Sensors of all kinds are being integrated with mobile and portable devices (tablets, smartphones). This opens up the possibility of context-aware applications to effectively be able to adapt their behavior, user interfaces and content according to the current user's situation. Frequently, contextaware applications require an infrastructure for acquisition, aggregation and reasoning of contextual information. However, existing context management infrastructures are not always appropriated to the heterogeneous and particular environment of mobile devices. In this paper, we present a context management middleware called LoCCAM (Loosely Coupled Context Acquisition Middeware) to provide selfadaptive acquisition of contextual information. It can execute both locally, on a single device, or distributed among nearby devices. The middleware proposes a model for publication, and notification of contextual information based on tuple spaces. As consequence, it offers a lower coupling among applications and the context acquisition layer. In this paper, we also present a performance evaluation of the adaptation mechanism. * CNPq Master Scholarship (MDCC/DC/UFC) † CAPES Master Scholarship (MDCC/DC/UFC) ‡ CNPq Research Scholarship (process
The evolution of mobile technologies allows the emerging of ubiquitous systems, able to anticipate user needs and to seamlessly adapt to context changes. These systems present the problem of dynamic adaptation in a highly distributed, heterogeneous and volatile environment, since it may be difficult to collect and process context information from distributed unknown sources. In order to facilitate the development of such systems, this paper extends an existing coordination framework based on tuple spaces, aiming at the management of distributed information. Hence, a decentralized coordination framework was created, offering primitives to developers to create ubiquitous systems able to interact and cooperate in scenarios of total decentralization. This paper reports some experimental results obtained in a testbed of smartphones and tablets which demonstrate the practical feasibility of our approach and pointed out how our solution can grant context data dissemination in ad hoc and infrastructured networks.
Resumo-Este artigo apresenta uma análise de sinais de voz baseada em medidas de quantificação obtidas de seus gráficos de recorrência. São avaliados três grupos de sinais: vozes saudáveis, vozes com paralisia e vozes com edema nas dobras vocais. O objetivo deste trabalhoé caracterizar os grupos de sinais de acordo com as medidas de quantificação de recorrência, a fim de potencializar o desenvolvimento de um sistema que discrimine os diferentes tipos de sinais. São abordadas quatro medidas: determinismo, entropia, taxa de recorrência e laminaridade.
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