The aim of this work is to build a computational model able to automatically identify, after training, dynamic speckle pattern regions with similar properties. The process is carried out using a set of descriptors applied to the intensity variations with time in every pixel of a speckle image sequence. An image obtained by projecting a self-organized map is converted into regions of similar activity that can be easily distinguished. We propose a general procedure that could be applied to numerous situations. As examples we show different situations: (a) an activity test in a simplified situation; (b) a non-biological example and (c) biological active specimens. The results obtained are encouraging; they significantly improve upon those obtained using a single descriptor and will eventually permit automatic quantitative assessment.

Electricity distribution companies constantly require improvements in service, and an appropriate reduction in costs of the system. Finding the optimal configuration of the distribution network reduces power losses, which impacts directly on costs, also leading to significant energy savings. Over recent decades, the problem of network reconfiguration for loss minimization and for other purposes has been widely studied. The algorithms improve the previously obtained minimum power loss and also reduce computational times. This paper proposes the study and application of an algorithm based on Ant Colony Optimization, method covered in the paradigm of Swarm Intelligence, sub-discipline of Computational Intelligence. We show an easy network codification and we performed a detailed study of the ranges of values for the configuration parameters of the algorithm. We achieved the optimal result for testing networks and we found appropriate configurations for more complex networks, taking low computational times.

We propose the use of a learning procedure to identify regions of similar dynamics in speckle image sequences that includes more than one descriptor. This procedure is based on the application of a naïve Bayes statistical classifier comprising the use of several descriptors. The class frontiers can be depicted so that the proportion of identified regions may be measured. To demonstrate the results, assembly of an RGB image, where each plane (R, G, and B) is associated with a particular region (class), was labeled according to its biospeckle dynamics. A high brightness in one color means a high probability of the pixel belonging to the corresponding class, and vice versa.