Analysis of Connectome Graphs Based on Boundary Scale.

The purpose of this work is to advance in the computational study of connectome graphs from a topological point of view. Specifically, starting from a sequence of hypergraphs associated to a brain graph (obtained using the Boundary Scale model, BS2), we analyze the resulting scale-space representation using classical topological features, such as Betti numbers and average node and edge degrees. In this way, the topological information that can be extracted from the original graph is substantially enriched, thus providing an insightful description of the graph from a clinical perspective.

Autonomous Underwater Vehicles: Identifying Critical Issues and Future Perspectives in Image Acquisition.

Underwater imaging has been present for many decades due to its relevance in vision and navigation systems. In recent years, advances in robotics have led to the availability of autonomous or unmanned underwater vehicles (AUVs, UUVs). Despite the rapid development of new studies and promising algorithms in this field, there is currently a lack of research toward standardized, general-approach proposals.

Graphics Processing Unit-Enhanced Genetic Algorithms for Solving the Temporal Dynamics of Gene Regulatory Networks.

Understanding the regulation of gene expression is one of the key problems in current biology. A promising method for that purpose is the determination of the temporal dynamics between known initial and ending network states, by using simple acting rules. The huge amount of rule combinations and the nonlinear inherent nature of the problem make genetic algorithms an excellent candidate for finding optimal solutions.

A parallel Homological Spanning Forest framework for 2D topological image analysis.

In [14], a topologically consistent framework to support parallel topological analysis and recognition for 2 digital objects was introduced. Based on this theoretical work, we focus on the problem of finding efficient algorithmic solutions for topological interrogation of a 2 digital object of interest of a presegmented digital image , using 4-adjacency between pixels of . In order to maximize the degree of parallelization of the topological processes, we use as many elementary unit processing as pixels the image has.

A Parallel Implementation for Computing the Region-Adjacency-Tree of a Segmentation of a 2D Digital Image.

A design and implementation of a parallel algorithm for computing the Region-Adjacency Tree of a given segmentation of a 2D digital image is given. The technique is based on a suitable distributed use of the algorithm for computing a Homological Spanning Forest (HSF) structure for each connected region of the segmentation and a classical geometric algorithm for determining inclusion between regions. The results show that this technique scales very well when executed in a multicore processor.