Heterogeneous multicore systems for signal processing

This thesis explores the capabilities of heterogeneous multi-core systems, based on multiple Graphics Processing Units (GPUs) in a standard desktop framework. Multi-GPU accelerated desk side computers are an appealing alternative to other high performance computing (HPC) systems: being composed of commodity hardware components fabricated in large quantities, their price-performance ratio is unparalleled in the world of high performance computing. Essentially bringing “supercomputing to the masses”, this opens up new possibilities for application fields where investing in HPC resources had been considered unfeasible before. One of these is the field of bioelectrical imaging, a class of medical imaging technologies that occupy a low-cost niche next to million-dollar systems like functional Magnetic Resonance Imaging (fMRI). In the scope of this work, several computational challenges encountered in bioelectrical imaging are tackled with this new kind of computing resource, striving to help these methods approach their true potential. Specifically, the following main contributions were made: Firstly, a novel dual-GPU implementation of parallel triangular matrix inversion (TMI) is presented, addressing an crucial kernel in computation of multi-mesh head models of encephalographic (EEG) source localization. This includes not only a highly efficient implementation of the routine itself achieving excellent speedups versus an optimized CPU implementation, but also a novel GPU-friendly compressed storage scheme for triangular matrices. Secondly, a scalable multi-GPU solver for non-hermitian linear systems was implemented. It is integrated into a simulation environment for electrical impedance tomography (EIT) that requires frequent solution of complex systems with millions of unknowns, a task that this solution can perform within seconds. In terms of computational throughput, it outperforms not only an highly optimized multi-CPU reference, but related GPU-based work as well. Finally, a GPU-accelerated graphical EEG real-time source localization software was implemented. Thanks to acceleration, it can meet real-time requirements in unpreceeded anatomical detail running more complex localization algorithms. Additionally, a novel implementation to extract anatomical priors from static Magnetic Resonance (MR) scansions has been included.

Microscopic Modeling on complex networks

The field of complex systems is a growing body of knowledge, It can be applied to countless different topics, from physics to computer science, biology, information theory and sociology. The main focus of this work is the use of microscopic models to study the behavior of urban mobility, which characteristics make it a paradigmatic example of complexity. In particular, simulations are used to investigate phase changes in a finite size open Manhattan-like urban road network under different traffic conditions, in search for the parameters to identify phase transitions, equilibrium and non-equilibrium conditions . It is shown how the flow-density macroscopic fundamental diagram of the simulation shows,like real traffic, hysteresis behavior in the transition from the congested phase to the free flow phase, and how the different regimes can be identified studying the statistics of road occupancy.

Diffusion in polymer systems studied by fluorescence correlation spectroscopy

Fluorescence correlation spectroscopy (FCS) is a powerful technique to determine the diffusion of fluorescence molecules in various environments. The technique is based on detecting and analyzing the fluctuation of fluorescence light emitted by fluorescence species diffusing through a small and fixed observation volume, formed by a laser focused into the sample. Because of its great potential and high versatility in addressing the diffusion and transport properties in complex systems, FCS has been successfully applied to a great variety of systems. In my thesis, I focused on the application of FCS to study the diffusion of fluorescence molecules in organic environments, especially in polymer melts. In order to examine our FCS setup and a developed measurement protocol, I first utilized FCS to measure tracer diffusion in polystyrene (PS) solutions, for which abundance data exist in the literature. I studied molecular and polymeric tracer diffusion in polystyrene solutions over a broad range of concentrations and different tracer and matrix molecular weights (Mw). Then FCS was further established to study tracer dynamics in polymer melts. In this part I investigated the diffusion of molecular tracers in linear flexible polymer melts [polydimethylsiloxane (PDMS), polyisoprene (PI)], a miscible polymer blend [PI and poly vinyl ethylene (PVE)], and star-shaped polymer [3-arm star polyisoprene (SPI)]. The effects of tracer sizes, polymer Mw, polymer types, and temperature on the diffusion coefficients of small tracers were discussed. The distinct topology of the host polymer, i.e. star polymer melt, revealed the notably different motion of the small tracer, as compared to its linear counterpart. Finally, I emphasized the advantage of the small observation volume which allowed FCS to investigate the tracer diffusions in heterogeneous systems; a swollen cross-linked PS bead and silica inverse opals, where high spatial resolution technique was required.

Modeling and simulations of nanoparticles in liquid crystalline systems

The aim of this work is to investigate, using extensive Monte Carlo computer simulations, composite materials consisting of liquid crystals doped with nanoparticles. These systems are currently of great interest as they offer the possibility of tuning the properties of liquid crystals used in displays and other devices as well as providing a way of obtaining regularly organized systems of nanoparticles exploiting the molecular organization of the liquid crystal medium. Surprisingly enough, there is however a lack of fundamental knowledge on the properties and phase behavior of these hybrid materials, making the route to their application an essentially empirical one. Here we wish to contribute to the much needed rationalization of these systems studying some basic effects induced by different nanoparticles on a liquid crystal host. We investigate in particular the effects of nanoparticle shape, size and polarity as well as of their affinity to the liquid crystal solvent on the stability of the system, monitoring phase transitions, order and molecular organizations. To do this we have proposed a coarse grained approach where nanoparticles are modelled as a suitably shaped (spherical, rod and disk like) collection of spherical Lennard-Jones beads, while the mesogens are represented with Gay-Berne particles. We find that the addition of apolar nanoparticles of different shape typically lowers the nematic–isotropic transition of a non-polar nematic, with the destabilization being greater for spherical nanoparticles. For polar mesogens we have studied the effect of solvent affinity of the nanoparticles showing that aggregation takes places for low solvation values. Interestingly, if the nanoparticles are polar the aggregates contribute to stabilizing the system, compensating the shape effect. We thus find the overall effects on stability to be a delicate balance of often contrasting contributions pointing to the relevance of simulations studies for understanding these complex systems.

EPR spectroscopy as a tool for the elucidation of non-covalent structuring principles in complex soft matter

This thesis aims at connecting structural and functional changes of complex soft matter systems due to external stimuli with non-covalent molecular interaction profiles. It addresses the problem of elucidating non-covalent forces as structuring principle of mainly polymer-based systems in solution. The structuring principles of a wide variety of complex soft matter types are analyzed. In many cases this is done by exploring conformational changes upon the exertion of external stimuli. The central question throughout this thesis is how a certain non-covalent interaction profile leads to solution condition-dependent structuring of a polymeric system.rnTo answer this question, electron paramagnetic resonance (EPR) spectroscopy is chosen as the main experimental method for the investigation of the structure principles of polymers. With EPR one detects only the local surroundings or environments of molecules that carry an unpaired electron. Non-covalent forces are normally effective on length scales of a few nanometers and below. Thus, EPR is excellently suited for their investigations. It allows for detection of interactions on length scales ranging from approx. 0.1 nm up to 10 nm. However, restriction to only one experimental technique likely leads to only incomplete pictures of complex systems. Therefore, the presented studies are frequently augmented with further experimental and computational methods in order to yield more comprehensive descriptions of the systems chosen for investigation.rnElectrostatic correlation effects in non-covalent interaction profiles as structuring principles in colloid-like ionic clusters and DNA condensation are investigated first. Building on this it is shown how electrostatic structuring principles can be combined with hydrophobic ones, at the example of host-guest interactions in so-called dendronized polymers (denpols).rnSubsequently, the focus is shifted from electrostatics in dendronized polymers to thermoresponsive alkylene oxide-based materials, whose structuring principles are based on hydrogen bonds and counteracting hydrophobic interactions. The collapse mechanism in dependence of hydrophilic-hydrophobic balance and topology of these polymers is elucidated. Complementarily the temperature-dependent phase behavior of elastin-like polypeptides (ELPs) is investigated. ELPs are the first (and so far only) class of compounds that is shown to feature a first-order inverse phase transition on nanoscopic length scales.rnFinally, this thesis addresses complex biological systems, namely intrinsically disordered proteins (IDPs). It is shown that the conformational space of the IDPs Osteopontin (OPN), a cytokine involved in metastasis of several kinds of cancer, and BASP1 (brain acid soluble protein one), a protein associated with neurite outgrowth, is governed by a subtle interplay between electrostatic forces, hydrophobic interaction, system entropy and hydrogen bonds. Such, IDPs can even sample cooperatively folded structures, which have so far only been associated with globular proteins.

Management control systems : subcomponents, optimal design and the role of time as a contingency

Management Control System (MCS) research is undergoing turbulent times. For a long time related to cybernetic instruments of management accounting only, MCS are increasingly seen as complex systems comprising not only formal accounting-driven instruments, but also informal mechanisms of control based on organizational culture. But not only have the means of MCS changed; researchers increasingly ap-ply MCS to organizational goals other than strategy implementation.rnrnTaking the question of "How do I design a well-performing MCS?" as a starting point, this dissertation aims at providing a comprehensive and integrated overview of the "current-state" of MCS research. Opting for a definition of MCS, broad in terms of means (all formal as well as informal MCS instruments), but focused in terms of objectives (behavioral control only), the dissertation contributes to MCS theory by, a) developing an integrated (contingency) model of MCS, describing its contingencies, as well as its subcomponents, b) refining the equifinality model of Gresov/Drazin (1997), c) synthesizing research findings from contingency and configuration research concerning MCS, taking into account case studies on research topics such as ambi-dexterity, equifinality and time as a contingency.

Diffusion in heterogeneous systems studied by laser scanning confocal microscopy and fluorescence correlation spectroscopy

Understanding and controlling the mechanism of the diffusion of small molecules, macromolecules and nanoparticles in heterogeneous environments is of paramount fundamental and technological importance. The aim of the thesis is to show, how by studying the tracer diffusion in complex systems, one can obtain information about the tracer itself, and the system where the tracer is diffusing. rnIn the first part of my thesis I will introduce the Fluorescence Correlation Spectroscopy (FCS) which is a powerful tool to investigate the diffusion of fluorescent species in various environments. By using the main advantage of FCS namely the very small probing volume (<1µm3) I was able to track the kinetics of phase separation in polymer blends at late stages by looking on the molecular tracer diffusion in individual domains of the heterogeneous structure of the blend. The phase separation process at intermediate stages was monitored with laser scanning confocal microscopy (LSCM) in real time providing images of droplet coalescence and growth. rnIn a further project described in my thesis I will show that even when the length scale of the heterogeneities becomes smaller than the FCS probing volume one can still obtain important microscopic information by studying small tracer diffusion. To do so, I will introduce a system of star shaped polymer solutions and will demonstrate that the mobility of small molecular tracers on microscopic level is nearly not affected by the transition of the polymer system to a “glassy” macroscopic state. rnIn the last part of the thesis I will introduce and describe a new stimuli responsive system which I have developed, that combines two levels of nanoporosity. The system is based on poly-N-isopropylacrylamide (PNIPAM) and silica inverse opals (iOpals), and allows controlling the diffusion of tracer molecules. rn

Modelling and simulating in systems biology: an approach based on multi-agent systems

Systems Biology is an innovative way of doing biology recently raised in bio-informatics contexts, characterised by the study of biological systems as complex systems with a strong focus on the system level and on the interaction dimension. In other words, the objective is to understand biological systems as a whole, putting on the foreground not only the study of the individual parts as standalone parts, but also of their interaction and of the global properties that emerge at the system level by means of the interaction among the parts. This thesis focuses on the adoption of multi-agent systems (MAS) as a suitable paradigm for Systems Biology, for developing models and simulation of complex biological systems. Multi-agent system have been recently introduced in informatics context as a suitabe paradigm for modelling and engineering complex systems. Roughly speaking, a MAS can be conceived as a set of autonomous and interacting entities, called agents, situated in some kind of nvironment, where they fruitfully interact and coordinate so as to obtain a coherent global system behaviour. The claim of this work is that the general properties of MAS make them an effective approach for modelling and building simulations of complex biological systems, following the methodological principles identified by Systems Biology. In particular, the thesis focuses on cell populations as biological systems. In order to support the claim, the thesis introduces and describes (i) a MAS-based model conceived for modelling the dynamics of systems of cells interacting inside cell environment called niches. (ii) a computational tool, developed for implementing the models and executing the simulations. The tool is meant to work as a kind of virtual laboratory, on top of which kinds of virtual experiments can be performed, characterised by the definition and execution of specific models implemented as MASs, so as to support the validation, falsification and improvement of the models through the observation and analysis of the simulations. A hematopoietic stem cell system is taken as reference case study for formulating a specific model and executing virtual experiments.

Communication in troubled waters: responses of fish communication systems to changing environments

Fish populations are increasingly being subjected to anthropogenic changes to their sensory environments. The impact of these changes on inter- and intra-specific communication, and its evolutionary consequences, has only recently started to receive research attention. A disruption of the sensory environment is likely to impact communication, especially with respect to reproductive interactions that help to maintain species boundaries. Aquatic ecosystems around the world are being threatened by a variety of environmental stressors, causing dramatic losses of biodiversity and bringing urgency to the need to understand how fish respond to rapid environmental changes. Here, we discuss current research on different communication systems (visual, chemical, acoustic, electric) and explore the state of our knowledge of how complex systems respond to environmental stressors using fish as a model. By far the bulk of our understanding comes from research on visual communication in the context of mate selection and competition for mates, while work on other communication systems is accumulating. In particular, it is increasingly acknowledged that environmental effects on one mode of communication may trigger compensation through other modalities. The strength and direction of selection on communication traits may vary if such compensation occurs. However, we find a dearth of studies that have taken a multimodal approach to investigating the evolutionary impact of environmental change on communication in fish. Future research should focus on the interaction between different modes of communication, especially under changing environmental conditions. Further, we see an urgent need for a better understanding of the evolutionary consequences of changes in communication systems on fish diversity.

Feature selection in the reconstruction of complex network representations of spectral data

Complex networks have been extensively used in the last decade to characterize and analyze complex systems, and they have been recently proposed as a novel instrument for the analysis of spectra extracted from biological samples. Yet, the high number of measurements composing spectra, and the consequent high computational cost, make a direct network analysis unfeasible. We here present a comparative analysis of three customary feature selection algorithms, including the binning of spectral data and the use of information theory metrics. Such algorithms are compared by assessing the score obtained in a classification task, where healthy subjects and people suffering from different types of cancers should be discriminated. Results indicate that a feature selection strategy based on Mutual Information outperforms the more classical data binning, while allowing a reduction of the dimensionality of the data set in two orders of magnitude

Collective Organization of Complex Networks: Emergent Scales, Vulnerability, and Targeting of Desired Dynamics

Cuando una colectividad de sistemas dinámicos acoplados mediante una estructura irregular de interacciones evoluciona, se observan dinámicas de gran complejidad y fenómenos emergentes imposibles de predecir a partir de las propiedades de los sistemas individuales. El objetivo principal de esta tesis es precisamente avanzar en nuestra comprensión de la relación existente entre la topología de interacciones y las dinámicas colectivas que una red compleja es capaz de mantener. Siendo este un tema amplio que se puede abordar desde distintos puntos de vista, en esta tesis se han estudiado tres problemas importantes dentro del mismo que están relacionados entre sí. Por un lado, en numerosos sistemas naturales y artificiales que se pueden describir mediante una red compleja la topología no es estática, sino que depende de la dinámica que se desarrolla en la red: un ejemplo son las redes de neuronas del cerebro. En estas redes adaptativas la propia topología emerge como consecuencia de una autoorganización del sistema. Para conocer mejor cómo pueden emerger espontáneamente las propiedades comúnmente observadas en redes reales, hemos estudiado el comportamiento de sistemas que evolucionan según reglas adaptativas locales con base empírica. Nuestros resultados numéricos y analíticos muestran que la autoorganización del sistema da lugar a dos de las propiedades más universales de las redes complejas: a escala mesoscópica, la aparición de una estructura de comunidades, y, a escala macroscópica, la existencia de una ley de potencias en la distribución de las interacciones en la red. El hecho de que estas propiedades aparecen en dos modelos con leyes de evolución cuantitativamente distintas que siguen unos mismos principios adaptativos sugiere que estamos ante un fenómeno que puede ser muy general, y estar en el origen de estas propiedades en sistemas reales. En segundo lugar, proponemos una medida que permite clasificar los elementos de una red compleja en función de su relevancia para el mantenimiento de dinámicas colectivas. En concreto, estudiamos la vulnerabilidad de los distintos elementos de una red frente a perturbaciones o grandes fluctuaciones, entendida como una medida del impacto que estos acontecimientos externos tienen en la interrupción de una dinámica colectiva. Los resultados que se obtienen indican que la vulnerabilidad dinámica es sobre todo dependiente de propiedades locales, por tanto nuestras conclusiones abarcan diferentes topologías, y muestran la existencia de una dependencia no trivial entre la vulnerabilidad y la conectividad de los elementos de una red. Finalmente, proponemos una estrategia de imposición de una dinámica objetivo genérica en una red dada e investigamos su validez en redes con diversas topologías que mantienen regímenes dinámicos turbulentos. Se obtiene como resultado que las redes heterogéneas (y la amplia mayora de las redes reales estudiadas lo son) son las más adecuadas para nuestra estrategia de targeting de dinámicas deseadas, siendo la estrategia muy efectiva incluso en caso de disponer de un conocimiento muy imperfecto de la topología de la red. Aparte de la relevancia teórica para la comprensión de fenómenos colectivos en sistemas complejos, los métodos y resultados propuestos podrán dar lugar a aplicaciones en sistemas experimentales y tecnológicos, como por ejemplo los sistemas neuronales in vitro, el sistema nervioso central (en el estudio de actividades síncronas de carácter patológico), las redes eléctricas o los sistemas de comunicaciones. ABSTRACT The time evolution of an ensemble of dynamical systems coupled through an irregular interaction scheme gives rise to dynamics of great of complexity and emergent phenomena that cannot be predicted from the properties of the individual systems. The main objective of this thesis is precisely to increase our understanding of the interplay between the interaction topology and the collective dynamics that a complex network can support. This is a very broad subject, so in this thesis we will limit ourselves to the study of three relevant problems that have strong connections among them. First, it is a well-known fact that in many natural and manmade systems that can be represented as complex networks the topology is not static; rather, it depends on the dynamics taking place on the network (as it happens, for instance, in the neuronal networks in the brain). In these adaptive networks the topology itself emerges from the self-organization in the system. To better understand how the properties that are commonly observed in real networks spontaneously emerge, we have studied the behavior of systems that evolve according to local adaptive rules that are empirically motivated. Our numerical and analytical results show that self-organization brings about two of the most universally found properties in complex networks: at the mesoscopic scale, the appearance of a community structure, and, at the macroscopic scale, the existence of a power law in the weight distribution of the network interactions. The fact that these properties show up in two models with quantitatively different mechanisms that follow the same general adaptive principles suggests that our results may be generalized to other systems as well, and they may be behind the origin of these properties in some real systems. We also propose a new measure that provides a ranking of the elements in a network in terms of their relevance for the maintenance of collective dynamics. Specifically, we study the vulnerability of the elements under perturbations or large fluctuations, interpreted as a measure of the impact these external events have on the disruption of collective motion. Our results suggest that the dynamic vulnerability measure depends largely on local properties (our conclusions thus being valid for different topologies) and they show a non-trivial dependence of the vulnerability on the connectivity of the network elements. Finally, we propose a strategy for the imposition of generic goal dynamics on a given network, and we explore its performance in networks with different topologies that support turbulent dynamical regimes. It turns out that heterogeneous networks (and most real networks that have been studied belong in this category) are the most suitable for our strategy for the targeting of desired dynamics, the strategy being very effective even when the knowledge on the network topology is far from accurate. Aside from their theoretical relevance for the understanding of collective phenomena in complex systems, the methods and results here discussed might lead to applications in experimental and technological systems, such as in vitro neuronal systems, the central nervous system (where pathological synchronous activity sometimes occurs), communication systems or power grids.

Resilience in Cloud Computing for Critical Systems. Resilience, Dependability, Reliability in the use of Cloud Computing in Critical Systems in Public Organizations.

La informática se está convirtiendo en la quinta utilidad (gas, agua, luz, teléfono) en parte debido al impacto de Cloud Computing en las mayorías de las organizaciones. Este uso de informática es usada por cada vez más tipos de sistemas, incluidos Sistemas Críticos. Esto tiene un impacto en la complejidad internad y la fiabilidad de los sistemas de la organización y los que se ofrecen a los clientes. Este trabajo investiga el uso de Cloud Computing por sistemas críticos, centrándose en las dependencias y especialmente en la fiabilidad de estos sistemas. Se han presentado algunos ejemplos de su uso, y aunque su utilización en sistemas críticos no está extendido, se presenta cual puede llegar a ser su impacto. El objetivo de este trabajo es primero definir un modelo que pueda representar de una forma cuantitativa las interdependencias en fiabilidad y interdependencia para las organizaciones que utilicen estos sistemas, y aplicar este modelo en un sistema crítico del campo de sanidad y mostrar sus resultados. Los conceptos de “macro-dependability” y “micro-dependability” son introducidos en el modelo para la definición de interdependencia y para analizar la fiabilidad de sistemas que dependen de otros sistemas. ABSTRACT With the increasing utilization of Internet services and cloud computing by most organizations (both private and public), it is clear that computing is becoming the 5th utility (along with water, electricity, telephony and gas). These technologies are used for almost all types of systems, and the number is increasing, including Critical Infrastructure systems. Even if Critical Infrastructure systems appear not to rely directly on cloud services, there may be hidden inter-dependencies. This is true even for private cloud computing, which seems more secure and reliable. The critical systems can began in some cases with a clear and simple design, but evolved as described by Egan to "rafted" networks. Because they are usually controlled by one or few organizations, even when they are complex systems, their dependencies can be understood. The organization oversees and manages changes. These CI systems have been affected by the introduction of new ICT models like global communications, PCs and the Internet. Even virtualization took more time to be adopted by Critical systems, due to their strategic nature, but once that these technologies have been proven in other areas, at the end they are adopted as well, for different reasons such as costs. A new technology model is happening now based on some previous technologies (virtualization, distributing and utility computing, web and software services) that are offered in new ways and is called cloud computing. The organizations are migrating more services to the cloud; this will have impact in their internal complexity and in the reliability of the systems they are offering to the organization itself and their clients. Not always this added complexity and associated risks to their reliability are seen. As well, when two or more CI systems are interacting, the risks of one can affect the rest, sharing the risks. This work investigates the use of cloud computing by critical systems, and is focused in the dependencies and reliability of these systems. Some examples are presented together with the associated risks. A framework is introduced for analysing the dependability and resilience of a system that relies on cloud services and how to improve them. As part of the framework, the concepts of micro and macro dependability are introduced to explain the internal and external dependability on services supplied by an external cloud. A pharmacovigilance model system has been used for framework validation.

An algorithm for controllability in complex networks

In this paper structural controllability of complex networks is anyzed. A new algorithm is proposed which constructs a structural control scheme for a given network by avoiding the absence of dilations and by guaranteeing the accessibility of all nodes. Such accessibility is solved via a wiring procedure; this procedure, based on determining the non-accessible regions of the network, has been improved in this new proposed algorithm. This way, the number of dedicated controllers is reduced with respect to the one provided by previous existing algorithms.

Time-multiscale methods for the simulation of slow transport problems in atomistic systems

En esta tesis presentamos una teoría adaptada a la simulación de fenómenos lentos de transporte en sistemas atomísticos. En primer lugar, desarrollamos el marco teórico para modelizar colectividades estadísticas de equilibrio. A continuación, lo adaptamos para construir modelos de colectividades estadísticas fuera de equilibrio. Esta teoría reposa sobre los principios de la mecánica estadística, en particular el principio de máxima entropía de Jaynes, utilizado tanto para sistemas en equilibrio como fuera de equilibrio, y la teoría de las aproximaciones del campo medio. Expresamos matemáticamente el problema como un principio variacional en el que maximizamos una entropía libre, en lugar de una energía libre. La formulación propuesta permite definir equivalentes atomísticos de variables macroscópicas como la temperatura y la fracción molar. De esta forma podemos considerar campos macroscópicos no uniformes. Completamos el marco teórico con reglas de cuadratura de Monte Carlo, gracias a las cuales obtenemos modelos computables. A continuación, desarrollamos el conjunto completo de ecuaciones que gobiernan procesos de transporte. Deducimos la desigualdad de disipación entrópica a partir de fuerzas y flujos termodinámicos discretos. Esta desigualdad nos permite identificar la estructura que deben cumplir los potenciales cinéticos discretos. Dichos potenciales acoplan las tasas de variación en el tiempo de las variables microscópicas con las fuerzas correspondientes. Estos potenciales cinéticos deben ser completados con una relación fenomenológica, del tipo definido por la teoría de Onsanger. Por último, aportamos validaciones numéricas. Con ellas ilustramos la capacidad de la teoría presentada para simular propiedades de equilibrio y segregación superficial en aleaciones metálicas. Primero, simulamos propiedades termodinámicas de equilibrio en el sistema atomístico. A continuación evaluamos la habilidad del modelo para reproducir procesos de transporte en sistemas complejos que duran tiempos largos con respecto a los tiempos característicos a escala atómica. ABSTRACT In this work, we formulate a theory to address simulations of slow time transport effects in atomic systems. We first develop this theoretical framework in the context of equilibrium of atomic ensembles, based on statistical mechanics. We then adapt it to model ensembles away from equilibrium. The theory stands on Jaynes' maximum entropy principle, valid for the treatment of both, systems in equilibrium and away from equilibrium and on meanfield approximation theory. It is expressed in the entropy formulation as a variational principle. We interpret atomistic equivalents of macroscopic variables such as the temperature and the molar fractions, wich are not required to be uniform, but can vary from particle to particle. We complement this theory with Monte Carlo summation rules for further approximation. In addition, we provide a framework for studying transport processes with the full set of equations driving the evolution of the system. We first derive a dissipation inequality for the entropic production involving discrete thermodynamic forces and fluxes. This discrete dissipation inequality identifies the adequate structure for discrete kinetic potentials which couple the microscopic field rates to the corresponding driving forces. Those kinetic potentials must finally be expressed as a phenomenological rule of the Onsanger Type. We present several validation cases, illustrating equilibrium properties and surface segregation of metallic alloys. We first assess the ability of a simple meanfield model to reproduce thermodynamic equilibrium properties in systems with atomic resolution. Then, we evaluate the ability of the model to reproduce a long-term transport process in complex systems.

A framework for mixed-criticality partitioned real-time embedded systems

Los sistemas empotrados son cada día más comunes y complejos, de modo que encontrar procesos seguros, eficaces y baratos de desarrollo software dirigidos específicamente a esta clase de sistemas es más necesario que nunca. A diferencia de lo que ocurría hasta hace poco, en la actualidad los avances tecnológicos en el campo de los microprocesadores de los últimos tiempos permiten el desarrollo de equipos con prestaciones más que suficientes para ejecutar varios sistemas software en una única máquina. Además, hay sistemas empotrados con requisitos de seguridad (safety) de cuyo correcto funcionamiento depende la vida de muchas personas y/o grandes inversiones económicas. Estos sistemas software se diseñan e implementan de acuerdo con unos estándares de desarrollo software muy estrictos y exigentes. En algunos casos puede ser necesaria también la certificación del software. Para estos casos, los sistemas con criticidades mixtas pueden ser una alternativa muy valiosa. En esta clase de sistemas, aplicaciones con diferentes niveles de criticidad se ejecutan en el mismo computador. Sin embargo, a menudo es necesario certificar el sistema entero con el nivel de criticidad de la aplicación más crítica, lo que hace que los costes se disparen. La virtualización se ha postulado como una tecnología muy interesante para contener esos costes. Esta tecnología permite que un conjunto de máquinas virtuales o particiones ejecuten las aplicaciones con unos niveles de aislamiento tanto temporal como espacial muy altos. Esto, a su vez, permite que cada partición pueda ser certificada independientemente. Para el desarrollo de sistemas particionados con criticidades mixtas se necesita actualizar los modelos de desarrollo software tradicionales, pues estos no cubren ni las nuevas actividades ni los nuevos roles que se requieren en el desarrollo de estos sistemas. Por ejemplo, el integrador del sistema debe definir las particiones o el desarrollador de aplicaciones debe tener en cuenta las características de la partición donde su aplicación va a ejecutar. Tradicionalmente, en el desarrollo de sistemas empotrados, el modelo en V ha tenido una especial relevancia. Por ello, este modelo ha sido adaptado para tener en cuenta escenarios tales como el desarrollo en paralelo de aplicaciones o la incorporación de una nueva partición a un sistema ya existente. El objetivo de esta tesis doctoral es mejorar la tecnología actual de desarrollo de sistemas particionados con criticidades mixtas. Para ello, se ha diseñado e implementado un entorno dirigido específicamente a facilitar y mejorar los procesos de desarrollo de esta clase de sistemas. En concreto, se ha creado un algoritmo que genera el particionado del sistema automáticamente. En el entorno de desarrollo propuesto, se han integrado todas las actividades necesarias para desarrollo de un sistema particionado, incluidos los nuevos roles y actividades mencionados anteriormente. Además, el diseño del entorno de desarrollo se ha basado en la ingeniería guiada por modelos (Model-Driven Engineering), la cual promueve el uso de los modelos como elementos fundamentales en el proceso de desarrollo. Así pues, se proporcionan las herramientas necesarias para modelar y particionar el sistema, así como para validar los resultados y generar los artefactos necesarios para el compilado, construcción y despliegue del mismo. Además, en el diseño del entorno de desarrollo, la extensión e integración del mismo con herramientas de validación ha sido un factor clave. En concreto, se pueden incorporar al entorno de desarrollo nuevos requisitos no-funcionales, la generación de nuevos artefactos tales como documentación o diferentes lenguajes de programación, etc. Una parte clave del entorno de desarrollo es el algoritmo de particionado. Este algoritmo se ha diseñado para ser independiente de los requisitos de las aplicaciones así como para permitir al integrador del sistema implementar nuevos requisitos del sistema. Para lograr esta independencia, se han definido las restricciones al particionado. El algoritmo garantiza que dichas restricciones se cumplirán en el sistema particionado que resulte de su ejecución. Las restricciones al particionado se han diseñado con una capacidad expresiva suficiente para que, con un pequeño grupo de ellas, se puedan expresar la mayor parte de los requisitos no-funcionales más comunes. Las restricciones pueden ser definidas manualmente por el integrador del sistema o bien pueden ser generadas automáticamente por una herramienta a partir de los requisitos funcionales y no-funcionales de una aplicación. El algoritmo de particionado toma como entradas los modelos y las restricciones al particionado del sistema. Tras la ejecución y como resultado, se genera un modelo de despliegue en el que se definen las particiones que son necesarias para el particionado del sistema. A su vez, cada partición define qué aplicaciones deben ejecutar en ella así como los recursos que necesita la partición para ejecutar correctamente. El problema del particionado y las restricciones al particionado se modelan matemáticamente a través de grafos coloreados. En dichos grafos, un coloreado propio de los vértices representa un particionado del sistema correcto. El algoritmo se ha diseñado también para que, si es necesario, sea posible obtener particionados alternativos al inicialmente propuesto. El entorno de desarrollo, incluyendo el algoritmo de particionado, se ha probado con éxito en dos casos de uso industriales: el satélite UPMSat-2 y un demostrador del sistema de control de una turbina eólica. Además, el algoritmo se ha validado mediante la ejecución de numerosos escenarios sintéticos, incluyendo algunos muy complejos, de más de 500 aplicaciones. ABSTRACT The importance of embedded software is growing as it is required for a large number of systems. Devising cheap, efficient and reliable development processes for embedded systems is thus a notable challenge nowadays. Computer processing power is continuously increasing, and as a result, it is currently possible to integrate complex systems in a single processor, which was not feasible a few years ago.Embedded systems may have safety critical requirements. Its failure may result in personal or substantial economical loss. The development of these systems requires stringent development processes that are usually defined by suitable standards. In some cases their certification is also necessary. This scenario fosters the use of mixed-criticality systems in which applications of different criticality levels must coexist in a single system. In these cases, it is usually necessary to certify the whole system, including non-critical applications, which is costly. Virtualization emerges as an enabling technology used for dealing with this problem. The system is structured as a set of partitions, or virtual machines, that can be executed with temporal and spatial isolation. In this way, applications can be developed and certified independently. The development of MCPS (Mixed-Criticality Partitioned Systems) requires additional roles and activities that traditional systems do not require. The system integrator has to define system partitions. Application development has to consider the characteristics of the partition to which it is allocated. In addition, traditional software process models have to be adapted to this scenario. The V-model is commonly used in embedded systems development. It can be adapted to the development of MCPS by enabling the parallel development of applications or adding an additional partition to an existing system. The objective of this PhD is to improve the available technology for MCPS development by providing a framework tailored to the development of this type of system and by defining a flexible and efficient algorithm for automatically generating system partitionings. The goal of the framework is to integrate all the activities required for developing MCPS and to support the different roles involved in this process. The framework is based on MDE (Model-Driven Engineering), which emphasizes the use of models in the development process. The framework provides basic means for modeling the system, generating system partitions, validating the system and generating final artifacts. The framework has been designed to facilitate its extension and the integration of external validation tools. In particular, it can be extended by adding support for additional non-functional requirements and support for final artifacts, such as new programming languages or additional documentation. The framework includes a novel partitioning algorithm. It has been designed to be independent of the types of applications requirements and also to enable the system integrator to tailor the partitioning to the specific requirements of a system. This independence is achieved by defining partitioning constraints that must be met by the resulting partitioning. They have sufficient expressive capacity to state the most common constraints and can be defined manually by the system integrator or generated automatically based on functional and non-functional requirements of the applications. The partitioning algorithm uses system models and partitioning constraints as its inputs. It generates a deployment model that is composed by a set of partitions. Each partition is in turn composed of a set of allocated applications and assigned resources. The partitioning problem, including applications and constraints, is modeled as a colored graph. A valid partitioning is a proper vertex coloring. A specially designed algorithm generates this coloring and is able to provide alternative partitions if required. The framework, including the partitioning algorithm, has been successfully used in the development of two industrial use cases: the UPMSat-2 satellite and the control system of a wind-power turbine. The partitioning algorithm has been successfully validated by using a large number of synthetic loads, including complex scenarios with more that 500 applications.