DEVELOPMENT OF A PREDICTIVE COMBUSTION MODEL OF A SPARK IGNITED ENGINE WITH GASOLINE DIRECT INJECTION, VARIABLE VALVE TIMING, DURATION AND LIFT TECHNOLOGIES

There is a need by engine manufactures for computationally efficient and accurate predictive combustion modeling tools for integration in engine simulation software for the assessment of combustion system hardware designs and early development of engine calibrations. This thesis discusses the process for the development and validation of a combustion modeling tool for Gasoline Direct Injected Spark Ignited Engine with variable valve timing, lift and duration valvetrain hardware from experimental data. Data was correlated and regressed from accepted methods for calculating the turbulent flow and flame propagation characteristics for an internal combustion engine. A non-linear regression modeling method was utilized to develop a combustion model to determine the fuel mass burn rate at multiple points during the combustion process. The computational fluid dynamic software Converge ©, was used to simulate and correlate the 3-D combustion system, port and piston geometry to the turbulent flow development within the cylinder to properly predict the experimental data turbulent flow parameters through the intake, compression and expansion processes. The engine simulation software GT-Power © is then used to determine the 1-D flow characteristics of the engine hardware being tested to correlate the regressed combustion modeling tool to experimental data to determine accuracy. The results of the combustion modeling tool show accurate trends capturing the combustion sensitivities to turbulent flow, thermodynamic and internal residual effects with changes in intake and exhaust valve timing, lift and duration.

A FIREWALL MODEL OF FILE SYSTEM SECURITY

File system security is fundamental to the security of UNIX and Linux systems since in these systems almost everything is in the form of a file. To protect the system files and other sensitive user files from unauthorized accesses, certain security schemes are chosen and used by different organizations in their computer systems. A file system security model provides a formal description of a protection system. Each security model is associated with specified security policies which focus on one or more of the security principles: confidentiality, integrity and availability. The security policy is not only about “who” can access an object, but also about “how” a subject can access an object. To enforce the security policies, each access request is checked against the specified policies to decide whether it is allowed or rejected. The current protection schemes in UNIX/Linux systems focus on the access control. Besides the basic access control scheme of the system itself, which includes permission bits, setuid and seteuid mechanism and the root, there are other protection models, such as Capabilities, Domain Type Enforcement (DTE) and Role-Based Access Control (RBAC), supported and used in certain organizations. These models protect the confidentiality of the data directly. The integrity of the data is protected indirectly by only allowing trusted users to operate on the objects. The access control decisions of these models depend on either the identity of the user or the attributes of the process the user can execute, and the attributes of the objects. Adoption of these sophisticated models has been slow; this is likely due to the enormous complexity of specifying controls over a large file system and the need for system administrators to learn a new paradigm for file protection. We propose a new security model: file system firewall. It is an adoption of the familiar network firewall protection model, used to control the data that flows between networked computers, toward file system protection. This model can support decisions of access control based on any system generated attributes about the access requests, e.g., time of day. The access control decisions are not on one entity, such as the account in traditional discretionary access control or the domain name in DTE. In file system firewall, the access decisions are made upon situations on multiple entities. A situation is programmable with predicates on the attributes of subject, object and the system. File system firewall specifies the appropriate actions on these situations. We implemented the prototype of file system firewall on SUSE Linux. Preliminary results of performance tests on the prototype indicate that the runtime overhead is acceptable. We compared file system firewall with TE in SELinux to show that firewall model can accommodate many other access control models. Finally, we show the ease of use of firewall model. When firewall system is restricted to specified part of the system, all the other resources are not affected. This enables a relatively smooth adoption. This fact and that it is a familiar model to system administrators will facilitate adoption and correct use. The user study we conducted on traditional UNIX access control, SELinux and file system firewall confirmed that. The beginner users found it easier to use and faster to learn then traditional UNIX access control scheme and SELinux.

Larcform 1 - single column model intercomparison of Arctic air formation, link to model results in NetCDF format

Weather and climate models struggle to represent lower tropospheric temperature and moisture profiles and surface fluxes in Arctic winter, not least because they lack or misrepresent physical processes that are specific to high latitudes. The Arctic boundary layer in winter has been observed to be in either a radiatively clear or cloudy state: The radiatively clear state is characterized by strong surface radiative cooling leading to the build-up of surface-based temperature inversions, whereas the cloudy state occurs when cloud liquid water is present in the atmospheric column, allowing little or no surface radiative cooling and leading to weaker and typically elevated temperature inversions. Many large-scale models have been shown to lack the cloudy state, and some do substantially underestimate stability in the clear state. We here present results from the first Lagrangian ARCtic air FORMation experiment (Larcform 1), a GASS (Global atmospheric system studies) single-column model intercomparison which reproduces these biases of large-scale models in an idealised setup.

Multi Domain-Specific Modeling of the Security Concerns of Service-Oriented Architectures

Service-Oriented Architectures (SOA), and Web Services (WS), the technology generally used to implement them, achieve the integration of heterogeneous technologies, providing interoperability, and yielding the reutilization of pre-existent systems. Model-driven development methodologies provide inherent benefits such as increased productivity, greater reuse, and better maintainability, to name a few. Efforts on achieving model-driven development of SOAs already exist, but there is currently no standard solution that addresses non-functional aspects of these services as well. This paper presents an approach to integrate these non-functional aspects in the development of web services, with an emphasis on security.

A Model for Proactivity in Mobile, Context-aware Recommender Systems

A proactive recommender system pushes recommendations to the user when the current situation seems appropriate, without explicit user request. This is conceivable in mobile scenarios such as restaurant or gas station recommendations. In this paper, we present a model for proactivity in mobile recommender systems. The model relies on domain-dependent context modeling in several categories. The recommendation process is divided into two phases to first analyze the current situation and then examine the suitability of particular items. We have implemented a prototype gas station recommender and conducted a survey for evaluation. Results showed good correlation of the output of our system with the assessment of users regarding the question when to generate recommendations.

A Development Methodology to Facilitate the Integration of Smart Spaces into the Web of Things

How to create or integrate large Smart Spaces (considered as mash-ups of sensors and actuators) into the paradigm of ?Web of Things? has been the motivation of many recent works. A cutting-edge approach deals with developing and deploying web-enabled embedded devices with two major objectives: 1) to integrate sensor and actuator technologies into everyday objects, and 2) to allow a diversity of devices to plug to Internet. Currently, developers who want to use this Internet-oriented approach need have solid understanding about sensorial platforms and semantic technologies. In this paper we propose a Resource-Oriented and Ontology-Driven Development (ROOD) methodology, based on Model Driven Architecture (MDA), to facilitate to any developer the development and deployment of Smart Spaces. Early evaluations of the ROOD methodology have been successfully accomplished through a partial deployment of a Smart Hotel.

Towards model-driven engineering for mixed-criticality systems: multiPARTES approach

Mixed criticality systems emerges as a suitable solution for dealing with the complexity, performance and costs of future embedded and dependable systems. However, this paradigm adds additional complexity to their development. This paper proposes an approach for dealing with this scenario that relies on hardware virtualization and Model-Driven Engineering (MDE). Hardware virtualization ensures isolation between subsystems with different criticality levels. MDE is intended to bridge the gap between design issues and partitioning concerns. MDE tooling will enhance the functional models by annotating partitioning and extra-functional properties. System partitioning and subsystems allocation will be generated with a high degree of automation. System configuration will be validated for ensuring that the resources assigned to a partition are sufficient for executing the allocated software components and that time requirements are met.

Model-to-Code transformation from product-line architecture models to aspectJ

Software Product Line Engineering has significant advantages in family-based software development. The common and variable structure for all products of a family is defined through a Product-Line Architecture (PLA) that consists of a common set of reusable components and connectors which can be configured to build the different products. The design of PLA requires solutions for capturing such configuration (variability). The Flexible-PLA Model is a solution that supports the specification of external variability of the PLA configuration, as well as internal variability of components. However, a complete support for product-line development requires translating architecture specifications into code. This complex task needs automation to avoid human error. Since Model-Driven Development allows automatic code generation from models, this paper presents a solution to automatically generate AspectJ code from Flexible-PLA models previously configured to derive specific products. This solution is supported by a modeling framework and validated in a software factory.

Probabilistic reasoning with an enzyme-driven DNA device

We present a biomolecular probabilistic model driven by the action of a DNA toolbox made of a set of DNA templates and enzymes that is able to perform Bayesian inference. The model will take single-stranded DNA as input data, representing the presence or absence of a specific molecular signal (the evidence). The program logic uses different DNA templates and their relative concentration ratios to encode the prior probability of a disease and the conditional probability of a signal given the disease. When the input and program molecules interact, an enzyme-driven cascade of reactions (DNA polymerase extension, nicking and degradation) is triggered, producing a different pair of single-stranded DNA species. Once the system reaches equilibrium, the ratio between the output species will represent the application of Bayes? law: the conditional probability of the disease given the signal. In other words, a qualitative diagnosis plus a quantitative degree of belief in that diagno- sis. Thanks to the inherent amplification capability of this DNA toolbox, the resulting system will be able to to scale up (with longer cascades and thus more input signals) a Bayesian biosensor that we designed previously.

From Analysis Model to Software Architecture: a PIM2PIM Mapping.

To our knowledge, no current software development methodology explicitly describes how to transit from the analysis model to the software architecture of the application. This paper presents a method to derive the software architecture of a system from its analysis model. To do this, we are going to use MDA. Both the analysis model and the architectural model are PIMs described with UML 2. The model type mapping designed consists of several rules (expressed using OCL and natural language) that, when applied to the analysis artifacts, generate the software architecture of the application. Specifically the rules act on elements of the UML 2 metamodel (metamodel mapping). We have developed a tool (using Smalltalk) that permits the automatic application of these rules to an analysis model defined in RoseTM to generate the application architecture expressed in the architectural style C2.

Structuring research methods and data with the research object model: genomics workflows as a case study

Background: One of the main challenges for biomedical research lies in the computer-assisted integrative study of large and increasingly complex combinations of data in order to understand molecular mechanisms. The preservation of the materials and methods of such computational experiments with clear annotations is essential for understanding an experiment, and this is increasingly recognized in the bioinformatics community. Our assumption is that offering means of digital, structured aggregation and annotation of the objects of an experiment will provide necessary meta-data for a scientist to understand and recreate the results of an experiment. To support this we explored a model for the semantic description of a workflow-centric Research Object (RO), where an RO is defined as a resource that aggregates other resources, e.g., datasets, software, spreadsheets, text, etc. We applied this model to a case study where we analysed human metabolite variation by workflows. Results: We present the application of the workflow-centric RO model for our bioinformatics case study. Three workflows were produced following recently defined Best Practices for workflow design. By modelling the experiment as an RO, we were able to automatically query the experiment and answer questions such as “which particular data was input to a particular workflow to test a particular hypothesis?”, and “which particular conclusions were drawn from a particular workflow?”. Conclusions: Applying a workflow-centric RO model to aggregate and annotate the resources used in a bioinformatics experiment, allowed us to retrieve the conclusions of the experiment in the context of the driving hypothesis, the executed workflows and their input data. The RO model is an extendable reference model that can be used by other systems as well.

Contributions to Speech Analytics based on Speech Recognition and Topic Identification

La última década ha sido testigo de importantes avances en el campo de la tecnología de reconocimiento de voz. Los sistemas comerciales existentes actualmente poseen la capacidad de reconocer habla continua de múltiples locutores, consiguiendo valores aceptables de error, y sin la necesidad de realizar procedimientos explícitos de adaptación. A pesar del buen momento que vive esta tecnología, el reconocimiento de voz dista de ser un problema resuelto. La mayoría de estos sistemas de reconocimiento se ajustan a dominios particulares y su eficacia depende de manera significativa, entre otros muchos aspectos, de la similitud que exista entre el modelo de lenguaje utilizado y la tarea específica para la cual se está empleando. Esta dependencia cobra aún más importancia en aquellos escenarios en los cuales las propiedades estadísticas del lenguaje varían a lo largo del tiempo, como por ejemplo, en dominios de aplicación que involucren habla espontánea y múltiples temáticas. En los últimos años se ha evidenciado un constante esfuerzo por mejorar los sistemas de reconocimiento para tales dominios. Esto se ha hecho, entre otros muchos enfoques, a través de técnicas automáticas de adaptación. Estas técnicas son aplicadas a sistemas ya existentes, dado que exportar el sistema a una nueva tarea o dominio puede requerir tiempo a la vez que resultar costoso. Las técnicas de adaptación requieren fuentes adicionales de información, y en este sentido, el lenguaje hablado puede aportar algunas de ellas. El habla no sólo transmite un mensaje, también transmite información acerca del contexto en el cual se desarrolla la comunicación hablada (e.g. acerca del tema sobre el cual se está hablando). Por tanto, cuando nos comunicamos a través del habla, es posible identificar los elementos del lenguaje que caracterizan el contexto, y al mismo tiempo, rastrear los cambios que ocurren en estos elementos a lo largo del tiempo. Esta información podría ser capturada y aprovechada por medio de técnicas de recuperación de información (information retrieval) y de aprendizaje de máquina (machine learning). Esto podría permitirnos, dentro del desarrollo de mejores sistemas automáticos de reconocimiento de voz, mejorar la adaptación de modelos del lenguaje a las condiciones del contexto, y por tanto, robustecer al sistema de reconocimiento en dominios con condiciones variables (tales como variaciones potenciales en el vocabulario, el estilo y la temática). En este sentido, la principal contribución de esta Tesis es la propuesta y evaluación de un marco de contextualización motivado por el análisis temático y basado en la adaptación dinámica y no supervisada de modelos de lenguaje para el robustecimiento de un sistema automático de reconocimiento de voz. Esta adaptación toma como base distintos enfoque de los sistemas mencionados (de recuperación de información y aprendizaje de máquina) mediante los cuales buscamos identificar las temáticas sobre las cuales se está hablando en una grabación de audio. Dicha identificación, por lo tanto, permite realizar una adaptación del modelo de lenguaje de acuerdo a las condiciones del contexto. El marco de contextualización propuesto se puede dividir en dos sistemas principales: un sistema de identificación de temática y un sistema de adaptación dinámica de modelos de lenguaje. Esta Tesis puede describirse en detalle desde la perspectiva de las contribuciones particulares realizadas en cada uno de los campos que componen el marco propuesto: _ En lo referente al sistema de identificación de temática, nos hemos enfocado en aportar mejoras a las técnicas de pre-procesamiento de documentos, asimismo en contribuir a la definición de criterios más robustos para la selección de index-terms. – La eficiencia de los sistemas basados tanto en técnicas de recuperación de información como en técnicas de aprendizaje de máquina, y específicamente de aquellos sistemas que particularizan en la tarea de identificación de temática, depende, en gran medida, de los mecanismos de preprocesamiento que se aplican a los documentos. Entre las múltiples operaciones que hacen parte de un esquema de preprocesamiento, la selección adecuada de los términos de indexado (index-terms) es crucial para establecer relaciones semánticas y conceptuales entre los términos y los documentos. Este proceso también puede verse afectado, o bien por una mala elección de stopwords, o bien por la falta de precisión en la definición de reglas de lematización. En este sentido, en este trabajo comparamos y evaluamos diferentes criterios para el preprocesamiento de los documentos, así como también distintas estrategias para la selección de los index-terms. Esto nos permite no sólo reducir el tamaño de la estructura de indexación, sino también mejorar el proceso de identificación de temática. – Uno de los aspectos más importantes en cuanto al rendimiento de los sistemas de identificación de temática es la asignación de diferentes pesos a los términos de acuerdo a su contribución al contenido del documento. En este trabajo evaluamos y proponemos enfoques alternativos a los esquemas tradicionales de ponderado de términos (tales como tf-idf ) que nos permitan mejorar la especificidad de los términos, así como también discriminar mejor las temáticas de los documentos. _ Respecto a la adaptación dinámica de modelos de lenguaje, hemos dividimos el proceso de contextualización en varios pasos. – Para la generación de modelos de lenguaje basados en temática, proponemos dos tipos de enfoques: un enfoque supervisado y un enfoque no supervisado. En el primero de ellos nos basamos en las etiquetas de temática que originalmente acompañan a los documentos del corpus que empleamos. A partir de estas, agrupamos los documentos que forman parte de la misma temática y generamos modelos de lenguaje a partir de dichos grupos. Sin embargo, uno de los objetivos que se persigue en esta Tesis es evaluar si el uso de estas etiquetas para la generación de modelos es óptimo en términos del rendimiento del reconocedor. Por esta razón, nosotros proponemos un segundo enfoque, un enfoque no supervisado, en el cual el objetivo es agrupar, automáticamente, los documentos en clusters temáticos, basándonos en la similaridad semántica existente entre los documentos. Por medio de enfoques de agrupamiento conseguimos mejorar la cohesión conceptual y semántica en cada uno de los clusters, lo que a su vez nos permitió refinar los modelos de lenguaje basados en temática y mejorar el rendimiento del sistema de reconocimiento. – Desarrollamos diversas estrategias para generar un modelo de lenguaje dependiente del contexto. Nuestro objetivo es que este modelo refleje el contexto semántico del habla, i.e. las temáticas más relevantes que se están discutiendo. Este modelo es generado por medio de la interpolación lineal entre aquellos modelos de lenguaje basados en temática que estén relacionados con las temáticas más relevantes. La estimación de los pesos de interpolación está basada principalmente en el resultado del proceso de identificación de temática. – Finalmente, proponemos una metodología para la adaptación dinámica de un modelo de lenguaje general. El proceso de adaptación tiene en cuenta no sólo al modelo dependiente del contexto sino también a la información entregada por el proceso de identificación de temática. El esquema usado para la adaptación es una interpolación lineal entre el modelo general y el modelo dependiente de contexto. Estudiamos también diferentes enfoques para determinar los pesos de interpolación entre ambos modelos. Una vez definida la base teórica de nuestro marco de contextualización, proponemos su aplicación dentro de un sistema automático de reconocimiento de voz. Para esto, nos enfocamos en dos aspectos: la contextualización de los modelos de lenguaje empleados por el sistema y la incorporación de información semántica en el proceso de adaptación basado en temática. En esta Tesis proponemos un marco experimental basado en una arquitectura de reconocimiento en ‘dos etapas’. En la primera etapa, empleamos sistemas basados en técnicas de recuperación de información y aprendizaje de máquina para identificar las temáticas sobre las cuales se habla en una transcripción de un segmento de audio. Esta transcripción es generada por el sistema de reconocimiento empleando un modelo de lenguaje general. De acuerdo con la relevancia de las temáticas que han sido identificadas, se lleva a cabo la adaptación dinámica del modelo de lenguaje. En la segunda etapa de la arquitectura de reconocimiento, usamos este modelo adaptado para realizar de nuevo el reconocimiento del segmento de audio. Para determinar los beneficios del marco de trabajo propuesto, llevamos a cabo la evaluación de cada uno de los sistemas principales previamente mencionados. Esta evaluación es realizada sobre discursos en el dominio de la política usando la base de datos EPPS (European Parliamentary Plenary Sessions - Sesiones Plenarias del Parlamento Europeo) del proyecto europeo TC-STAR. Analizamos distintas métricas acerca del rendimiento de los sistemas y evaluamos las mejoras propuestas con respecto a los sistemas de referencia. ABSTRACT The last decade has witnessed major advances in speech recognition technology. Today’s commercial systems are able to recognize continuous speech from numerous speakers, with acceptable levels of error and without the need for an explicit adaptation procedure. Despite this progress, speech recognition is far from being a solved problem. Most of these systems are adjusted to a particular domain and their efficacy depends significantly, among many other aspects, on the similarity between the language model used and the task that is being addressed. This dependence is even more important in scenarios where the statistical properties of the language fluctuates throughout the time, for example, in application domains involving spontaneous and multitopic speech. Over the last years there has been an increasing effort in enhancing the speech recognition systems for such domains. This has been done, among other approaches, by means of techniques of automatic adaptation. These techniques are applied to the existing systems, specially since exporting the system to a new task or domain may be both time-consuming and expensive. Adaptation techniques require additional sources of information, and the spoken language could provide some of them. It must be considered that speech not only conveys a message, it also provides information on the context in which the spoken communication takes place (e.g. on the subject on which it is being talked about). Therefore, when we communicate through speech, it could be feasible to identify the elements of the language that characterize the context, and at the same time, to track the changes that occur in those elements over time. This information can be extracted and exploited through techniques of information retrieval and machine learning. This allows us, within the development of more robust speech recognition systems, to enhance the adaptation of language models to the conditions of the context, thus strengthening the recognition system for domains under changing conditions (such as potential variations in vocabulary, style and topic). In this sense, the main contribution of this Thesis is the proposal and evaluation of a framework of topic-motivated contextualization based on the dynamic and non-supervised adaptation of language models for the enhancement of an automatic speech recognition system. This adaptation is based on an combined approach (from the perspective of both information retrieval and machine learning fields) whereby we identify the topics that are being discussed in an audio recording. The topic identification, therefore, enables the system to perform an adaptation of the language model according to the contextual conditions. The proposed framework can be divided in two major systems: a topic identification system and a dynamic language model adaptation system. This Thesis can be outlined from the perspective of the particular contributions made in each of the fields that composes the proposed framework: _ Regarding the topic identification system, we have focused on the enhancement of the document preprocessing techniques in addition to contributing in the definition of more robust criteria for the selection of index-terms. – Within both information retrieval and machine learning based approaches, the efficiency of topic identification systems, depends, to a large extent, on the mechanisms of preprocessing applied to the documents. Among the many operations that encloses the preprocessing procedures, an adequate selection of index-terms is critical to establish conceptual and semantic relationships between terms and documents. This process might also be weakened by a poor choice of stopwords or lack of precision in defining stemming rules. In this regard we compare and evaluate different criteria for preprocessing the documents, as well as for improving the selection of the index-terms. This allows us to not only reduce the size of the indexing structure but also to strengthen the topic identification process. – One of the most crucial aspects, in relation to the performance of topic identification systems, is to assign different weights to different terms depending on their contribution to the content of the document. In this sense we evaluate and propose alternative approaches to traditional weighting schemes (such as tf-idf ) that allow us to improve the specificity of terms, and to better identify the topics that are related to documents. _ Regarding the dynamic language model adaptation, we divide the contextualization process into different steps. – We propose supervised and unsupervised approaches for the generation of topic-based language models. The first of them is intended to generate topic-based language models by grouping the documents, in the training set, according to the original topic labels of the corpus. Nevertheless, a goal of this Thesis is to evaluate whether or not the use of these labels to generate language models is optimal in terms of recognition accuracy. For this reason, we propose a second approach, an unsupervised one, in which the objective is to group the data in the training set into automatic topic clusters based on the semantic similarity between the documents. By means of clustering approaches we expect to obtain a more cohesive association of the documents that are related by similar concepts, thus improving the coverage of the topic-based language models and enhancing the performance of the recognition system. – We develop various strategies in order to create a context-dependent language model. Our aim is that this model reflects the semantic context of the current utterance, i.e. the most relevant topics that are being discussed. This model is generated by means of a linear interpolation between the topic-based language models related to the most relevant topics. The estimation of the interpolation weights is based mainly on the outcome of the topic identification process. – Finally, we propose a methodology for the dynamic adaptation of a background language model. The adaptation process takes into account the context-dependent model as well as the information provided by the topic identification process. The scheme used for the adaptation is a linear interpolation between the background model and the context-dependent one. We also study different approaches to determine the interpolation weights used in this adaptation scheme. Once we defined the basis of our topic-motivated contextualization framework, we propose its application into an automatic speech recognition system. We focus on two aspects: the contextualization of the language models used by the system, and the incorporation of semantic-related information into a topic-based adaptation process. To achieve this, we propose an experimental framework based in ‘a two stages’ recognition architecture. In the first stage of the architecture, Information Retrieval and Machine Learning techniques are used to identify the topics in a transcription of an audio segment. This transcription is generated by the recognition system using a background language model. According to the confidence on the topics that have been identified, the dynamic language model adaptation is carried out. In the second stage of the recognition architecture, an adapted language model is used to re-decode the utterance. To test the benefits of the proposed framework, we carry out the evaluation of each of the major systems aforementioned. The evaluation is conducted on speeches of political domain using the EPPS (European Parliamentary Plenary Sessions) database from the European TC-STAR project. We analyse several performance metrics that allow us to compare the improvements of the proposed systems against the baseline ones.

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.

Dynamic regulation of the tryptophan operon: A modeling study and comparison with experimental data

A mathematical model for regulation of the tryptophan operon is presented. This model takes into account repression, feedback enzyme inhibition, and transcriptional attenuation. Special attention is given to model parameter estimation based on experimental data. The model's system of delay differential equations is numerically solved, and the results are compared with experimental data on the temporal evolution of enzyme activity in cultures of Escherichia coli after a nutritional shift (minimal + tryptophan medium to minimal medium). Good agreement is obtained between the numeric simulations and the experimental results for wild-type E. coli, as well as for two different mutant strains.

Development of the DEPLOY computer math model for the investigation of various airbag and crash parameters on the out-of-position occupant. Final report.

National Highway Traffic Safety Administration, Washington, D.C.