In this study, we present a novel ultra-wideband passive polarization conversion metasurface (PCM) that integrates double V-shaped patterns with circular split-ring resonators. Operating without any external power supply or active components, this design effectively manipulates the polarization state of incident electromagnetic waves. Numerical and experimental results demonstrate that the proposed PCM can convert incident linear polarization into orthogonal states across a wide frequency range of 7.1–22.3 GHz, encompassing the C-, X-, Ku-, and K-bands. A fabricated prototype confirms that the polarization conversion ratio (PCR) exceeds 90% throughout the specified band. Furthermore, we explore an additional application of this passive metasurface for electromagnetic stealth, wherein it achieves over 10 dB of monostatic radar cross-section (RCS) reduction from 7.6 to 21.5 GHz. This broad effectiveness is attributed to strong electromagnetic resonances between the top and bottom layers, as well as the Fabry–Pérot cavity effect, as evidenced by detailed analyses of the underlying physical mechanisms and induced surface currents. These findings confirm the effectiveness of the proposed design and highlight its potential for future technological applications, including 6G communications, radar imaging, anti-interference measures, and electromagnetic stealth.

Satellite sensors often capture remote sensing images that contain various types of stripe noise. The presence of these stripes significantly reduces the quality of the remote images and severely affects their subsequent applications in other fields. Despite the existence of many stripe noise removal methods in the research, they often result in the loss of fine details during the destriping process, and some methods even generate artifacts. In this paper, we proposed a new unidirectional variational model to remove horizontal stripe noise. The proposed model fully considered the directional characteristics and structural sparsity of the stripe noise, as well as the prior features of the underlying image, to design different sparse constraints, and the ${\mathsf{\ell }}_p$ quasinorm was introduced in these constraints to better describe these sparse characteristics, thus achieving a more excellent destriping effect. Moreover, we employed the fast alternating direction method of multipliers (ADMM) to solve the proposed non-convex model. This significantly improved the efficiency and robustness of the proposed method. The qualitative and quantitative results from simulated and real data experiments confirm that our method outperforms existing destriping approaches in terms of stripe noise removal and preservation of image details.

Metamaterial absorbers have gained widespread applications in fields such as sensing, imaging, and electromagnetic cloaking due to their unique absorption characteristics. This paper presents the design and fabrication of a novel K-band polarization-sensitive metamaterial absorber, which operates in the frequency range of 20.76 to 24.20 GHz for both TE and TM modes, achieving an absorption rate exceeding 90% and a bandwidth of up to 3.44 GHz. The structure of the metamaterial absorber consists of a rectangular aperture metallic patch, two metallic rings, and two metallic strips, with a metallic patch structure on the back. Both metallic patches are printed on a 1.575 mm-thick FR-4 substrate. In the TE mode, the performance shows diagonal symmetry, with a minimum absorption bandwidth of 1.4 GHz at 45° and a maximum of 3.44 GHz at 0°. The absorption rate exceeds 90% across various polarization angles. In terms of conventional modes, both the TE and TM modes can achieve ultra-wideband absorption. For specific scenarios requiring single-frequency or multi-frequency absorption, the desired functionality can be realized by varying the incident angle. These exceptional characteristics confer strong applicability for high-bandwidth electromagnetic wave absorption and specific frequency point absorption, indicating significant potential and practical value in the field of wireless communication.

Road cracks pose a significant safety hazard to transportation, making timely detection crucial for traffic safety. Traditional crack segmentation methods face three main issues: (1) Tree shadow background affects crack recognition in real-world environments. (2) Conventional convolutional neural networks fail to detect complete cracks. (3) Direct deconvolution during upsampling results in unclear crack details. To address these challenges, this paper proposes the WFU-Unet model for road crack detection and segmentation. First, the WCM module, constructed with wavelet transform, ConvNext, and MobileNet, reduces shadow interference, enabling the network to distinguish between cracks and tree shadows. Second, the Fuse module replaces traditional convolutional blocks, enhancing the network’s ability to extract crack features. Finally, the Up module substitutes conventional upsampling techniques to minimize spatial information loss of cracks. Experimental results show that the WFU-Unet model achieves a Miou of 81.68%, precision of 91.43%, recall of 86.63%, and F1-Score of 88.93%. Compared to other models, WFU-Unet demonstrates superior generalization ability and segmentation accuracy, making it more suitable for crack detection in shadowed environments.