| 2 | Design of core-shell nickel oxide/silicon carbide whiskers towards excellent microwave absorption property显示文摘Toward the increasingly serious problem of electromagnetic wave pollution,the development of absorbing material with the properties of the light,thin,wide,strong,and multiple applications scenarios is still a huge challenge.Herein,the core-shell nickel oxide/silicon carbide whiskers(NiO/SiCw)with variational NiO morphologies(tulle-,flower,and rod-like)were designed and fabricated via a facile prehydrothermal method and post-annealing process.For the NiO shell morphology,it can effectively be controlled by the proportion of Ni(NO_(3))_(2)·6H_(2)O,NH_(4)Cl,and CO(NH_(2))_(2).The structure of NiO/SiCw samples was investigated by XRD,SEM,XPS,TEM,and N2 absorption-desorption.Compared to other morphologies of NiO,the flower-like NiO/SiCw possess a porous structure and large surface area that can benefit from the multiple reflections and attenuation of the microwave.As an absorber,the composite with the flower-like NiO/SiCw filler loading of 50%manifests a superior microwave attenuation capability due to its special porous structure,good impedance matching,and large dielectric loss induced from heterojunction interfacial polarization.The minimum reflection loss of flower-like NiO/SiCw is up to-56.8 dB with a thickness of 1.9 mm,and the maximum effective absorption bandwidth(EAB)reaches 5.17 GHz.The as-prepared flower-like NiO/SiCw with strong absorption,thin thickness,and width EAB can meet the potential requirements for microwave absorption materials under oxidation environments. | Jingpeng Chen Ge Song Zhuo Liu Lijing Xie Shoushun Zhang Chengmeng Chen | 2021 | Chinese Journal of Chemical Engineering2021，34，9： | 0 |
| 3 | A robust tensor watermarking algorithm for diffusion-tensor images显示文摘Watermarking algorithms that use convolution neural networks have exhibited good robustness in studies of deep learning networks.However,after embedding watermark signals by convolution,the feature fusion eficiency of convolution is relatively low;this can easily lead to distortion in the embedded image.When distortion occurs in medical images,especially in diffusion tensor images(DTIs),the clinical value of the DTI is lost.To address this issue,a robust watermarking algorithm for DTIs implemented by fusing convolution with a Transformer is proposed to ensure the robustness of the watermark and the consistency of sampling distance,which enhances the quality of the reconstructed image of the watermarked DTIs after embedding the watermark signals.In the watermark-embedding network,Ti-weighted(Tlw)images are used as prior knowledge.The correlation between T1w images and the original DTI is proposed to calculate the most significant features from the T1w images by using the Transformer mechanism.The maximum of the correlation is used as the most significant feature weight to improve the quality of the reconstructed DTI.In the watermark extraction network,the most significant watermark features from the watermarked DTI are adequately learned by the Transformer to robustly extract the watermark signals from the watermark features.Experimental results show that the average peak signal-to-noise ratio of the watermarked DTI reaches 50.47 dB,the diffusion characteristics such as mean diffusivity and fractional anisotropy remain unchanged,and the main axis deflection angleαAc is close to 1.Our proposed algorithm can effectively protect the copyright of the DTI and barely affects the clinical diagnosis. | Chengmeng LIU Zhi LI Guomei WANG Long ZHENG | 2024 | Frontiers of Information Technology & Electronic Engineering2024，25，3： | 0 |
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