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Energy and storage restrictions are relevant variables that software applications should be concerned about when running in low-power environments. In particular, computer vision (CV) applications exemplify well that concern, since conventional uniform image sensors typically capture large amounts of data to be further handled by the appropriate CV algorithms. Moreover, much of the acquired data are often redundant and outside of the application’s interest, which leads to unnecessary processing and energy spending. In the literature, techniques for sensing and re-sampling images in non-uniform fashions have emerged to cope with these problems. In this study, we propose Application-Oriented Retinal Image Models that define a space-variant configuration of uniform images and contemplate requirements of energy consumption and storage footprints for CV applications. We hypothesize that our models might decrease energy consumption in CV tasks. Moreover, we show how to create the models and validate their use in a face detection/recognition application, evidencing the compromise between storage, energy, and accuracy.

The Sugarcane EST project (SUCEST) produced 291,904 expressed sequence tags (ESTs) in a consortium that involved 74 sequencing and data mining laboratories. We created a web site for this project that served as a ‘meeting point’ for receiving, processing, analyzing, and providing services to help explore the sequence data. In this paper we describe the information pathway that we implemented to support this project and a brief explanation of the clustering procedure, which resulted in 43,141 clusters.O projeto SUCEST (Sugarcane EST Project) produziu 291.904 ESTs de cana-de-açúcar. Nesse projeto, o Laboratório de Bioinformática criou o web site que foi o "ponto de encontro" dos 74 laboratórios de sequenciamento e data mining que fizeram parte do consórcio para o projeto. O Laboratório de Bioinformática (LBI) recebeu, processou, analisou e disponibilizou ferramentas para a exploração dos dados. Neste artigo os dados, serviços e programas implementados pelo LBI para o projeto são descritos, incluindo o procedimento de clustering que gerou 43.141 clusters.