Abstract

Abstract
Automated Fare Collection (AFC) systems produce a large amount of very detailed data, which analysis may be very useful to authorities and transport planners to define future service delivery strategies. Such analysis can be further improved by relating to other data sources, such as points-of-interest (POI) data. As a result public transport operators are able to identify the city service providers with whom it would be more interesting to establish partnerships and propose joint value propositions benefiting both service providers. The objective of such partnerships is to attract new customers and retain those that already exist by providing combined offers, discounts or loyalty schemes. The potential of such analysis is demonstrated by using data related to the city of Porto, Portugal. This study relies on two different data sources: AFC system data and points-of interest data. Urban mobility data is used to identify mobility patterns of different segments of passengers and points-of-interest data is used to analyse the type of services that are likely to concentrate around public transport stations. The results allowed to identify the potential city services to establish partnerships according to the mobility profiles of passengers and the concentration levels of services around public transport stations. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Abstract
Optical modulation characteristics of resonant tunneling diode photodetectors (RTD-PD) are investigated. Intensity modulated light excites the RTD-PDs to conduct data experiments. Simple and complex data patterns are used with results showing data rates up to 80 and 200 Mbit/s, respectively. This is the first demonstration of complex modulation using resonant tunneling diodes.

Abstract
Smart Transformers (ST) are power-electronic based apparatuses that bring new opportunities for defining innovative operating strategies for Medium Voltage (MV) and Low Voltage (LV) distribution grids in future scenarios with increased shares of small-scale generation units. A distinctive advantage of ST is the possibility of connecting a storage device in the DC link when considering a three-stage configuration (AC/DC/AC). In this paper, ST is exploited within the context of Multi-Microgrids (MMG) in order to enhance the possibility of islanding operation through the identification of new control functionalities. The identification of robust control strategies to coordinate the flexibility of all the available resources (distributed resources at the LV and MV grids and storage units connected to the ST) is required to guarantee the successful operation of the MMG in the islanded mode. In order to address the need of specific control requirements, two different configurations are considered, being the proposed control strategies properly described: (1) ST with an energy storage unit in the DC link which fully decouples all the control possibilities for the MV and LV grid sides; (2) ST without an energy storage unit, requiring proper coordination between the MV and LV grid levels.

Abstract
Smartphones are increasingly present in human’s life. For example, for entertainment many people use their smartphones to watch videos or listen to music. Many users, however, stream or play videos with the intention to only listen to the audio track. This way, some battery energy, which is critical to most users, is unnecessarily consumed thus and switching between video and audio can increase the time of use of the smartphone between battery recharges. In this paper, we present a first approach that, based on the user context, can automatically switch between video and audio. A supervised learning approach is used along with the classifiers K-Nearest Neighbors, Hoeffding Trees and Naive Bayes, individually and combined to create an ensemble classifier. We investigate the accuracy for recognizing the context of the user and the overhead that this system can have on the smartphone energy consumption. We evaluate our approach with several usage scenarios and an average accuracy of 88.40% was obtained for the ensemble classifier. However, the actual overhead of the system on the smartphone energy consumption highlights the need for researching further optimizations and techniques.

Abstract