<p style='text-indent:20px;'>A common task in inverse problems and imaging is finding a solution that sparse, the sense most of its components vanish. In framework compressed sensing, general results guaranteeing exact recovery have been proven. practice, sparse solutions are often computed combining <inline-formula><tex-math id="M1">\begin{document}$ \ell_1 $\end{document}&lt...

The use of machine-learning algorithms in optimizing the energy efficiency HVAC systems has been widely studied recent years. Previous research focused mainly on data-driven model predictive controls and reinforcement learning. Both approaches require a large amount online interactive data; therefore, they are not efficient stable enough for large-scale practical applications. In this paper, Ba...

Multifractal analysis has become a reference tool for signal and image processing. Grounded in the quantification of local regularity fluctuations, it proven useful an increasing range applications, yet so far involving only univariate data (scalar valued time series or single channel images). Recently theoretical ground multivariate multifractal been devised, showing potential quantifying tran...

To enhance Patient’s safety, we need effective methods for risk management. This work aims to propose an integrated approach to risk management for a hospital system. To improve patient’s safety, we should develop flexible methods where different aspects of risk and type of information are taken into consideration. This paper proposes a fuzzy Bayesian network to model and analyze risk in the op...

The paper proposes an application framework to be used for medicine assisted diagnosis based on ontology and Bayesian Network (DBNO). There are two goals: (1) to separate the domain knowledge from the probabilistic information and (2) to create an intuitive user interface. The framework architecture has three layers: knowledge, uncertainty model and user interface. The contributions of the doma...

This paperconsiders the problem of representing complex systems that evolve stochastically over time. Dynamic Bayesian networks provide a compact representation for stochastic processes. Unfortunately, they are often unwieldy since they cannot explicitly model the complex organizational structure of many real life systems: the fact that processes are typically composed of several interacting su...