A transparent metamaterial absorber with broadband microwave absorption and polarization insensitiveness is presented in this paper. Consisting of a two-layer closed square ring, one-layer patch-shaped indium tin oxide films, and a three-layer soda-lime glass substrate, the proposed absorber has advantages of broadband absorption with absorptivity higher than 85% ranging from 4.6 to 18 GHz, transparency, good polarization insensitiveness, wide-incident-angle stability, and high shielding efficiency. A prototype sample is fabricated and measured to demonstrate its excellent performance. The experimental results agree well with the numerical simulations.

Traditional digital camouflage is mainly designed for a single background and state. Its camouflage performance is appealing in the specified time and place, but with the change of place, season, and time, its camouflage performance is greatly weakened. Therefore, camouflage technology, which can change with the environment in real-time, is the inevitable development direction of the military camouflage field in the future. In this paper, a fast-self-adaptive digital camouflage design method based on deep learning is proposed for the new generation of adaptive optical camouflage. Firstly, we trained a YOLOv3 model that could identify four typical military targets with mean average precision (mAP) of 91.55%. Secondly, a pre-trained deepfillv1 model was used to design the preliminary camouflage texture. Finally, the preliminary camouflage texture was standardized by the k-means algorithm. The experimental results show that the camouflage pattern designed by our proposed method is consistent with the background in texture and semantics, and has excellent camouflage performance in optical camouflage. Meanwhile, the whole pattern generation process takes a short time, less than 0.4 s, which meets the camouflage design requirements of the near-real-time camouflage in the future.

In hyperspectral image (HSI) classification, convolutional neural networks (CNNs) and transformer architectures have each contributed to considerable advancements. CNNs possess potent local feature representation skills, whereas transformers excel in learning global features, offering a complementary strength. Nevertheless, both architectures are limited by static receptive fields, which hinder their accuracy in delineating subtle boundary discrepancies. To mitigate the identified limitations, we introduce a novel dual-branch adaptive convolutional transformer (DBACT) network architecture featuring an adaptive multi-head self-attention mechanism. The architecture begins with a triadic parallel stem structure for shallow feature extraction and reduction of the spectral dimension. A global branch with adaptive receptive fields performs high-level global feature extraction. Simultaneously, a local branch with a cross-attention module provides detailed local insights, enriching the global perspective. This methodical integration synergizes the advantages of both branches, capturing representative spatial-spectral features from HSI. Comprehensive evaluation across three benchmark datasets reveals that the DBACT model exhibits superior classification performance compared to leading-edge models.

A highly transparent polarization-insensitive metamaterial absorber with wideband microwave absorption is presented. The broadband absorption (6.0~16.7 GHz, absorptance > 85%) is achieved using three patterned resistive metasurfaces. The visible light transmittance of the absorber is as high as 85.7%. The thickness of the absorber is 4.42 mm, which is only 0.088 times of the upper-cutoff wavelength. A prototype sample is fabricated and measured to demonstrate its excellent performance. The experimental results agree well with the simulation results. In view of its wide band absorption, high transmittance, low profile, polarization insensitivity and wide incidence angle stability, the presented absorber has a wide range of potential applications.

Convolutional neural networks (CNNs) and transformers have achieved great success in hyperspectral image (HSI) classification. However, CNNs are inefficient in establishing long-range dependencies, and transformers may overlook some local information. To overcome these limitations, we propose a U-shaped convolution-aided transformer (UCaT) that incorporates convolutions into a novel transformer architecture to aid classification. The group convolution is employed as parallel local descriptors to extract detailed features, and then the multi-head self-attention recalibrates these features in consistent groups, emphasizing informative features while maintaining the inherent spectral–spatial data structure. Specifically, three components are constructed using particular strategies. First, the spectral groupwise self-attention (spectral-GSA) component is developed for spectral attention, which selectively emphasizes diagnostic spectral features among neighboring bands and reduces the spectral dimension. Then, the spatial dual-scale convolution-aided self-attention (spatial-DCSA) encoder and spatial convolution-aided cross-attention (spatial-CCA) decoder form a U-shaped architecture for per-pixel classifications over HSI patches, where the encoder utilizes a dual-scale strategy to explore information in different scales and the decoder adopts the cross-attention for information fusion. Experimental results on three datasets demonstrate that the proposed UCaT outperforms the competitors. Additionally, a visual explanation of the UCaT is given, showing its ability to build global interactions and capture pixel-level dependencies.