An approach to implement data fusion techniques in wireless sensor networks using genetic machine learning algorithms

Wireless Sensor Networks (WSNs) can be used to monitor hazardous and inaccessible areas. In these situations, the power supply (e.g. battery) of each node cannot be easily replaced. One solution to deal with the limited capacity of current power supplies is to deploy a large number of sensor nodes, since the lifetime and dependability of the network will increase through cooperation among nodes. Applications on WSN may also have other concerns, such as meeting temporal deadlines on message transmissions and maximizing the quality of information. Data fusion is a well-known technique that can be useful for the enhancement of data quality and for the maximization of WSN lifetime. In this paper, we propose an approach that allows the implementation of parallel data fusion techniques in IEEE 802.15.4 networks. One of the main advantages of the proposed approach is that it enables a trade-off between different user-defined metrics through the use of a genetic machine learning algorithm. Simulations and field experiments performed in different communication scenarios highlight significant improvements when compared with, for instance, the Gur Game approach or the implementation of conventional periodic communication techniques over IEEE 802.15.4 networks. © 2013 Elsevier B.V. All rights reserved.

Applying machine learning based on multiscale classifiers to detect remote phenology patterns in Cerrado savanna trees

Plant phenology is one of the most reliable indicators of species responses to global climate change, motivating the development of new technologies for phenological monitoring. Digital cameras or near remote systems have been efficiently applied as multi-channel imaging sensors, where leaf color information is extracted from the RGB (Red, Green, and Blue) color channels, and the changes in green levels are used to infer leafing patterns of plant species. In this scenario, texture information is a great ally for image analysis that has been little used in phenology studies. We monitored leaf-changing patterns of Cerrado savanna vegetation by taking daily digital images. We extract RGB channels from the digital images and correlate them with phenological changes. Additionally, we benefit from the inclusion of textural metrics for quantifying spatial heterogeneity. Our first goals are: (1) to test if color change information is able to characterize the phenological pattern of a group of species; (2) to test if the temporal variation in image texture is useful to distinguish plant species; and (3) to test if individuals from the same species may be automatically identified using digital images. In this paper, we present a machine learning approach based on multiscale classifiers to detect phenological patterns in the digital images. Our results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; (2) different plant species present a different behavior with respect to the color change information; and (3) texture variation along temporal images is promising information for capturing phenological patterns. Based on those results, we suggest that individuals from the same species and functional group might be identified using digital images, and introduce a new tool to help phenology experts in the identification of new individuals from the same species in the image and their location on the ground. © 2013 Elsevier B.V. All rights reserved.

Incorporación de la dimensión ambiental en la planificación del desarrollo de la cuenca del río Guayas y la península de Santa Elena (Ecuador)

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Algunas características de la urbanización en Río de Janeiro = Some characteristics of urbanization in the city of Rio de Janeiro

Los documentos del Seminario fueron publicados por UNESCO en 1961 con el título: La urbanización en América Latina/Urbanization in Latin America

¿Qué hacer después de Río?: lo que no se hizo antes de Estocolmo

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La municipalidad como agente de desarrollo económico: la experiencia del Estado de Río de Janeiro

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Proyección de la población por sexo y grupos de edades a nivel municipal de la Provincia de Pinar del Río, período 1981-1985; trabajo de investigación

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Proyecto de recuperación urbana de la ribera norte del río Bio Bio, Concepción: hacia la integración de las variables de población

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Informe de la Reunión de Expertos designados por los gobiernos de América Latina y el Caribe preparatoria de Río+20: economía verde en el contexto del desarrollo sostenible y de la erradicación de la pobreza y marco institucional para desarrollo sostenible

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Ideas generales que pueden ser de utilidad para los gobiernos de América Latina y el Caribe en la formulación de una posición regional para Río +20: nota de la secretaría

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