Análise da paisagem para o planejamento e a gestão da conservação da Mata Atlântica

Landscape is dynamic, having complex nature, with tangible and intangible dimensions, presenting a continuous evolution process. The aim of this research were based on the identification and classification of landscapes in units and subunits, from the ownership by individuals; the development of a methodology to assist in the planning and management, conciliating conservation of natural areas with anthropic activities; and, from the information gathered, evaluate the different social groups aiming to design a landscape from the sustainable development perspective; thus better understanding both cultural and forest fragmentation processes, in the city of Ouro Preto, Minas Gerais. The research analyzed the current landscape and its historical evolution, distinguishing between material and immaterial dimensions. Information was raised from field trips, soil types, relief, slope, drainage, conservation units, administrative zoning, urban areas, natural resources, economy, tax raising, transport and building infrastructure, satellite images, types of management applied to the preservation or conservation of forests and fields, and semi-structured interviews with the various actors that modify and transform the territory, thus making a balance between the built landscape and the demands of the society and ecosystems. Results were composed by a map of land use in 2011; a map of landscape units and their subunits, with their appropriate definitions; a map with five levels of activities intensity, with their respective descriptions; and raising barriers to improving the welfare of the actors and the integrity of ecosystems. The number of generated ecosystem services are difficult to measure, but its benefits are useful for everyone. The physical changes are a reflection of the economy, which caused environmental impacts, mainly related to mining activities, tourism, agriculture and conservation of natural areas, all requiring ideally a shared management. In this sense, the landscape needs a management to create sustainable alternatives to anthropic activities. The dynamics of the landscape has been shaped by a slow evolution, set by mining activities due to the high financial revenues, there were areas of revegetation after clearcuts in the past, and now tourism lacks structure. The city has great potential for development projects with payments for environmental services, however, gaps for shared management exists.

SIRCo: uma arquitetura para Sensoriamento Inteligente de Rádios Cognitivos

The increased demand for using the Industrial, Scientific and Medical (ISM) unlicensed frequency spectrum has caused interference problems and lack of resource availability for wireless networks. Cognitive radio (CR) have emerged as an alternative to reduce interference and intelligently use the spectrum. Several protocols were proposed aiming to mitigate these problems, but most have not been implemented in real devices. This work presents an architecture for Intelligent Sensing for Cognitive Radios (ISCRa), and a spectrum decision model (SDM) based on Artificial Neural Networks (ANN), which uses as input a database with local spectrum behavior and a database with primary users information. For comparison, a spectrum decision model based on AHP, which employs advanced techniques in its spectrum decision method was implemented. Another spectrum decision model that considers only a physical parameter for channel classification was also implemented. Spectrum decision models evaluated, as well as ISCRa's architecture were developed in GNU-Radio framework and implemented on real nodes. Evaluation of SDMs considered metrics of: delivery rate, latency (Round Trip Time - RTT) and handoff. Experiments on real nodes showed that ISCRa architecture with ANN based SDM increased packet delivery rate and presented fewer frequency variation (handoff) while maintaining latency. Considering higher bandwidth as application's Quality of Service requirement, ANN-SDM obtained the best results when compared to other SDM for cognitive radio networks (CRN).