Implementation of a New Memristor Based Chaotic System

Li, Yuxia; Zhao, Liangyu; Chi, Wenqing; Lu, Shuli; Huang, Xia

doi:10.1109/iwcfta.2012.75

Cited by 6 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From this, several authors have made modifications both in the memory function memconductance and in the circuit for specific applications. For example, in [21,22] new functions are implemented to generate chaotic systems. In 2016 and 2017 new circuits were proposed for encryption and decryption of images and texts [23].…”

Section: Related Workmentioning

confidence: 99%

“…What was stated by [7] allowed to conclude that the models used until that moment were incapable of capturing this property, opening the way to the characterization and modeling of modern traffic. This need led to the study of fractal traffic through the Wavelet transform [18][19][20][21][28][29][30][31]. For this reason, Riedi et al [10] proposed a wavelet multifractal model (WMM) which characterizes and synthesizes positive data with long-range dependence (LRD) and Tuberquia et al [32] made an algorithm to generate multifractal time series with Hurst parameter and multifractal spectrum width, sample and adjustable.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Random Number Generator with Long-Range Dependence and Multifractal Behavior Based on Memristor

et al. 2020

View full text Add to dashboard Cite

Random number generators are used in areas such as encryption and system modeling, where some of these exhibit fractal behaviors. For this reason, it is interesting to make use of the memristor characteristics for the random number generation. Accordingly, the objective of this article is to evaluate the performance of a chaotic memristive system as a random number generator with fractal behavior and long-range dependence. To achieve the above, modeling memristor and its corresponding chaotic systems is performed, from which a random number generator is constructed. Subsequently, the Hurst parameter for the detection of long-range dependence is estimated and a fractal analysis of the synthesized data is performed. Finally, a comparison between the model proposed in the research and the β-MWM algorithm is made. The results obtained show that the data synthesized from the proposed generator have a variable Hurst parameter and both monofractal and multifractal behavior. The main contribution of this research is the proposal of a new model for the synthesis of traces with long-range dependence and fractal behavior based on the non-linearity of the memristor.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Random Number Generator with Long-Range Dependence and Multifractal Behavior Based on Memristor

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The main conclusion from this research is that memristors are useful for designing nonlinear oscillators. The existence of chaotic dynamics have been observed in simulations of various electronic oscillators containing memristors [7,11,12,22]. Certain bifurcation phenomena existing in nonlinear oscillators based on mem-devices are reported in [18].…”

Section: Introductionmentioning

confidence: 99%