Multiservice QoS-Enabled MAC for Optical Burst Switching

The emergence of a broad range of network-driven applications (e.g., multimedia, online gaming) brings in the need for a network environment able to provide multiservice capabilities with diverse quality-of-service (QoS) guarantees. In this paper, a medium access control protocol is proposed to support multiple services and QoS levels in optical burst-switched mesh networks without wavelength conversion. The protocol provides two different access mechanisms, queue-arbitrated and prearbitrated for connectionless and connection-oriented burst transport, respectively. It has been evaluated through extensive simulations and its simplistic form makes it very promising for implementation and deployment. Results indicate that the protocol can clearly provide a relative quality differentiation for connectionless traffic and guarantee null (or negligible, and thus acceptable) burst loss probability for a wide range of network (or offered) load while ensuring low access delay for the higher-priority traffic. Furthermore, in the multiservice scenario mixing connectionless and connection-oriented burst transmissions, three different prearbitrated slot scheduling algorithms are evaluated, each one providing a different performance in terms of connection blocking probability. The overall results demonstrate the suitability of this architecture for future integrated multiservice optical networks

Statistical Learning and Inverse Problems: An Stochastic Gradient Approach

Inverse problems are paramount in Science and Engineering. In this paper, we consider the setup of Statistical Inverse Problem (SIP) and demonstrate how Stochastic Gradient Descent (SGD) algorithms can be used in the linear SIP setting. We provide consistency and finite sample bounds for the excess risk. We also propose a modification for the SGD algorithm where we leverage machine learning methods to smooth the stochastic gradients and improve empirical performance. We exemplify the algorithm in a setting of great interest nowadays: the Functional Linear Regression model. In this case we consider a synthetic data example and examples with a real data classification problem

ESTIMATES OF FOREST CHARACTERISTICS DERIVED FROM REMOTELY SENSED IMAGERY AND FIELD SAMPLES: APPLICABLE SCALES, APPROPRIATE STUDY DESIGN, AND RELEVANCE TO FOREST MANAGEMENT

Information and knowledge about a given forested landscape drives forest management decisions. Within forest management though, information that adequately describes various characteristics of the forested environment in the spatial detail desired to make fully informed management decisions is often limited. Key metrics such as species composition, tree basal area, and tree density are typically too expensive to collect using ground-based inventory methods alone across broad extents for forest level planning (thousands of ha) at fine spatial detail that permit use at tactical spatial scales (tens of ha). However, quantifying these metrics accurately, in spatial detail, across broad landscapes is important to inform the management process. While relating remotely sensed data to classical ground-based survey data through modeling has shown promise for describing landscapes at the spatial detail need to inform planning and tactical scale projects, questions remain related to integrating both sources of data, sample design, and linking plots to remotely sensed data. This dissertation addresses critical aspects of these questions by: quantifying and mitigating the impact of co-registration errors; comparing various sample designs and estimation techniques using simulated ground-based information, remotely sensed data, and a variety of modeling techniques; developing enhanced image normalization routines; and creating an ensemble approach to estimating various forest characteristics that describe species composition, basal area, and tree density. This dissertation address knowledge gaps in the fields of forestry, remote sensing, data science, and decision science that can be used to efficiently and effectively inform the natural resource management decision-making process at fine spatial resolutions across broad extents

Steps towards adaptive situation and context-aware access: a contribution to the extension of access control mechanisms within pervasive information systems

L'évolution des systèmes pervasives a ouvert de nouveaux horizons aux systèmes d'information classiques qui ont intégré des nouvelles technologies et des services qui assurent la transparence d'accès aux resources d'information à n'importe quand, n'importe où et n'importe comment. En même temps, cette évolution a relevé des nouveaux défis à la sécurité de données et à la modélisation du contrôle d'accès. Afin de confronter ces challenges, differents travaux de recherche se sont dirigés vers l'extension des modèles de contrôles d'accès (en particulier le modèle RBAC) afin de prendre en compte la sensibilité au contexte dans le processus de prise de décision. Mais la liaison d'une décision d'accès aux contraintes contextuelles dynamiques d'un utilisateur mobile va non seulement ajouter plus de complexité au processus de prise de décision mais pourra aussi augmenter les possibilités de refus d'accès. Sachant que l'accessibilité est un élément clé dans les systèmes pervasifs et prenant en compte l'importance d'assurer l'accéssibilité en situations du temps réel, nombreux travaux de recherche ont proposé d'appliquer des mécanismes flexibles de contrôle d'accès avec des solutions parfois extrêmes qui depassent les frontières de sécurité telle que l'option de "Bris-de-Glace". Dans cette thèse, nous introduisons une solution modérée qui se positionne entre la rigidité des modèles de contrôle d'accès et la flexibilité qui expose des risques appliquées pendant des situations du temps réel. Notre contribution comprend deux volets : au niveau de conception, nous proposons PS-RBAC - un modèle RBAC sensible au contexte et à la situation. Le modèle réalise des attributions des permissions adaptatives et de solution de rechange à base de prise de décision basée sur la similarité face à une situation importanteÀ la phase d'exécution, nous introduisons PSQRS - un système de réécriture des requêtes sensible au contexte et à la situation et qui confronte les refus d'accès en reformulant la requête XACML de l'utilisateur et en lui proposant une liste des resources alternatives similaires qu'il peut accéder. L'objectif est de fournir un niveau de sécurité adaptative qui répond aux besoins de l'utilisateur tout en prenant en compte son rôle, ses contraintes contextuelles (localisation, réseau, dispositif, etc.) et sa situation. Notre proposition a été validé dans trois domaines d'application qui sont riches des contextes pervasifs et des scénarii du temps réel: (i) les Équipes Mobiles Gériatriques, (ii) les systèmes avioniques et (iii) les systèmes de vidéo surveillance.The evolution of pervasive computing has opened new horizons to classical information systems by integrating new technologies and services that enable seamless access to information sources at anytime, anyhow and anywhere. Meanwhile this evolution has opened new threats to information security and new challenges to access control modeling. In order to meet these challenges, many research works went towards extending traditional access control models (especially the RBAC model) in order to add context awareness within the decision-making process. Meanwhile, tying access decisions to the dynamic contextual constraints of mobile users would not only add more complexity to decision-making but could also increase the possibilities of access denial. Knowing that accessibility is a key feature for pervasive systems and taking into account the importance of providing access within real-time situations, many research works have proposed applying flexible access control mechanisms with sometimes extreme solutions that depass security boundaries such as the Break-Glass option. In this thesis, we introduce a moderate solution that stands between the rigidity of access control models and the riskful flexibility applied during real-time situations. Our contribution is twofold: on the design phase, we propose PS-RBAC - a Pervasive Situation-aware RBAC model that realizes adaptive permission assignments and alternative-based decision-making based on similarity when facing an important situation. On the implementation phase, we introduce PSQRS - a Pervasive Situation-aware Query Rewriting System architecture that confronts access denials by reformulating the user's XACML access request and proposing to him a list of alternative similar solutions that he can access. The objective is to provide a level of adaptive security that would meet the user needs while taking into consideration his role, contextual constraints (location, network, device, etc.) and his situation. Our proposal has been validated in three application domains that are rich in pervasive contexts and real-time scenarios: (i) Mobile Geriatric Teams, (ii) Avionic Systems and (iii) Video Surveillance Systems

Telecommunications Networks

This book guides readers through the basics of rapidly emerging networks to more advanced concepts and future expectations of Telecommunications Networks. It identifies and examines the most pressing research issues in Telecommunications and it contains chapters written by leading researchers, academics and industry professionals. Telecommunications Networks - Current Status and Future Trends covers surveys of recent publications that investigate key areas of interest such as: IMS, eTOM, 3G/4G, optimization problems, modeling, simulation, quality of service, etc. This book, that is suitable for both PhD and master students, is organized into six sections: New Generation Networks, Quality of Services, Sensor Networks, Telecommunications, Traffic Engineering and Routing

Uncertainty in Regional Air Quality Modeling

Effective pollution mitigation is the key to successful air quality management. Although states invest millions of dollars to predict future air quality, the regulatory modeling and analysis process to inform pollution control strategy remains uncertain. Traditionally deterministic ‘bright-line’ tests are applied to evaluate the sufficiency of a control strategy to attain an air quality standard. A critical part of regulatory attainment demonstration is the prediction of future pollutant levels using photochemical air quality models. However, because models are uncertain, they yield a false sense of precision that pollutant response to emission controls is perfectly known and may eventually mislead the selection of control policies. These uncertainties in turn affect the health impact assessment of air pollution control strategies. This thesis explores beyond the conventional practice of deterministic attainment demonstration and presents novel approaches to yield probabilistic representations of pollutant response to emission controls by accounting for uncertainties in regional air quality planning. Computationally-efficient methods are developed and validated to characterize uncertainty in the prediction of secondary pollutant (ozone and particulate matter) sensitivities to precursor emissions in the presence of uncertainties in model assumptions and input parameters. We also introduce impact factors that enable identification of model inputs and scenarios that strongly influence pollutant concentrations and sensitivity to precursor emissions. We demonstrate how these probabilistic approaches could be applied to determine the likelihood that any control measure will yield regulatory attainment, or could be extended to evaluate probabilistic health benefits of emission controls, considering uncertainties in both air quality models and epidemiological concentration–response relationships. Finally, ground-level observations for pollutant (ozone) and precursor concentrations (oxides of nitrogen) have been used to adjust probabilistic estimates of pollutant sensitivities based on the performance of simulations in reliably reproducing ambient measurements. Various observational metrics have been explored for better scientific understanding of how sensitivity estimates vary with measurement constraints. Future work could extend these methods to incorporate additional modeling uncertainties and alternate observational metrics, and explore the responsiveness of future air quality to project trends in emissions and climate change

IPAC Image Processing and Data Archiving for the Palomar Transient Factory

The Palomar Transient Factory (PTF) is a multiepochal robotic survey of the northern sky that acquires data for the scientific study of transient and variable astrophysical phenomena. The camera and telescope provide for wide-field imaging in optical bands. In the five years of operation since first light on 2008 December 13, images taken with Mould-R and SDSS-g′ camera filters have been routinely acquired on a nightly basis (weather permitting), and two different Hα filters were installed in 2011 May (656 and 663 nm). The PTF image-processing and data-archival program at the Infrared Processing and Analysis Center (IPAC) is tailored to receive and reduce the data, and, from it, generate and preserve astrometrically and photometrically calibrated images, extracted source catalogs, and co-added reference images. Relational databases have been deployed to track these products in operations and the data archive. The fully automated system has benefited by lessons learned from past IPAC projects and comprises advantageous features that are potentially incorporable into other ground-based observatories. Both off-the-shelf and in-house software have been utilized for economy and rapid development. The PTF data archive is curated by the NASA/IPAC Infrared Science Archive (IRSA). A state-of-the-art custom Web interface has been deployed for downloading the raw images, processed images, and source catalogs from IRSA. Access to PTF data products is currently limited to an initial public data release (M81, M44, M42, SDSS Stripe 82, and the Kepler Survey Field). It is the intent of the PTF collaboration to release the full PTF data archive when sufficient funding becomes available