In this paper, a novel medical image encryption method based on multi-mode synchronization of hyper-chaotic systems is presented. The synchronization of hyper-chaotic systems is of great significance in secure communication tasks such as encryption of images. Multi-mode synchronization is a novel and highly complex issue, especially if there is uncertainty and disturbance. In this work, an adaptive-robust controller is designed for multimode synchronized chaotic systems with variable and unknown parameters, despite the bounded disturbance and uncertainty with a known function in two modes. In the first case, it is a main system with some response systems, and in the second case, it is a circular synchronization. Using theorems it is proved that the two synchronization methods are equivalent. Our results show that, we are able to obtain the convergence of synchronization error and parameter estimation error to zero using Lyapunov’s method. The new laws to update time-varying parameters, estimating disturbance and uncertainty bounds are proposed such that stability of system is guaranteed. To assess the performance of the proposed synchronization method, various statistical analyzes were carried out on the encrypted medical images and standard benchmark images. The results show effective performance of the proposed synchronization technique in the medical images encryption for telemedicine application.

In this paper, a new scheme for diagnosis of epileptic seizures in EEG signals using Tunable-Q wavelet transform (TQWT) framework is proposed and benchmarked with Bonn dataset. First, a segmentation of the EEG signals into smaller windows is performed, then a high-pass Butterworth filter with a cutoff frequency of 0.5 Hz is applied to eliminate possible noise. After that, a TQWT with parameters J=8, r=3, Q=1 has been utilized for decomposing the segmented EEG Signals into nine sub-bands. Next, a combination of statistical, entropy-based, and fractal dimension features are extracted from each sub-band. Finally, different ensemble learning-based classifiers, specifically, Adaboost, Gradient Boosting (GB), Hist Gradient Boosting (HistGB), and Random Forest (RF) are employed to classify signals. Also, a feature ranking is driven from classifiers to further analyze the importance of each feature in this particular task. Comparing our method to previous ones, introduced scheme outperforms most of the state-of-the-art works in this field, indicating the effectiveness of the proposed epileptic seizures detection method.

In this paper, a robust adaptive control strategy is proposed to synchronize a class of uncertain chaotic systems with unknown time delays. Using Lyapunov theory and Lipschitz conditions in chaotic systems, the necessary adaptation rules for estimating uncertain parameters and unknown time delays are determined. Based on the proposed adaptation rules, an adaptive controller is recommended for the robust synchronization of the aforementioned uncertain systems that prove the robust stability of the proposed control mechanism utilizing the Lyapunov theorem. Finally, to evaluate the proposed robust and adaptive control mechanism, the synchronization of two Jerk chaotic systems with finite non-linear uncertainty and external disturbances as well as unknown fixed and variable time delays are simulated. The simulation results confirm the ability of the proposed control mechanism in robust synchronization of the uncertain chaotic systems as well as to estimate uncertain and unknown parameters.