Electromagnetic modelling of bundle of single-walled carbon nanotubes with circular geometry for antenna applications

This paper aims to present an effective electromagnetic (EM) modelling approach for circular bundle of single-walled carbon nanotubes (CBSWCNTs), based on the electrical conductivity, relative complex permittivity and linear distribution impedance by applying General Ohms law for this bundle. The equivalent single conductor material (ESCM) model for personification the CB-SWCNTs is presented in this paper. The main target of this modelling approach is to estimate and investigate the EM properties of CBSWCNTs using common EM engineering tool solver CST (MWS). For this purpose, the CB-SWCNTs and ESCM dipole antennas will be designed and implemented using CST (MWS). Mathematically, the equivalent conductivity model, relative complex permittivity and other parameters of the CB-SWCNTs will be derived in this paper and considered as equivalent material parameters for the ESCM. This modelling technique is expected to provide new avenues for designing different antenna structures

Region of Interest Extraction in 3D Face Using Local Shape Descriptor

Recently, numerous efforts were focused on 3D face models due to its geometrical information and its reliability against pose estimation and identification problems. The major objective of this work is to reduce the massive amount of information contained the entire 3D face image into a distinctive and informative subset interested regions based 3D face analysis systems. The interested regions are represented by nose and eyes regions of frontal and profile 3D images. These regions are detected based on distance to local plan descriptor only which is copes well with profile views of 3D images. The statistical distribution of distance to local plane descriptor is predicted using Gaussian distribution. The framework of the proposed approach involves two modes: training mode and testing mode. In the training mode, a learning process for local shape descriptor related to the interested regions is carried out. The interested regions (nose and eyes) are extracted automatically in the testing mode. The performance evaluation of the proposed approach has been conducted using 3D images taken from GAVADB 3D face database which consists of both frontal and profile views. The proposed approach achieved high detection rate of interested regions for both frontal and profile views