ESEIG Mobile: an m-learning approach in a superior school

In recent years, mobile learning has emerged as an educational approach to decrease the limitation of learning location and adapt the teaching-learning process to all type of students. However, the large number and variety of Web-enabled devices poses challenges for Web content creators who want to automatic get the delivery context and adapt the content to mobile devices. In this paper we study several approaches to adapt the learning content to mobile phones. We present an architecture for deliver uniform m-Learning content to students in a higher School. The system development is organized in two phases: firstly enabling the educational content to mobile devices and then adapting it to all the heterogeneous mobile platforms. With this approach, Web authors will not need to create specialized pages for each kind of device, since the content is automatically transformed to adapt to any mobile device capabilities from WAP to XHTML MP-compliant devices.

Using mobile device detection approaches to augment the accuracy of web delivery content

Recent studies of mobile Web trends show a continuous explosion of mobile-friendly content. However, the increasing number and heterogeneity of mobile devices poses several challenges for Web programmers who want to automatically get the delivery context and adapt the content to mobile devices. In this process, the devices detection phase assumes an important role where an inaccurate detection could result in a poor mobile experience for the enduser. In this paper we compare the most promising approaches for mobile device detection. Based on this study, we present an architecture for a system to detect and deliver uniform m-Learning content to students in a Higher School. We focus mainly on the devices capabilities repository manageable and accessible through an API. We detail the structure of the capabilities XML Schema that formalizes the data within the devices capabilities XML repository and the REST Web Service API for selecting the correspondent devices capabilities data according to a specific request. Finally, we validate our approach by presenting the access and usage statistics of the mobile web interface of the proposed system such as hits and new visitors, mobile platforms, average time on site and rejection rate.

Moxtra ‘o meu’, ‘o teu’, ‘o nosso’, ‘o vosso’ mobile learning

Neste workshop pretende-se apresentar uma aplicação móvel (Moxtra) que integra uma experiência de inovação pedagógica no âmbito do mobile-learning que está em pleno desenvolvimento, com a participação ativa dos estudantes e docentes das unidades curriculares de Hematologia Laboratorial I e II do curso de Ciências Biomédicas Laboratoriais. A adesão dos estudantes ao projeto mobile-learning é inédita no nosso país e tem sido muito positiva. O workshop terá dois objetivos: a) Conhecer os principais atributos da aplicação Moxtra; b) Construir um modelo de gestão de aprendizagem para uma unidade curricular.

JSON on Mobile: is there an efficient parser?

The two largest causes for battery consumption on mobile devices are related with the display and network operations. Since most application need to share data and communicate with remote servers, communications should be as lightweight and efficient as possible. In network communication, serialization plays a central role as the process of converting an object into a stream of bytes. One of the most popular data-interchange format is JSON (JavaScript Object Notation). This paper presents a survey on JSON parsers in mobile scenarios. The aim of the survey is to find the most efficient JSON parser in mobile communications characterised by high transfer rate of small amounts of data. In the performance benchmark we compare the time required to read and write data with several popular JSON parser implementations such as Gson, Jackson, org.json and others. The results of this survey are important for others that need to select an efficient parser for mobile communication.

Reconfigurable antenna for mobile terminal

Trabalho Final de Mestrado para obtenção do grau de Mestrado em Engenharia Electrónica e Telecomunicações

Mining social individuals movements and friends based on mobile devices

Trabalho de Projeto realizado para obtenção do grau de Mestre em Engenharia Informática e de Computadores

Mobile cockpit system for enhanced electric bicycle use

This paper presents the project of a mobile cockpit system (MCS) for smartphones, which provides assistance to electric bicycle (EB) cyclists in smart cities' environment. The presented system introduces a mobile application (MCS App) with the goal to provide useful personalized information to the cyclist related to the EB's use, including EB range prediction considering the intended path, management of the cycling effort performed by the cyclist, handling of the battery charging process, and the provisioning of information regarding available public transport. This work also introduces the EB cyclist profile concept, which is based on historical data analysis previously stored in a database and collected from mobile devices' sensors. From the tests performed, the results show the importance of route guidance, taking into account the energy savings. The results also show significant changes on range prediction based on user and route taken. It is important to say that the proposed system can be used for all bicycles in general.

LADAR based mapping and obstacle detection system for service robots

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia Electrotécnica e de Computadores

Plano de Negócio para a aplicação mobile Nutri Consult

Trabalho de projeto apresentado à Escola Superior de Comunicação Social como parte dos requisitos para obtenção de grau de mestre em Publicidade e Marketing.

App mobile “My Corine de Farme”

Trabalho de projeto apresentado à Escola Superior de Comunicação Social como parte dos requisitos para obtenção de grau de mestre em Publicidade e Marketing.

ESEIG Mobile: an m-learning approach in a superior school

In recent years, mobile learning has emerged as an educational approach to decrease the limitation of learning location and adapt the teaching-learning process to all type of students. However, the large number and variety of Web-enabled devices poses challenges for Web content creators who want to automatic get the delivery context and adapt the content to mobile devices. This paper studies several approaches to adapt the learning content to mobile phones. It presents an architecture for deliver uniform m-Learning content to students in a higher School. The system development is organized in two phases: firstly enabling the educational content to mobile devices and then adapting it to all the heterogeneous mobile platforms. With this approach, Web authors will not need to create specialized pages for each kind of device, since the content is automatically transformed to adapt to any mobile device capabilities from WAP to XHTML MP-compliant devices.

Mobile device-to-device distributed computing using data sets

The rapidly increasing computing power, available storage and communication capabilities of mobile devices makes it possible to start processing and storing data locally, rather than offloading it to remote servers; allowing scenarios of mobile clouds without infrastructure dependency. We can now aim at connecting neighboring mobile devices, creating a local mobile cloud that provides storage and computing services on local generated data. In this paper, we describe an early overview of a distributed mobile system that allows accessing and processing of data distributed across mobile devices without an external communication infrastructure. Copyright © 2015 ICST.

Robot visual localization through local feature fusion: an evaluation of multiple classifiers combination approaches

In the last decade, local image features have been widely used in robot visual localization. In order to assess image similarity, a strategy exploiting these features compares raw descriptors extracted from the current image with those in the models of places. This paper addresses the ensuing step in this process, where a combining function must be used to aggregate results and assign each place a score. Casting the problem in the multiple classifier systems framework, in this paper we compare several candidate combiners with respect to their performance in the visual localization task. For this evaluation, we selected the most popular methods in the class of non-trained combiners, namely the sum rule and product rule. A deeper insight into the potential of these combiners is provided through a discriminativity analysis involving the algebraic rules and two extensions of these methods: the threshold, as well as the weighted modifications. In addition, a voting method, previously used in robot visual localization, is assessed. Furthermore, we address the process of constructing a model of the environment by describing how the model granularity impacts upon performance. All combiners are tested on a visual localization task, carried out on a public dataset. It is experimentally demonstrated that the sum rule extensions globally achieve the best performance, confirming the general agreement on the robustness of this rule in other classification problems. The voting method, whilst competitive with the product rule in its standard form, is shown to be outperformed by its modified versions.

Flow resistance in open-channel flows with mobile hydraulically rough beds

River Flow 2008, Vol.1

Experimental dam-break waves profile analysis: horizontal mobile bed

River Flow, Vol. 2