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.     

RUSHES—an annotation and retrieval engine for multimedia semantic units

Multimedia analysis and reuse of raw un-edited audio visual content known as rushes is gaining acceptance by a large number of research labs and companies. A set of research projects are considering multimedia indexing, annotation, search and retrieval in the context of European funded research, but only the FP6 project RUSHES is focusing on automatic semantic annotation, indexing and retrieval of raw and un-edited audio-visual content. Even professional content creators and providers as well as home-users are dealing with this type of content and therefore novel technologies for semantic search and retrieval are required. In this paper, we present a summary of the most relevant achievements of the RUSHES project, focusing on specific approaches for automatic annotation as well as the main features of the final RUSHES search engine.     

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.     

Aircraft design optimization

In this paper we describe briefly a set of procedures for the optimal design of full mission aerospace systems. This involves multi-physics simulations at various fidelity levels, surrogates, distributed computing and multi-objective optimization. Low-fidelity analysis is used to populate a database of inputs and outputs of the system simulation and Neural Networks are then designed to generate inexpensive surrogates. Higher fidelity is used only where is warranted and also to do a local exploration after global optimization techniques have been used on the surrogates in order to provide plausible initial values. The ideas are exemplified on a generic supersonic aircraft configuration, where one of the main goals is to reduce the ground sonic boom.     

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.     

A compiler for parsing schemata

We present a compiler that can be used to automatically obtain efficient Java implementations of parsing algorithms from formal specifications expressed as parsing schemata. The system performs an analysis of the inference rules in the input schemata in order to determine the best data structures and indexes to use, and to ensure that the generated implementations are efficient. The system described is general enough to be able to handle all kinds of schemata for different grammar formalisms, such as context‐free grammars and tree‐adjoining grammars, and it provides an extensibility mechanism allowing the user to define custom notational elements. This compiler has proven very useful for analyzing, prototyping and comparing natural‐language parsers in real domains, as can be seen in the empirical examples provided at the end of the paper. Copyright © 2008 John Wiley & Sons, Ltd.     

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.