Competence Monitoring in Project Teams by using Web based portfolio management systems

This paper reports a learning experience related to the acquisition of project management competences. Students from three different universities and backgrounds, cooperate in a common project that drives the learning-teaching process. Previous related works on this initiative have already evaluated the goodness of this multidisciplinary, project-based learning approach in the context of a new educative paradigm. Yet the innovative experience has allowed the authors to define a rubric in order to measure specific competences in project management. The study shows the rubric’s main aspects as well as competence acquisition evaluation alternatives, based in the metrics defined. Key indicators and specific reports obtained from data base fields in the web tool will support this work. As a result, new competences can be assessed, such ones like teamwork, problem solving, communication and leadership. Final goal is to provide an overall competence map to the students at the same time they improve their skills.

Ultra Low Power FPGA-Based Architecture for Wake-up Radio in Wireless Sensor Networks

In this paper the capabilities of ultra low power FPGAs to implement Wake-up Radios (WuR) for ultra low energy Wireless Sensor Networks (WSNs) are analyzed. The main goal is to evaluate the utilization of very low power configurable devices to take advantage of their speed, flexibility and low power consumption instead of the more common approaches based on ASICs or microcontrollers. In this context, energy efficiency is a key aspect, considering that usually the instant power consumption is considered a figure of merit, more than the total energy consumed by the application.

Making Transport Safer: V2V-Based Automated Emergency Braking System

An important goal in the field of intelligent transportation systems (ITS) is to provide driving aids aimed at preventing accidents and reducing the number of traffic victims. The commonest traffic accidents in urban areas are due to sudden braking that demands a very fast response on the part of drivers. Attempts to solve this problem have motivated many ITS advances including the detection of the intention of surrounding cars using lasers, radars or cameras. However, this might not be enough to increase safety when there is a danger of collision. Vehicle to vehicle communications are needed to ensure that the other intentions of cars are also available. The article describes the development of a controller to perform an emergency stop via an electro-hydraulic braking system employed on dry asphalt. An original V2V communication scheme based on WiFi cards has been used for broadcasting positioning information to other vehicles. The reliability of the scheme has been theoretically analyzed to estimate its performance when the number of vehicles involved is much higher. This controller has been incorporated into the AUTOPIA program control for automatic cars. The system has been implemented in Citroën C3 Pluriel, and various tests were performed to evaluate its operation.

Upper Limb Portable Motion Analysis System Based on Inertial Technology for Neurorehabilitation Purposes

Here an inertial sensor-based monitoring system for measuring and analyzing upper limb movements is presented. The final goal is the integration of this motion-tracking device within a portable rehabilitation system for brain injury patients. A set of four inertial sensors mounted on a special garment worn by the patient provides the quaternions representing the patient upper limb’s orientation in space. A kinematic model is built to estimate 3D upper limb motion for accurate therapeutic evaluation. The human upper limb is represented as a kinematic chain of rigid bodies with three joints and six degrees of freedom. Validation of the system has been performed by co-registration of movements with a commercial optoelectronic tracking system. Successful results are shown that exhibit a high correlation among signals provided by both devices and obtained at the Institut Guttmann Neurorehabilitation Hospital.

Relating goal scheduling, precedence, and memory management in and-parallel execution of logic programs

The interactions among three important issues involved in the implementation of logic programs in parallel (goal scheduling, precedence, and memory management) are discussed. A simplified, parallel memory management model and an efficient, load-balancing goal scheduling strategy are presented. It is shown how, for systems which support "don't know" non-determinism, special care has to be taken during goal scheduling if the space recovery characteristics of sequential systems are to be preserved. A solution based on selecting only "newer" goals for execution is described, and an algorithm is proposed for efficiently maintaining and determining precedence relationships and variable ages across parallel goals. It is argued that the proposed schemes and algorithms make it possible to extend the storage performance of sequential systems to parallel execution without the considerable overhead previously associated with it. The results are applicable to a wide class of parallel and coroutining systems, and they represent an efficient alternative to "all heap" or "spaghetti stack" allocation models.

Intelligent context-aware services based on internet of things architectures

La Internet de las Cosas (IoT), como parte de la Futura Internet, se ha convertido en la actualidad en uno de los principales temas de investigación; en parte gracias a la atención que la sociedad está poniendo en el desarrollo de determinado tipo de servicios (telemetría, generación inteligente de energía, telesanidad, etc.) y por las recientes previsiones económicas que sitúan a algunos actores, como los operadores de telecomunicaciones (que se encuentran desesperadamente buscando nuevas oportunidades), al frente empujando algunas tecnologías interrelacionadas como las comunicaciones Máquina a Máquina (M2M). En este contexto, un importante número de actividades de investigación a nivel mundial se están realizando en distintas facetas: comunicaciones de redes de sensores, procesado de información, almacenamiento de grandes cantidades de datos (big--‐data), semántica, arquitecturas de servicio, etc. Todas ellas, de forma independiente, están llegando a un nivel de madurez que permiten vislumbrar la realización de la Internet de las Cosas más que como un sueño, como una realidad tangible. Sin embargo, los servicios anteriormente mencionados no pueden esperar a desarrollarse hasta que las actividades de investigación obtengan soluciones holísticas completas. Es importante proporcionar resultados intermedios que eviten soluciones verticales realizadas para desarrollos particulares. En este trabajo, nos hemos focalizado en la creación de una plataforma de servicios que pretende facilitar, por una parte la integración de redes de sensores y actuadores heterogéneas y geográficamente distribuidas, y por otra lado el desarrollo de servicios horizontales utilizando dichas redes y la información que proporcionan. Este habilitador se utilizará para el desarrollo de servicios y para la experimentación en la Internet de las Cosas. Previo a la definición de la plataforma, se ha realizado un importante estudio focalizando no sólo trabajos y proyectos de investigación, sino también actividades de estandarización. Los resultados se pueden resumir en las siguientes aseveraciones: a) Los modelos de datos definidos por el grupo “Sensor Web Enablement” (SWE™) del “Open Geospatial Consortium (OGC®)” representan hoy en día la solución más completa para describir las redes de sensores y actuadores así como las observaciones. b) Las interfaces OGC, a pesar de las limitaciones que requieren cambios y extensiones, podrían ser utilizadas como las bases para acceder a sensores y datos. c) Las redes de nueva generación (NGN) ofrecen un buen sustrato que facilita la integración de redes de sensores y el desarrollo de servicios. En consecuencia, una nueva plataforma de Servicios, llamada Ubiquitous Sensor Networks (USN), se ha definido en esta Tesis tratando de contribuir a rellenar los huecos previamente mencionados. Los puntos más destacados de la plataforma USN son: a) Desde un punto de vista arquitectónico, sigue una aproximación de dos niveles (Habilitador y Gateway) similar a otros habilitadores que utilizan las NGN (como el OMA Presence). b) Los modelos de datos están basado en los estándares del OGC SWE. iv c) Está integrado en las NGN pero puede ser utilizado sin ellas utilizando infraestructuras IP abiertas. d) Las principales funciones son: Descubrimiento de sensores, Almacenamiento de observaciones, Publicacion--‐subscripcion--‐notificación, ejecución remota homogénea, seguridad, gestión de diccionarios de datos, facilidades de monitorización, utilidades de conversión de protocolos, interacciones síncronas y asíncronas, soporte para el “streaming” y arbitrado básico de recursos. Para demostrar las funcionalidades que la Plataforma USN propuesta pueden ofrecer a los futuros escenarios de la Internet de las Cosas, se presentan resultados experimentales de tres pruebas de concepto (telemetría, “Smart Places” y monitorización medioambiental) reales a pequeña escala y un estudio sobre semántica (sistema de información vehicular). Además, se está utilizando actualmente como Habilitador para desarrollar tanto experimentación como servicios reales en el proyecto Europeo SmartSantander (que aspira a integrar alrededor de 20.000 dispositivos IoT). v Abstract Internet of Things, as part of the Future Internet, has become one of the main research topics nowadays; in part thanks to the pressure the society is putting on the development of a particular kind of services (Smart metering, Smart Grids, eHealth, etc.), and by the recent business forecasts that situate some players, like Telecom Operators (which are desperately seeking for new opportunities), at the forefront pushing for some interrelated technologies like Machine--‐to--‐Machine (M2M) communications. Under this context, an important number of research activities are currently taking place worldwide at different levels: sensor network communications, information processing, big--‐ data storage, semantics, service level architectures, etc. All of them, isolated, are arriving to a level of maturity that envision the achievement of Internet of Things (IoT) more than a dream, a tangible goal. However, the aforementioned services cannot wait to be developed until the holistic research actions bring complete solutions. It is important to come out with intermediate results that avoid vertical solutions tailored for particular deployments. In the present work, we focus on the creation of a Service--‐level platform intended to facilitate, from one side the integration of heterogeneous and geographically disperse Sensors and Actuator Networks (SANs), and from the other the development of horizontal services using them and the information they provide. This enabler will be used for horizontal service development and for IoT experimentation. Prior to the definition of the platform, we have realized an important study targeting not just research works and projects, but also standardization topics. The results can be summarized in the following assertions: a) Open Geospatial Consortium (OGC®) Sensor Web Enablement (SWE™) data models today represent the most complete solution to describe SANs and observations. b) OGC interfaces, despite the limitations that require changes and extensions, could be used as the bases for accessing sensors and data. c) Next Generation Networks (NGN) offer a good substrate that facilitates the integration of SANs and the development of services. Consequently a new Service Layer platform, called Ubiquitous Sensor Networks (USN), has been defined in this Thesis trying to contribute to fill in the previous gaps. The main highlights of the proposed USN Platform are: a) From an architectural point of view, it follows a two--‐layer approach (Enabler and Gateway) similar to other enablers that run on top of NGN (like the OMA Presence). b) Data models and interfaces are based on the OGC SWE standards. c) It is integrated in NGN but it can be used without it over open IP infrastructures. d) Main functions are: Sensor Discovery, Observation Storage, Publish--‐Subscribe--‐Notify, homogeneous remote execution, security, data dictionaries handling, monitoring facilities, authorization support, protocol conversion utilities, synchronous and asynchronous interactions, streaming support and basic resource arbitration. vi In order to demonstrate the functionalities that the proposed USN Platform can offer to future IoT scenarios, some experimental results have been addressed in three real--‐life small--‐scale proofs--‐of concepts (Smart Metering, Smart Places and Environmental monitoring) and a study for semantics (in--‐vehicle information system). Furthermore we also present the current use of the proposed USN Platform as an Enabler to develop experimentation and real services in the SmartSantander EU project (that aims at integrating around 20.000 IoT devices).

Response threshold models, stochastic learning automata and ant colony optimization-based decentralized self-coordination algorithms for heterogeneous multi-tasks distribution in multi-robot systems

In recent decades, there has been an increasing interest in systems comprised of several autonomous mobile robots, and as a result, there has been a substantial amount of development in the eld of Articial Intelligence, especially in Robotics. There are several studies in the literature by some researchers from the scientic community that focus on the creation of intelligent machines and devices capable to imitate the functions and movements of living beings. Multi-Robot Systems (MRS) can often deal with tasks that are dicult, if not impossible, to be accomplished by a single robot. In the context of MRS, one of the main challenges is the need to control, coordinate and synchronize the operation of multiple robots to perform a specic task. This requires the development of new strategies and methods which allow us to obtain the desired system behavior in a formal and concise way. This PhD thesis aims to study the coordination of multi-robot systems, in particular, addresses the problem of the distribution of heterogeneous multi-tasks. The main interest in these systems is to understand how from simple rules inspired by the division of labor in social insects, a group of robots can perform tasks in an organized and coordinated way. We are mainly interested on truly distributed or decentralized solutions in which the robots themselves, autonomously and in an individual manner, select a particular task so that all tasks are optimally distributed. In general, to perform the multi-tasks distribution among a team of robots, they have to synchronize their actions and exchange information. Under this approach we can speak of multi-tasks selection instead of multi-tasks assignment, which means, that the agents or robots select the tasks instead of being assigned a task by a central controller. The key element in these algorithms is the estimation ix of the stimuli and the adaptive update of the thresholds. This means that each robot performs this estimate locally depending on the load or the number of pending tasks to be performed. In addition, it is very interesting the evaluation of the results in function in each approach, comparing the results obtained by the introducing noise in the number of pending loads, with the purpose of simulate the robot's error in estimating the real number of pending tasks. The main contribution of this thesis can be found in the approach based on self-organization and division of labor in social insects. An experimental scenario for the coordination problem among multiple robots, the robustness of the approaches and the generation of dynamic tasks have been presented and discussed. The particular issues studied are: Threshold models: It presents the experiments conducted to test the response threshold model with the objective to analyze the system performance index, for the problem of the distribution of heterogeneous multitasks in multi-robot systems; also has been introduced additive noise in the number of pending loads and has been generated dynamic tasks over time. Learning automata methods: It describes the experiments to test the learning automata-based probabilistic algorithms. The approach was tested to evaluate the system performance index with additive noise and with dynamic tasks generation for the same problem of the distribution of heterogeneous multi-tasks in multi-robot systems. Ant colony optimization: The goal of the experiments presented is to test the ant colony optimization-based deterministic algorithms, to achieve the distribution of heterogeneous multi-tasks in multi-robot systems. In the experiments performed, the system performance index is evaluated by introducing additive noise and dynamic tasks generation over time.

Environmental variables modeling based on remote sensing data using time series analysis: forestry and agricultural applications

En la actualidad, el seguimiento de la dinámica de los procesos medio ambientales está considerado como un punto de gran interés en el campo medioambiental. La cobertura espacio temporal de los datos de teledetección proporciona información continua con una alta frecuencia temporal, permitiendo el análisis de la evolución de los ecosistemas desde diferentes escalas espacio-temporales. Aunque el valor de la teledetección ha sido ampliamente probado, en la actualidad solo existe un número reducido de metodologías que permiten su análisis de una forma cuantitativa. En la presente tesis se propone un esquema de trabajo para explotar las series temporales de datos de teledetección, basado en la combinación del análisis estadístico de series de tiempo y la fenometría. El objetivo principal es demostrar el uso de las series temporales de datos de teledetección para analizar la dinámica de variables medio ambientales de una forma cuantitativa. Los objetivos específicos son: (1) evaluar dichas variables medio ambientales y (2) desarrollar modelos empíricos para predecir su comportamiento futuro. Estos objetivos se materializan en cuatro aplicaciones cuyos objetivos específicos son: (1) evaluar y cartografiar estados fenológicos del cultivo del algodón mediante análisis espectral y fenometría, (2) evaluar y modelizar la estacionalidad de incendios forestales en dos regiones bioclimáticas mediante modelos dinámicos, (3) predecir el riesgo de incendios forestales a nivel pixel utilizando modelos dinámicos y (4) evaluar el funcionamiento de la vegetación en base a la autocorrelación temporal y la fenometría. Los resultados de esta tesis muestran la utilidad del ajuste de funciones para modelizar los índices espectrales AS1 y AS2. Los parámetros fenológicos derivados del ajuste de funciones permiten la identificación de distintos estados fenológicos del cultivo del algodón. El análisis espectral ha demostrado, de una forma cuantitativa, la presencia de un ciclo en el índice AS2 y de dos ciclos en el AS1 así como el comportamiento unimodal y bimodal de la estacionalidad de incendios en las regiones mediterránea y templada respectivamente. Modelos autorregresivos han sido utilizados para caracterizar la dinámica de la estacionalidad de incendios y para predecir de una forma muy precisa el riesgo de incendios forestales a nivel pixel. Ha sido demostrada la utilidad de la autocorrelación temporal para definir y caracterizar el funcionamiento de la vegetación a nivel pixel. Finalmente el concepto “Optical Functional Type” ha sido definido, donde se propone que los pixeles deberían ser considerados como unidades temporales y analizados en función de su dinámica temporal. ix SUMMARY A good understanding of land surface processes is considered as a key subject in environmental sciences. The spatial-temporal coverage of remote sensing data provides continuous observations with a high temporal frequency allowing the assessment of ecosystem evolution at different temporal and spatial scales. Although the value of remote sensing time series has been firmly proved, only few time series methods have been developed for analyzing this data in a quantitative and continuous manner. In the present dissertation a working framework to exploit Remote Sensing time series is proposed based on the combination of Time Series Analysis and phenometric approach. The main goal is to demonstrate the use of remote sensing time series to analyze quantitatively environmental variable dynamics. The specific objectives are (1) to assess environmental variables based on remote sensing time series and (2) to develop empirical models to forecast environmental variables. These objectives have been achieved in four applications which specific objectives are (1) assessing and mapping cotton crop phenological stages using spectral and phenometric analyses, (2) assessing and modeling fire seasonality in two different ecoregions by dynamic models, (3) forecasting forest fire risk on a pixel basis by dynamic models, and (4) assessing vegetation functioning based on temporal autocorrelation and phenometric analysis. The results of this dissertation show the usefulness of function fitting procedures to model AS1 and AS2. Phenometrics derived from function fitting procedure makes it possible to identify cotton crop phenological stages. Spectral analysis has demonstrated quantitatively the presence of one cycle in AS2 and two in AS1 and the unimodal and bimodal behaviour of fire seasonality in the Mediterranean and temperate ecoregions respectively. Autoregressive models has been used to characterize the dynamics of fire seasonality in two ecoregions and to forecasts accurately fire risk on a pixel basis. The usefulness of temporal autocorrelation to define and characterized land surface functioning has been demonstrated. And finally the “Optical Functional Types” concept has been proposed, in this approach pixels could be as temporal unities based on its temporal dynamics or functioning.

An abstract machine based execution model for computer architecture design and efficient implementation of logic programs in parallel.

The term "Logic Programming" refers to a variety of computer languages and execution models which are based on the traditional concept of Symbolic Logic. The expressive power of these languages offers promise to be of great assistance in facing the programming challenges of present and future symbolic processing applications in Artificial Intelligence, Knowledge-based systems, and many other areas of computing. The sequential execution speed of logic programs has been greatly improved since the advent of the first interpreters. However, higher inference speeds are still required in order to meet the demands of applications such as those contemplated for next generation computer systems. The execution of logic programs in parallel is currently considered a promising strategy for attaining such inference speeds. Logic Programming in turn appears as a suitable programming paradigm for parallel architectures because of the many opportunities for parallel execution present in the implementation of logic programs. This dissertation presents an efficient parallel execution model for logic programs. The model is described from the source language level down to an "Abstract Machine" level suitable for direct implementation on existing parallel systems or for the design of special purpose parallel architectures. Few assumptions are made at the source language level and therefore the techniques developed and the general Abstract Machine design are applicable to a variety of logic (and also functional) languages. These techniques offer efficient solutions to several areas of parallel Logic Programming implementation previously considered problematic or a source of considerable overhead, such as the detection and handling of variable binding conflicts in AND-Parallelism, the specification of control and management of the execution tree, the treatment of distributed backtracking, and goal scheduling and memory management issues, etc. A parallel Abstract Machine design is offered, specifying data areas, operation, and a suitable instruction set. This design is based on extending to a parallel environment the techniques introduced by the Warren Abstract Machine, which have already made very fast and space efficient sequential systems a reality. Therefore, the model herein presented is capable of retaining sequential execution speed similar to that of high performance sequential systems, while extracting additional gains in speed by efficiently implementing parallel execution. These claims are supported by simulations of the Abstract Machine on sample programs.

Dimensional synthesis of a spherical parallel manipulator based on the evaluation of global performance indexes

In this work, the dimensional synthesis of a spherical Parallel Manipulator (PM) with a -1S kinematic chain is presented. The goal of the synthesis is to find a set of parameters that defines the PM with the best performance in terms of workspace capabilities, dexterity and isotropy. The PM is parametrized in terms of a reference element, and a non-directed search of these parameters is carried out. First, the inverse kinematics and instantaneous kinematics of the mechanism are presented. The latter is found using the screw theory formulation. An algorithm that explores a bounded set of parameters and determines the corresponding value of global indexes is presented. The concepts of a novel global performance index and a compound index are introduced. Simulation results are shown and discussed. The best PMs found in terms of each performance index evaluated are locally analyzed in terms of its workspace and local dexterity. The relationship between the performance of the PM and its parameters is discussed, and a prototype with the best performance in terms of the compound index is presented and analyzed.

HF spectrum activity prediction model based on HMM for cognitive radio applications

Although most of the research on Cognitive Radio is focused on communication bands above the HF upper limit (30 MHz), Cognitive Radio principles can also be applied to HF communications to make use of the extremely scarce spectrum more efficiently. In this work we consider legacy users as primary users since these users transmit without resorting to any smart procedure, and our stations using the HFDVL (HF Data+Voice Link) architecture as secondary users. Our goal is to enhance an efficient use of the HF band by detecting the presence of uncoordinated primary users and avoiding collisions with them while transmitting in different HF channels using our broad-band HF transceiver. A model of the primary user activity dynamics in the HF band is developed in this work to make short-term predictions of the sojourn time of a primary user in the band and avoid collisions. It is based on Hidden Markov Models (HMM) which are a powerful tool for modelling stochastic random processes and are trained with real measurements of the 14 MHz band. By using the proposed HMM based model, the prediction model achieves an average 10.3% prediction error rate with one minute-long channel knowledge but it can be reduced when this knowledge is extended: with the previous 8 min knowledge, an average 5.8% prediction error rate is achieved. These results suggest that the resulting activity model for the HF band could actually be used to predict primary users activity and included in a future HF cognitive radio based station.

Goal-oriented self-adaptive hp-strategies for finite element analysis of electromagnetic scattering and radiation problems

In this paper, a fully automatic goal-oriented hp-adaptive finite element strategy for open region electromagnetic problems (radiation and scattering) is presented. The methodology leads to exponential rates of convergence in terms of an upper bound of an user-prescribed quantity of interest. Thus, the adaptivity may be guided to provide an optimal error, not globally for the field in the whole finite element domain, but for specific parameters of engineering interest. For instance, the error on the numerical computation of the S-parameters of an antenna array, the field radiated by an antenna, or the Radar Cross Section on given directions, can be minimized. The efficiency of the approach is illustrated with several numerical simulations with two dimensional problem domains. Results include the comparison with the previously developed energy-norm based hp-adaptivity.

Integrated luminescent chemical microsensors based on GaN LEDs for security applications using smartphones

Development of PCB-integrateable microsensors for monitoring chemical species is a goal in areas such as lab-on-a-chip analytical devices, diagnostics medicine and electronics for hand-held instruments where the device size is a major issue. Cellular phones have pervaded the world inhabitants and their usefulness has dramatically increased with the introduction of smartphones due to a combination of amazing processing power in a confined space, geolocalization and manifold telecommunication features. Therefore, a number of physical and chemical sensors that add value to the terminal for health monitoring, personal safety (at home, at work) and, eventually, national security have started to be developed, capitalizing also on the huge number of circulating cell phones. The chemical sensor-enabled “super” smartphone provides a unique (bio)sensing platform for monitoring airborne or waterborne hazardous chemicals or microorganisms for both single user and crowdsourcing security applications. Some of the latest ones are illustrated by a few examples. Moreover, we have recently achieved for the first time (covalent) functionalization of p- and n-GaN semiconductor surfaces with tuneable luminescent indicator dyes of the Ru-polypyridyl family, as a key step in the development of innovative microsensors for smartphone applications. Chemical “sensoring” of GaN-based blue LED chips with those indicators has also been achieved by plasma treatment of their surface, and the micrometer-sized devices have been tested to monitor O2 in the gas phase to show their full functionality. Novel strategies to enhance the sensor sensitivity such as changing the length and nature of the siloxane buffer layer are discussed in this paper.

A Recommendation Framework Based on the Analytic Network Process and its Application in the Semantic Technology Domain

When users face a certain problem needing a product, service, or action to solve it, selecting the best alternative among them can be a dicult task due to the uncertainty of their quality. This is especially the case in the domains where users do not have an expertise, like for example in Software Engineering. Multiple criteria decision making (MCDM) methods are methods that help making better decisions when facing the complex problem of selecting the best solution among a group of alternatives that can be compared according to different conflicting criteria. In MCDM problems, alternatives represent concrete products, services or actions that will help in achieving a goal, while criteria represent the characteristics of these alternatives that are important for making a decision.

Wake-up architecture for Wireless sensor nodes based on ultra low power FPGA

In this work a novel wake-up architecture for wireless sensor nodes based on ultra low power FPGA is presented. A simple wake up messaging mechanism for data gathering applications is proposed. The main goal of this work is to evaluate the utilization of low power configurable devices to take advantage of their speed, flexibility and low power consumption compared with traditional approaches, based on ASICs or microcontrollers, for frame decoding and data control. A test bed based on infrared communications has been built to validate the messaging mechanism and the processing architecture.