Rawan received her B.Sc. degree (Hons.) in electronics and communications engineering from the Faculty of Engineering, Cairo University, Giza, Egypt, in 2019. Currently pursuing the M.Sc. degree with the Nanoelectronics Integrated System Center (NISC), Nile University in the program of Microelectronics System Design (MSD), Giza, Egypt. Currently, she is a teaching assistant at the School of Engineering and Applied Sciences, Nile University. Her research interests include fractional-order systems, memristive applications, and Ternary system applications in memories, processors and neural networks.
Memristive Bio-Impedance Modeling of Fruits and Vegetables
Recent works show that the plants can exhibit nonlinear memristive behavior when excited with low-frequency signals. However, in the literature, only linear bio-impedance models are extensively considered to model the electrical properties of biological tissues without acknowledging the nonlinear behavior. In this paper, we show with experiments, for the first time, the pinched hysteresis behavior
Comparative Study of CNTFET Implementations of 1-trit Multiplier
Ternary logic has become a promising alternative to traditional binary logic due to low power consumption and reduced circuits such as interconnects and chip areas. The efficiency of the multiplier circuit can be much better using a ternary logic system. Carbon nanotube field-effect transistor (CNTFET) is a promising technology as it achieves more advantages than MOSFET due to its low off-current
Do the Bio-impedance Models Exhibit Pinched Hysteresis?
Recently, pinched hysteresis has been found in the electrical modelling of regular plant tissues. Usually, the biological tissues are characterized in the frequency domain using bio-impedance analyzers without investigating the time domain, which would show the pinched hysteresis. In this paper, the current-voltage analysis of some of the widely known electrical bio-impedance models is studied
- Multi-valued Logic
- Ternary System
- Digital Circuits
- fractional-order Systems
- Memristive Applications
- Neural Networks