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

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

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

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

Soft Robots: Implementation, Modeling, and Methods of control

Soft robotics is a branch of robotics that focuses on technologies with physical features that are like those of live biological creatures. Additionally, they have many details that are hard, if not impossible, to realize with traditional robots which are composed of solid materials. This study concentrates on the current expansion of soft pneumatic actuators for modern soft robotics in recent years, with an emphasis on three areas: Implementation of soft robots, Modeling, and Methods of control systems. Therefore, numerous soft robotic designs and ways to make them suitable for medical, manufacturing, and agricultural applications have been presented. Moreover, a set of functional and technological aspects have been given to review models similar to human hand functionality and motions. To realize the advanced soft robotic hand manipulation function, robotic hands must be equipped with tactile sensing, that sensing is required to provide continuous data on the volume and direction of forces at all contact locations. The research examines achievements in material science, actuation, sensing techniques, and manufacturing technologies, as well as how to model and control a soft robot's motion, all of which are scientifically challenging and, more importantly, practical