Desenvolvimento de um Sistema de Reconhecimento de Atividades Humanas e Monitoramento Remoto Utilizando um Dispositivo Vestível

Fatores como o envelhecimento da população e o consequente aumento do número de pessoas com doenças crônicas implicam um crescimento exponencial dos custos de assistência médica, visto que o sistema de saúde deve ser capaz de atender a um número cada vez maior de pessoas, mantendo a qualidade do atendimento. Visando redução de custos e melhoria da qualidade, seria desejável um sistema de saúde focado no paciente, no qual se poderia detectar precocemente condições médicas, evitando hospitalizações, bem como acompanhá-los remotamente, evitando a permanência destes no hospital. Nesse contexto, dispositivos de monitoramento remoto tornam-se essenciais para coletar informações importantes de pacientes e torná-las disponíveis ao provedor de saúde. O avanço tecnológico conseguido com a miniaturização de sensores e as novas tecnologias de comunicação sem fio de baixo consumo energético impulsionam o desenvolvimento de sistemas de monitoramento remoto de saúde com dispositivos vestíveis. O presente trabalho propõe o desenvolvimento de um sistema de reconhecimento de atividades humanas e de monitoramento remoto, utilizando três diferentes abordagens. Para a primeira abordagem, conseguiu-se uma acurácia de 89,11% e precisão de 91,45% na classificação de seis diferentes atividades. Já para as duas últimas abordagens, construiu-se a estrutura completa de monitoramento remoto da intensidade das atividades realizadas por uma pessoa, desde a coleta dos dados até o envio por e-mail para acompanhamento à distância pelo provedor de saúde. Os resultados obtidos com o sistema desenvolvido demonstram a sua viabilidade tanto para o reconhecimento de atividades humanas quanto para monitoramento remoto. Palavras-chave: Sistemas Embarcados, Dispositivos Vestíveis, Reconhecimento de Padrões, Reconhecimento de Atividades Humanas, Monitoramento Remoto

Estimating equivalent bottom geoacoustial parameters from broadband inversion

A simple and fast approach to retrieve equivalent geoacoustic parameters is presented in this paper. The method is based upon the processing of 300-800 Hz broadband signals on a single hydrophone.Two stable characteristics of the impulse response of the shallow water waveguide are estimated: the time dispersion and the bottom reflection amplitudes. This two features are analytically linked to the compressional speed and to the attenuation coefficient of the medium. The inversion of the two latter geoacoustic parameters is straightforward since it relies on an analytical expression. The method is tested on INTIMATE96 data. The results show an excellent agreement between the reflection of the true medium and the reflection coefficient of the equivalent medium.The partners of the INTIMATE project wish to thank the staff of NRP ANDROMEDA, the staff of BO D’ENTRECASTEAUX and people of Mission Océanographique de l’Atlantique (aboard D’ENTRECASTEAUX). We also wish to thank the SACLANT Undersea Research Center for lending the Portable Array System and Roberto Chiarabini (SACLANTCEN) for his participation in the array preparation, deployment and use. Thanks to T. Folegot and G. Bonnaillie (CMO) for their active contribution in this work. The study was jointly sponsored by SHOM (exploratory program 95901), the Portuguese Ministery of Research (PRAXIS XXI) and ONR (contract N00014-95-1-0558)

Development of an image analysis procedure for identifying protozoa and metazoa typical of activated sludge system

A procedure for the semi-automatic identification of the main protozoa and metazoa species present in the activated sludge of wastewater treatment plants was developed. This procedure was based on both image processing and multivariable statistical methodologies, leading to the use of the image analysis morphological descriptors by discriminant analysis and neural network techniques. The image analysis programwritten in Matlab has proved to be adequate in terms of protozoa and metazoa recognition, as well as for the operating conditions assessment.National Council of Scientific and Technological Development of Brazil (CNPq); BIEURAM III ALFA co-operation project (European Commission); Fundação para a Ciência e a Tecnologia (FCT

Stalked protozoa identification by image analysis and multivariable statistical techniques

Protozoa are considered good indicators of the treatment quality in activated sludge systems as they are sensitive to physical, chemical and operational processes. Therefore, it is possible to correlate the predominance of certain species or groups and several operational parameters of the plant. This work presents a semi-automatic image analysis procedure for the recognition of the stalked protozoa species most frequently found in WWTP by determining the physical, morphological and signature data and subsequent processing by discriminant analysis and neural network techniques. Physical descriptors were found to be responsible the largest identification ability and the crucial Opercularia and V. microstoma micro-organisms identification provided some degree of confidence to establish their presence in WWTP

Recognition of protozoa and metazoa using image analysis tools, discriminant analysis, neural networks and decision trees

Protozoa and metazoa are considered good indicators of the treatment quality in activated sludge systems due to the fact that these organisms are fairly sensitive to physical, chemical and operational processes. Therefore, it is possible to establish close relationships between the predominance of certain species or groups of species and several operational parameters of the plant, such as the biotic indices, namely the Sludge Biotic Index (SBI). This procedure requires the identification, classification and enumeration of the different species, which is usually achieved manually implying both time and expertise availability. Digital image analysis combined with multivariate statistical techniques has proved to be a useful tool to classify and quantify organisms in an automatic and not subjective way. Thiswork presents a semi-automatic image analysis procedure for protozoa and metazoa recognition developed in Matlab language. The obtained morphological descriptors were analyzed using discriminant analysis, neural network and decision trees multivariable statistical techniques to identify and classify each protozoan or metazoan. The obtained procedure was quite adequate for distinguishing between the non-sessile protozoa classes and also for the metazoa classes, with high values for the overall species recognition with the exception of sessile protozoa. In terms of the wastewater conditions assessment the obtained results were found to be suitable for the prediction of these conditions. Finally, the discriminant analysis and neural networks results were found to be quite similar whereas the decision trees technique was less appropriate.National Council of Scientific and Technological Development of Brazil (CNPq); BI-EURAM III ALFA co-operation project (European Commission); Fundação para a Ciência e a Tecnologia (FCT)

Aplicação de técnicas de análise de imagem e de estatística multivariável no reconhecimento de protozoários e metazoários típicos de sistemas por lodos ativados

Os protozoários e pequenos metazoários são organismos abundantes nos tanques de aeração das Estações de Tratamento de Efluentes (ETEs) por lodos ativados. A distribuição de espécies e sua abundância têm sido apontadas como indicadores da qualidade do tratamento fornecendo um instrumento útil para avaliar o desempenho destes sistemas. O presente trabalho teve como objetivo o desenvolvimento em ambiente Matlab de um procedimento de análise digital de imagens combinado com as técnicas multivariáveis de Redes Neurais, Análise Discriminante e Árvores de Decisão para efetuar o reconhecimento dos principais grupos de protozoários e pequenos metazoários típicos dos sistemas de tratamento de efluentes por lodos ativados. O procedimento obtido mostrou-se adequado para distinguir entre as classes de protozoários e metazoários incluídos no estudo. Os desempenhos globais de reconhecimento alcançados podem ser considerados de razoáveis a bons para todos as espécies avaliadas com exceção dos organismos pedunculados. Em relação com o reconhecimento dos organismos indicadores das condições operacionais das ETEs os resultados obtidos foram razoáveis para efetuar o seu diagnóstico, com melhoras na identificação dos organismos indicadores de condições críticas de operação em relação a estudos anteriores. Dentre as técnicas de análise estatística multivariável aplicadas, as Redes Neurais e a Análise Discriminante alcançaram níveis de Desempenho Global de Reconhecimento comparáveis, enquanto que as Árvores de Decisão mostraram-se menos apropriadas para os objetivos deste estudo. Por último, os resultados obtidos provaram que a técnica de análise digital de imagens combinada com as técnicas estatísticas de análise multivariável constitui uma ferramenta promissora para avaliar e monitorar populações de protozoários e matazoários nas ETEs por lodos ativados

Recognition of protozoa and metazoa using image analysis tools, discriminant analysis and neural network

A mixed culture of microorganisms is usually present in biological wastewater treatment processes such as the activated sludge system in aeration tanks. These microorganisms are capable of reducing the organic matter and other pollutants in the sewage. Protozoa and metazoa play an important role in this system because they maintain the density of bacterial populations by predation and contribute to the flocculation process, being responsible for an mprovement in the quality of the effluent. Moreover, protozoa and metazoa are considered to be important bioindicators of the activated sludge process due to their association with physical, chemical and operational parameters of the treatment plant. Furthermore, the analysis of the number and classes of the predominant groups of these organisms is used to predict the effectiveness of the aeration, extent of the nitrification process, sludge age and final effluent conditions1,2. Classical microfauna analysis is frequently done by microscopic observation and assessment of the different protozoa and metazoa species present. However, this task is not only timeconsuming and labour intensive but also requires the expertise of a zoologist or protozoologist. Therefore, digital image analysis can be seen as a useful tool to achieve taxonomic classification and organism’s quantification in an automatic, non subjective manner. Some studies have already been carried out using this technique combined with statistic multivariable analysis such as Neural Networks, Discriminant Analysis, and Principal Components Analysis to perform the recognition of protozoa and metazoa commonly present in the aeration tank of wastewater treatment plants activated sludge, including the works of Amaral et al. (2004)3. In this work an image analysis programme was developed in MATLAB code for the semi-automatic recognition of several groups of protozoa and metazoa commonly present in wastewater treatment plants. The protozoa and metazoa were characterized by different morphological parameters of Euclidean and fractal geometry, with or without their external structures (peduncles, cirri, tentacles). Finally, the morphological parameters (around 40) of the above-mentioned geometries were analysed using the multivariable statistical techniques Discriminant Analysis and Neural Network to identify and classify each protozoan or metazoan image. The procedure obtained was adequate for distinguishing between amoebas, sessile ciliates, crawling ciliates, large flagellates and free swimming ciliates in terms of the protozoa classes and also for the metazoa. Furthermore, with the exception of some sessile species, the value of overall species recognition was high. In terms of the wastewater conditions assessment such as aeration, nitrification, sludge age and effluent quality the obtained results were found to be suitable for the prediction of these conditions.ALFA cooperation project; the Biological Engineering Department of Minho University; Chemistry School – Federal University of Rio de Janeiro

Practical approximation scheme for the pion dynamics in the three-nucleon system

We discuss a working approximation scheme to a recently developed formulation of the coupled piNNN-NNN problem. The approximation scheme is based on the physical assumption that, at low energies, the 2N-subsystem dynamics in the elastic channel is conveniently described by the usual 2N-potential approach, while the explicit pion dynamics describes small, correction-type effects. Using the standard separable-expansion method, we obtain a dynamical equation of the Alt-Grassberger-Sandhas (AGS) type. This is an important result, because the computational techniques used for solving the normal AGS equation can also be used to describe the pion dynamics in the 3N system once the matrix dimension is increased by one component. We have also shown that this approximation scheme treats the conventional 3N problem once the pion degrees of freedom are projected out. Then the 3N system is described with an extended AGS-type equation where the spin-off of the pion dynamics (beyond the 2N potential) is taken into account in additional contributions to the driving term. These new terms are shown to reproduce the diagrams leading to modern 3N-force models. We also recover two sets of irreducible diagrams that are commonly neglected in 3N-force discussions, and conclude that these sets should be further investigated, because a claimed cancellation is questionable.Comment: 18 pages, including 5 figures, RevTeX, Eps