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

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

Real time monitoring and control for advanced microwave biodiesel reactor

Intelligent control techniques that emulate characteristics of biological system offer opportunities for creating control products with new capabilities. Intelligence is a mental quality that consists of the abilities to learn from experience, adapt to new situations. Artificial intelligence as the ability of a digital computer to perform tasks commonly associated with intelligent beings. The objective of this PhD studies is to undertake extensive research activities to simulate, design and implement various types of intelligent controllers such as error-base adaptive, conventional fuzzy logic, self-tuning fuzzy using Iterative Learning Control (ILC), inverse Adaptive NeuroFuzzy Inference System (ANFIS) controller, genetic-ANFIS controller, and adaptive PID controller. These techniques aimed to control and monitor in real time the performance of the microwave reactor to produce a biodiesel from any fats or waste cooking oil, with a potential of scale up system to be characterised for use in industrial environments. The other objective of the project is to use for the first time a microwave reactor to speed up the process of transesterification reaction in order to produce higher yield. Within the biodiesel production system the microwave reactor plays an important role. However, due to its non-linear nature then a complex control of the reactor is required as unsuccessful reaction step due to any disturbances or changes in the reaction conditions can have a significant impact on the transesterification reaction, leading to an incomplete conversion of waste oil to biodiesel. Ultimately this can lead to a reduction in product yield and quality, an issue which is further compounded by complex heat and mass transfer characteristics, frequent overshoot of temperature and oscillation of pressure within the reactor. Therefore, good control is essential for quality biodiesel production.EThOS - Electronic Theses Online ServiceGBUnited Kingdo