Data in the smart grid is generated from various sources including for example, energy consumption data measured by smart meters, operation data from devices in the power infrastructure, data from sensors distributed over ...

The goal of this thesis is to take a step to understanding the resource management of massively and scattered distributed systems. A framework to predict grid resources behavior and leverage the execution of long running ...

This thesis describes the integration of radar network and grid computing technologies to provide a set of grid services to manipulate and store data from different radars, to execute algorithms on different platforms in ...

Efficient and fast management of complex systems requires the development of efficient interfaces. The purpose of this document is to describe the development of a set of interfaces to provide sensor management capabilities ...

This research presents a sensitivity analysis of a semi-analytical inversion model for hyperspectral remote sensing of shallow coral ecosystems. Using this inversion model, five parameters describing water column bioptical ...

Electric grids are an important aspect of the civil infrastructure of our society. Traditional energy grids are being modernized with the introduction of smart grids and transactive energy concepts. In a smart grid ...

In this thesis the scheduling process in production environments is improved through the development of a framework that implements a distributed dynamic scheduling methodology based on quality of service. Such methodology ...

The deployment of grid infrastructures is a time consuming and arduous task, therefore, the goal of this work was to establish an easy, “out of the box”, grid por- tal environment that allows administrators to go through ...

Hyperspectral imaging provides the capability to identify and classify materials remotely. The applications of such technology is applied everywhere from medical devices and military targets to environmental sciences. With ...

Hyperspectral imaging analysis demands large input data sets and in turn re- quires signi¯cant CPU time and memory capacity. Grid Computing has the potential of improving the performance of these types of data and computational ...