Implementation of a Chua Circuit to Demonstrate Bifurcations and Strange Attractors in a Class

2023

IJ-AI

Image encryption is an important issue in protecting the content of images and in the area of information security. This article proposes a novel method for image encryption and decryption using the structure of the artificial neural network (ANN)-based chua chaotic system (CCS). This structure was efficiently designed on a field-programmable gate array (FPGA) chip utilizing the xilinx system generator (XSG) tool with the IEEE-754-1985 32-bit floating-point number format. For ANN-based CCS design, a multilayer feed forward neural network (FFNN) structure with three inputs and three outputs was created. This structure consists of one hidden layer with four neurons, each of which has a Tangent Sigmoid activation function. The training of ANN-based CCS yielded a 3.602e-13 mean square error (MSE) value. After successfully training the ANN-based CCS, the design was carried out on FPGA, utilizing the ANN structure's bias and weight values as a reference. The xilinx vivado (2017.4) design suite was used to synthesis and test the ANN-based CCS on the FPGA. The histogram, correlation coefficient, and entropy are used to perform security analysis on various images. Finally, FPGA hardware co-simulation using a Xilinx Artix7 xc7a100t-1csg324 chip was utilized to verify that the encryption and decryption of the images were successful.

Section: Chaotic Systemsmentioning

confidence: 99%

Artificial intelligence techniques for encrypt images based on the chaotic system implemented on field-programmable gate array

2023

IJ-AI

“…For the implementation of the chaotic system Chua, we use the Xilinx system generator (XSG) method in this section. The relevance of the Chua circuit has recently made possible the birth of a big family of multi-scroll oscillators and techniques to control the chaos [17]. Chua's circuit has found many applications in physics, communication, and control, mechanics, as well as chemistry, economics, and medicine [18].…”

Section: Chua's Circuit Designmentioning

confidence: 99%

Field-programmable gate array design of image encryption and decryption using Chua’s chaotic masking

2022

IJECE

This article presents a simple and efficient masking technique based on Chua chaotic system synchronization. It includes feeding the masked signal back to the master system and using it to drive the slave system for synchronization purposes. The proposed system is implemented in a field programmable gate array (FPGA) device using the Xilinx system generator tool. To achieve synchronization, the Pecora-Carroll identical cascading synchronization approach was used. The transmitted signal should be mixed or masked with a chaotic carrier and can be processed by the receiver without any distortion or loss. For different images, the security analysis is performed using the histogram, correlation coefficient, and entropy. In addition, FPGA hardware co-simulation based Xilinx Artix7 xc7a100t-1csg324 was used to check the reality of the encryption and decryption of the images.

“…In 1983, Leon Chua developed a nonlinear circuit that is capable of demonstrating a rich collection of dynamical phenomena, ranging from fixed points to cycle points, standard bifurcations (period-doubling), other standard routes to chaos, and chaos itself. The relevance of the Chua circuit has recently made possible the birth of a big family of multi-scroll oscillators and techniques to control the chaos [18]. Chua's circuit has found many applications in physics, communication, and control, mechanics, as well as, chemistry, economics, and medicine [19].…”

Section: Chua Chaotic Systemmentioning

confidence: 99%

New artificial neural network design for Chua chaotic system prediction using FPGA hardware co-simulation

2022

IJECE

This study aims to design a new architecture of the artificial neural networks (ANNs) using the Xilinx system generator (XSG) and its hardware co-simulation equivalent model using field programmable gate array (FPGA) to predict the behavior of Chua’s chaotic system and use it in hiding information. The work proposed consists of two main sections. In the first section, MATLAB R2016a was used to build a 3×4×3 feed forward neural network (FFNN). The training results demonstrate that FFNN training in the Bayesian regulation algorithm is sufficiently accurate to directly implement. The second section demonstrates the hardware implementation of the network with the XSG on the Xilinx artix7 xc7a100t-1csg324 chip. Finally, the message was first encrypted using a dynamic Chua system and then decrypted using ANN’s chaotic dynamics. ANN models were developed to implement hardware in the FPGA system using the IEEE 754 Single precision floating-point format. The ANN design method illustrated can be extended to other chaotic systems in general.