BEMDEC: An Adaptive and Robust Methodology for Digital Image Feature Extraction

The intriguing study of feature extraction, and edge detection in particular, has, as a result of the increased use of imagery, drawn even more attention not just from the field of computer science but also from a variety of scientific fields. However, various challenges surrounding the formulation of feature extraction operator, particularly of edges, which is capable of satisfying the necessary properties of low probability of error (i.e., failure of marking true edges), accuracy, and consistent response to a single edge, continue to persist. Moreover, it should be pointed out that most of the work in the area of feature extraction has been focused on improving many of the existing approaches rather than devising or adopting new ones. In the image processing subfield, where the needs constantly change, we must equally change the way we think. In this digital world where the use of images, for variety of purposes, continues to increase, researchers, if they are serious about addressing the aforementioned limitations, must be able to think outside the box and step away from the usual in order to overcome these challenges. In this dissertation, we propose an adaptive and robust, yet simple, digital image features detection methodology using bidimensional empirical mode decomposition (BEMD), a sifting process that decomposes a signal into its two-dimensional (2D) bidimensional intrinsic mode functions (BIMFs). The method is further extended to detect corners and curves, and as such, dubbed as BEMDEC, indicating its ability to detect edges, corners and curves. In addition to the application of BEMD, a unique combination of a flexible envelope estimation algorithm, stopping criteria and boundary adjustment made the realization of this multi-feature detector possible. Further application of two morphological operators of binarization and thinning adds to the quality of the operator

A Mixed-Bouncing Based Non-Stationarity and Consistency 6G V2V Channel Model with Continuously Arbitrary Trajectory

In this paper, a novel three-dimensional (3D) irregularshaped geometry-based stochastic model (IS-GBSM) is proposedfor sixth-generation (6G) millimeter wave (mmWave) massivemultiple-input multiple-output (MIMO) vehicle-to-vehicle(V2V) channels. To investigate the impact of vehicular trafficdensity (VTD) on channel statistics, clusters are divided into staticclusters and dynamic clusters, which are further distinguishedinto static/dynamic single/twin-clusters to capture the mixed bouncingpropagation. A new method, which integrates thevisibility region and birth-death process methods, is developedto model space-time-frequency (S-T-F) non-stationarity of V2Vchannels with time-space (T-S) consistency. The continuouslyarbitrary vehicular movement trajectory (VMT) and soft clusterpower handover are modeled to further ensure channel T-Sconsistency. From the proposed model, key channel statistics arederived. Simulation results show that S-T-F non-stationarity ofchannels with T-S consistency is modeled and the impacts of VTDand VMT on channel statistics are analyzed. The generality ofthe proposed model is validated by comparing simulation resultsand measurement/ray-tracing (RT)-based results

Population-based algorithms for improved history matching and uncertainty quantification of Petroleum reservoirs

In modern field management practices, there are two important steps that shed light on a multimillion dollar investment. The first step is history matching where the simulation model is calibrated to reproduce the historical observations from the field. In this inverse problem, different geological and petrophysical properties may provide equally good history matches. Such diverse models are likely to show different production behaviors in future. This ties the history matching with the second step, uncertainty quantification of predictions. Multiple history matched models are essential for a realistic uncertainty estimate of the future field behavior. These two steps facilitate decision making and have a direct impact on technical and financial performance of oil and gas companies. Population-based optimization algorithms have been recently enjoyed growing popularity for solving engineering problems. Population-based systems work with a group of individuals that cooperate and communicate to accomplish a task that is normally beyond the capabilities of each individual. These individuals are deployed with the aim to solve the problem with maximum efficiency. This thesis introduces the application of two novel population-based algorithms for history matching and uncertainty quantification of petroleum reservoir models. Ant colony optimization and differential evolution algorithms are used to search the space of parameters to find multiple history matched models and, using a Bayesian framework, the posterior probability of the models are evaluated for prediction of reservoir performance. It is demonstrated that by bringing latest developments in computer science such as ant colony, differential evolution and multiobjective optimization, we can improve the history matching and uncertainty quantification frameworks. This thesis provides insights into performance of these algorithms in history matching and prediction and develops an understanding of their tuning parameters. The research also brings a comparative study of these methods with a benchmark technique called Neighbourhood Algorithms. This comparison reveals the superiority of the proposed methodologies in various areas such as computational efficiency and match quality

ESSE 2017. Proceedings of the International Conference on Environmental Science and Sustainable Energy

Environmental science is an interdisciplinary academic field that integrates physical-, biological-, and information sciences to study and solve environmental problems. ESSE - The International Conference on Environmental Science and Sustainable Energy provides a platform for experts, professionals, and researchers to share updated information and stimulate the communication with each other. In 2017 it was held in Suzhou, China June 23-25, 2017

Towards a multilevel ant colony optimization

Masteroppgave i Informasjons- og kommunikasjonsteknologi IKT590 Universitetet i Agder 2014Ant colony optimization is a metaheuristic approach for solving combinatorial optimization problems which belongs to swarm intelligence techniques. Ant colony optimization algorithms are one of the most successful strands of swarm intelligence which has already shown very good performance in many combinatorial problems and for some real applications. This thesis introduces a new multilevel approach for ant colony optimization to solve the NP-hard problems shortest path and traveling salesman. We have reviewed different elements of multilevel algorithm which helped us in construction of our proposed multilevel ant colony optimization solution. We for comparison purposes implemented our own multi-threaded variant Dijkstra for solving shortest path to compare it with single level and multilevel ant colony optimization and reviewed different techniques such as genetic algorithms and Dijkstra’s algorithm. Our proposed multilevel ant colony optimization was developed based on the single level ant colony optimization which we both implemented. We have applied the novel multilevel ant colony optimization to solve the shortest path and traveling salesman problem. We show that the multilevel variant of ant colony optimization outperforms single level. The experimental results conducted demonstrate the overall performance of multilevel in comparison to the single level ant colony optimization, displaying a vast improvement when employing a multilevel approach in contrast to the classical single level approach. These results gave us a better understanding of the problems and provide indications for further research

Ein dienstgütebasiertes Routingprotokoll für ein selbstorganisiertes Kommunikationsnetz

Mobile Ad-hoc Networks (MANETs) are characterized by two dimensions namely, anywhere and anytime. The freely moving participating nodes can form an ad hoc network anywhere, and the mobile nodes can join or leave the network anytime. A particular mobile node in a MANET can communicate with all the other nodes using the multihop communication. Thus, MANETs offer a vast range of applications in various domains like entertainment, military, emergency, etc. However, the implementation of real-time applications like voice/video calling that demands stringent quality requirements over MANETs is a major challenge. This challenge arises due to the unplanned and dynamic nature of MANETs, due to the unreliability of wireless links, due to the scarcity of resources like battery, bandwidth, processing power, due to the large-scale nature of MANETs, etc. This issue can be addressed at the network layer or the routing protocol, which establishes multiple routes from source to destination and adapts to the dynamicity of MANETs without compromising on the quality requirements. The primary goal of this work is the investigation and development of such a routing algorithm that supports real-time applications over MANETs. For adaptive multipath routing, we studied Ant Colony Optimization (ACO) algorithms originate from the fields of Swarm Intelligence (SI) while Quality of Service (QoS) computation is carried out by cleverly utilizing the monitoring feature of the Simple Network Management Protocol (SNMP). So, combining these two mechanisms we propose a powerful adaptive multipath QoS-aware Routing protocol based on ACO (QoRA). We discuss and investigate the internal working of QoRA and perform detailed simulation studies in the network simulator ns-3. Finally, we discuss the implementation of QoRA routing algorithms in a real world testbed.Mobile Ad-hoc-Netze (MANETs) ermöglichen eine Kommunikation überall zu jedem Zeitpunkt. Frei sich bewegende Knoten können überall ein solches Netz bilden, wobei die Teilnehmer zu jeder Zeit dem Netz beitreten oder es wieder verlassen können. Ein teilnehmender Knoten in einem MANET kommuniziert mit allen anderen über Multi-Hop-Kommunikation. So ermöglicht ein MANET viele unterschiedliche Anwendungen aus verschiedenen Domänen wie beispielsweise Unterhaltungskommunikation, Notfallkommunikation oder Einsatzkommunikation. Allerdings benötigen Echtzeitanwendungen wie Telefonie oder Videokommunikation eine stringente Kommunikationsdienstgüte, was für MANETs eine große Herausforderung darstellt. Diese Herausforderung hat viele Gründe: das dynamische und unvorhersehbare Verhalten der Knoten im MANET, die Unzuverlässigkeit der drahtlosen Kommunikation, die Beschränkung der zur Verfügung stehenden Kommunikationsressourcen (wie Batterielaufzeit, Bandbreite oder Prozessorleistung), die relativ große Abdeckung durch ein MANET. Die Herausforderung kann in der Vermittlungsschicht durch ein spezielles Routingprotokoll gelöst werden, das mehrere gleichzeitige Pfade von der Quelle zum Ziel verwendet, sodass die Dynamik in einem MANET Berücksichtigung findet ohne dass die Dienstgüte kompromittiert werden muss. Das vorrangige Ziel dieser Arbeit ist die Erforschung und Entwicklung eines solchen Routingverfahrens, das Echtzeitanwendungen in einem MANET unterstützt. Für das adaptive Mehrwegerouting wurde ein Ameisenalgorithmus (Ant Colony Optimization, ACO) angewendet, der das Prinzip der Schwarmintelligenz ausnutzt. Die Bestimmung der aktuell möglichen Kommunikationsdienstgüte erfolgt über die Informationen, die das Netzmanagementprotokoll Simple Network Management Protocol SNMP standardmäßig zur Verfügung stellt. Durch die Kombination dieser beiden Ansätze wurde das adaptive Mehrwegeroutingprotokoll "QoS-aware Routing Protocol based on ACO" (QoRA) vorgeschlagen. In der vorliegenden Dissertation werden das Konzept von QoRA vorgestellt und die interne Funktionsweise erläutert. Anhand umfangreicher Simulationen auf Basis des Simulationswerkzeug ns-3 werden die Vorteile des Verfahrens nachgewiesen. Den Abschluss bildet die Diskussion einer Implementierung von QoRA in einer realen Testumgebung

Bio-Inspired Robotics

Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field

Natural Language Processing: Emerging Neural Approaches and Applications

This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains