6 resultados para Image-to-Image Variation

em SAPIENTIA - Universidade do Algarve - Portugal


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Tese de doutoramento, Ciências do Mar, da Terra e do Ambiente, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2015

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The EU-funded project UAN - Underwater Acoustic Network aims at conceiving, developing and testing at sea an innovative and operational concept for integrating in a unique communication system submerged, surface and aerial sensors with the objective of protecting off-shore and coastline critical infrastructures. A crucial aspect of the project consisted in the use of autonomous underwater vehicles (AUVs) as mobile nodes in the underwater acoustic communication network. In particular, AUVs have the role of adapting the network geometry to the variation of the acoustic channel. This paper reports on the project concept and vision as well as on the progress of its various development phases. The recent at-sea successes that have been demonstrated within the UAN framework are detailed and results of the final UAN project demonstration, UAN11, held in the May of 2011, are reported. The UAN network was in operation for five continuous days with up to five nodes, of which three of them were mobile nodes. © IFAC.

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Tese de Doutoramento, Gestão da Inovação e do Território, Faculdade de Economia, Universidade do Algarve, 2016

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Understanding the factors that affect seagrass meadows encompassing their entire range of distribution is challenging yet important for their conservation. We model the environmental niche of Cymodocea nodosa using a combination of environmental variables and landscape metrics to examine factors defining its distribution and find suitable habitats for the species. The most relevant environmental variables defining the distribution of C. nodosa were sea surface temperature (SST) and salinity. We found suitable habitats at SST from 5.8 ºC to 26.4 ºC and salinity ranging from 17.5 to 39.3. Optimal values of mean winter wave height ranged between 1.2 m and 1.5 m, while waves higher than 2.5 m seemed to limit the presence of the species. The influence of nutrients and pH, despite having weight on the models, was not so clear in terms of ranges that confine the distribution of the species. Landscape metrics able to capture variation in the coastline enhanced significantly the accuracy of the models, despite the limitations caused by the scale of the study. By contrasting predictive approaches, we defined the variables affecting the distributional areas that seem unsuitable for C. nodosa as well as those suitable habitats not occupied by the species. These findings are encouraging for its use in future studies on climate-related marine range shifts and meadow restoration projects of these fragile ecosystems.

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Dependence of some species on landscape structure has been proved in numerous studies. So far, however, little progress has been made in the integration of landscape metrics in the prediction of species associated with coastal features. Specific landscape metrics were tested as predictors of coastal shape using three coastal features of the Iberian Peninsula (beaches, capes and gulfs) at different scales. We used the landscape metrics in combination with environmental variables to model the niche and find suitable habitats for a seagrass species (Cymodocea nodosa) throughout its entire range of distribution. Landscape metrics able to capture variation in the coastline enhanced significantly the accuracy of the models, despite the limitations caused by the scale of the study. We provided the first global model of the factors that can be shaping the environmental niche and distribution of C. nodosa throughout its range. Sea surface temperature and salinity were the most relevant variables. We identified areas that seem unsuitable for C. nodosa as well as those suitable habitats not occupied by the species. We also present some preliminary results of testing historical biogeographical hypotheses derived from distribution predictions under Last Glacial Maximum conditions and genetic diversity data.

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Complete image ontology can be obtained by formalising a top-down meta-language wich must address all possibilities, from global message and composition to objects and local surface properties.