Abstract
Robustness, real-time properties and resource efficiency are key properties to embedded devices of the CPS/IoT era. In this paper we propose a language approach RTFM-core, and show its potential to facilitate the development process and provide highly efficient and statically verifiable implementations. Our programming model is reactive, based on the familiar notions of concurrent tasks and (single-unit) resources. The language is kept minimalistic, capturing the static task, communication and resource structure of the system. Whereas C-source can be arbitrarily embedded in the model, and/or externally referenced, the instep to mainstream development is minimal, and a smooth transition of legacy code is possible. A prototype compiler implementation for RTFM-core is presented. The compiler generates C-code output that compiled together with the RTFM-kernel primitives runs on bare metal. The RTFM-kernel guarantees deadlock-lock free execution and efficiently exploits the underlying interrupt hardware for static priority scheduling and resource management under the Stack Resource Policy. This allows a plethora of well-known methods to static verification (response time analysis, stack memory analysis, etc.) to be readily applied. The proposed language and supporting tool-chain is demonstrated by showing the complete process from RTFM-core source code into bare metal executables for a lightweight ARM-Cortex M3 target.

Abstract
Fingerprint segmentation is a crucial step of an automatic fingerprint identification system, since an accurate segmentation promote both the elimination of spurious minutiae close to the foreground boundaries and the reduction of the computation time of the following steps. In this paper, a new, and more robust fingerprint segmentation algorithm is proposed. The main novelty is the introduction of a more robust binarization process in the framework, mainly based on the fuzzy C-means clustering algorithm. Experimental results demonstrate significant benchmark progress on three existing FVC datasets.

Abstract
Traditionally, travel demand modelling focused on long-term multiple socio-economic scenarios and land-use configurations to estimate the required transport supply. However, the limited number of transportation requests in demand-responsive flexible transport systems require a higher resolution zoning. This work analyses users short-term destination choice patterns, with a careful analysis of the available data coming from various different sources, such as GPS traces and social networks. We use a Multinomial Logit Model, with a social component for utility and characteristics, both derived from Social Network Analyses. The results from the model show meaningful relationships between distance and attractiveness for all the different alternatives, with the variable distance being the most significant.

Abstract
Intelligent wheelchairs (IW) are technologies that can increase the autonomy and independence of elderly people and patients suffering from some kind of disability. Nowadays the intelligent wheelchairs and the human-machine studies are very active research areas. This paper presents a methodology and a Data Analysis System (DAS) that provides an adapted command language to an user of the IW. This command language is a set of input sequences that can be created using inputs from an input device or a combination of the inputs available in a multimodal interface. The results show that there are statistical evidences to affirm that the mean of the evaluation of the DAS generated command language is higher than the mean of the evaluation of the command language recommended by the health specialist (p value = 0.002) with a sample of 11 cerebral palsy users. This work demonstrates that it is possible to adapt an intelligent wheelchair interface to the user even when the users present heterogeneous and severe physical constraints.

Abstract
Adaptive Cruise Control (ACC) is a system that maintains driver-selected speed and headway to a preceding vehicle. The system presents some limitations that are, in part or totally, unknown to the users. Hence, many drivers exhibit a rudimentary mental model of the system and place excessive trust in the device. As a consequence, negative effects on road safety can easily occur. However, to date, many studies conducted on ACC have comprised participants who had never used ACC previously. Therefore, there is limited knowledge of how ACC affects the driving performance of experienced users of the system. To shed light on this point, twenty-six participants, divided into two groups (ACC users and non-users) drove twice in the simulated environment (once with the ACC and once manually). During both drives, the participants experienced a critical situation (stationary vehicle stopped in the cruising lane of the highway). The results show that negative behavioural adaptations to the ACC resulted from the usage of the system with regard to the critical situation: the risk of collision during the driving with ACC was increased compared with the manual driving for both groups of drivers. Besides, the research stresses the negative large correlation between the driver's mental model of ACC operation in the critical situation and the safety margins maintained by the ACC users during the same situation. Finally, it was found that the drivers' trust in the system does not have an influence on the drivers' behaviour during the trial with the ACC.

Abstract
Energy planning is a complex process involving multiple and conflicting objectives with many agents able to influence decisions. This complexity is frequently addressed with the use of multicriteria tools, relying on a set of criteria and different methods to aggregate all the information in a final ranking of the available alternatives. This paper describes the application of a multicriteria decision tool for the analysis of Portuguese electricity scenarios. A set of criteria is proposed aiming to include social, economic, environmental and technical aspects. Criteria weighting was directly addressed considering 5 approaches: equitable weights, financial, technological, social and environmental perspectives. Results indicate that close to 100% RES scenario is the best option under a social perspective, base scenario represents the best option on a technical approach and scenarios relying on natural gas and wind power units are the best options for the electricity system under equitable weights, economic and environmental approaches.