By F. Nielson, P. Cousot, M. Dam, P. Degano (auth.), Mads Dam (eds.)

This e-book originates from the fifth LOMAPS Workshop on research and Verification of Multiple-Agent Languages, held in Stockholm, Sweden, in June 1996. LOMAPS is an ESPRIT venture dedicated to software research and verification recommendations acceptable to rising multi-paradigm programming languages.
The quantity offers 14 revised complete papers chosen from the workshop submissions including four invited contributions; additionally incorporated is an introductory evaluate surveying the cutting-edge within the sector and placing the contributions into this context.

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Extra resources for Analysis and Verification of Multiple-Agent Languages: 5th LOMAPS Workshop Stockholm, Sweden, June 24–26, 1996 Selected Papers

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2 The λ-MST Tangent Estimator The λ-MST tangent estimator at one point is designed to take into account the various orientations of the MS in the pencil weighted by a functional of their respective eccentricity with respect to the point of interest: Definition 3. The λ-maximal segment tangent direction at point Ck (λ-MST) λ(ei (k))θi ˆ is defined as θ(k) = i∈P(k) λ(ei (k)) , where θi is the angle of the slope of the i∈P(k) i-th MS with the x-abscissa. Considering the properties of the eccentricity and the non-emptyness of pencils, this value is always defined and may be computed locally.

It is well-known that the resulting skeletons obtained from sequential and from parallel implementations of the same thinning algorithm can be quite distinct, and, that it is far from trivial whether a parallel implementation of a thinning algorithm, preserves topology. We will show in the next section that the parallel implementation of Kovalevsky’s algorithm preserves topology. 6 Parallel Thinning Due to Kovalevsky’s Algorithm Recall from definition 9 that each iteration of Kovalevsky’s algorithm consists in two steps.

This is precisely the goal of this paper which is achieved by experimental comparison between the λ-MST estimator and two representative classes of continuous estimators. We naturally examine classical criteria like the average absolute error. Furthermore, we propose to use the product precision by computational cost to compare them as objectively as possible. Besides, our aim is not only to compare these estimators but to see if they can benefit from one another. This is the case here where we show how an optimal computation window (problem (2)) can be chosen for the Gaussian derivative technique.

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