Achieving giant magneto-optical responses at the nanoscale remains a fundamental challenge for robust polarimetric sensing. Here, we theoretically propose an ultra-sensitive dual-parameter refractive index sensor based on a 3D cylindrical anisotropic photonic crystal slab. Utilizing a rigorous full-tensor Fourier Series Expansion Method (FSEM), we capture the structure’s scattering properties. We demonstrate that exciting a high-Q resonance drastically prolongs the photon dwell time, thereby magnifying the gyrotropic cross-polarization. This yields a giant Faraday rotation exceeding 26◦. Operating in the linear regime, the sensor achieves a competitive wavelength sensitivity of 116.7 nm/RIU alongside a giant polarimetric response, paving a new avenue for ultra-sensitive, cavity-enhanced biochemical sensors.

Glucose plays a crucial role in maintaining human health as an indispensable source of energy in living organisms. Accurate monitoring of glucose levels in living organisms and detecting it in food is essential. In this study, gold nanoclusters (AuNCs) with unique aggregation-induced emission (AIE) effects were encapsulated within zeolite imidazole framework (ZIF-8) to fabricate a pH-responsive AuNCs@ZIF-8 fluorescent nanocomposite. The fluorescence intensity had significant sevenfold enhancement compared to AuNCs alone due to the structural domain-limiting effects exerted by ZIF-8, which effectively inhibited the rotations and vibrations of the AuNCs ligands. Based on the increased acidity generated by glucose catalytic oxidation via glucose oxidase (GOx), the subsequent degradation of ZIF-8 structure and the consequent reduction of AuNCs with AIE effects were achieved, and a rapid and efficient fluorescence quantification for glucose was performed. The constructed AuNCs@ZIF-8-based fluorescent probe demonstrated a favorable linear response to glucose, achieving a detection limit of 0.096 mmol/L and providing a rapid and efficient approach suitable for assessing glucose levels in both blood and beverage samples.

Industrial surface defect segmentation plays a crucial strategic role in modern manufacturing and quality control, significantly impacting product quality and operational efficiency. Nevertheless, this field faces several critical challenges, including the wide spectrum of defect sizes, indistinct defect boundaries, and the complex tradeoff between segmentation accuracy and computational efficiency. This paper proposes an adaptive axial squeeze transformer network (AASFormer), a novel convolutional neural network (CNN) Transformer hybrid architecture designed to address these challenges through three innovative components. First, the adaptive axial squeeze attention (AASA) mechanism dynamically adapts row and column compression rates to capture long-range contextual dependencies and fine-grained local details, enabling robust modeling of multi-scale defect geometries. Second, the edge enhancement reverse attention (EERA) mechanism synergistically integrates multi-directional Sobel operators and edge sharpening techniques with a reverse attention mechanism to sharpen defect boundaries and suppress background noise, leveraging traditional edge detection prior with deep learning attention for enhanced boundary precision, Third, the lightweight multi-scale feature fusion decoder (LMFFD) efficiently fuses hierarchical features across scales, balancing high-level semantic information with low-level spatial details to improve segmentation consistency. To facilitate comprehensive evaluation and address the scarcity of relevant datasets in this domain, a novel gear surface defect segmentation (Gear-Seg) dataset is introduced. Extensive experimental results on MSD, Gear-Seg, and NEU-Seg datasets of semantic segmentation of defects demonstrate that AASFormer consistently outperforms existing methods, achieving state-of-the-art performance in terms of mean intersection over union (MIoU) (MSD: 90.46%, Gear-Seg: 89.78%, and NEU-Seg: 84.85%).

This letter presents a high-selectivity three-dimensional (3-D) dual-polarized frequency-selective rasorber (FSR). The proposed design comprises a 3-D array of multiple lossy strip-type resonators integrated with a planar bandpass frequency-selective surface (FSS). While the multiple resonances of the strips provide wideband absorption, a parallel LC structure is loaded within each resonator to achieve a low-loss transmission band. Numerical and experimental results demonstrate an ultra-wide low-reflection band with a fractional bandwidth (FBW) of 162.2% from 2.0 to 19.2 GHz. This includes a transmission band at 10 GHz with an insertion loss of 0.47 dB, alongside a lower frequency absorption band (2.0-9.5 GHz, FBW 130.4%) and an upper frequency absorption band (10.5-19.2 GHz, FBW 58.6%). The operating mechanism is further validated by an equivalent circuit model and measurement of a fabricated prototype, showing good agreement between theory and experiment.

Numerous 3D shape descriptors have been proposed in recent years, among which spectral descriptors have gained significant prominence. However, widely used spectral signatures, such as the Heat Kernel Signature (HKS), Scale-Invariant HKS (SIHKS), and Wave Kernel Signature (WKS), suffer from parameter dependence, where heuristic and sub-optimal scale selection limits their robustness and generalizability. To address this limitation, this paper introduces a novel class of descriptors termed Geometric Moments of Spectral Shape Descriptors (GMSDs). By integrating temporal and spatial domains, GMSDs leverage invariant moment theory to calculate six moment terms, creating a theoretical framework that significantly enhances performance in non-rigid 3D shape analysis. GMSDs not only inherit the desirable properties of standard spectral signatures, such as isometric invariance and robustness to noise and topological changes, but also effectively mitigate parameter sensitivity. Extensive experiments on the TOSCA, SCAPE, SHREC 2011, and SHREC 2015 benchmarks demonstrate that GMSDs achieve superior performance in both shape correspondence and retrieval tasks compared to state-of-the-art methods.

During the image compositing process, there may be inconsistencies in tone and illumination between the foreground and background, leading to poor visual quality and low realism in the composite images. To address these issues, image harmonization techniques can be employed. This paper proposes an image harmonization method based on multi-scale and global feature guidance (MSGF). In general, images captured in different scenes may exhibit inconsistencies in lighting after composition. The goal of image harmonization is to adjust the foreground illumination to match that of the background. Traditional methods often attempt to blend pixels directly, which can result in unrealistic outcomes. The proposed approach combines multi-scale feature extraction with global feature guidance, forming the MSGF framework. The experiment was conducted on the iHarmony4 dataset. Comparative experiments showed that MSGF achieved the best performance on three subset indicators, including HCOCO. Ablation studies demonstrated the effectiveness of the proposed module. Efficiency evaluation results indicated that it took 0.01s and had 20.9 million parameters, outperforming comparative methods and effectively achieving high-quality image harmonization.

A wideband high-gain microstrip patch/Fabry-Perot (FP) resonator cavity antenna with low radar cross section (RCS) is proposed, which consists of a microstrip patch, a ground plane, a double-layer partially reflective surface (PRS), a resonant complementary metasurface (RCM), and a checkerboard metasurface (CBM). A PRS is designed as circular patches and rectangular slot patches corresponding to the top and bottom, for achieving high-gain with wideband. RCM consists of three rectangular patch arrays of varied sizes for bandwidth enhancement and compensation using characteristic mode analysis (CMA). CBM adopts a fusion of two 4 × 4 metasurface modes to achieve low RCS. The three types of metasurfaces collaboratively enable frequency band resonance in the proposed antenna, resulting in enhanced gain, wide bandwidth, and reduced RCS. The measured results agree well with the simulated ones, showing that the antenna has an impedance bandwidth of 37.3%, a peak gain of 14.9 dBi at 7.0 GHz, and its cross-polarization greater than 20 dB. Monostatic RCS reduction of 10 dB is achieved across the frequency range from 5.0 GHz to 17.6 GHz (111.5%).

The research on 3D model reconstruction from a single image using deep learning technology has achieved remarkable progress. However, compared with images, sketches lack sufficient visual information, which challenges the reconstruction algorithm’s ability to correctly interpret sketches. Herein, we introduce a streamlined network architecture for sketch-to-3D mesh generation, designed to address the challenge of reconstructing high-fidelity 3D models from single-hand sketches. Our approach deploys the expressive PowerMLP architecture within an encoder-decoder framework, surpassing traditional MLP implementations in representation capability. By integrating 3D shape constraints instead of relying on conventional discriminators, we achieve geometric fidelity in a collaborative generation process. Experimental results demonstrate state-of-the-art (SOTA) performance on both synthetic stylized sketches and real-world handwritten inputs, validating the method’s robustness and adaptability.

By combining the Fourier series expansion method with the perfect matching layer strategy, this study provides a detailed analysis of symmetric periodic chains consisting of two-dimensional monolayer dielectric cylinders. The numerical and field distribution characteristics were systematically compared by varying the semidiameter and dielectric constant of the monolayer cylindrical periodic structure. The results show that, under a constant dielectric constant, structural chains with larger radii support both odd and even modes, enabling multimode communication. Additionally, when the radius is fixed, structural chains with a lower dielectric constant exhibit more stable single-mode behavior under the same radius conditions. These findings provide valuable theoretical insights for the design of high-density optical interconnects and reconfigurable photonic networks, contributing to the advancement of next-generation optical communication systems.

This work presents a novel approach to broadband proximity-coupled millimeter-wave microstrip array design based on a substrate integrated waveguide (SIW) feeding network, tailored specifically to automotive radar applications. The antenna array includes a central microstrip line with a series of indirectly adjacent parasitic coupled trapezoidal patches periodically arranged on both sides. By precisely adjusting the gap between these parasitic patches and lines to control the normalized impedance of the radiating elements, it helps to achieve broadband and low sidelobe levels (SLLs) characteristics. To validate the proposed design, two antennas-a 1 x 16 linear array and an 8 x 16 planar array based on the SIW feeding network-are developed to operate within the 77- to 81-GHz range. The measured SLLs are lower than -20 dB, with measured gains exceeding 15 dBi for the 1 x 16 array and 20 dBi for the 8 x 16 array across the 77- to 81-GHz band. The impedance bandwidth of the 8 x 16 planar array reaches 6.8% (76.2 GHz-81.5 GHz).

A low‐profile wideband circularly polarized (CP), single‐layer antenna with low radar cross section (RCS) reduction based on staggered elliptical metasurface using characteristic mode analysis (CMA) is presented. The staggered arrangement of the elliptical metasurface has a 90° relationship, ensuring the generation of circular polarization, and their modes of fusion can achieve RCS reduction by CMA. The defective ground shows that the difference in characteristic angle (CA) between the two pairs of modes is about 90°. The measured results show that the antenna has a 40.6% (5.5 GHz–8.3 GHz) impedance bandwidth (IBW), a 26.2% (6.3 GHz–8.2 GHz) 3‐dB axial ratio bandwidth (ARBW), and a peak gain of 5.1 dBic is achieved at 6.3 GHz, and with a large RCS reduces across the frequency range from 2.0 GHz to 22.0 GHz (166.7%). Monostatic RCS reduction of more than 5 dBsm is achieved within the frequency range from 4.6 GHz to 22.0 GHz (130.8%).

This paper presents a low-profile, single-layer, broadband, polarization-reconfigurable metasurface antenna with a low radar cross section (RCS). The design leverages characteristic mode analysis (CMA) applied to a staggered elliptical-shaped unit cell metasurface. The unique arrangement of the elliptical-shaped elements, oriented at 90 degrees, promotes mode fusion through CMA, effectively reducing RCS. Additionally, the analysis of a defective floor with branching reveals a critical characteristic angle (CA) of 90 degrees, essential for generating circular polarization (CP) waves. Experimental measurements indicate that the antenna prototype achieves a -10 dB impedance bandwidth (IBW) from 5.5 to 8.3 GHz (40.6%), a 3-dB axial ratio bandwidth (ARBW) from 6.3 to 8.2 GHz (26.2%) and a peak gain of 5.1 dBi at 6.3 GHz. By integrating resistors, the antenna supports switching between CP and linear polarization (LP), as well as across different frequency bands, resulting in three distinct operating states (with IBWs of 26.5%, 26.9%, and 26.9%, respectively). Additionally, the metasurface structure achieves a notable reduction in monostatic RCS across the frequency range from 2.0 to 22.0 GHz, resulting in a substantial 166.7% reduction and a 6 dBsm decrease in RCS within the IBW of 6.3 to 7.2 GHz. The exploded view of low-profile low-RCS wideband metasurface antenna with polarization reconfigurable.

This study investigates the propagation characteristics and field distribution of photonic crystals composed of epsilon-near-zero (ENZ) materials and metal cylinders. The research reveals that the cutoff frequency of the photonic crystal formed by combining metal cylinders with an ENZ background is independent of the volume fraction of the metal cylinders and exhibits a stop-band profile within the measured frequency range. This unique behavior is attributed to the scattering of long-wavelength light when the wavelength approaches the effective wavelength range of the ENZ material. Taking advantage of this feature, the study selectively filters specific wavelength ranges from the mid-frequency band by varying the ratio of cylinder radius to lattice constant (R/a). Decreasing the R/a ratio enables the design of waveguide devices that operate over a broader guided wavelength range within the intermediate-frequency band. The findings emphasize the importance of the interaction between light and ENZ materials in shaping the transmission characteristics of photonic crystal structures.

This paper presents a novel ultra-wideband three-dimensional electromagnetic absorber. The proposed absorber is composed of a three-dimensional (3-D) lossy layer structure and a two-dimensional (2-D) magnetic material (MM) layer. The 3-D lossy layer structure is constructed using cross-oriented FR-4 dielectric substrates. On the front side of each substrate, two cascaded lossy metallic strips integrated with open-ended coplanar striplines (CPS) elements are printed, while the backside contains a single lossy metallic strip. The 2-D magnetic material is filled at the bottom of the 3-D lossy layer structure. The absorber achieves an ultra-wide absorption bandwidth from 0.99 GHz to 18 GHz with a reflection coefficient of vertical bar S-11 vertical bar <= -10 dB, corresponding to a fractional bandwidth (FBW) of 179.1%. The overall thickness of the proposed absorber reaches only 0.066 wavelength at the lowest absorption frequency. Moreover, it maintains angular stability up to 45 degrees under transverse electric (TE) polarization and up to 60 degrees under transverse magnetic (TM) polarization. To investigate its operating mechanism, an equivalent circuit model is established. The results obtained from the equivalent circuit simulation are in good agreement with the full-wave simulation results.

A radio frequency power amplifier operating at 2.45 GHz was designed utilizing Ampleon’s LDMOS amplifier chip, the BLC2425M9LS250Z, as the active device. The biasing circuit for the BLC2425M9LS250Z was designed and simulated using Advanced Design System (ADS). Input matching was achieved through a relatively wide microstrip line, while output matching was implemented using a Type-II impedance matching network consisting of capacitors. Simulation results indicate that, within the frequency range of 2.4 GHz to 2.5 GHz, the small-signal S₂₁ gain ranges from 19.343 dB to 20.006 dB, the large-signal output power is 53.487 dBm, and the power-added efficiency (PAE) reaches 57.681%, satisfying the design specifications.

Semantic segmentation plays a crucial role in autonomous driving systems, serving as a key technology for understanding and interpreting the road environment. Most existing semantic segmentation networks strive for high accuracy, but achieving true real-time performance while maintaining high accuracy remains a challenge. However, autonomous driving systems require extremely high reaction speed and real-time processing capabilities, and any processing delay may lead to safety risks. To solve this problem, this paper proposes a lightweight dual-branch multi-scale network (LDMSNet) to achieve real-time semantic segmentation. First, the effective dilated bottleneck (EDB) is proposed to efficiently extract semantic information and spatial information using complementary dual-branch structure and depth-wise dilated convolution. Second, the multi-scale pyramid pooling module (MSPPM) is proposed, which uses a hierarchical residual structure and combines with dilated convolution to extract detailed information from low-resolution branches. Third, the polarized self-attention mechanism (PSA) is introduced to further enhance the interaction and correlation between features and improve the ability to perceive global information. The experimental results show that LDMSNet achieves 74.46

Shape reconstruction from 3D point clouds is one of the most important topic in the field of computer graphics. In this paper, we propose a subdivision-based framework for this topic. The framework includes two parts: distance field optimization and mesh generation. The first part optimizes a point cloud into an approximately isotropic one based on a subdivision structure. The second part is to generate a triangular mesh from the optimized point cloud. The mesh is regarded as the result of shape reconstruction. The advantages of our method includes accurate geometric consistency, improved mesh quality, controllable point number, and fast speed. Experiments indicate that our method has good performance for shape reconstruction (compare to the state-of-the-art, our method achieves five and six times improvement in Hausdorff distance-based measurement and density estimation). The executable file is available: ( https://github.com/vvvwo/Parallel-Structure-ShapeReconstruction )

Millimeter-wave radar is experiencing an increasing demand for higher resolution and elevation measurement, necessitating its evolution from 3D millimeter-wave radar systems to 4D millimeter-wave radar. Unlike front automotive radar, corner radar is particularly interested in close-range targets. This article proposes a composite waveform tailored for automotive corner radar, employing different waveform schemes for various distances. This allows millimeter-wave angle radar to achieve superior range resolution and velocity resolution in the close and medium ranges. However, enhancing velocity resolution inevitably results in a reduction in the maximum unambiguous velocity. Consequently, a velocity ambiguity resolution method based on target parameter matching for front and rear frames is proposed to address this issue. The feasibility of the composite waveform and method are verified through simulation. Finally, a radar designed with the ALPS PRO chip is employed to test the designed waveform and velocity ambiguity resolution method, yielding results that are largely consistent with the simulation outcomes. The findings indicate a notable enhancement in resolution for medium distances ranging from 60 to 100 m when employing the proposed waveform. Moreover, the velocity ambiguity resolution method effectively resolves velocity ambiguity, leading to enhanced accuracy in velocity measurements. This suggests that the composite waveform and algorithm are well suited for current automotive angle radar applications.