PROJECT TITLE :

A Novel Shielding Effectiveness Matrix of Small Shield Cans Based on Equivalent Dipole Moments for Radio-Frequency Interference Analysis

ABSTRACT:

A definition of a shielding effectiveness (SE) matrix for tiny protect cans used to suppress intra-system noise coupling in mobile devices is proposed. Assuming protect cans enclosing a noise supply can be modeled as equivalent dipole moments along with integrated circuits (ICs), a replacement SE matrix for radio-frequency interference analysis is defined as a ratio of equivalent dipole moments with and without a shield will. The equivalent dipole moments are experimentally extracted employing a GTEM cell and 2 sorts of probes (monopole and loop), that mimic well the radiations of ICs. Furthermore, the estimation of suppression of noise coupling to an antenna using the proposed SE matrix is successfully validated through numerical simulations.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : Novel Secure Outsourcing of Modular Inversion for Arbitrary and Variable Modulus ABSTRACT: In the fields of cryptography and algorithmic number theory, modular inversion is regarded as one of the operations that
PROJECT TITLE : A Novel Blockchain Based Secured and QoS Aware IoT Vehicular Network in Edge Cloud Computing ABSTRACT: An IoT (Internet of Things)-based vehicular ad-hoc network and a software-defined network are the two components
PROJECT TITLE : Progressive Self-Supervised Clustering With Novel Category Discovery ABSTRACT: Clustering is one of the most classical methods used to analyze data structures in machine learning and pattern recognition at the
PROJECT TITLE : Large-Scale Affine Matrix Rank Minimization With a Novel Nonconvex Regularizer ABSTRACT: The goal of low-rank minimization is to recover a matrix with the lowest possible rank while still satisfying the constraints
PROJECT TITLE : A Novel Discriminative Dictionary Pair Learning Constrained by Ordinal Locality for Mixed Frequency Data Classification ABSTRACT: One of the challenges that classification must overcome is the fact that the data