In this paper, hybrid observers based approach for faults detection and isolation, for a class of MIMO hybrid systems, is proposed. The faults herein considered can affect the sensors or the discrete trajectory of the considered system. The proposed approach is based on a Dedicated Switched Robust Observer Scheme (DSROS) using three blocks in interaction. A block generating the mode signatures, a one for the fault signature generation and the last for the diagnosis of observed discrete events. Some LMIs conditions are provided to guarantee both of the robustness and the convergence of the proposed observers. Finally some simulation results illustrate the efficiency of the proposed approach.

The effect of additions up to 9.6%wt BaTiO ₃ on grain growth and microstructure in ZnO samples sintered at 1300°C has been studied using scanning electron microscopy, energy dispersive X‐ray, X‐ray diffraction and impedance analyser as techniques. The sample doped with 1.6%wt BaTiO ₃ , leads to grain size increasing and forms (BaO _10.89 Ti _3.93 Zn _2.03 and Ba ₄ O ₂₇ Ti ₁₁ Zn) solid solutions with ZnO. A homogeneous structure was obtained whereas with further additions 3%wt the structure was inhomogeneous and the solid solutions formed in the first segregate to grain boundaries. Afterwards, an excess of 9.6%wt BaTiO ₃ leads to BaTiO ₃ phase segregation locating on the surface of the sample and in the grain boundaries near the junctions between matrix grains. Experimental I–V current‐voltage characteristics show that BaTiO ₃ as additive in ZnO varistors, increases the non‐linear coefficient (α) and the breakdown voltage. The highest non‐linearity was obtained for 9.6%wt BaTiO ₃ content with α = 121.03 and 1.79 µA in leakage current. The average breakdown voltage per grain boundary (Vgb) was evaluated in the ranges 1.7–3.46 V/gb and 1.34–2.54 V/gb in agreement with the literature.

The field of medical image segmentation is incredibly broad. Several works has been conducted on the study of such a field, especially Magnetic Resonance Imaging (MRI). This article aims to perform brain tumor segmentation on MRI images using an improved clustering method based on K-Means (KM) algorithm. The proposed approach deals with some limitations of the standard KM algorithm such as, the random initialization of clusters centroids, and the noise sensitivity. The main idea consists to combine the Darwinian Particle Swarm Optimization (DPSO) technique, the KM algorithm and the Morphological Reconstruction (MR) operation. The DPSO technique is applied to the MRI medical images for initialization of cluster centroids. Moreover, the MR filter is used to eliminate the noise and generate more compact and well separated clusters. The performance of the proposed method was evaluated by comparing it to some state of the art segmentation algorithms such as, standard KM clustering algorithm and DPSO-based multilevel thresholding. The results show the effectiveness of the proposed method.