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Traditional microwave absorbing materials (MAMs) have exposed more and more problems in multi-spectrum detection and harsh service environment, which hinder their further application. Bionic materials and structures have attracted more...

Microwave absorbing materials (MAMs) are materials that effectively absorb incident electromagnetic (EM) wave energy, reducing reflection and scattering. They play a crucial role in enhancing electronic reliability, healthcare, and defense security. However, traditional MAMs like ferrites, magnetic metals, and polymers possess certain limitations, including low impedance matching, narrow absorption bandwidth, poor chemical stability, and high filling ratio, which hinder their further development. To address the requirements of lightweight, wideband, and high‐efficiency absorption, precise structural design has emerged as a captivating research focus. Additionally, comprehending the structure–property relationships between these unique microstructures and EM response and loss mechanisms still poses significant challenges. Herein, a comprehensive review of MAMs is presented with varied structural designs encompassing various scales, providing a detailed introduction of the relationship between various potential structural designs of MAMs and their corresponding EM characteristics and loss mechanisms. Moreover, EM theoretical calculation models, characterization, and analysis methods are discussed. Finally, the article proposes the challenges and prospects for the development of structural design EM wave absorbers.

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According to the correlation between adjacent blocks of the carrier image， two algorithms, called STDM-CO-MW and STDM-PCO-MW, are proposed. By using the quantization step calculated from the previous secondary block to modulate the latter secondary block, STDM-CO-MW solves the problem of the difference of quantization step between embedding and decoding. In order to raise the correlation between adjacent secondary blocks, STDM-PCO-MW exchanges the pixels between the previous secondary block and the latter one before embedding.As a result STDM-PCO-MW is more robust. Thanks to the quantization step calculated above based on modified Watson model , we find that the quantization step of our algorithms varies linearly with the brightness of the carrier image adaptively .From the results of numerical simulation, it is obvious that our proposed algorithms are robust to JPEG compression, Gaussian noise and gain attack. Compared with AQIM and general STDM based on Watson model, our algorithms have a great improvement in performance.

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