Nonlinear Circuits and Systems: Latest Advances and Prospects

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Circuit and Signal Processing".

Deadline for manuscript submissions: 15 December 2024 | Viewed by 1185

Special Issue Editors


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Guest Editor
Department of Information Engineering and Mathematics, University of Siena, 53100 Siena, Italy
Interests: mixed-signal electronics; front-end electronics; sensors and sensing systems; nonlinear circuits and systems

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Guest Editor
Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy
Interests: analog electronic design; switching power supply; chaotic circuits; random number generators

Special Issue Information

Dear Colleagues,

The study of nonlinear circuits and systems has played a crucial role in recent advances in microelectronics, with particular emphasis on analog and mixed-signal electronics, and power electronics.

The goal of this Special Issue is to gather original research papers as well as review articles that highlight the latest developments and breakthroughs in this area, with particular emphasis on multidisciplinary and interdisciplinary applied research.

Topics of interest for this Special Issue include, but are not limited to, the following:

  • Cryptography and true random number generators;
  • Hardware security and physically unclonable functions;
  • Nonlinear signal processing;
  • Integrated chaotic circuits;
  • Power electronics and energy conversion circuits;
  • Analog computing and memristor-based circuits;
  • Nonlinear circuits for artificial intelligence;
  • Nonlinear circuits for biomedical applications;
  • Nonlinear control systems;
  • Complex networks;
  • Applications in telecommunication.

We look forward to receiving your contributions.

Dr. Tommaso Addabbo
Dr. Fabio Pareschi
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cryptography
  • nonlinear signal processing
  • chaotic circuits
  • energy conversion
  • memristor-based circuits
  • nonlinear circuits for artificial intelligence
  • biomedical systems
  • control systems
  • complex networks
  • telecommunications

Published Papers (2 papers)

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Research

19 pages, 13018 KiB  
Article
Secure Encryption of Biomedical Images Based on Arneodo Chaotic System with the Lowest Fractional-Order Value
by Berkay Emin, Akif Akgul, Fahrettin Horasan, Abdullah Gokyildirim, Haris Calgan and Christos Volos
Electronics 2024, 13(11), 2122; https://doi.org/10.3390/electronics13112122 - 29 May 2024
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Abstract
Fractional-order (FO) chaotic systems exhibit richer and more complex dynamic behaviors compared to integer-order ones. This inherent richness and complexity enhance the security of FO chaotic systems against various attacks in image cryptosystems. In the present study, a comprehensive examination of the dynamical [...] Read more.
Fractional-order (FO) chaotic systems exhibit richer and more complex dynamic behaviors compared to integer-order ones. This inherent richness and complexity enhance the security of FO chaotic systems against various attacks in image cryptosystems. In the present study, a comprehensive examination of the dynamical characteristics of the fractional-order Arneodo (FOAR) system with cubic nonlinearity is conducted. This investigation involves the analysis of phase planes, bifurcation diagrams, Lyapunov exponential spectra, and spectral entropy. Numerical studies show that the Arneodo chaotic system exhibits chaotic behavior when the lowest fractional-order (FO) value is set to 0.55. In this context, the aim is to securely encrypt biomedical images based on the Arneodo chaotic system with the lowest FO value using the Nvidia Jetson Nano development board. However, though the lowest FO system offers enhanced security in biomedical image encryption due to its richer dynamic behaviors, it necessitates careful consideration of the trade-off between high memory requirements and increasing complexity in encryption algorithms. Within the scope of the study, a novel random number generator (RNG) is designed using the FOAR chaotic system. The randomness of the random numbers is proven by using internationally accepted NIST 800-22 and ENT test suites. A biomedical image encryption application is developed using pseudo-random numbers. The images obtained as a result of the application are evaluated with tests such as histogram, correlation, differential attack, and entropy analyses. As a result of the study, it has been shown that encryption and decryption of biomedical images can be successfully performed on a mobile Nvidia Jetson Nano development card in a secure and fast manner. Full article
(This article belongs to the Special Issue Nonlinear Circuits and Systems: Latest Advances and Prospects)
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20 pages, 1768 KiB  
Article
A Deterministic Chaos-Model-Based Gaussian Noise Generator
by Serhii Haliuk, Dmytro Vovchuk, Elisabetta Spinazzola, Jacopo Secco, Vjaceslavs Bobrovs and Fernando Corinto
Electronics 2024, 13(7), 1387; https://doi.org/10.3390/electronics13071387 - 6 Apr 2024
Viewed by 596
Abstract
The abilities of quantitative description of noise are restricted due to its origin, and only statistical and spectral analysis methods can be applied, while an exact time evolution cannot be defined or predicted. This emphasizes the challenges faced in many applications, including communication [...] Read more.
The abilities of quantitative description of noise are restricted due to its origin, and only statistical and spectral analysis methods can be applied, while an exact time evolution cannot be defined or predicted. This emphasizes the challenges faced in many applications, including communication systems, where noise can play, on the one hand, a vital role in impacting the signal-to-noise ratio, but possesses, on the other hand, unique properties such as an infinite entropy (infinite information capacity), an exponentially decaying correlation function, and so on. Despite the deterministic nature of chaotic systems, the predictability of chaotic signals is limited for a short time window, putting them close to random noise. In this article, we propose and experimentally verify an approach to achieve Gaussian-distributed chaotic signals by processing the outputs of chaotic systems. The mathematical criterion on which the main idea of this study is based on is the central limit theorem, which states that the sum of a large number of independent random variables with similar variances approaches a Gaussian distribution. This study involves more than 40 mostly three-dimensional continuous-time chaotic systems (Chua’s, Lorenz’s, Sprott’s, memristor-based, etc.), whose output signals are analyzed according to criteria that encompass the probability density functions of the chaotic signal itself, its envelope, and its phase and statistical and entropy-based metrics such as skewness, kurtosis, and entropy power. We found that two chaotic signals of Chua’s and Lorenz’s systems exhibited superior performance across the chosen metrics. Furthermore, our focus extended to determining the minimum number of independent chaotic signals necessary to yield a Gaussian-distributed combined signal. Thus, a statistical-characteristic-based algorithm, which includes a series of tests, was developed for a Gaussian-like signal assessment. Following the algorithm, the analytic and experimental results indicate that the sum of at least three non-Gaussian chaotic signals closely approximates a Gaussian distribution. This allows for the generation of reproducible Gaussian-distributed deterministic chaos by modeling simple chaotic systems. Full article
(This article belongs to the Special Issue Nonlinear Circuits and Systems: Latest Advances and Prospects)
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