No abstract

No abstract

The computation of geodesic distances is an important research topic in Geometry Processing and 3D Shape Analysis as it is a basic component of many methods used in these areas. In this work, we present a minimalistic parallel algorithm based on front propagation to compute approximate geodesic distances on meshes. Our method is practical and simple to implement, and does not require any heavy pre-processing. The convergence of our algorithm depends on the number of discrete level sets around the source points from which distance information propagates. To appropriately implement our method on GPUs taking into account memory coalescence problems, we take advantage of a graph representation based on a breadth-first search traversal that works harmoniously with our parallel front propagation approach. We report experiments that show how our method scales with the size of the problem. We compare the mean error and processing time obtained by our method with such measures computed using other methods. Our method produces results in competitive times with almost the same accuracy, especially for large meshes. We also demonstrate its use for solving two classical geometry processing problems: the regular sampling problem and the Voronoi tessellation on meshes.

Fuzzy Cognitive Maps (FCM) is a paradigm used to represent knowledge in a simple and concise way, expressing the grade of relation that exists between concepts and causal relationships. Due to its flexibility, FCM has been successfully applied in numerous applications in diverse research fields, such as, robotics, medical diagnosis, decision problems in information technology, games, and so forth. However, one critical drawback is the determination of the weights in the representation graph, which is generally done by an expert. The present paper proposes a semi-automated method for calibrating the weights in a solution for the problem of dynamic game difficulty balancing (DGB) using Evolutionary Fuzzy Cognitive Maps (E-FCM). The proposed algorithm adjusts the weights in real time, ensuring an equilibrium between the values generated according to the expert’s contribution (based on a static analysis) and the changes produced in the values of the concepts by the calibration process during the simulation (a dynamic analysis).