Recent Advances in Graph Partitioning

We survey recent trends in practical algorithms for balanced graph partitioning together with applications and future research directions

The hArtes Tool Chain

This chapter describes the different design steps needed to go from legacy code to a transformed application that can be efficiently mapped on the hArtes platform

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Analyse und Optimierung von Hybriden Software-Defined Networks

Hybrid IP networks that use both control plane paradigms - distributed and centralized - promise the best of two worlds: programmability and flexible control of Software-Defined Networking (SDN), and at the same time the reliability and fault tolerance of distributed routing protocols like Open Shortest Path First (OSPF). Hybrid SDN/OSPF networks typically deploy OSPF to assure care-free operation of best effort traffic, while SDN can control prioritized traffic. This "ships-passing-in-the-night" approach, where both control planes are unaware of each other's configurations, only require hybrid SDN/OSPF routers that can participate in the domain-wide legacy routing protocol and additionally connect to a central SDN controller. This mode of operation is however known for a number of challenges in operational networks, including those related to network failures, size of forwarding tables, routing convergence time, and the increased complexity of network management. There are alternative modes of hybrid operation that provide a more holistic network control paradigm, either through an OSPF-enabled SDN controller, or a common network management system that allows the joint monitoring and configuration of both control planes, or via the partitioning of the legacy routing domain with SDN border nodes. The latter mode of operation offers to some extent to steer the working of the legacy routing protocol inside the sub-domains, which is new. The analysis, modeling, and evaluative comparison of this approach called SDN Partitioning with other modes of operation is the main contribution of this thesis. This thesis addresses important network planning tasks in hybrid SDN/OSPF networks and provides the according mathematical models to optimize network clustering, capacity planning, SDN node placement, and resource provisioning for a fault tolerant operation. It furthermore provides the mathematical models to optimize traffic engineering, failure recovery, reconfiguration scheduling, and traffic monitoring in hybrid SDN/OSPF networks, which are vital network operational tasks.Hybride IP-Netzwerke, die beide Control-Plane-Paradigmen einsetzen - verteilt und zentralisiert - versprechen das Beste aus beiden Welten: Programmierbarkeit und flexible Kontrolle des Software-Defined Networking (SDN) und gleichzeitig die Zuverlässigkeit und Fehlertoleranz von verteilten Routingprotokollen wie Open Shortest Path First (OSPF). Hybride SDN/OSPF-Netze nutzen typischerweise OSPF für die wartungsarme Bedienung des Best-Effort-Datenverkehrs, während SDN priorisierte Datenströme kontrolliert. Bei diesem Ansatz ist beiden Kontrollinstanzen die Konfiguration der jeweils anderen unbekannt, wodurch hierbei hybride SDN/OSPF Router benötigt werden, die am domänenweiten Routingprotokoll teilnehmen können und zusätzlich eine Verbindung zu einem SDN-Controller herstellen. Diese Arbeitsweise bereitet jedoch bekanntermaßen eine Reihe von Schwierigkeiten in operativen Netzen, wie zum Beispiel die Reaktion auf Störungen, die Größe der Forwarding-Tabellen, die benötigte Zeit zur Konvergenz des Routings, sowie die höhere Komplexität der Netzwerkadministration. Es existieren alternative Betriebsmodi für hybride Netze, die einen ganzheitlicheren Kontrollansatz bieten, entweder mittels OSPF-Erweiterungen im SDN-Controller, oder mittels eines übergreifenden Netzwerkmanagementsystems, dass das Monitoring und die Konfiguration aller Netzelemente erlaubt. Eine weitere Möglichkeit stellt das Clustering der ursprünglichen Routingdomäne in kleinere Subdomänen mittels SDN-Grenzknoten dar. Dieser neue Betriebsmodus erlaubt es zu einem gewissen Grad, die Operationen des Routingprotokolls in den Subdomänen zu steuern. Die Analyse, Modellierung und die vergleichende Evaluation dieses Ansatzes mit dem Namen SDN-Partitionierung und anderen hybriden Betriebsmodi ist der Hauptbeitrag dieser Dissertation. Diese Dissertation behandelt grundlegende Fragen der Netzplanung in hybriden SDN/OSPF-Netzen und beinhaltet entsprechende mathematische Modelle zur Optimierung des Clusterings, zur Kapazitätsplanung, zum Platzieren von SDN-Routern, sowie zur Bestimmung der notwendigen Ressourcen für einen fehlertoleranten Betrieb. Desweiteren enthält diese Dissertation Optimierungsmodelle für Traffic Engineering, zur Störungsbehebung, zur Ablaufplanung von Konfigurationsprozessen, sowie zum Monitoring des Datenverkehrs in hybriden SDN/OSPF-Netzen, was entscheidende Aufgaben der Netzadministration sind

Traveling Salesman Problem

This book is a collection of current research in the application of evolutionary algorithms and other optimal algorithms to solving the TSP problem. It brings together researchers with applications in Artificial Immune Systems, Genetic Algorithms, Neural Networks and Differential Evolution Algorithm. Hybrid systems, like Fuzzy Maps, Chaotic Maps and Parallelized TSP are also presented. Most importantly, this book presents both theoretical as well as practical applications of TSP, which will be a vital tool for researchers and graduate entry students in the field of applied Mathematics, Computing Science and Engineering

Swarm intelligence: novel tools for optimization, feature extraction, and multi-agent system modeling

Abstract Animal swarms in nature are able to adapt to dynamic changes in their envi-ronment, and through cooperation they can solve problems that are crucial for their survival. Only by means of local interactions with other members of the swarm and with the environment, they can achieve a common goal more efficiently than it would be done by a single individual. This problem-solving behavior that results from the multiplicity of such interactions is referred to as Swarm Intelligence. The mathematical models of swarming behavior in nature were initially proposed to solve optimization problems. Nevertheless, this decentralized approach can be a valuable tool for a variety of applications, where emerging global patterns represent a solution to the task at hand. Methods for the solution of difficult computational problems based on Swarm Intelligence have been experimentally demonstrated and reported in the literature. However, a general framework that would facilitate their design does not exist yet. In this dissertation, a new general design methodology for Swarm Intelligence tools is proposed. By defining a discrete space in which the members of the swarm can move, and by modifying the rules of local interactions and setting the adequate objective function for solutions evaluation, the proposed methodology is tested in various domains. The dissertation presents a set of case studies, and focuses on two general approaches. One approach is to apply Swarm Intelligence as a tool for optimization and feature extraction, and the other approach is to model multi-agent systems such that they resemble swarms of animals in nature providing them with the ability to autonomously perform a task at hand. Artificial swarms are designed to be autonomous, scalable, robust, and adaptive to the changes in their environment. In this work, the methods that exploit one or more of these features are presented. First, the proposed methodology is validated in a real-world scenario seen as a combinatorial optimization problem. Then a set of novel tools for feature extraction, more precisely the adaptive edge detection and the broken-edge linking in digital images is proposed. A novel data clustering algorithm is also proposed and applied to image segmentation. Finally, a scalable algorithm based on the proposed methodology is developed for distributed task allocation in multi-agent systems, and applied to a swarm of robots. The newly proposed general methodology provides a guideline for future developers of the Swarm Intelligence tools. Los enjambres de animales en la naturaleza son capaces de adaptarse a cambios dinamicos en su entorno y, por medio de la cooperación, pueden resolver problemas ´ cruciales para su supervivencia. Unicamente por medio de interacciones locales con otros miembros del enjambre y con el entorno, pueden lograr un objetivo común de forma más eficiente que lo haría un solo individuo. Este comportamiento problema-resolutivo que es resultado de la multiplicidad de interacciones se denomina Inteligencia de Enjambre. Los modelos matemáticos de comportamiento de enjambres en entornos naturales fueron propuestos inicialmente para resolver problemas de optimización. Sin embargo, esta aproximación descentralizada puede ser una herramienta valiosa en una variedad de aplicaciones donde patrones globales emergentes representan una solución de las tareas actuales. Aunque en la literatura se muestra la utilidad de los métodos de Inteligencia de Enjambre, no existe un entorno de trabajo que facilite su diseño. En esta memoria de tesis proponemos una nueva metodologia general de diseño para herramientas de Inteligencia de Enjambre. Desarrollamos herramientas noveles que representan ejem-plos ilustrativos de su implementación. Probamos la metodología propuesta en varios dominios definiendo un espacio discreto en el que los miembros del enjambre pueden moverse, modificando las reglas de las interacciones locales y fijando la función objetivo adecuada para evaluar las soluciones. La memoria de tesis presenta un conjunto de casos de estudio y se centra en dos aproximaciones generales. Una aproximación es aplicar Inteligencia de Enjambre como herramienta de optimización y extracción de características mientras que la otra es modelar sistemas multi-agente de tal manera que se asemejen a enjambres de animales en la naturaleza a los que se les confiere la habilidad de ejecutar autónomamente la tarea. Los enjambres artificiales están diseñados para ser autónomos, escalables, robustos y adaptables a los cambios en su entorno. En este trabajo, presentamos métodos que explotan una o más de estas características. Primero, validamos la metodología propuesta en un escenario del mundo real visto como un problema de optimización combinatoria. Después, proponemos un conjunto de herramientas noveles para ex-tracción de características, en concreto la detección adaptativa de bordes y el enlazado de bordes rotos en imágenes digitales, y el agrupamiento de datos para segmentación de imágenes. Finalmente, proponemos un algoritmo escalable para la asignación distribuida de tareas en sistemas multi-agente aplicada a enjambres de robots. La metodología general recién propuesta ofrece una guía para futuros desarrolladores deherramientas de Inteligencia de Enjambre

Optical flow estimation via steered-L1 norm

Global variational methods for estimating optical flow are among the best performing methods due to the subpixel accuracy and the ‘fill-in’ effect they provide. The fill-in effect allows optical flow displacements to be estimated even in low and untextured areas of the image. The estimation of such displacements are induced by the smoothness term. The L1 norm provides a robust regularisation term for the optical flow energy function with a very good performance for edge-preserving. However this norm suffers from several issues, among these is the isotropic nature of this norm which reduces the fill-in effect and eventually the accuracy of estimation in areas near motion boundaries. In this paper we propose an enhancement to the L1 norm that improves the fill-in effect for this smoothness term. In order to do this we analyse the structure tensor matrix and use its eigenvectors to steer the smoothness term into components that are ‘orthogonal to’ and ‘aligned with’ image structures. This is done in primal-dual formulation. Results show a reduced end-point error and improved accuracy compared to the conventional L1 norm

Optical flow estimation via steered-L1 norm

Global variational methods for estimating optical flow are among the best performing methods due to the subpixel accuracy and the ‘fill-in’ effect they provide. The fill-in effect allows optical flow displacements to be estimated even in low and untextured areas of the image. The estimation of such displacements are induced by the smoothness term. The L1 norm provides a robust regularisation term for the optical flow energy function with a very good performance for edge-preserving. However this norm suffers from several issues, among these is the isotropic nature of this norm which reduces the fill-in effect and eventually the accuracy of estimation in areas near motion boundaries. In this paper we propose an enhancement to the L1 norm that improves the fill-in effect for this smoothness term. In order to do this we analyse the structure tensor matrix and use its eigenvectors to steer the smoothness term into components that are ‘orthogonal to’ and ‘aligned with’ image structures. This is done in primal-dual formulation. Results show a reduced end-point error and improved accuracy compared to the conventional L1 norm

Sustainable Agriculture and Advances of Remote Sensing (Volume 2)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publication of the results, among others

Recent Trends in Communication Networks

In recent years there has been many developments in communication technology. This has greatly enhanced the computing power of small handheld resource-constrained mobile devices. Different generations of communication technology have evolved. This had led to new research for communication of large volumes of data in different transmission media and the design of different communication protocols. Another direction of research concerns the secure and error-free communication between the sender and receiver despite the risk of the presence of an eavesdropper. For the communication requirement of a huge amount of multimedia streaming data, a lot of research has been carried out in the design of proper overlay networks. The book addresses new research techniques that have evolved to handle these challenges