Electronic, mechanical, and thermodynamic properties of americium dioxide

By performing density functional theory (DFT) +$U$ calculations, we systematically study the electronic, mechanical, tensile, and thermodynamic properties of AmO$_{2}$. The experimentally observed antiferromagnetic insulating feature [J. Chem. Phys. 63, 3174 (1975)] is successfully reproduced. It is found that the chemical bonding character in AmO$_{2}$ is similar to that in PuO$_{2}$, with smaller charge transfer and stronger covalent interactions between americium and oxygen atoms. The valence band maximum and conduction band minimum are contributed by 2$p-5f$ hybridized and 5$f$ electronic states respectively. The elastic constants and various moduli are calculated, which show that AmO$_{2}$ is less stable against shear forces than PuO$_{2}$. The stress-strain relationship of AmO$_{2}$ is examined along the three low-index directions by employing the first-principles computational tensile test method. It is found that similar to PuO$_{2}$, the [100] and [111] directions are the strongest and weakest tensile directions, respectively, but the theoretical tensile strengths of AmO$_{2}$ are smaller than those of PuO$_{2}$. The phonon dispersion curves of AmO$_{2}$ are calculated and the heat capacities as well as lattice expansion curve are subsequently determined. The lattice thermal conductance of AmO$_{2}$ is further evaluated and compared with attainable experiments. Our present work integrally reveals various physical properties of AmO$_{2}$ and can be referenced for technological applications of AmO$_{2}$ based materials.Comment: 23 pages, 8 figure

Enhanced CNN for image denoising

Owing to flexible architectures of deep convolutional neural networks (CNNs), CNNs are successfully used for image denoising. However, they suffer from the following drawbacks: (i) deep network architecture is very difficult to train. (ii) Deeper networks face the challenge of performance saturation. In this study, the authors propose a novel method called enhanced convolutional neural denoising network (ECNDNet). Specifically, they use residual learning and batch normalisation techniques to address the problem of training difficulties and accelerate the convergence of the network. In addition, dilated convolutions are used in the proposed network to enlarge the context information and reduce the computational cost. Extensive experiments demonstrate that the ECNDNet outperforms the state-of-the-art methods for image denoising.Comment: CAAI Transactions on Intelligence Technology[J], 201

Miniaturization of Branch-Line Coupler Using Composite Right/Left-Handed Transmission Lines with Novel Meander-shaped-slots CSSRR

A novel compact-size branch-line coupler using composite right/left-handed transmission lines is proposed in this paper. In order to obtain miniaturization, composite right/left-handed transmission lines with novel complementary split single ring resonators which are realized by loading a pair of meander-shaped-slots in the split of the ring are designed. This novel coupler occupies only 22.8% of the area of the conventional approach at 0.7 GHz. The proposed coupler can be implemented by using the standard printed-circuit-board etching processes without any implementation of lumped elements and via-holes, making it very useful for wireless communication systems. The agreement between measured and stimulated results validates the feasible configuration of the proposed coupler