Electrostatic Field Invisibility Cloak

Invisibility cloak is drawing much attention due to its special camouflage when exposed to physical field varing from wave (electromagnetic field, acoustic field, elastic wave, etc.) to scalar field (thermal field, static magnetic field, dc electric field and mass diffusion). Here, an electrostatic field invisibility cloak has been theoretically investigated, and experimentally demonstrated for the first time to perfectly hide a certain region from sight without disturbing the external electrostatic field. The desired cloaking effect has been achieved via both scattering cancelling technology and transformation optics (TO).This present work will pave a novel way for manipulating of electrostatic field where would enable a wide range of potential applications and sustainable products made available.Comment: 17 page

A Small-Divergence-Angle Orbital Angular Momentum Metasurface Antenna

Electromagnetic waves carrying an orbital angular momentum (OAM) are of great interest. However, most OAM antennas present disadvantages such as a complicated structure, low efficiency, and large divergence angle, which prevents their practical applications. So far, there are few papers and research focuses on the problem of the divergence angle. Herein, a metasurface antenna is proposed to obtain the OAM beams with a small divergence angle. The circular arrangement and phase gradient were used to simplify the structure of the metasurface and obtain the small divergence angle, respectively. The proposed metasurface antenna presents a high transmission coefficient and effectively decreases the divergence angle of the OAM beam. All the theoretical analyses and derivation calculations were validated by both simulations and experiments. This compact structure paves the way to generate OAM beams with a small divergence angle

Deep Residual Fusion Network for Single Image Super-Resolution

Abstract Convolutional neural networks have been applied in the field of single-image super-resolution (SISR) and have achieved a series of outstanding results. However, most of the SISR research still attempt to pursue wider and deeper network structure, without paying enough attention to the correlations between different features. In order to solve these problems, deep residual fusion network (DRFN) is proposed for more powerful feature expression and feature learning. Specifically, we propose a feature fusion group (FFG) structure, which can effectively use the relevant features extracted from the residual attention group (RAG) and fuse them to be more discriminative. Residual attention group (RAG) includes channel attention module (CAM) and spatial attention module (SAM), which uses attention mechanism to refine features. DRFN also makes full use of nested residual connections, skipping redundant low-frequency information to enhance circulation, thereby focusing the calculation on more important high-frequency components. Extensive experimental results have proved the effectiveness of our model. And our model finally achieves excellent performance in terms of both quantitative metrics and visual quality.</jats:p

Residual Attention Fusion Network for Single Image Super-Resolution

Abstract Recently, a very deep convolutional neural network (CNN) demonstrated influential performance in the field of single image super-resolution (SISR). However, most of the CNN-based methods focus on designing deeper and wider network structures alone and do not use the hierarchical and global features in the input image. Therefore, we proposed a residual attention fusion network (RAFN), which is an improved residual fusion (RF) framework, to effectively extract hierarchical features for use in single-image super-resolution. The proposed framework comprises two residual fusion structures composed of several residual and fusion modules, and a continuous memory mechanism is realized by adding a long and short jump connection. The network focuses on learning more effective features. Furthermore, to maximize the power of the RF framework, we introduced global context attention (GCA) module that can model the global context and capture long-distance dependencies. The final RAFN was constructed by applying the proposed RF framework to the GCA blocks. Extensive experiments showed that the proposed network achieved improved performance in the SISR method with fewer parameters, as compared to the methods proposed in previous studies.</jats:p