6533b7d6fe1ef96bd1266d51

RESEARCH PRODUCT

Memristors in Nonlinear Network : Application to Information (Signal and Image) Processing

subject

description

Memristor is a two-terminal nonlinear dynamic electronic device. Typically, it is a passive nano-device whose conductivity is controlled by the flux, time-integral of the voltage across its terminals, or by the charge, time-integral of the current flowing through it, and it presents interesting features for versatile applications. This thesis considers memristor use as a neighborhood connection for 2D cellular nonlinear or neural network (CNN), essentially for information (image and signal) processing and electronic prosthesis. We develop a model of the memristor based 2D cellular nonlinear networks CNNs compatible to image applications by incorporating memristor in the adjacent neighborhood connection. This approach will offer many advantages with respect to previous known designs. Some of these advantages are higher pixel density due to the nano-nature of the memristor, lower power consumption, high-density connection flexibility and compatibility to CMOS technology, etc. Firstly, we present the State of the Art, that is, what is known about this new passive component - the memristor, along with an analog model of memristor for practical and demonstration purposes. Then, we present the quantitative and qualitative behaviour of a charge-controlled memristor by considering RC networks with memristor in the coupling mode, focusing specifically on the system of two initially charged RC cells. We extensively study the interaction of two Fitzhugh-Nagumo cells via a memristor by observing the transient and the steady state response of each cell, allowing us to have a good foresight of the memristor functionality in the memristor based 2D CNNs and the diffusion effect in a 1D cellular nonlinear electrical lattice. Furthermore, we present the generalized model of the memristor based 2D CNNs reliable for processing any number of cells.