Lino Figueiredo

This paper proposes a system to monitor, through the internet, the data of a logistics distribution truck. For this, it was implemented a reliable and flexible wireless sensor network with low energy consumption. The technology used for the radio system was Bluetooth Low Energy (BLE). Each node in the network contains one type of sensor. The sensors information together with GPS and On-Board Diagnostics (OBD) data collected by the central unit, and later transmitted to the cloud by GSM or Wi-Fi.

Nowadays, online learning seems to be more and more in demand, with several offers being presented to the students. Online learning platforms that can be at any time that is convenient for them at any pace, using any technology available in any place. This also confers to the students the responsibility of been responsible for their own learning. To assist the students in this task the Emotional Adaptive Platforms (EAP) can be used. The EAP can perceived the student emotional state, personality and learning preference and adjust the learning course to it. So a question is asked how do we build an EAP? In this paper, is described how to build an EAP highlighting the major problems and concern issues from the initial research phase to the test of a working prototype. The research was carried out based on the assumption that emotion can influence several aspects of human life, knowledge acquisition, perception, learning process to the way people communicate and the way rational decisions are made.

An electrocardiogram (ECG) system deals with several challenges related with noise sources. The denoising process is a challenge due to ECG signal amplitude with respect to noise amplitude. The filtering process can be executed by analogue filters or digital filters, however, modern systems uses digital filters mainly because their flexibility, efficiency and hardware costs reduction. This paper compares the ability of two digital techniques used in ECG denoising, namely Adaptive Filter (AF) and Singular Value Decomposition (SVD). The techniques were applied to real ECG signals contaminated with the most common noise sources; artefacts caused by electromyography (EMG) and power line noise (Hum).

A pedestrian inertial navigation system is typically used to suppress the Global Navigation Satellite System limitation to track persons in indoor or in dense environments. However, low- cost inertial systems provide huge location estimation errors due to sensors and pedestrian dead reckoning inherent characteristics. To suppress some of these errors we propose a system that uses two inertial measurement units spread in person’s body, which measurements are aggregated using learning algorithms that learn the gait behaviors. In this work we present our results on using different machine learning algorithms which are used to characterize the step according to its direction and length. This characterization is then used to adapt the navigation algorithm according to the performed classifications.

Ambient intelligence (AmI) paradigm has been the catalyst for the creation of several types of smart spaces, and the smart meeting rooms (SMR) is one of them. One of the main aims of AmI spaces is the development of middleware able to provide context aware computing specifically in SMR face-to-face, and distributed/asynchronous meeting settings are envisaged. This paper describes OLAVAmI - a context aware middleware system which was tested in LAID environment, a smart meeting room. OLAVAmI allows video production focusing on the speaker, an audio-to-text conversion service, and a multimedia database of meetings produced in an autonomous way. To experiment OLAVAmI usage and functionalities, two tools present in LAID test bed were used. These AmI intelligent layer tools cover two application scenarios: a ubiquitous group decision-making and a service reposition in power systems. The results and insights are presented in this article.