Abstract
In this paper we consider two very different case studies explored using scalable analysis techniques and stochastic process algebra. The first case study is a classical computer science problem: determining the efficiency of two non-repudiation protocols. We use PEPA to specify the model derived from the protocol specification and mean value analysis and fluid approximation to derive the desired metrics. In the second case study we model a human-centric system, concerning patient flow through a hospital clinic. The model is derived from the clinic practice and observed takt times are used to populate the model. We use PEPA and fluid approximations to derive measures. The two case studies demonstrate the power and versatility of the modelling and analysis approaches used. © 2013 Springer-Verlag.

Abstract
This paper presents a study were the Rough Set Theory and Data Mining Technique are applied to the electrical power system. The Data Mining technique classifies the system operation in four possible states: normal, alert, emergency (emergency I and emergency II). The states, that correspond to the normal state can be classified as secure and insecure the remaining ones. In this security studies, the overloads in transmition lines and the violation of the voltage limits are used to classify and rank these contingencies. This technique was applied to the 118IEEE busbar test power network and the results obtained are analyzed. Finally, some conclusions that provide a valuable contribution to the understanding of the power system security analysis are pointed out. © 2013, Springer Science+Business Media Dordrecht.

Abstract
The duration of 802.11 user sessions has been widely studied in the context of analyzing user behavior and mobility. Short (smaller-than-5-minutes) sessions are never used or characterized in these analyses as they are unrelated to user behavior and considered as artifacts introduced by the wireless network. In this paper we characterize short 802.11 sessions as recorded through RADIUS authentication. We show that 50% of access points have 70% of smaller than 5 minutes sessions in a 5 months trace from the Eduroam academic wireless network in the University of Porto. Exactly because they are artifacts introduced by the network, short sessions are an important indicator for network management and the quality of the wireless access. Network managers typically do not collect and process session information but rely on SNMP to provide summaries of 802.11 usage data. We develop a modeling framework to provide predictions for the number of short sessions from SNMP data. We model the data stream of each access point using two methods of regression and one classification technique. We evaluate these models based on short session prediction accuracy. The models are trained on the 5 months data and the best results show prediction accuracy of 95.27% in polynomial regression at degree of 3.

Abstract
The European Project Semester at ISEP (EPS@ISEP) is a one semester project-based learning programme addressed to engineering students from diverse scientific backgrounds and nationalities. The students, organized in multicultural teams, are challenged to solve real world multidisciplinary problems, accounting for 30 ECTU. The EPS package, although focused on project development (20 ECTU), includes a series of complementary seminars aimed at fostering soft, project-related and engineering transversal skills (10 ECTU). This paper presents the study plan, resources, operation and results of the EPS@ISEP that was created in 2011 to apply the best engineering education practices and promote the internationalization of ISEP. The results show that the EPS@ISEP students acquire during one semester the scientific, technical and soft competences necessary to propose, design and implement a solution for a multidisciplinary problem.

Abstract
Robotic autonomous driving is a very complex task that tries to replicate the human behavior when performing such task. This paper presents a high-level overview of an architecture applicable for small-scale vehicles in autonomous driving competitions. A special emphasis is given on the sensory and navigation sub-systems since they are the most prominent intelligent decision layers. The former is almost entirely based on computer vision, processing the raw image content from two ordinary and inexpensive "web-cams", conveying further relevant information to the later, on a real-time basis. The proposed architecture was implemented with minimal interventions to an Ackermann-like vehicle which was originally designed for recreational purposes, serving as a "proof of concept" of the developed system. Results show that a low-cost, scalable and modular system can be easily integrated on regular small-scale vehicles obtaining exciting results at a minimal cost.

Abstract
Ensuring a mobile ad-hoc network (MANET) in real and complex environments is an arduous task since the strength of the connection between two robots can rapidly change over time or even disappear. An extension of the Particle Swarm Optimization to multi-robot applications was recently proposed and denoted as Robotic Darwinian PSO (RDPSO). This paper contributes with a further extension of the RDPSO, by integrating a fault-tolerant distributed search in order to prevent communication network splits. To that end, each robot performs packet forwarding according to a paradigm of multi-hop communication, thus maintaining a maximum range or minimum signal quality between its "best" neighbors. This results in a sum of virtual forces for each robot to ensure a multi-connected MANET over time. Experimental results with 15 physical robots show that a more fault-tolerant strategy clearly influences the time needed to converge to the final solution but is less susceptible to robot failures.