Hyper-chaotic Feeded GA (HFGA): a reversible optimization technique for robust and sensitive image encryption

In recent years, due to their straightforward structure and efficiency, the chaos-based cryptographic algorithms have become a good candidate for image encryption. However, they still suffer from many weaknesses, such as insensitivity to the plain image, weak key streams, small key space, non-resistance to some attacks and failure to meet some security criteria. For this purpose in this paper, a novel hybrid image encryption algorithm named Hyper-chaotic Feeded GA (HFGA) is proposed to fill the gaps in two stages; initial encryption by using a hyper-chaotic system, and then outputs reinforcement by employing a customized Genetic Algorithm (GA). By applying an innovative technique, called gene-labelling, the proposed algorithm not only optimizes the preliminary encrypted images in terms of security criteria but also allows the legal receiver to easily and securely decrypt the optimized cipher image. In fact, in the first stage, besides unpredictable random sequences generated by a hyper-chaotic system, a new sensitive diffusion function is proposed which makes the algorithm resistant to differential attacks. In the second stage, the generated cipher images, which are labeled in a special way, will be used as the initial population of a GA which enhances randomness of the cipher images. The results of several experiments and statistical analysis show that the proposed image encryption scheme provides an efficient and secure way for fast image encrypting as well as providing robustness against some well-known statistical attacks.     

Dynamic modeling and CPG-based trajectory generation for a masticatory rehab robot

Human mastication is a complex and rhythmic biomechanical process which is regulated by a brain stem central pattern generator (CPG). Masticatory patterns, frequency and amplitude of mastication are different from person to person and significantly depend on food properties. The central nervous system controls the activity of muscles to produce smooth transitions between different movements. Therefore, to rehab human mandibular system, there is a real need to use the concept of CPG for development of a new methodology in jaw exercises and to help jaw movements recovery. This paper proposes a novel method for real-time trajectory generation of a mastication rehab robot. The proposed method combines several methods and concepts including kinematics, dynamics, trajectory generation and CPG. The purpose of this article is to provide a methodology to enable physiotherapists to perform the human jaw rehabilitation. In this paper, the robotic setup includes two Gough–Stewart platforms. The first platform is used as the rehab robot, while the second one is used to model the human jaw system. Once the modeling is completed, the second robot will be replaced by an actual patient for the selected physiotherapy. Gibbs–Appell’s formulation is used to obtain the dynamics equations of the rehab robot. Then, a method based on the Fourier series is employed to tune parameters of the CPG. It is shown that changes in leg lengths, due to the online changes of the mastication parameters, occur in a smooth and continuous manner. The key feature of the proposed method, when applied to human mastication, is its ability to adapt to the environment and change the chewing pattern in real-time parameters, such as amplitudes as well as jaw movements velocity during mastication.