can be generated. Conventional methods primarily rely on semantic information for image colorization. These methods still suffer from color contamination and semantic confusion. This is largely due to the limited capacity of convolutional neural networks to learn deep semantic information inherent in images effectively. In this paper, we propose a network structure that addresses these limitations by leveraging multi-level semantic information classification and fusion. Additionally, we introduce a global semantic fusion network to combat the issues of color contamination. The proposed coloring encoder accurately extracts object-level semantic information from images. To further enhance visual plausibility, we employ a self-supervised adversarial training method. We train the network structure on various datasets with varying amounts of data and evaluate its performance using the ImageNet validation set and COCO validation set. Experimental results demonstrate that our proposed algorithm can generate more realistic images compared to previous approaches, showcasing its high generalization ability.

Abstract:In order to solve the challenge of breast cancer region segmentation, we improved the U-Net. The convolutional block attention module with prioritized attention (CBAM-PA) and dilated transformer (Dformer) modules were designed to replace the convolutional layers at the encoding side in the base U-Net, the input logic of the U-Net was improved by dynamically adjusting the input size of each layer, and the short connections in the U-Net were replaced with cross-layer connections to enhance the image restoration capability at the decoding side. On the breast ultrasound images (BUSI) dataset, we obtain a Dice coefficient of 0.803 1 and an intersection-over-union (IoU) value of 0.736 2. The experimental results show that the proposed enhancement method effectively improves the accuracy and quality of breast cancer lesion region segmentation.