Analysis and visualization of seismic data using mutual information

Seismic data is difficult to analyze and classical mathematical tools reveal strong limitations in exposing hidden relationships between earthquakes. In this paper, we study earthquake phenomena in the perspective of complex systems. Global seismic data, covering the period from 1962 up to 2011 is analyzed. The events, characterized by their magnitude, geographic location and time of occurrence, are divided into groups, either according to the Flinn-Engdahl (F-E) seismic regions of Earth or using a rectangular grid based in latitude and longitude coordinates. Two methods of analysis are considered and compared in this study. In a first method, the distributions of magnitudes are approximated by Gutenberg-Richter (G-R) distributions and the parameters used to reveal the relationships among regions. In the second method, the mutual information is calculated and adopted as a measure of similarity between regions. In both cases, using clustering analysis, visualization maps are generated, providing an intuitive and useful representation of the complex relationships that are present among seismic data. Such relationships might not be perceived on classical geographic maps. Therefore, the generated charts are a valid alternative to other visualization tools, for understanding the global behavior of earthquakes.

RSS and LEA adaptation for indoor location using fingerprinting

Fingerprinting is an indoor location technique, based on wireless networks, where data stored during the offline phase is compared with data collected by the mobile device during the online phase. In most of the real-life scenarios, the mobile node used throughout the offline phase is different from the mobile nodes that will be used during the online phase. This means that there might be very significant differences between the Received Signal Strength values acquired by the mobile node and the ones stored in the Fingerprinting Map. As a consequence, this difference between RSS values might contribute to increase the location estimation error. One possible solution to minimize these differences is to adapt the RSS values, acquired during the online phase, before sending them to the Location Estimation Algorithm. Also the internal parameters of the Location Estimation Algorithms, for example the weights of the Weighted k-Nearest Neighbour, might need to be tuned for every type of terminal. This paper focuses both approaches, using Direct Search optimization methods to adapt the Received Signal Strength and to tune the Location Estimation Algorithm parameters. As a result it was possible to decrease the location estimation error originally obtained without any calibration procedure.

Optimization of River Water Quality Surveys by Multivariate Analysis of Physicochemical, Bacteriological and Ecotoxicological Data

This study aims to optimize the water quality monitoring of a polluted watercourse (Leça River, Portugal) through the principal component analysis (PCA) and cluster analysis (CA). These statistical methodologies were applied to physicochemical, bacteriological and ecotoxicological data (with the marine bacterium Vibrio fischeri and the green alga Chlorella vulgaris) obtained with the analysis of water samples monthly collected at seven monitoring sites and during five campaigns (February, May, June, August, and September 2006). The results of some variables were assigned to water quality classes according to national guidelines. Chemical and bacteriological quality data led to classify Leça River water quality as “bad” or “very bad”. PCA and CA identified monitoring sites with similar pollution pattern, giving to site 1 (located in the upstream stretch of the river) a distinct feature from all other sampling sites downstream. Ecotoxicity results corroborated this classification thus revealing differences in space and time. The present study includes not only physical, chemical and bacteriological but also ecotoxicological parameters, which broadens new perspectives in river water characterization. Moreover, the application of PCA and CA is very useful to optimize water quality monitoring networks, defining the minimum number of sites and their location. Thus, these tools can support appropriate management decisions.

An Open Source BI Approach: Concept Proof Tracking Fleet

É possível assistir nos dias de hoje, a um processo tecnológico evolutivo acentuado por toda a parte do globo. No caso das empresas, quer as pequenas, médias ou de grandes dimensões, estão cada vez mais dependentes dos sistemas informatizados para realizar os seus processos de negócio, e consequentemente à geração de informação referente aos negócios e onde, muitas das vezes, os dados não têm qualquer relacionamento entre si. A maioria dos sistemas convencionais informáticos não são projetados para gerir e armazenar informações estratégicas, impossibilitando assim que esta sirva de apoio como recurso estratégico. Portanto, as decisões são tomadas com base na experiência dos administradores, quando poderiam serem baseadas em factos históricos armazenados pelos diversos sistemas. Genericamente, as organizações possuem muitos dados, mas na maioria dos casos extraem pouca informação, o que é um problema em termos de mercados competitivos. Como as organizações procuram evoluir e superar a concorrência nas tomadas de decisão, surge neste contexto o termo Business Intelligence(BI). A GisGeo Information Systems é uma empresa que desenvolve software baseado em SIG (sistemas de informação geográfica) recorrendo a uma filosofia de ferramentas open-source. O seu principal produto baseia-se na localização geográfica dos vários tipos de viaturas, na recolha de dados, e consequentemente a sua análise (quilómetros percorridos, duração de uma viagem entre dois pontos definidos, consumo de combustível, etc.). Neste âmbito surge o tema deste projeto que tem objetivo de dar uma perspetiva diferente aos dados existentes, cruzando os conceitos BI com o sistema implementado na empresa de acordo com a sua filosofia. Neste projeto são abordados alguns dos conceitos mais importantes adjacentes a BI como, por exemplo, modelo dimensional, data Warehouse, o processo ETL e OLAP, seguindo a metodologia de Ralph Kimball. São também estudadas algumas das principais ferramentas open-source existentes no mercado, assim como quais as suas vantagens/desvantagens relativamente entre elas. Em conclusão, é então apresentada a solução desenvolvida de acordo com os critérios enumerados pela empresa como prova de conceito da aplicabilidade da área Business Intelligence ao ramo de Sistemas de informação Geográfica (SIG), recorrendo a uma ferramenta open-source que suporte visualização dos dados através de dashboards.