A chaotic image cryptosystem based on Toeplitz and Hankel matrices

Abstract

Because of properties in chaos system such as the sensitive dependence on initial conditions, system parameters, pseudorandom property and ergodicity, chaotic image encryption algorithm can suggest a new and efficient way of encryption scheme, which has been studied more and more in recent years. A novel chaotic image encryption algorithm based on Toeplitz matrix and Hankel matrix is proposed in this paper. We shuffle totally the positions of image pixels to confuse the relationship between the plain image and cipher image combined with Toeplitz matrix, Hankel matrix and logistic chaotic system. Another hyper-chaos system of Chen's chaotic system is taken to change the grey values of image pixels to enhance the security further. Experimental results in Sections 3 and 4 demonstrate that the key space is large enough and the key is sensitive to initial conditions to resist the brute force attack in the proposed algorithm. Additionally, the distribution of grey values in encrypted image has a random-like behaviour to resist statistical analysis.     

Navigation system including associative memory

A new adaptive linear robot control system for a robot work cell that can visually track and intercept stationary and moving objects undergoing arbitrary motion anywhere along its predicted trajectory within the robot's workspace is presented in this paper. The proposed system was designed by integrating a stationary monocular CCD camera with off-the-shelf frame grabber and an industrial robot operation into a single application on the MATLAB platform. A combination of the model based object recognition technique and a learning vector quantization network is used for classifying stationary objects without overlapping. The optical flow technique and the MADALINE network are used for determining the target trajectory and generating the predicted robot trajectory based on visual servoing, respectively. The necessity of determining a model of the robot, camera, all the stationary and moving objects, and environment is eliminated. The location and image features of these objects need not be preprogrammed, marked and known before, and any change in a task is possible without changing the robot program. After the learning process on the robot, it is shown that the KUKA robot is capable of tracking and intercepting both stationary and moving objects at an optimal rendezvous point on the conveyor accurately in real-time.