@inproceedings{d53222ac178b404087a26581aac7cfb1,
title = "UWB based dielectric material characterization using PCNN based ASIN framework",
abstract = "A non-destructive method for shape invariant dielectric material characterization by estimating their relative dielectric constant using Pulse Coupled Neural Network (PCNN) based Application Specific Instrumentation (ASIN) Framework with Ultra Wide Band (UWB) sensors is discussed in this paper. The property of an electromagnetic wave changes due to the effects of relative dielectric constant & conductivity of a dielectric material, which changes reflection or transmission signal in terms of it's amplitude and spread. This property can be utilized to estimate the relative dielectric constant of a dielectric material. First, our implementation is compared to existing approaches to establish the superiority of the proposed method. In the next step, we established the geometric shape invariance property of our work i.e. this method can estimate the dielectric property of a material irrespective of its geometric shape. These approaches are validated using Finite Difference Time Domain (FDTD) simulation.",
keywords = "ASIN, conductivity, dielectric constant, FDTD, PCNN, UWB",
author = "Santu Sardar and Mishra, {Amit K.}",
year = "2014",
month = jan,
day = "9",
doi = "10.1109/ICAEE.2014.6838428",
language = "English",
isbn = "9781479935420",
series = "2014 International Conference on Advances in Electrical Engineering, ICAEE 2014",
publisher = "IEEE Press",
booktitle = "2014 International Conference on Advances in Electrical Engineering, ICAEE 2014",
address = "United States of America",
note = "2014 International Conference on Advances in Electrical Engineering, ICAEE 2014 ; Conference date: 09-01-2014 Through 11-01-2014",
}