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With the evolution of the communication technology, fast and efficient tools for secure exchanged data are highly required. Through this research work, we introduce a simplified and fast chaos-based scheme for multimedia data encryption and in particular for color image encryption application. The new algorithm is based on an extracted four-dimension (4-D) discrete time map. The proposed 4-D chaos system includes seven (07) nonlinear terms and four (04) controllers to generate a robust chaos that can satisfy the encryption requirements. The performance of this image encryption algorithm are analyzed with the help of four important factors which are key space, correlation, complexity and running time. Results of the security analysis compared to some of similar proposals, show that our encryption scheme is more effective in terms of key stream cipher space, correlation, complexity and running time.

International audience; Prostate Cancer is one of the most prevalent cancers in men and a leading cause of death. Prostate Brachytherapy (BT) is one of the promising treatment techniques. The use of BT techniques is made easier, thanks to the developments of associated imaging technologies like Ultrasound and Magnetic Resonance Imaging (MRI). MRI has the capacity to detect small lesions providing a wider scope of research and enabling better accuracy in BT treatments. In order to enhance the diagnosis of prostate cancer and its treatment options in the 2 seas region, Europe, Interreg 2 Seas co-financed a European Union Project Cooperative Brachytherapy called CoBra. The aim of the CoBra project is to perform a robotized biopsy and BT under real-time MRI-guidance. The concept is under development, such as the robot's task should be achieved within the MRI-scanner without any repositioning of the patient.

In the present paper, a new joint eigenvalue decomposition (JEVD) method is developed by considering generalized Givens rotations. This method deals with a set of square matrices sharing a same eigenstructure. Several Jacobi-like methods exist already for solving the aforementioned problem. The differences reside in the way of estimating the Shear rotation. Herein, we clarify these differences, highlight the weaknesses of the existing solutions and develop a new robust method named Efficient and Stable Joint eigenvalue Decomposition (ESJD). Simulation results are provided to highlight the effectiveness of the proposed technique especially in difficult scenario.

International audience; An unmanned aerial vehicle (UAV) has grown rapidly the last years. They are being engaged in many types of missions, ranging from military to agriculture passing from entertainment and rescue or even delivery. This work consist to ensure the convergence of positions and yaw angle, to their desired trajectories while maintaining stability of roll and pitch angle. For this, formulate this problem to an optimal control problem which minimizes the distance between state and desired state in free final time. Our contribution is to use the Bocop software to solve this problem. And to implement Shooting method with Matlab software.

International audience; The main objective of an energy regulation of a building is to maintain internal thermal comfort, as well as minimize energy consumption, or reduce the peak of electrical consumption. The dynamic programming has been used to minimize a cost function, accounting for a high peak electricity tariff, under constraints related to comfort (minimal temperature, maximal temperature variation) and the maximum heating power. The proposed energy management consists in over-heating the building during the hours before the peak knowing in advance the weather, occupation and internal gains for the day. The method has been tested in a case study corresponding to a house of a four-person family with performance levels: high construction and poorly insulated old house.

International audience; In multisensor tracking systems, the state fusion also known as track to track fusion is a crucial issue where the derivation of the best track combination is obtained according to a stochastic criteria in a minimum variance sense. Recently, sub-optimal weighted combination fusion algorithms involving matrices and scalars were developed. However, hence they only depend on the initial parameters of the system motion model and noise characteristics, these techniques are not robust against erroneous measures and unstable environment. To overcome this drawbacks, this work introduces a new approach to the optimal decentralized state fusion that copes with erroneous observations and system shortcomings. The simulations results show the effectiveness of the proposed approach. Moreover, the reduced complexity of the designed algorithm is well suited for real-time implementation.