SENFIS: a Sensor Node File System for increasing the scalability and reliability of Wireless Sensor Networks applications

In the last years the Wireless Sensor Networks’ (WSN) technology has been increasingly employed in various application domains. The extensive use of WSN posed new challenges in terms of both scalability and reliability. This paper proposes Sensor Node File System (SENFIS), a novel file system for sensor nodes, which addresses both scalability and reliability concerns. SENFIS can be mainly used in two broad scenarios. First, it can transparently be employed as a permanent storage for distributed TinyDB queries, in order to increase the reliability and scalability. Second, it can be directly used by a WSN application for permanent storage of data on the WSN nodes. The experimental section shows that SENFIS implementation makes an efficient use of resources in terms of energy consumption, memory footprint, flash wear levelling, while achieving execution times similarly with existing WSN file systems.     

The kinematic design of spatial,hybrid closed chains including planar parallelograms

It is presented an integral approach for the kinematic design of spatial, hybrid closed chains which include planar parallelograms into their kinematic structure. It is based on a systematic application of recursive formulae intended for describing the evolution of screws through time. Due to the particular nature of the proposed approach, it can be closely related with Lie algebras and allows to overcome the lacking of group structure offered by a parallelogram when it is going to be considered as a component of a hybrid closed chain. Several application examples are presented in order to show the potential of the proposed approach.     

Providing EAP-based Kerberos pre-authentication and advanced authorization for network federations

Kerberos is a well-known standard protocol which is becoming one of the most widely deployed for authentication and key distribution in application services. However, whereas service providers use the protocol to control their own subscribers, they do not widely deploy Kerberos infrastructures to handle subscribers coming from foreign domains, as happens in network federations. Instead, the deployment of Authentication, Authorization and Accounting (AAA) infrastructures has been preferred for that operation. Thus, the lack of a correct integration between these infrastructures and Kerberos limits the service access only to service provider's subscribers. To avoid this limitation, we design an architecture which integrates a Kerberos pre-authentication mechanism, based on the use of the Extensible Authentication Protocol (EAP), and advanced authorization, based on the standards SAML and XACML, to link the end user authentication and authorization performed through an AAA infrastructure with the delivery of Kerberos tickets in the service provider's domain. We detail the interfaces, protocols, operation and extensions required for our solution. Moreover, we discuss important aspects such as the implications on existing standards.     

On the addition of integral action to port-controlled Hamiltonian systems

A technique that provides closed loop integral action depending on the passive outputs of port-controlled Hamiltonian systems is already available. This paper addresses a new method that allows us to add integral action also on system variables having relative degree higher than one, while still preserving the Hamiltonian form and, thus, closed loop stability. The new approach is applied to design speed regulation controllers for the permanent magnet synchronous motor. Closed loop stability and asymptotic rejection of unknown piecewise constant load torques are formally proved. This theoretically predicted control system performance is illustrated via simulation experiments, which also show that the properties hold under parameter uncertainties. This is in line with the usual practice of including integral action in a controller with the aim of improving its closed loop robustness. The fact that the method enhances the range of possible integral actions in the controller, enriched with this robustness property, allows us to assess it as a practically important complement to the well-known interconnection and damping assignment techniques developed in the framework of port-controlled Hamiltonian systems.     

Michigan Particle Swarm Optimization for Prototype Reduction in Classification Problems

This paper presents a new approach to Particle Swarm Optimization, called Michigan Approach PSO (MPSO), and its application to continuous classification problems as a Nearest Prototype (NP) classifier. In Nearest Prototype classifiers, a collection of prototypes has to be found that accurately represents the input patterns. The classifier then assigns classes based on the nearest prototype in this collection. The MPSO algorithm is used to process training data to find those prototypes. In the MPSO algorithm each particle in a swarm represents a single prototype in the solution and it uses modified movement rules with particle competition and cooperation that ensure particle diversity. The proposed method is tested both with artificial problems and with real benchmark problems and compared with several algorithms of the same family. Results show that the particles are able to recognize clusters, find decision boundaries and reach stable situations that also retain adaptation potential. The MPSO algorithm is able to improve the accuracy of 1-NN classifiers, obtains results comparable to the best among other classifiers, and improves the accuracy reported in literature for one of the problems.

MPC for battery/fuel cell hybrid vehicles including fuel cell dynamics and battery performance improvement

In this paper, the performance and durability of hybrid PEM fuel cell vehicles are investigated. To that end, a hybrid predictive controller is proposed to improve battery performance and to avoid fuel cell and battery degradation. Such controller deals with this complex control problem by handling binary and continuous variables, piecewise affine models and constraints. Moreover, the control strategy is to track motor power demand and keep batteries close to a desired battery state of charge which is appropriately chosen to minimize hydrogen consumption. It is important to highlight the consideration of constraints which are directly related to the goals of this paper, such as minimum fuel cell power threshold and time limitation between fuel cell startups and shutdowns. Furthermore, different models have been elaborated and particularized for a vehicle prototype. These models include few innovations such as a reference governor which smooths fuel cell power demand during sharp power profiles, forcing batteries to supply such peaks and resulting a longer fuel cell lifetime. Battery thermal dynamics are also taken into account in these models in order to analyze the effect of battery temperature on its degradation. Finally, this paper studies the feasibility of the real implementation, presenting an explicit formulation as a solution to reduce execution time. This explicit controller exhibits the same performance as the hybrid predictive controller does with a reduced computational effort. All the results have been validated in several simulations.     

Assisting requirements analysts to find latent concerns with <Emphasis Type="Italic">REAssistant</Emphasis>

Textual requirements are very common in software projects. However, this format of requirements often keeps relevant concerns (e.g., performance, synchronization, data access, etc.) from the analyst’s view because their semantics are implicit in the text. Thus, analysts must carefully review requirements documents in order to identify key concerns and their effects. Concern mining tools based on NLP techniques can help in this activity. Nonetheless, existing tools cannot always detect all the crosscutting effects of a given concern on different requirements sections, as this detection requires a semantic analysis of the text. In this work, we describe an automated tool called REAssistant that supports the extraction of semantic information from textual use cases in order to reveal latent crosscutting concerns. To enable the analysis of use cases, we apply a tandem of advanced NLP techniques (e.g, dependency parsing, semantic role labeling, and domain actions) built on the UIMA framework, which generates different annotations for the use cases. Then, REAssistant allows analysts to query these annotations via concern-specific rules in order to identify all the effects of a given concern. The REAssistant tool has been evaluated with several case-studies, showing good results when compared to a manual identification of concerns and a third-party tool. In particular, the tool achieved a remarkable recall regarding the detection of crosscutting concern effects.     

Beyond social graphs: mining patterns underlying social interactions

This work aims at discovering and extracting relevant patterns underlying social interactions. To do so, some knowledge extracted from Facebook, a social networking site, is formalised by means of an Extended Social Graph, a data structure which goes beyond the original concept of a social graph by also incorporating information on interests. When the Extended Social Graph is built, state-of-the-art techniques are applied over it in order to discover communities. Once these social communities are found, statistical techniques will look for relevant patterns common to each of those, in such a way that each cluster of users is characterised by a set of common features. The resulting knowledge will be used to develop and evaluate a social recommender system, which aims at suggesting users in a social network with possible friends or interests.