Tunis University

About: Tunis University is a education organization based out in Tunis, Tunisia. It is known for research contribution in the topics: Population & Thin film. The organization has 11745 authors who have published 15400 publications receiving 154900 citations. The organization is also known as: University of Tunis & UT.

An image encryption scheme based on a new hybrid chaotic map and optimized substitution box

Abstract: This paper proposes a new hybrid chaotic map and a different way of using optimization technique to improve the performance of encryption algorithms. Compared to other chaotic functions, the proposed chaotic map establishes an excellent randomness performance and sensitivity. Based on its Lyapunov exponents and entropy measure, the characteristics of the new mathematical function are better than those of classical maps. We propose a new image cipher based on confusion/diffusion Shannon properties. The substitution phase of the proposed encryption algorithm, which depends on a new optimized substitution box, was carried out by chaotic Jaya optimization algorithm to generate S-boxes according to their nonlinearity score. The goal of the optimization process is to have a bijective matrix with high nonlinearity score. Furthermore, a dynamic key depending on the output of encrypted image is proposed. Security analysis indicates that the proposed encryption scheme can withstand different crypt analytics attacks.

Application of higher order spectral features and support vector machines for bearing faults classification

Abstract: Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed The average of these performance measures is computed to report the overall performance of the support vector machine classifier In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity The sensitivity and robustness of the proposed method are explored by running a series of experiments A receiver operating characteristic (ROC) curve made the results more convincing The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals