FROM A MODEL OF CONCURRENCY TO A TEST MODEL: A GRAPH TRANSFORMATION BASED APPROACH

Maximality-based Labeled Transition Systems (MLTS) is semantic model for true concurrency. In other hand Mixed Refusal Graphs  (MRG)  are models  for  formal  testing.  In  this  paper, we  propose  an  approach  to  transform  an MLTS model  to  an equivalent  MRG  model.  Since  the  input  and  output  models  are  graphs,  we  use  graph  transformation  to  perform  this transformation automatically. So, we propose  two meta-models; one for the input model and the other for the output model. Then,  based  on  these meta-models we  propose  a  graph  grammar  that  deals with  the  transformation  process.    The meta-modeling tool ATOM3 is used. Our approach is illustrated through an example

Télémétrie laser aéroportée et transformée en ondelettes pour la détection des réseaux hydrographiques artificiels des paysages ruraux méditerranéens

Artificial drainage networks such as ditches networks are landscape elements that control many hydrological transfers. To introduce these elements in hydrological models, we need to develop methods that allow their representation in space. We aimed here to assess the utilities of LiDAR data in the detecting and characterizing of ditches in Mediterranean cultivated rural landscapes. To perform the detection, we combined jointly laser-scanning and parcel boundaries data and developed a methodology based on : 1- estimation from last pulse laser points of terrain altimetric profiles that are perpendicular to parcel boundaries; 2- 1D wavelet transforms of the profiles ; 3- a supervised classification of profiles with CART on the wavelet coefficients that are relevant for the size of the shapes we want to detect. The methodology we developed was applied in France on a typical basin of the Mediterranean vineyard landscapes. Compared to visual interpretation of the profiles, we obtained satisfactory detection rate. According to these results, high resolution laser-scanning data appears reliable for ditches detection but, due to vegetation and sampling resolution, misclassification on ditches typology is still important