In this paper, Finite Difference Time Domain (FDTD) is utilized to simulate metamaterials of Double Negative (DNG) origin that refers to those materials having simultaneous negative permittivity and permeability. The ...In this paper, Finite Difference Time Domain (FDTD) is utilized to simulate metamaterials of Double Negative (DNG) origin that refers to those materials having simultaneous negative permittivity and permeability. The problem regarding space formulation is achieved by means of auxiliary differential equation method (ADE), which is easy, reliable and also causal process in nature thus making it proficient. It uses fair approximations to explicate the model. Mur’s boundary condition is used for 1-D problem space and convolution perfectly matched layer boundary is implemented for 2-D problem space. The properties of metamaterial conform their speculations of energy absorption, enhancement and backward propagation property with the aid of graphs engineered by Matlab simulation both in 1-D and 2-D. Also, the interaction of fields on DNG and Double Positive (DPS) layers is contrasted. The results achieved elucidate the validity and effectiveness of the ADE method and the Convolution Perfectly Match Layer (CPML) in designing DNG metamaterials.展开更多
This paper proposes a novel equalizer, termed here as Evolutionary MPNN, where a complex modified probabilistic Neural Networks (MPNN) acts as a filter for the detected signal pattern. The neurons were embedded with o...This paper proposes a novel equalizer, termed here as Evolutionary MPNN, where a complex modified probabilistic Neural Networks (MPNN) acts as a filter for the detected signal pattern. The neurons were embedded with optimization algorithms. We have considered two optimization algorithms, Bacteria Foraging Optimization (BFO) and Ant Colony Optimization (ACO). The proposed structure have the ability to process complex signals also can perform for slowly varying channels. Also, Simulation results prove the superior performance of the proposed equalizer over the existing MPNN equalizers.展开更多
文摘In this paper, Finite Difference Time Domain (FDTD) is utilized to simulate metamaterials of Double Negative (DNG) origin that refers to those materials having simultaneous negative permittivity and permeability. The problem regarding space formulation is achieved by means of auxiliary differential equation method (ADE), which is easy, reliable and also causal process in nature thus making it proficient. It uses fair approximations to explicate the model. Mur’s boundary condition is used for 1-D problem space and convolution perfectly matched layer boundary is implemented for 2-D problem space. The properties of metamaterial conform their speculations of energy absorption, enhancement and backward propagation property with the aid of graphs engineered by Matlab simulation both in 1-D and 2-D. Also, the interaction of fields on DNG and Double Positive (DPS) layers is contrasted. The results achieved elucidate the validity and effectiveness of the ADE method and the Convolution Perfectly Match Layer (CPML) in designing DNG metamaterials.
文摘This paper proposes a novel equalizer, termed here as Evolutionary MPNN, where a complex modified probabilistic Neural Networks (MPNN) acts as a filter for the detected signal pattern. The neurons were embedded with optimization algorithms. We have considered two optimization algorithms, Bacteria Foraging Optimization (BFO) and Ant Colony Optimization (ACO). The proposed structure have the ability to process complex signals also can perform for slowly varying channels. Also, Simulation results prove the superior performance of the proposed equalizer over the existing MPNN equalizers.
