Desenvolvimento de uma aplicação para suporte ao t-learning

Pós-graduação em Televisão Digital: Informação e Conhecimento - FAAC

Think: guia de desenvolvimento mobile ágil em equipes reduzidas

Currently, applications for smartphones and tablets, called apps, are becoming increasingly relevant and attract more attention from users and finally the developers. With the Application Stores, services provided by the company that maintains the platform, access to such applications is as or more simplified than to web sites, with the advantage of anenhanced user experience and focused on the mobile device, and enjoy natives resources as camera, audio, storage, integration with other applications, etc. They present a great opportunity for independent developers, who can now develop an application and make it availabl e to all users of that platform, at free or at a cost that is usually low. Even students may create their applications in the intervals of their classes and sell them in stores. Making use of tools and services, free or at low cost, anyone can develop quality applications, that can be marketed and have a large number of users even in adverse situations in which the application is not the focus of developer productivity. However, such to ols do not seem to be well used, or are unknown, or its purpose is not considered important, and this paper tries to show the real importance of these tools in the rapid development of quality software. This project presents several tools, services and practices, which together make it possible to develop an application for various mobile platforms, independently and with a team of a few people, as demonstrated. However, this paper aims not to say that the development of software today it is easy and simple, but there are currently a large set of tools, for various platforms, that assists and enhances the work of the programmer

Environment mapping for mobile robots navigation using hierarchical neural network and omnivision

Autonomous robots must be able to learn and maintain models of their environments. In this context, the present work considers techniques for the classification and extraction of features from images in joined with artificial neural networks in order to use them in the system of mapping and localization of the mobile robot of Laboratory of Automation and Evolutive Computer (LACE). To do this, the robot uses a sensorial system composed for ultrasound sensors and a catadioptric vision system formed by a camera and a conical mirror. The mapping system is composed by three modules. Two of them will be presented in this paper: the classifier and the characterizer module. The first module uses a hierarchical neural network to do the classification; the second uses techiniques of extraction of attributes of images and recognition of invariant patterns extracted from the places images set. The neural network of the classifier module is structured in two layers, reason and intuition, and is trained to classify each place explored for the robot amongst four predefine classes. The final result of the exploration is the construction of a topological map of the explored environment. Results gotten through the simulation of the both modules of the mapping system will be presented in this paper. © 2008 IEEE.

Environment for teaching and development of mobile robot systems

Using robots for teaching is one approach that has gathered good results on Middle-School, High-School and Universities. Robotics gives chance to experiment concepts of a broad range of disciplines, principally those from Engineering courses and Computer Science. However, there are not many kits that enables the use of robotics in classroom. This article describes the methodologies to implement tools which serves as test beds for the use of robotics to teach Computer Science and Engineering. Therefore, it proposes the development of a flexible, low cost hardware to integrate sensors and control actuators commonly found on mobile robots, the development of a mobile robot device whose sensors and actuators allows the experimentation of different concepts, and an environment for the implementation of control algorithms through a computer network. This paper describes each one of these tools and discusses the implementation issues and future works. © 2010 IEEE.

Autenticação biométrica de usuários em sistemas de E-learning baseada em reconhecimento de faces a partir de vídeo

Pós-graduação em Ciência da Computação - IBILCE

An evolutionary approach to improve connectivity prediction in mobile wireless sensor networks

Connectivity is the basic factor for the proper operation of any wireless network. In a mobile wireless sensor network it is a challenge for applications and protocols to deal with connectivity problems, as links might get up and down frequently. In these scenarios, having knowledge of the node remaining connectivity time could both improve the performance of the protocols (e.g. handoff mechanisms) and save possible scarce nodes resources (CPU, bandwidth, and energy) by preventing unfruitful transmissions. The current paper provides a solution called Genetic Machine Learning Algorithm (GMLA) to forecast the remainder connectivity time in mobile environments. It consists in combining Classifier Systems with a Markov chain model of the RF link quality. The main advantage of using an evolutionary approach is that the Markov model parameters can be discovered on-the-fly, making it possible to cope with unknown environments and mobility patterns. Simulation results show that the proposal is a very suitable solution, as it overcomes the performance obtained by similar approaches.

O ensino de física com lousa digital: atividades lúdicas como ferramenta mediadora na aprendizagem

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)