No abstract

Artículo de publicación ISIOptical flow approaches calculate vector fields which determine the apparent velocities of objects in timevarying image sequences. They have been analyzed extensively in computer science using both natural and synthetic video sequences. In life sciences, there is an increasing need to extract kinetic information from temporal image sequences which reveals the interplay between form and function of microscopic biological structures. In this work, we test different variational optical flow techniques to quantify the displacements of biological objects in 2D fluorescent image sequences. The accuracy of the vector fields is tested for defined displacements of fluorescent point sources in synthetic image series which mimic protein traffic in neuronal dendrites, and for GABABR1 receptor subunits in dendrites of hippocampal neurons. Our results reveal that optical flow fields predict the movement of fluorescent point sources within an error of 3% for a maximum displacement of 160 nm. Displacement of agglomerated GABABR1 receptor subunits can be predicted correctly for maximum displacements of 640 nm. Based on these results, we introduce a criteria to derive the optimum parameter combinations for the calculation of the optical flow fields in experimental images. From these results, temporal sampling frequencies for image acquisition can be derived to guarantee correct motion estimation for biological objects.J. Delpiano and J. Jara are funded by a PhD scholarship from CONICYT (Chile). O. A. Ramírez is funded by FONDECYT (3110157). Research in SCIAN-Lab (S. Härtel) is funded by FONDECYT (1090246) and FONDEF (D07I1019). SCIAN-Lab is member of the German-Chilean Center of Excellence Initiative for Medical Informatics (DAAD), BNI (ICM P09-015-F), and the Advanced Imaging & Bioinformatics Initiative AI·BI (http://www.aibi.cl)

The application of artificial intelligence-based techniques has covered a wide range of applications related to electric power systems (EPS). Particularly, a metaheuristic technique known as Particle Swarm Optimization (PSO) has been chosen for the tuning of parameters for Power System Stabilizers (PSS) with success for relatively small systems. This article proposes a tuning methodology for PSSs based on the use of PSO that works for systems with ten or even more machines. Our new methodology was implemented using the source language of the commercial simulation software DigSilent PowerFactory. Therefore, it can be translated into current practice directly. Our methodology was applied to different test systems showing the effectiveness and potential of the proposed technique.