Efficient structural outlooks for vertex product networks

In this thesis, a new classification for a large set of interconnection networks, referred to as "Vertex Product Networks" (VPN), is provided and a number of related issues are discussed including the design and evaluation of efficient structural outlooks for algorithm development on this class of networks. The importance of studying the VPN can be attributed to the following two main reasons: first an unlimited number of new networks can be defined under the umbrella of the VPN, and second some known networks can be studied and analysed more deeply. Examples of the VPN include the newly proposed arrangement-star and the existing Optical Transpose Interconnection Systems (OTIS-networks). Over the past two decades many interconnection networks have been proposed in the literature, including the star, hyperstar, hypercube, arrangement, and OTIS-networks. Most existing research on these networks has focused on analysing their topological properties. Consequently, there has been relatively little work devoted to designing efficient parallel algorithms for important parallel applications. In an attempt to fill this gap, this research aims to propose efficient structural outlooks for algorithm development. These structural outlooks are based on grid and pipeline views as popular structures that support a vast body of applications that are encountered in many areas of science and engineering, including matrix computation, divide-and- conquer type of algorithms, sorting, and Fourier transforms. The proposed structural outlooks are applied to the VPN, notably the arrangement-star and OTIS-networks. In this research, we argue that the proposed arrangement-star is a viable candidate as an underlying topology for future high-speed parallel computers. Not only does the arrangement-star bring a solution to the scalability limitations from which the Abstract existing star graph suffers, but it also enables the development of parallel algorithms based on the proposed structural outlooks, such as matrix computation, linear algebra, divide-and-conquer algorithms, sorting, and Fourier transforms. Results from a performance study conducted in this thesis reveal that the proposed arrangement-star supports efficiently applications based on the grid or pipeline structural outlooks. OTIS-networks are another example of the VPN. This type of networks has the important advantage of combining both optical and electronic interconnect technology. A number of studies have recently explored the topological properties of OTIS-networks. Although there has been some work on designing parallel algorithms for image processing and sorting, hardly any work has considered the suitability of these networks for an important class of scientific problems such as matrix computation, sorting, and Fourier transforms. In this study, we present and evaluate two structural outlooks for algorithm development on OTIS-networks. The proposed structural outlooks are general in the sense that no specific factor network or problem domain is assumed. Timing models for measuring the performance of the proposed structural outlooks are provided. Through these models, the performance of various algorithms on OTIS-networks are evaluated and compared with their counterparts on conventional electronic interconnection systems. The obtained results reveal that OTIS-networks are an attractive candidate for future parallel computers due to their superior performance characteristics over networks using traditional electronic interconnects

INTEROPERABILITY FOR MODELING AND SIMULATION IN MARITIME EXTENDED FRAMEWORK

This thesis reports on the most relevant researches performed during the years of the Ph.D. at the Genova University and within the Simulation Team. The researches have been performed according to M&S well known recognized standards. The studies performed on interoperable simulation cover all the environments of the Extended Maritime Framework, namely Sea Surface, Underwater, Air, Coast & Land, Space and Cyber Space. The applications cover both the civil and defence domain. The aim is to demonstrate the potential of M&S applications for the Extended Maritime Framework, applied to innovative unmanned vehicles as well as to traditional assets, human personnel included. A variety of techniques and methodology have been fruitfully applied in the researches, ranging from interoperable simulation, discrete event simulation, stochastic simulation, artificial intelligence, decision support system and even human behaviour modelling

Holistic System Design for Distributed National eHealth Services

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Acta Universitatis Sapientiae - Electrical and Mechanical Engineering

Series Electrical and Mechanical Engineering publishes original papers and surveys in various fields of Electrical and Mechanical Engineering

Learning to segment in images and videos with different forms of supervision

Much progress has been made in image and video segmentation over the last years. To a large extent, the success can be attributed to the strong appearance models completely learned from data, in particular using deep learning methods. However, to perform best these methods require large representative datasets for training with expensive pixel-level annotations, which in case of videos are prohibitive to obtain. Therefore, there is a need to relax this constraint and to consider alternative forms of supervision, which are easier and cheaper to collect. In this thesis, we aim to develop algorithms for learning to segment in images and videos with different levels of supervision. First, we develop approaches for training convolutional networks with weaker forms of supervision, such as bounding boxes or image labels, for object boundary estimation and semantic/instance labelling tasks. We propose to generate pixel-level approximate groundtruth from these weaker forms of annotations to train a network, which allows to achieve high-quality results comparable to the full supervision quality without any modifications of the network architecture or the training procedure. Second, we address the problem of the excessive computational and memory costs inherent to solving video segmentation via graphs. We propose approaches to improve the runtime and memory efficiency as well as the output segmentation quality by learning from the available training data the best representation of the graph. In particular, we contribute with learning must-link constraints, the topology and edge weights of the graph as well as enhancing the graph nodes - superpixels - themselves. Third, we tackle the task of pixel-level object tracking and address the problem of the limited amount of densely annotated video data for training convolutional networks. We introduce an architecture which allows training with static images only and propose an elaborate data synthesis scheme which creates a large number of training examples close to the target domain from the given first frame mask. With the proposed techniques we show that densely annotated consequent video data is not necessary to achieve high-quality temporally coherent video segmentation results. In summary, this thesis advances the state of the art in weakly supervised image segmentation, graph-based video segmentation and pixel-level object tracking and contributes with the new ways of training convolutional networks with a limited amount of pixel-level annotated training data.In der Bild- und Video-Segmentierung wurden im Laufe der letzten Jahre große Fortschritte erzielt. Dieser Erfolg beruht weitgehend auf starken Appearance Models, die vollständig aus Daten gelernt werden, insbesondere mit Deep Learning Methoden. Für beste Performanz benötigen diese Methoden jedoch große repräsentative Datensätze für das Training mit teuren Annotationen auf Pixelebene, die bei Videos unerschwinglich sind. Deshalb ist es notwendig, diese Einschränkung zu überwinden und alternative Formen des überwachten Lernens in Erwägung zu ziehen, die einfacher und kostengünstiger zu sammeln sind. In dieser Arbeit wollen wir Algorithmen zur Segmentierung von Bildern und Videos mit verschiedenen Ebenen des überwachten Lernens entwickeln. Zunächst entwickeln wir Ansätze zum Training eines faltenden Netzwerkes (convolutional network) mit schwächeren Formen des überwachten Lernens, wie z.B. Begrenzungsrahmen oder Bildlabel, für Objektbegrenzungen und Semantik/Instanz- Klassifikationsaufgaben. Wir schlagen vor, aus diesen schwächeren Formen von Annotationen eine annähernde Ground Truth auf Pixelebene zu generieren, um ein Netzwerk zu trainieren, das hochwertige Ergebnisse ermöglicht, die qualitativ mit denen bei voll überwachtem Lernen vergleichbar sind, und dies ohne Änderung der Netzwerkarchitektur oder des Trainingsprozesses. Zweitens behandeln wir das Problem des beträchtlichen Rechenaufwands und Speicherbedarfs, das der Segmentierung von Videos mittels Graphen eigen ist. Wir schlagen Ansätze vor, um sowohl die Laufzeit und Speichereffizienz als auch die Qualität der Segmentierung zu verbessern, indem aus den verfügbaren Trainingsdaten die beste Darstellung des Graphen gelernt wird. Insbesondere leisten wir einen Beitrag zum Lernen mit must-link Bedingungen, zur Topologie und zu Kantengewichten des Graphen sowie zu verbesserten Superpixeln. Drittens gehen wir die Aufgabe des Objekt-Tracking auf Pixelebene an und befassen uns mit dem Problem der begrenzten Menge von dicht annotierten Videodaten zum Training eines faltenden Netzwerkes. Wir stellen eine Architektur vor, die das Training nur mit statischen Bildern ermöglicht, und schlagen ein aufwendiges Schema zur Datensynthese vor, das aus der gegebenen ersten Rahmenmaske eine große Anzahl von Trainingsbeispielen ähnlich der Zieldomäne schafft. Mit den vorgeschlagenen Techniken zeigen wir, dass dicht annotierte zusammenhängende Videodaten nicht erforderlich sind, um qualitativ hochwertige zeitlich kohärente Resultate der Segmentierung von Videos zu erhalten. Zusammenfassend lässt sich sagen, dass diese Arbeit den Stand der Technik in schwach überwachter Segmentierung von Bildern, graphenbasierter Segmentierung von Videos und Objekt-Tracking auf Pixelebene weiter entwickelt, und mit neuen Formen des Trainings faltender Netzwerke bei einer begrenzten Menge von annotierten Trainingsdaten auf Pixelebene einen Beitrag leistet

Next-Generation Self-Organizing Networks through a Machine Learning Approach

Fecha de lectura de Tesis Doctoral: 17 Diciembre 2018.Para reducir los costes de gestión de las redes celulares, que, con el tiempo, aumentaban en complejidad, surgió el concepto de las redes autoorganizadas, o self-organizing networks (SON). Es decir, la automatización de las tareas de gestión de una red celular para disminuir los costes de infraestructura (CAPEX) y de operación (OPEX). Las tareas de las SON se dividen en tres categorías: autoconfiguración, autooptimización y autocuración. El objetivo de esta tesis es la mejora de las funciones SON a través del desarrollo y uso de herramientas de aprendizaje automático (machine learning, ML) para la gestión de la red. Por un lado, se aborda la autocuración a través de la propuesta de una novedosa herramienta para una diagnosis automática (RCA), consistente en la combinación de múltiples sistemas RCA independientes para el desarrollo de un sistema compuesto de RCA mejorado. A su vez, para aumentar la precisión de las herramientas de RCA mientras se reducen tanto el CAPEX como el OPEX, en esta tesis se proponen y evalúan herramientas de ML de reducción de dimensionalidad en combinación con herramientas de RCA. Por otro lado, en esta tesis se estudian las funcionalidades multienlace dentro de la autooptimización y se proponen técnicas para su gestión automática. En el campo de las comunicaciones mejoradas de banda ancha, se propone una herramienta para la gestión de portadoras radio, que permite la implementación de políticas del operador, mientras que, en el campo de las comunicaciones vehiculares de baja latencia, se propone un mecanismo multicamino para la redirección del tráfico a través de múltiples interfaces radio. Muchos de los métodos propuestos en esta tesis se han evaluado usando datos provenientes de redes celulares reales, lo que ha permitido demostrar su validez en entornos realistas, así como su capacidad para ser desplegados en redes móviles actuales y futuras

Threats to Soil Quality in Europe

During the recent years, there has been a surge of concern and attention in Europe to soil degradation processes. One of the most innovative aspects of the newly proposed Soil Thematic Strategy for the EU is the recognition of the multifunctionality of soils. This report is summarizing the reserch results on the fields of soil degradation and soil quality reserach. Chapters of the report include: Preface Characterisation of soil degradation risk: an overview Soil quality in the European Union Main threats to soil quality in Europe The Natural Susceptibility on European Soils to Compaction Soil Erosion: a main threats to the soils in Europe Soil Erosion risk assessment in the alpine area according to the IPCC scenarios An example of the threat of wind erosion using DSM techniques Updated map of salt affected soils in the European Union A framework to estimate the distribution of heavy metals in European Soils Application of Soil Organic Carbon Status Indicators for policy-decision making in the EU Main threats on soil biodiversity: The case of agricultural activities impacts on soil microarthropods Implications of soil threats on agricultural areas in Europe MEUSIS, a Multi-Scale European Soil Information System (MEUSIS): novel ways to derive soil indicators through UpscalingJRC.H.7-Land management and natural hazard