Plasmon-phonon coupling in graphene-hyperbolic bilayer heterostructures

Polar dielectrics are important optical materials enabling the subwavelength manipulation of light in infrared due to their capability to excite phonon polaritons.In practice,it is highly desired to actively modify these hyperbolic phonon polaritons（HPPs） to optimize or tune the response of the device.In this work,we investigate the plasmonic material,a monolayer graphene,and study its hybrid structure with three kinds of hyperbolic thin films grown on SiO_2 substrate.The inter-mode hybridization and their tunability have been thoroughly clarified from both the band dispersions and the mode patterns numerically calculated through a transfer matrix method.Our results show that these hybrid multilayer structures are of strong potentials for applications in plasmonic waveguides,modulators and detectors in infrared.

An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems

Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial benefits.There is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies,along with data processing and advanced tools.The main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information security.The original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI mechanisms.Here,an AdaBelief Exponential Feature Selection(AEFS)technique is used to efficiently handle the input huge datasets from the smart grid for boosting security.Then,a Kernel based Extreme Neural Network(KENN)technique is used to anticipate security vulnerabilities more effectively.The Polar Bear Optimization(PBO)algorithm is used to efficiently determine the parameters for the estimate of radial basis function.Moreover,several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network(AEFS-KENN)big data security framework.The results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5%with precision and AUC of 99%for all smart grid big datasets used in this study.

Low power mapping for AND/XOR circuits and its application in searching the best mixed-polarity

A low power mapping algorithm for technology independent AND/XOR circuits is proposed. In this algorithm, the average power of the static mixed-polarity Reed-Muller （MPRM） circuits is minimized by generating a two-input gates circuit to optimize the switching active of nodes, and the power and area of MPRM circuits are estimated by using gates from a given library. On the basis of obtaining an optimal power MPRM circuit, the best mixed-polarity is found by combining an exhaustive searching method with polarity conversion algorithms. Our experiments over 18 benchmark circuits show that compared to the power optimization for fixed-polarity Reed-Muller circuits and AND/OR circuits, power saving is up to 44.22% and 60.09%, and area saving is up to 14.13% and 32.72%, respectively.

An efficient and fast polarity optimization approach for mixed polarity Reed-Muller logic circuits

Although the genetic algorithm has been widely used in the polarity optimization of mixed polarity Reed- Muller （MPRM） logic circuits, few studies have taken into account the polarity conversion sequence. In order to im- prove the efficiency of polarity optimization of MPRM logic circuits, we propose an efficient and fast polarity optimiza- tion approach （FPOA） considering the polarity conversion se- quence. The main idea behind the FPOA is that, firstly, the best polarity conversion sequence of the polarity set wait- ing for evaluation is obtained by using the proposed hybrid genetic algorithm （HGA）; secondly, each of polarity in the polarity set is converted according to the best polarity con- version sequence obtained by HGA. Our proposed FPOA is implemented in C and a comparative analysis has been pre- sented for MCNC benchmark circuits. The experimental re- suits show that for the circuits with more variables, the FPOA is highly effective in improving the efficiency of polarity op- timization of MPRM logic circuits compared with the tradi- tional polarity optimization approach which neglects the po- larity conversion sequence and the improved polarity opti- mization approach with heuristic technique.