A visual chaotic system simulation in Arduino platform controlled by Android app

Silva, Pedro H. O.; Nepomuceno, Erivelton G.; Vitorino, Alex; Martins, Samir A. M.

doi:10.1109/edunine.2017.7918183

Cited by 6 publications

(7 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Bit error rate analysis in FPGA/FPAA based communication systems by using Arduino in a Matlab/Simulink environment with Sprott chaotic generator performed by Günay and Altun in [15]. The approach of chaotic system simulation in the Arduino platform is described by Silva, et al in [16]. Dual-channel Chaotic Generator of Mackey-Glass system is implemented on Arduino Due in [17] by Voliansky et al A Raspberry Pi 3 implementation of Lorenz Chaotic System with estimation by Hybrid Jaya-Powell Algorithm is described by Zhuang,et al in [18].…”

Section: Brief Overview Of System Hardware Implementation Issuesmentioning

confidence: 99%

“…To compare the obtained results, the chaotic system was estimated using the least squares method in Section 3.7. In Section 4.1, some hardware system implementation options on an Arduino, Raspberry Pi and FPGAs are discussed based on examples from other studies described earlier [14][15][16][17][18][19][20]. Besides Section 4 provides examples of system software implementation, including an example of digital data software transmission (Section 4.2) and secured image transmission via the designed system in Section 4.3.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Data Secured Communication System Design Procedure with a Chaotic Carrier and Synergetic Observer

Mushenko

Dzuba²,

Nekrasov

et al. 2020

Electronics

View full text Add to dashboard Cite

We have considered the problem of secure communication by means of chaotic carrier communication channels. Data protection is provided if the signal in the communication channel looks like a stochastic or noisy one, i.e., by the steganography property, and also by using advanced and secured approaches of mixing a useful signal into a chaotic carrier. The problem is to find effective and efficient techniques for useful signal reconstruction at the receiver side. We firstly use a synergetic nonlinear mathematical observer to recover the two useful signals transmitted simultaneously over a single communication channel. Compared to a known observer, the synergetic one operates with initial nonlinear models, i.e., it may be applied directly to chaotic systems. In that system structure, we consider the useful signals as unobservable variables and estimate them by the given model of a chaotic generator. A chaotic data transmission system with two channels is demonstrated; the presented mathematical procedure explains the steps of synergetic two-channel observer design. Computer simulation results prove the viability of our approach. The proposed data transmission scheme and the observer design procedure provide effective transmission and reconstruction of informational signals. Reconstruction error is up to approximately 25% but has a very short pulses shape and could be eliminated with further analog or digital filtering algorithms. As an example application, simulation of image transmission and recovery is demonstrated. The study's results may be used as a basis for further research into secure data transmission system design.Electronics 2020, 9, 497 2 of 24 Related Works and Methods OverviewThe overall issues and approaches of chaotic communications when using chaotic modulation schemes were briefly described by Riaz and Ali [21]. According to their study [21], the most common modulation schemes are: chaos shift keying (CSK); differential chaos shift keying (DCSK); additive chaos modulation (ACM); and multiplicative chaos modulation (MCM). CSK and DCKS provide secure communication by shifting between two identical or different chaotic generators. ACM and MCM provide mixing of the useful signal directly into the mathematical model of a chaotic generator in a linear (additive) or nonlinear (multiplicative) manner. In all of these schemes, one of the data secure factors is a chaotic carrier signal frequency ten times greater than the useful signal frequency or more. ACM with a combination of MCM was explored by Kevin Short in the 1990s [22] in several Electronics 2020, 9, 497 3 of 24 papers, where he proves that this kind of combined modulation does not provide security for a 3rd order system. In addition, we note that using only MCM is a more secure approach due to nonlinear mixing of the useful signal and MCM as a secure scheme is thus widely used in much research as indicated below. Different approaches and applications of chaotic data communication systems design have been illustrated in several recent papers [23][2...

show abstract

Section: Brief Overview Of System Hardware Implementation Issuesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Data Secured Communication System Design Procedure with a Chaotic Carrier and Synergetic Observer

Mushenko

Dzuba²,

Nekrasov

et al. 2020

Electronics

View full text Add to dashboard Cite

show abstract

“…The created interface allows you to change the parameters related to the established limits, which notify the data that do not match the limits entered in the application. The interface was created in App Inventor [18], software intended for the construction of Android applications. A simple and intuitive software based on the development of blocks, enabling reconfigurable programming and a structure for easy adaptation and implementation of new functions.…”

Section: Remote User Interfacementioning

confidence: 99%

A Prototype for Monitoring Railway Vehicle Dynamics Using Inertial Measurement Units

Medeiros

Silva

Valente

et al. 2018

2018 13th IEEE International Conference on Industry Applications (INDUSCON)

View full text Add to dashboard Cite

The dynamics of railway vehicles have changed from an essentially mechanical application into one that requires knowledge of sensors, electronics and computational processing. Industry 4.0 is a term that describes industrial activity by intelligent systems and solutions established on the concept of the Internet of Things (IoT). The present work illustrates the application of a conceptual cycle of an Internet of Things building a prototype, consisting on acquisition, processing and communication of data in the industrial context. Specifically, the monitoring of the dynamics of wagons using inertial sensors and algorithms that establish the fusion of sensors. The use of refined methods shows more reliable results in the use of low-cost sensors. Furthermore, the implementation of wireless communication enables the use of data in the creation of more complex analyses that can be applied in computational and instrumentation tools.

show abstract

“…Além disso, o MATLAB também é uma ferramenta bastante utilizada no ensino de disciplinas que envolvam programac ¸ão e métodos numéricos [31], [32]. Experiências bem sucedidas nessa linha para área de sistemas não-lineares e Arduino podem ser vistas em [33], [34]. Assim, baseando-se nisso, o MATLAB foi adotado para a implementac ¸ão da interface gráfica proposta neste trabalho.…”

Section: Introduc ¸ãOunclassified

Development of a Pedagogical Graphical Interface for the Reinforcement Learning

Ottoni

Nepomuceno

Oliveira

2020

IEEE Latin Am. Trans.

View full text Add to dashboard Cite

A visual chaotic system simulation in Arduino platform controlled by Android app

Cited by 6 publications

References 15 publications

A Data Secured Communication System Design Procedure with a Chaotic Carrier and Synergetic Observer

A Data Secured Communication System Design Procedure with a Chaotic Carrier and Synergetic Observer

A Prototype for Monitoring Railway Vehicle Dynamics Using Inertial Measurement Units

Development of a Pedagogical Graphical Interface for the Reinforcement Learning

Contact Info

Product

Resources

About