By P. Ciarlini, E Filipe, A B Forbes, F Pavese, C Perruchet, B R L Siebert

This quantity collects the refereed contributions in accordance with the displays made on the 7th Workshop on complex Mathematical and Computational instruments in Metrology, a discussion board for metrologists, mathematicians and software program engineers that would inspire a better synthesis of abilities, services and assets. the quantity includes articles via global well known metrologists and mathematicians considering dimension technological know-how and, including the six past volumes during this sequence, constitutes an authoritative resource of the mathematical, statistical and software program instruments precious in glossy metrology.

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B 26, 211 (1964). 4. L. K. Chan, K. P. Hapuarachchi and B. D. Macpherson, IEEE Transactions on Reliability 37 1, 117 (1988). 5. L. K. Chan and J. K. Heng, Naval Research Logistics50, 555 (2003). 6. Y. S. Chang and D. S. Bai, Qual. Reliab. Engng. Int. 17, 397 (2001). 7. M. G. Cox and E. P. Iguzquiza, Advanced Mathematical and Computational Tools in Metrology, 5, 106 (2001). 8. O. Figueiredo, Proc. 13th European Young Statisticians Meeting, 53 (2003b). 9. O. I. Gomes, Applied Stochastic Models in Business and Industry 20, 339 (2004).

Practically, the concepts associated to each ontology are tuples of common entities. ,n(r))> where r e / ? • Concepts in the functional ontology are goals defined such as the pair (A,Q, where A is an action symbol and C is a non-empty set of physical contexts defined as above. , functions) guarded by input conditions specifying the way of achievement of a given goal. In [9], Richard Dapoigny shows how the physical and the functional ontologies give enough constraints to automate the design of the behavioral causal graph.

Pp. 47-59) MULTISENSOR DATA FUSION AND ITS APPLICATION TO DECISION MAKING PEDRO SILVA GIRAO, JOSE DIAS PEREIRA, OCTAVIAN POSTOLACHE Telecommunications Institute, DEEC, 1ST Avenida Rovisco Pais, 1, 1049-001, Lisboa, Portugal In this paper the authors present their experience on the use of data fusion in multisensor systems signal processing, reviewing the different levels of data fusion, some of the techniques mostly used and domains of application, with emphasis on decision making. Examples taken from the authors past work are presented and discussed.

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