Extremely Low Effective Impedance in Stratified Graphene-Dielectric Metamaterials

The periodic reflections in frequency were observed in a stack of graphene layers and reported as a series of mini photonic bandgaps owing to the multiple interference by the graphene layers. In this research, the effective medium approach was employed to obtain the effective refractive index and Bloch impedance for understanding the wave propagation characteristic therein. Specifically, the pure real effective refractive index without attenuation as well as an extremely low Bloch impedance were found at the frequencies exhibiting periodic reflections. Some numerical examples were demonstrated to show that the series bandgap-like reflections in fact are attributed to considerable impedance mismatch caused by this ultra low Bloch impedance.

Simultaneous Representation and Separation for Multiple Interference Allied with Approximation Message Passing

<p>Broadband reliable communication is a competitive 5G technology for cognitive communication scenarios, but meanwhile introduces multiform interference to existing broadband transform domain communication system (TDCS) transmission. In order to facilitate the improvement of the anti-jamming performance for the coexistence of diverse interference and TDCS signals in wireless heterogeneous networks, it is important to separated and eliminate various interference to TDCS systems. In this paper, a novel sparse learning method-based cognitive transformation framework of interference separation is formulated for accurate interference recovery, which can be efficiently solved by iteratively learning the prior sparse probability distribution of the interference support. To further improve the separation accuracy and iterative convergence, the principal component analysis and Bayesian perspective in orthogonal base learning are exploited to singly recover the multiple interference and TDCS signals. Moreover, utilizing different sparsity states of spectrum analysis, the proposed novel interference separation algorithm is extended to simultaneous separation based on state evolving of approximation message passing, which iteratively learns the belief propagation posteriors and keeps shrunk by iterative shrinkage threshold. Simulation results demonstrate that the proposed methods are effective in separating and recovering the sparse diversities of interference to TDCS systems, and significantly outperform the state-of-the-art methods.</p>

Simultaneous Representation and Separation for Multiple Interference Allied with Approximation Message Passing

<p>Broadband reliable communication is a competitive 5G technology for cognitive communication scenarios, but meanwhile introduces multiform interference to existing broadband transform domain communication system (TDCS) transmission. In order to facilitate the improvement of the anti-jamming performance for the coexistence of diverse interference and TDCS signals in wireless heterogeneous networks, it is important to separated and eliminate various interference to TDCS systems. In this paper, a novel sparse learning method-based cognitive transformation framework of interference separation is formulated for accurate interference recovery, which can be efficiently solved by iteratively learning the prior sparse probability distribution of the interference support. To further improve the separation accuracy and iterative convergence, the principal component analysis and Bayesian perspective in orthogonal base learning are exploited to singly recover the multiple interference and TDCS signals. Moreover, utilizing different sparsity states of spectrum analysis, the proposed novel interference separation algorithm is extended to simultaneous separation based on state evolving of approximation message passing, which iteratively learns the belief propagation posteriors and keeps shrunk by iterative shrinkage threshold. Simulation results demonstrate that the proposed methods are effective in separating and recovering the sparse diversities of interference to TDCS systems, and significantly outperform the state-of-the-art methods.</p>

Measuring Optical Properties of Advanced Nano/Micro-Periodic Structures Fabricated by Multi-Exposure Interference Lithography

Functional optical elements based on nano/micro-periodic structures have attracted much attention. Since the fabrication of these dual-periodic structures requires precise control of periodicity, the semiconductor process such as an electron beam lithography has been mainly employed. However, these techniques have problems with expensive and low throughput for industrial applications. Therefore, there remains a need for low cost and high throughput fabrication methods of dual-periodic structures. Then we developed a multi-exposure interference lithography (MEIL) system using rotational Lloyd’s mirror interferometer to overcome these problems. The advantages of interference lithography are a large processing area and low cost. Our developed rotational Lloyd’s mirror setup enables us to a highly precise superposition of multiple interference fringes by multi-exposure. Furthermore, we developed a measurement setup for reflective diffractive elements using a two axial rotating stage and measured the diffraction properties of the fabricated dual-periodic diffraction gratings. In this paper, as a demonstration, we succeeded in the fabrication of high-dispersion diffraction grating with an enhanced diffraction efficiency of the −3rd order light. The fabricated shapes have a periodicity of 1997 nm and 665 nm. Furthermore, it was confirmed that the intensity of the −3rd order light was enhanced by about 10 times compared to the single periodic grating.