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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)
— 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)
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)
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 network. Petri nets and their causality semantics are used to model concurrency. Special partially stochastic Petri nets are developed, that establish some kind(More)
— For distributed systems, i.e., large complex net-worked 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(More)
This paper presents a class of non-linear hierarchical algorithms for the fusion of multiresolution image data in low-level vision. The approach combines non-linear causal Markov models defined on hierarchical graph structures, with standard bayesian estimation theory. Two random processes defined on simple hierarchical graphs (quadtrees or " ternary graphs(More)
— We consider a distributed system modeled as a possibly large network of automata. Planning in this system consists in selecting and organizing actions in order to reach a goal state in an optimal manner, assuming actions have a cost. To cope with the complexity of the system, we propose a distributed/modular planning approach. In each automaton or(More)
Monitoring or diagnosis of large scale distributed Discrete Event Systems with asynchronous communication is a demanding task. Ensuring that the methods developed for Discrete Event Systems properly scale up to such systems is a challenge. In this paper we explain why the use of partial orders cannot be avoided in order to achieve this objective. To support(More)
Factored planning methods aim to exploit locality to efficiently solve large but " loosely coupled " planning problems by computing solutions locally and propagating limited information between components. However, all factored planning methods presented so far work with representations that require certain parameters to be bounded (e.g. number of(More)