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In this paper, we propose an approach to the diagnosis of Petri nets in a decentralized setting that combines the decentralized scheme for automata presented by Debouk with the diagnosis approach for Petri nets based on the notion of basis markings and justifications presented by some of the authors of this paper. The decentralized architecture that we use is composed of a set of sites communicating their diagnosis information with a coordinator that is responsible for detecting the occurrence of failures in the system. In particular, we define three protocols that differ in the amount of information exchanged between the local sites and the coordinator and the rules adopted by the coordinator to compute the global diagnosis states. Finally, we prove that, as in the case of automata, diagnosability is strictly related to the presence of failure ambiguous strings. [ABSTRACT FROM AUTHOR]

In this paper, we present a new methodology for clustering hyperspectral images. It aims at simultaneously solving the following three different issues: 1) estimation of the class statistical parameters; 2) detection of the best discriminative bands without requiring the a priori setting of their number by the user; and 3) estimation of the number of data classes characterizing the considered image. It is formulated within a multiobjective particle swarm optimization (MOPSO) framework and is guided by three different optimization criteria, which are the log-likelihood function, the Bhattacharyya statistical distance between classes, and the minimum description length (MDL). A detailed experimental analysis was conducted on both simulated and real hyperspectral images. In general, the obtained results show that interesting classification performances can be achieved by the proposed methodology despite its completely unsupervised nature. [ABSTRACT FROM AUTHOR]

A remark related to our recent paper is made. Using a counterexample we show that we need more strict assumptions with respect to those used to prove that if a system is diagnosable in a decentralized setting, then it is also diagnosable in a centralized one. Specifically, we require that the central diagnoser sees all the events that can be seen by all the local diagnosers, and that the central diagnoser can distinguish all observable events that the local diagnosers can distinguish on their own. [ABSTRACT FROM PUBLISHER]