Moisés Espínola

A new virtual task to evaluate human place learning

This study assesses the effectiveness of a new virtual task to evaluate human place learning. This test was based on the hole-board maze, developed for rodent research. Its design provides an easy set of levels of difficulty. Sixty-three undergraduate students (30 men and 33 women) were randomly distributed into three testing conditions; they had to find 3, 5 and 7 rewards, respectively, in a virtual room with 16 possible rewarded positions. Subjects were asked to use the minimum amount of attempts to discover all the rewards in 10 trials. In the initial trial subjects needed to visit almost all the positions to discover the rewards. However, in the last trial an important percentage of subjects did not err. Results showed that all subjects acquired the task but with different amounts of mistakes directly related to the level of difficulty of the condition. In addition, women were slower and less accurate than men. These results agree with previous results in other virtual tasks, and support the spatial component of this test.

Contextual and Hierarchical Classification of Satellite Images Based on Cellular Automata

Satellite image classification is an important technique used in remote sensing for the computerized analysis and pattern recognition of satellite data, which facilitates the automated interpretation of a large amount of information. Today, there exist many types of classification algorithms, such as parallelepiped and minimum distance classifiers, but it is still necessary to improve their performance in terms of accuracy rate. On the other hand, over the last few decades, cellular automata have been used in remote sensing to implement processes related to simulations. Although there is little previous research of cellular automata related to satellite image classification, they offer many advantages that can improve the results of classical classification algorithms. This paper discusses the development of a new classification algorithm based on cellular automata which not only improves the classification accuracy rate in satellite images by using contextual techniques but also offers a hierarchical classification of pixels divided into levels of membership degree to each class and includes a spatial edge detection method of classes in the satellite image.

Characterization of Texture in Images by Using a Cellular Automata Approach

Spatial patterns in an image that shows a visual perception of roughness or softness of the surface is known as the texture of the image. Most of the analysis and description of texture found in the literature is based on statistical or structural properties of this attribute [2]. The field of cellular automata (CA), which has been developed mainly to model the dynamical behavior of systems, is based on the behavior or arrangements of pixel values and their neighborhood which, according to some rules behaves in different manners [2, 8]. In this paper, within the frame of structural approach, a novel method based on the properties of linear cellular automata is proposed to characterize different sort of textures. To this purpose, it is assumed that a binary version of the image under study was generated by a cellular automata technique. By using this model a number of textural primitives are found which allows the production of a characterizing image. In order to verify the feasibility of the proposed method, texture images generated by CA techniques as well as natural images has been used.

Mii School: New 3D Technologies Applied in Education to Detect Drug Abuses and Bullying in Adolescents

Mii School is a 3D school simulator developed with Blender and used by psychology researchers for the detection of drugs abuses, bullying and mental disorders in adolescents. The school simulator created is an interactive video game where the players, in this case the students, have to choose, along 17 scenes simulated, the options that better define their personalities. In this paper we present a technical characteristics description and the first results obtained in a real school.

Classification of Satellite Images Using the Cellular Automata Approach

Nowadays, remote sensing allows us the acquisition of information using techniques that do not require be in contact with the object or area being observed. This science can be used in many environmental applications, helping to solve and improve the social problems derived from them. Examples of re- motely sensed applications are in soil quality, water resources, environmental management and protection or meteorology, among others. The classification algorithms are one of the most important techniques used in remote sensing that help developers to interpret the information contained in the satellite images. At present, there are several classification processes, i.e., maximum likelihood, paralelepiped or minimum distance classifier, among others. In this paper, we investigate a new Classification Algorithm based on Cellular Automata (ACA): a technique usually used by researchers on Complex Systems. This kind of clas- sifier will be validated and experimented in the SOLERES framework.

Cellular automata applied in remote sensing to implement contextual Pseudo-fuzzy classification

Nowadays, remote sensing is used in many environmental applications, helping to solve and improve the social problems derived from them. Examples of remotely sensed applications include soil quality studies, water resources searching, environmental protection or meteorology simulations. The classification algorithms are one of the most important techniques used in remote sensing that help developers to interpret the information contained in the satellite images. At present, there are several classification processes, i.e., maximum likelihood, paralelepiped or minimum distance classifier. In this paper we investigate a new satellite image classification Algorithm based on Cellular Automata (ACA), a technique usually used by researchers on complex systems. There are not previous works related to satellite image classification with cellular automata. This new kind of satellite image classifier, that improves the results obtained by classical algorithms in several aspects, has been validated and experimented in the SOLERES framework.

Modeling Rainfall Features Dynamics in a DEM Satellite Image with Cellular Automata

Cellular automata have been widely used in the field of remote sensing for simulating natural phenomena over two-dimensional satellite images. Simulations on DEM (Digital Elevation Model), three-dimensional satellite images, are very rare. This paper presents a study of modeling and simulation of the weather phenomenon of precipitation over DEM satellite images through a new algorithm, RACA (RAinfall with Cellular Automata). The aim of RACA is to obtain, from the simulation, numerical and 3D results related to the water level that allow us to make decisions on important issues such as avoiding the destruction of human life and property from future natural disasters, establishing future urbanized areas away from locations with high probability of flooding or estimating the future water supply for arid regions.

ACA Multiagent System for Satellite Image Classification

In this paper, we present a multiagent system for satellite image classification. With this aim we will describe a new classification algorithm based on cellular automata called ACA (Algorithm based on Cellular Automata). This algorithm can be modeled by agents. Actually, there are different classification algorithms, such as minimum distance and parallelepiped classifiers, but none is fullreliable in terms of quality. One of the main advantages of ACA is to provide a mechanism which offers a hierarchical classification divided into levels of reliability with a final quality optimized through contextual techniques. Finally, we have developed a multiagent system which allows to classify satellite images in the SOLERES framework.

Ecological Sectorization Process Improvement through Neural Networks: Synthesis of Vegetation Data from Satellite Images Using RBFs

This paper presents an application of neural networks that uses radial basis function net architecture as a tool for simplifying and reducing the cost of ecological mapping. The process speeds up and replaces the classic means of obtaining ecological variables through field studies. The radial basis function networks were applied to estimate field data remotely, using data captured by the Landsat satellite and correlating it with ecological variables in order to substitute for them in the mapping process. The trial was undertaken for an area in south-eastern Spain, whereby, in 43 out of the 45 cases, the ecological variables could be obtained using satellite data. This approach substantially reduces the time and cost of ecological mapping, limiting field studies and automating the generation of the ecological variables.