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Image encryption is a commonly used method to secure medical data on a public network, playing a crucial role in the healthcare industry. Because of their complex dynamics, memristors are often used in developing novel chaotic systems that can improve the efficiency of encryption algorithms based on chaos. In this work, we propose a novel locally active memristor-based chaotic circuit model and present a real time hybrid image encryption application developed on a PYNQ-Z1 (Python Productivity for Zynq) low-cost FPGA board using Jupiter programming environment. The proposed hybrid algorithm combines memristor-based chaos with a DNA (deoxyribonucleic acid) encryption algorithm exploiting diffusion-confusion technique. We initially present a new compact and inductorless chaotic circuit, derive the model equations, and then verify its chaotic dynamics numerically through the investigation of the phase portraits, Lyapunov exponents and the bifurcation diagrams. We further implement the chaotic circuit experimentally with discrete elements. The randomness of the chaotic sequence is improved using Trivium and von Neumann post-processor algorithms and assessed through the NIST tests. Finally, the performance of the encryption algorithm is evaluated through various metrics, including histogram and correlation analyses, differential attack, information entropy, as well as data-loss and noise attack, demonstrating its security and suitability for real-time encryption systems. [ABSTRACT FROM AUTHOR]

Random telegraph noise (RTN) owns its very name to its assumed stochastic nature. In this paper, we follow up previous works that questioned this stochastic nature, and we investigate this assumption using experimentally measured noise coming from properly biased Ni/HfO 2 unipolar Resistive RAM memristor nanodevices. We have used established, well–known tools from nonlinear theory to examine the current–noise temporal series. Evaluation results show that this series appears to exhibit not a stochastic, but a deterministic chaotic behavior, also demostrating interesting fractal characteristics in 2D and 3D phase space projections. The presented results clearly advocate for a strong component of complex (chaotic) fluctuation of deterministic origin, instead of a typical (fully stochastic) RTN. This result could pave the path for an enhanced understanding of the mechanisms behind RTN emergence, as well as improve its noise models. • We have analyzed experimental time series RTN from Ni/HfO 2 unipolar Resistive RAM memristor nanodevices. • Established nonlinear dynamics evaluation tools have been utilized in order to characterize • The recorded RTN has been proved to be deterministic chaotic, possessing a fractal structure. • The studied nanodevice dynamics can embedded in a 5-dimensional phase space. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the reset transition in bipolar TiN/Ti/HfO 2 (10 nm)/Al 2 O 3 (2 nm)/W ReRAM devices using a tool that allows studying the temporal behaviour of these devices. This tool, the Dynamic Route Map (DRM), provides information about the temporal evolution of the state variable that governs the behaviour of the device, thus allowing an increased insight into resistive switching processes. Here, we show that this DRM is a powerful tool, that may help explaining some non intuitive behaviours of memristors, like the difference in the reset voltage when the inputs are from different frequency or shape. Using this tool, this fact can be explained as a different trajectory on a unique surface defining the device. As a first step, we have used two different models, one based on a physical description, and another one based on the mathematical definition of memristor as a non linear relation between charge and flux. We check that similar DRM can be obtained from both models. Additionally, several series of set-reset transitions have been measured using voltage ramps of different slopes. From the measured transitions, the corresponding resistance has been extracted and, assuming conductive filaments (CF) as the switching mechanism, the corresponding CF radius has been calculated. Using these data, we show that explanations from the model are also supported when using experimental data, thus proving the validity of the approach. [ABSTRACT FROM AUTHOR]