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This article proposes a fast reaching finite time synchronization approach for chaotic systems along with its application to medical image encryption. First, an adaptive terminal sliding mode tracking approach with fast reaching condition is designed to synchronize the chaotic systems at the transmitter and receiver ends in finite time. Then, a chaotic cryptosystem, using synchronized chaotic systems as secret keys generator, is proposed to enhance the security of medical image transmission and/or storage. The applicability and efficiency of the proposed synchronization approach is assessed using a simulation as well as an analytical study. The analysis encompassed security tools such as histogram analysis, correlation test, and information entropy change the rate of the number of pixels and unified average changing intensity. The obtained results confirmed the robustness and fast convergence rate of the proposed synchronization approach. The security analysis also shows that the proposed cryptosystem displays acceptable levels of resistance to various attacks. INDEX TERMSChaos synchronization; fast reaching condition; medical image encryption; MORE method encryption.

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This paper considers the finite-time chaos synchronization of Chua chaotic oscillators based on the secure communication scheme in wireless sensor networks. The modified Chua oscillators are added to the base station and sensor nodes to generate the chaotic signals. Two methods are proposed for the finite-time synchronization of the modified Chua systems with uncertain parameters. In the first method, by using the Lyapunov stability theory, control law is suggested to achieve finite-time chaos synchronization. In order to increase the robustness of the controller, in the second method, a sliding mode controller is applied to the wireless sensor network. Synchronization between the base station and each of the sensor nodes is realized by multiplying a selection matrix by the specified chaotic signal, which is broadcasted by the base station to the sensor nodes. The mathematical proofs confirm that the proposed control law is correct and finally, the simulation results are presented to show the efficiency of the proposed technique.

This paper proposes a novel chaotic system with infinite number of equilibria located on an exponential curve. It signifies an exciting category of dynamical systems which display many features of regular and chaotic motions. The proposed chaotic system belongs to the general category of chaotic systems with hidden attractors. Moreover, some theoretical analyses of the chaotic system’s dynamical characteristics are presented. Using the developed chaotic system, the new random number generator and encryption algorithm have been designed. Encryption application and security analysis are presented verifying its feasibility.

This paper proposes a novel exponential hyper–chaotic system with complex dynamic behaviors. It also analyzes the chaotic attractor, bifurcation diagram, equilibrium points, Poincare map, Kaplan–Yorke dimension, and Lyapunov exponent behaviors. A fast terminal sliding mode control scheme is then designed to ensure the fast synchronization and stability of the new exponential hyper–chaotic system. Stability analysis was performed using the Lyapunov stability theory. One of the main features of the proposed controller is the finite time stability of the terminal sliding surface designed with high–order power function of error and derivative of error. The approach was implemented for image cryptosystem. Color image encryption was carried out to confirm the performance of the new hyper–chaotic system. For image encryption, the DNA encryption-based RGB algorithm was used. Performance assessment of the proposed approach confirmed the ability of the proposed hyper–chaotic system to increase the security of image encryption.

In this paper a Farsi handwritten word recognition system for reading city names in postal addresses is presented. The method is based on vector quantization (VQ) and hidden Markov model (HMM). The sliding right to left window is used to extract the proper features(we have proposed four features). After feature extraction, K-means clustering is used for generation a codebook and VQ generates a codeword for each word image. In the next stage, HMM is trained by Baum Welch algorithm for each city name. A test image is recognized by finding the best match (likelihood) between the image and all of the HMM words models using forward algorithm. Experimental results show the advantages of using VQIHMM recognizer engine instead of conventional discrete HMM.

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