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Journals and Conferences
In this paper we consider the diagnosis of asynchronous discrete event systems. We follow a so-called true concurrency approach, in which no global state and no global time is available. Instead, we use only local states in combination with a partial order model of time. Our basic mathematical tool is that of net unfoldings originating from the Petri net… (More)
We analyze the dependencies between the variables involved in the source and channel coding chain. This analysis is carried out in the framework of Bayesian networks, which provide both an intuitive representation for the global model of the coding chain, and a way of deriving joint (soft) decoding algorithms. Three sources of dependencies are involved in… (More)
We address the problem of alarm correlation in large distributed systems. The key idea is to make use of the concurrence of events in order to separate and simplify the state estimation in a faulty system. Petri nets and their causality semantics are used to model concurrency. Special partially stochastic Petri nets are developed, that establish some kind… (More)
This paper presents a framework to deal with large systems, which cannot be handled as a whole. We propose to model them as a graph of interacting subsystems, and to base all processings on this factorization of the large system.
Developing applications over a distributed and asynchronous architecture without the need for synchronization services is going to become a central track for distributed computing. This research track will be central for the domain of autonomic computing and self-management. Distributed constraint solving, distributed observation, and distributed… (More)
For distributed systems, i.e., large complex networked systems, there is a drastic difference between a local view and knowledge of the system, and its global view. Distributed systems have local state and time, but do not possess global state and time in the usual sense. In this paper, motivated by the monitoring of distributed systems and in particular of… (More)
This paper presents a class of non-linear hierarchical algorithms for the fusion of multiresolution image d a t a in low-level vision. The approach combines nonlinear causal Markov models defined on hierarchical graph structures, with standard bayesian estimation theory. Two random processes defined on simple hierarchical graphs (quadtrees or “ternary… (More)
We consider the smoothing problem for multiscale stochastic models based on the wavelet transform. These models involve processes indexed by the nodes of a dyadic tree. Each level of the dyadic tree represents one scale or resolution of the process, thus moving upward on the tree divides the resolution by 2 while moving downward multiplies it by 2. The… (More)
Distributed architectures for network management have been the subject of a large research effort, but distributed algorithms that implement the corresponding functions have been much less investigated. In this paper we describe novel algorithms for model-based distributed fault diagnosis.
In truly asynchronous, distributed systems, neither global state nor global time are available. The diagnosis approach with Petri net unfoldings, motivated by the problem of event correlation in telecommunications network management and proposed in , uses only local states in combination with a partial order model of time. Here, we give a definition of… (More)