In this paper we present a modification of the strongly diluted Hopfield model in which the dilution scheme, instead of being random, is inspired on biological grounds. We also show that the resulting sparsely connected neural network shows striking features in terms of performance as an...

This work proposes to obtain novel fractal descriptors from gray-level texture images by combining information from interior and boundary measures of the Minkowski dilation applied to the texture surface. At first, the image is converted into a surface where the height of each point is the gray...

This work presents a methodology to the morphology analysis and characterization of nanostructured material images acquired from FEG-SEM (Field Emission Gun-Scanning Electron Microscopy) technique. The metrics were extracted from the image texture (mathematical surface) by the volumetric fractal...

We developed two new tools to deal with synthetic aperture radar (SAR) images, based on two different and powerful mathematical formalism: wavelets (WT) and Karhunen Loève transforms (KLT). WT are used to remove the undesirable speckle, which comes from the specific measurement process of SAR...

We study the predictability of extreme events in records with linear and nonlinear long-range memory in the presence of additive white noise using two different approaches: (i) the precursory pattern recognition technique (PRT) that exploits solely the information about short-term precursors,...

This work proposes a method for data clustering based on complex networks theory. A data set is represented as a network by considering different metrics to establish the connection between each pair of objects. The clusters are obtained by taking into account five community detection...

An entropy-based image segmentation approach is introduced and applied to color images obtained from Google Earth. Segmentation refers to the process of partitioning a digital image in order to locate different objects and regions of interest. The application to satellite images paves the way to...

This research extends a method previously applied to music and philosophy (Vilson Vieira et al., 2012), representing the evolution of art as a time-series where relations like dialectics are measured quantitatively. For that, a corpus of paintings of 12 well-known artists from baroque and...

We have studied the effect of various kinds of damaging that may occur in a neural network whose synaptic bonds have been trained (before damaging) so as to preserve a definite number of patterns. We have used the Hopfield model of the neural network, and applied the Hebbian rule of training...

We introduce the concept of “negative bubbles” as the mirror (but not necessarily exactly symmetric) image of standard financial bubbles, in which positive feedback mechanisms may lead to transient accelerating price falls. To model these negative bubbles, we adapt the...