Decentralized multi-tasks distribution in heterogeneous robot teams by means of ant colony optimization and learning automata

This paper focuses on the general problem of coordinating multiple robots. More specifically, it addresses the self-election of heterogeneous specialized tasks by autonomous robots. In this paper we focus on a specifically distributed or decentralized approach as we are particularly interested on decentralized solution where the robots themselves autonomously and in an individual manner, are responsible of selecting a particular task so that all the existing tasks are optimally distributed and executed. In this regard, we have established an experimental scenario to solve the corresponding multi-tasks distribution problem and we propose a solution using two different approaches by applying Ant Colony Optimization-based deterministic algorithms as well as Learning Automata-based probabilistic algorithms. We have evaluated the robustness of the algorithm, perturbing the number of pending loads to simulate the robot’s error in estimating the real number of pending tasks and also the dynamic generation of loads through time. The paper ends with a critical discussion of experimental results.

Fusion of probabilistic knowledge-based classification rules and learning automata for automatic recognition of digital images

In this paper, the fusion of probabilistic knowledge-based classification rules and learning automata theory is proposed and as a result we present a set of probabilistic classification rules with self-learning capability. The probabilities of the classification rules change dynamically guided by a supervised reinforcement process aimed at obtaining an optimum classification accuracy. This novel classifier is applied to the automatic recognition of digital images corresponding to visual landmarks for the autonomous navigation of an unmanned aerial vehicle (UAV) developed by the authors. The classification accuracy of the proposed classifier and its comparison with well-established pattern recognition methods is finally reported.

Enseñanza (y aprendizaje) virtual de Geología por parte del alumno=Virtual education (and learning) of Geology by students

Una de las maneras más efectivas para asentar conocimientos se produce cuando, además de realizar un aprendizaje práctico, se intentan transmitir a otra persona. De hecho, los alumnos muchas veces prestan más atención a sus compañeros que al profesor. En la E.T.S.I. Minas de Madrid se ha llevado a cabo un programa de innovación educativa en asignaturas relacionadas con la Geología mediante nuevas tecnologías para mejorar el aprendizaje basado en el trabajo práctico personal del alumno, con la realización de vídeos en el medio físico (campo) en los que explican los aspectos geológicos visibles a diferentes escalas. Estos vídeos se han subido a las plataformas “moodle”, “facebook” y canal “youtube” donde compañeros, alumnos de otras Universidades y personas interesadas pueden consultarlos. De esta manera se pretende que, además de adquirir conocimientos geológicos, los alumnos adquieren el hábito de expresarse en público con un lenguaje técnico. Los alumnos manifestaron su satisfacción por esta actividad, aunque idea del rodaje de vídeos no resultó inicialmente muy popular. Se ha observado una mejora en las calificaciones, así como un incremento de la motivación. De hecho, los estudiantes manifestaron haber adquirido, además de los conceptos geológicos, seguridad a la hora de expresarse en público. Palabras clave: innovación educativa, nuevas tecnologías (TIC), Geología Abstract- Knowledge is gained by practice, but one of the most effective ways is when one tries to transmit it to others. Likewise, students pay more attention to their classmates than to teachers. In the Geological Engineering Department of the Madrid School of Mines, we have run an educational innovation program in courses related to Geology using new technologies (ITC) in order to increase the acquisition of geological knowledge. This program is designed mainly on the basis of individual and group work with video recordings in the field in which students explain geological concepts at various scales. These videos have been uploaded to the “Moodle”, “Facebook” and “YouTube” channel of the Madrid School of Mines, where other students from the same university or elsewhere can view them. Students acquire geological knowledge and the ability to address the general public using technical language. The realization of these videos has been warmly welcomed by students. Notably, they show increased motivation, accompanied by an improvement in grades, although at the beginning this program was not very popular because of student insecurity. Students have expressed that they learnt geological concepts but also gained confidence in public speaking using technical language

ASYTRAIN: a new methodology for teaching and learning antennas

This paper presents ASYTRAIN, a new tool to teach and learn antennas, based on the use of a modular building kit and a low cost portable antenna measurement system that lets the students design and build different types of antennas and observe their characteristics while learning the insights of the subjects. This tool has a methodology guide for try-and-test project development and, makes the students be active antenna engineers instead of passive learners. This experimental learning method arises their motivation during the antenna courses.

PUE attack detection in CWSN using collaboration and learning behavior

Cognitive Wireless Sensor Network (CWSN) is a new paradigm which integrates cognitive features in traditional Wireless Sensor Networks (WSNs) to mitigate important problems such as spectrum occupancy. Security in Cognitive Wireless Sensor Networks is an important problem because these kinds of networks manage critical applications and data. Moreover, the specific constraints of WSN make the problem even more critical. However, effective solutions have not been implemented yet. Among the specific attacks derived from new cognitive features, the one most studied is the Primary User Emulation (PUE) attack. This paper discusses a new approach, based on anomaly behavior detection and collaboration, to detect the PUE attack in CWSN scenarios. A nonparametric CUSUM algorithm, suitable for low resource networks like CWSN, has been used in this work. The algorithm has been tested using a cognitive simulator that brings important results in this area. For example, the result shows that the number of collaborative nodes is the most important parameter in order to improve the PUE attack detection rates. If the 20% of the nodes collaborates, the PUE detection reaches the 98% with less than 1% of false positives.

iCarer Project: Intelligent Care Guidance and Learning Services Platform for Informal Carers of the Elderly

The increasing ageing population is demanding new care approaches to maintain the quality of life of elderly people. Informal carers are becoming crucial agents in the care and support of elderly people, which can lead to those carers suffering from additional stress due to competing priorities with employment or due to lack of knowledge about elderly people?s care needs. Thus, support and stress relief in carers should be a key issue in the home-care process of these older adults. Considering this context, this work presents the iCarer project aimed at developing a personalized and adaptive platform to offer informal carers support by means of monitoring their activities of daily care and psychological state, as well as providing an orientation to help them improve the care provided. Additionally, iCarer will provide e-Learning services and an informal carers learning network. As a result, carers will be able to expand their knowledge, supported by the experience provided by expert counsellors and fellow carers. Additionally, the coordination between formal and informal carers will be improved, offering the informal carers flexibility to organize and combine their assistance and social activities.