Construção de mosaicos georreferenciados usando imagens aéreas de pequeno formato

In this work we propose a technique that uses uncontrolled small format aerial images, or SFAI, and stereohotogrammetry techniques to construct georeferenced mosaics. Images are obtained using a simple digital camera coupled with a radio controlled (RC) helicopter. Techniques for removing common distortions are applied and the relative orientation of the models are recovered using projective geometry. Ground truth points are used to get absolute orientation, plus a definition of scale and a coordinate system which relates image measures to the ground. The mosaic is read into a GIS system, providing useful information to different types of users, such as researchers, governmental agencies, employees, fishermen and tourism enterprises. Results are reported, illustrating the applicability of the system. The main contribution is the generation of georeferenced mosaics using SFAIs, which have not yet broadly explored in cartography projects. The proposed architecture presents a viable and much less expensive solution, when compared to systems using controlled pictures

Uma abordagem baseada em aspectos e composição dinâmica para a construção de aplicações adaptativas cientes ao contexto

Ubiquitous computing systems operate in environments where the available resources significantly change during the system operation, thus requiring adaptive and context aware mechanisms to sense changes in the environment and adapt to new execution contexts. Motivated by this requirement, a framework for developing and executing adaptive context aware applications is proposed. The PACCA framework employs aspect-oriented techniques to modularize the adaptive behavior and to keep apart the application logic from this behavior. PACCA uses abstract aspect concept to provide flexibility by addition of new adaptive concerns that extend the abstract aspect. Furthermore, PACCA has a default aspect model that considers habitual adaptive concerns in ubiquitous applications. It exploits the synergy between aspect-orientation and dynamic composition to achieve context-aware adaptation, guided by predefined policies and aim to allow software modules on demand load making possible better use of mobile devices and yours limited resources. A Development Process for the ubiquitous applications conception is also proposed and presents a set of activities that guide adaptive context-aware developer. Finally, a quantitative study evaluates the approach based on aspects and dynamic composition for the construction of ubiquitous applications based in metrics

AO-OIL: um middleware orientado a aspectos baseado em uma arquitetura de referência

Middleware platforms have been widely used as an underlying infrastructure to the development of distributed applications. They provide distribution and heterogeneity transparency and a set of services that ease the construction of distributed applications. Nowadays, the middlewares accommodate an increasing variety of requirements to satisfy distinct application domains. This broad range of application requirements increases the complexity of the middleware, due to the introduction of many cross-cutting concerns in the architecture, which are not properly modularized by traditional programming techniques, resulting in a tangling and spread of theses concerns in the middleware code. The presence of these cross-cutting concerns limits the middleware scalability and aspect-oriented paradigm has been used successfully to improve the modularity, extensibility and customization capabilities of middleware. This work presents AO-OiL, an aspect-oriented (AO) middleware architecture, based on the AO middleware reference architecture. This middleware follows the philosophy that the middleware functionalities must be driven by the application requirements. AO-OiL consists in an AO refactoring of the OiL (Orb in Lua) middleware in order to separate basic and crosscutting concerns. The proposed architecture was implemented in Lua and RE-AspectLua. To evaluate the refactoring impact in the middleware architecture, this paper presents a comparative analysis of performance between AO-OiL and OiL

Abordagem de construção de arquitetura homogênea para comitês via meta-aprendizagem

In the world we are constantly performing everyday actions. Two of these actions are frequent and of great importance: classify (sort by classes) and take decision. When we encounter problems with a relatively high degree of complexity, we tend to seek other opinions, usually from people who have some knowledge or even to the extent possible, are experts in the problem domain in question in order to help us in the decision-making process. Both the classification process as the process of decision making, we are guided by consideration of the characteristics involved in the specific problem. The characterization of a set of objects is part of the decision making process in general. In Machine Learning this classification happens through a learning algorithm and the characterization is applied to databases. The classification algorithms can be employed individually or by machine committees. The choice of the best methods to be used in the construction of a committee is a very arduous task. In this work, it will be investigated meta-learning techniques in selecting the best configuration parameters of homogeneous committees for applications in various classification problems. These parameters are: the base classifier, the architecture and the size of this architecture. We investigated nine types of inductors candidates for based classifier, two methods of generation of architecture and nine medium-sized groups for architecture. Dimensionality reduction techniques have been applied to metabases looking for improvement. Five classifiers methods are investigated as meta-learners in the process of choosing the best parameters of a homogeneous committee.