Cognitive radio (CR) networks and heterogeneous cellular networks are promising approaches to satisfy the demand for higher data rates and better connectivity. A CR network increases the utilization of the radio spectrum by opportunistically using it. Heterogeneous networks provide high data rates and improved connectivity by spatially reusing the spectrum and by bringing the network closer to the user. Interference presents a critical challenge for reliable communication in these networks. Accurately modeling it is essential in ensuring a successful design and deployment of these networks. We first propose modeling the aggregate interference power at a primary receiver (PU-Rx) caused from transmissions by randomly located cognitive users (CUs) in a CR network as a shifted lognormal random process. Its parameters are determined using a moment matching method. Extensive benchmarking shows that the proposed model is more accurate than the lognormal and Gaussian process models considered in the literature, even for a relatively dense deployment of CUs. It also compares favorably with the asymptotically exact stable and symmetric truncated stable distribution models, except at high CU densities. Our model accounts for the effect of imperfect spectrum sensing, interweave and underlay modes of CR operation, and path-loss, time-correlated shad-owing and fading of the various links in the network. It leads to new expressions for the probability distribution function, level crossing rate (LCR), and average exceedance duration (AED). The impact of cooperative spectrum sensing is also characterized. We also apply and validate the proposed model by using it to redesign the primary exclusive zone to account for the time-varying nature of interference. Next we model the uplink inter-cell aggregate interference power in homogeneous and heterogeneous cellular systems as a simpler lognormal random variable. We develop a new moment generating function (MGF) matching method to determine the lognormal’s parameters. Our model accounts for the transmit power control, peak transmit power constraint, small scale fading and large scale shadowing, and randomness in the number of interfering mobile stations and their locations. In heterogeneous net-works, the random nature of the number and locations of low power base stations is also accounted for. The accuracy of the proposed model is verified for both small and large values of interference. While not perfect, it is more accurate than the conventional Gaussian and moment-matching-based lognormal and Gamma distribution models. It is also performs better than the symmetric-truncated stable and stable distribution models, except at higher user density.

This term project deals with the functionality of Spanning Tree protocols, especially the Rapid Spanning Tree Protocol, and the Multiple Spanning Tree Protocol. The primary usage of spanning tree protocols is the prevention of loops within the data link layer, the prevention of a broadcast storm, and also dealing with redundancy in the network. Moreover, the project contains the description of configuration of these protocols on Cisco devices. The main goal of this thesis is to implement the Multiple Spanning Tree protocol into INET framework within the OMNeT++ simulation system. Then, the implemented solution is tested and it's functionality is compared with the referential behavior in a Cisco network.

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The dissertation is generally focused on the use of artificial intelligence tools in practice and with regard to the focus of study in the field of Management and Business Economics at using the tools of artificial intelligence in corporate practice, as a tool for decision support at the operational and tactical level management. In the narrower sense, the task deals with the proposal of the prediction sales model of selected food commodities. The proposed model is designed to serve as a substitute for a human expert in support decision-making process in the purchase of selected commodities, especially when training new staff and extend the currently used methods of managerial decision-making about artificial intelligence tools for company management and existing employees. The aim of this dissertation is the design prediction sales model of selected food commodities (apples and potatoes) for specific wholesale of fruit and vegetable operating in the Czech Republic. To become familiar with the behaviour of selected commodities were used primary and secondary research as well and knowledge gained from Czech and foreign literature sources and research. The resulting predictive model is developed using statistical analysis of time series and the sales prediction proceeds using the tools of artificial intelligence and is modeled by an artificial neural network. The dissertation in the practical part also contains proposals for the use of the prediction model and partial processing procedures for: • practice, • theory, • pedagogical activities.

Approved for public release, distribution is unlimited / Wireless mesh networking has emerged as the successor of the traditional ad hoc networks. New technological advances, the standardization of protocols and interfaces and the maturity of key components have made it possible for current mesh research groups to set goals that are really close to the world's expectations. The objective of this research is to design and implement a simulation toolkit for wireless mesh clusters that can be used as an additional performance evaluation technique for the Tactical Network Topology program of Naval Postgraduate School. This toolkit is implemented in the OPNET simulation environment and it incorporates various nodes running different ad hoc routing protocols. Furthermore, the investigation of a suitable combination of protocols for the Tactical Network Topology is achieved by creating scenarios and running a number of simulations using the mesh toolkit. / Captain, Hellenic Air Force

Flow and transport phenomena in the subsurface often span a wide range of length (nanometers to kilometers) and time (nanoseconds to years) scales, and frequently arise in applications of CO₂ sequestration, pollutant transport, and near-well acid stimulation. Reliable field-scale predictions depend on our predictive capacity at each individual scale as well as our ability to accurately propagate information across scales. Pore-scale modeling (coupled with experiments) has assumed an important role in improving our fundamental understanding at the small scale, and is frequently used to inform/guide modeling efforts at larger scales. Among the various methods, there often exists a trade-off between computational efficiency/simplicity and accuracy. While high-resolution methods are very accurate, they are computationally limited to relatively small domains. Since macroscopic properties of a porous medium are statistically representative only when sample sizes are sufficiently large, simple and efficient pore-scale methods are more attractive. In this work, two Eulerian pore-network models for simulating single-phase flow and solute transport are developed. The models focus on capturing two key pore-level mechanisms: a) partial mixing within pores (large void volumes), and b) shear dispersion within throats (narrow constrictions connecting the pores), which are shown to have a substantial impact on transverse and longitudinal dispersion coefficients at the macro scale. The models are verified with high-resolution pore-scale methods and validated against micromodel experiments as well as experimental data from the literature. Studies regarding the significance of different pore-level mixing assumptions (perfect mixing vs. partial mixing) in disordered media, as well as the predictive capacity of network modeling as a whole for ordered media are conducted. A mortar domain decomposition framework is additionally developed, under which efficient and accurate simulations on even larger and highly heterogeneous pore-scale domains are feasible. The mortar methods are verified and parallel scalability is demonstrated. It is shown that they can be used as “hybrid” methods for coupling localized pore-scale inclusions to a surrounding continuum (when insufficient scale separation exists). The framework further permits multi-model simulations within the same computational domain. An application of the methods studying “emergent” behavior during calcite precipitation in the context of geologic CO₂ sequestration is provided. / text

The primary objective was to predict the relative storage capacity of carbonate rocks relevant for carbon dioxide sequestration. To achieve this, a detailed pore scale characterization of model carbonate rocks, Indiana Limestone and Pink Dolomite, was conducted utilizing micro-computed tomography (microCT) data using pore network modeling and invasion percolation simulations. For the first time in literature, Pink Dolomite’s pore space characteristics were analyzed. A secondary objective was to compare thresholding techniques as applied to carbonates which exhibit dual porosity (porosity at multiple length scales). The analysis showed the sensitivity of existing methods to the thresholding technique, imaging method and material. Overall, the contributions of this work provide an assessment of two carbonates relevant for carbon capture and storage at the pore scale; and a preliminary assessment into thresholding dual porosity carbonates.

The primary objective was to predict the relative storage capacity of carbonate rocks relevant for carbon dioxide sequestration. To achieve this, a detailed pore scale characterization of model carbonate rocks, Indiana Limestone and Pink Dolomite, was conducted utilizing micro-computed tomography (microCT) data using pore network modeling and invasion percolation simulations. For the first time in literature, Pink Dolomite’s pore space characteristics were analyzed. A secondary objective was to compare thresholding techniques as applied to carbonates which exhibit dual porosity (porosity at multiple length scales). The analysis showed the sensitivity of existing methods to the thresholding technique, imaging method and material. Overall, the contributions of this work provide an assessment of two carbonates relevant for carbon capture and storage at the pore scale; and a preliminary assessment into thresholding dual porosity carbonates.

Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-08-14T16:42:54Z No. of bitstreams: 1 EWALDO EDER CARVALHO SANTANA - TESE PPGEE 2009..pdf: 5646465 bytes, checksum: d83cd716193f68815a22b066836f3ae6 (MD5) / Made available in DSpace on 2018-08-14T16:42:54Z (GMT). No. of bitstreams: 1 EWALDO EDER CARVALHO SANTANA - TESE PPGEE 2009..pdf: 5646465 bytes, checksum: d83cd716193f68815a22b066836f3ae6 (MD5) Previous issue date: 2009-11-06 / Neste trabalho é proposto o desenvolvimento de uma rede neuronial com aprendizagem não supervisionada, para modelar a organização topográfica do córtex visual primário. Para isto, estuda-se o comportamento dos campos receptivos do córtex visual primário(V1), e, para o modelamento da rede utilizam-se os conceitos de divergência local e de interação entre neurônios vizinhos, bem como da característica de não linearidades dos neurônios. Para treinamento da rede desenvolveu-se um algoritmo de ponto fixo. / In this work it is proposed an unsupervised neural network model, which seems biologically plausible in modeling the primary visual cortex (V1). It is, also, studied de behavior of the receptive fields of V1. In order to modeling the net it was used the concepts of local discrepancy and interactions between neighbor neurons, as well the non-linearity characteristics of neurons. It was designed a fixed-point algorithm to train the neural network.

Dans un cadre de modélisation des machines électriques, la méthode qui connaît une grande popularité, car réputée pour la qualité de ses résultats est la méthode des éléments finis. Cependant, les temps de calcul deviennent importants lorsque les modèles éléments finis sont associés à une démarche d’optimisation et de prédimensionnement dans le cadre d’un cahier des charges complexe. Les méthodes de modélisation bien souvent utilisées comme alternative aux éléments finis sont les approches de modélisation par constantes localisées. Ces dernières se prêtent bien aux différentes physiques impliquées dans le fonctionnement des machines électriques, i.e. électromagnétique, mécanique et thermique. Ainsi, les stratégies de conception optimisée d’une machine électrique utilisent ces modèles pour déterminer les propriétés et performances de cette dernière dans différentes conditions de fonctionnement. Cependant, la mise en place de ce type de modèles nécessite un temps de développement important par manque d’outils dédiés tels que ceux existants pour les éléments finis. Dans le contexte électromagnétique, les travaux de cette thèse présentent une contribution à l’approche de modélisation par réseaux de réluctances par le développement d’outils permettant leurs générations automatiques. Cette approche est intégrée dans un outil permettant le traitement automatisé d'une géométrie, fournissant un modèle précis dans un délai plus court que celui nécessaire à la construction d'un modèle dédié. L’outil, intégralement développé sur MATLAB®, a été appelé MRNsoftware (pour Mesh-based Reluctance Network Software). Ce mémoire s’organise en quatre parties. Le premier chapitre est consacré à un état de l’art détaillé sur les méthodes de modélisation par réseaux de réluctances. Dans la deuxième partie, nous abordons les méthodologies mises en place en se basant sur un maillage de l’espace d'étude par des blocs élémentaires bidirectionnels et dans le cadre d’un maillage conforme. Le maillage non conforme fera l’objet du troisième chapitre. Une interpolation des potentiels se révélera utile pour connecter les différentes branches des interfaces de non-conformité dans le maillage de l’espace d’étude. Différents découpages d’une même structure sont testés et la précision ainsi que le temps d’évaluation des modèles en réseaux de réluctances sont comparés aux modèles de référence réalisés par éléments finis. Le quatrième chapitre présente en premier lieu l’interface graphique de l’outil. Par la suite, les approches de modélisation développées seront utilisées pour réaliser les modèles de la machine linéaire à aimants permanents et la machine linéaire à commutation de flux à excitation bobinée. Ces approches de modélisation sont le fruit de la collaboration entre les laboratoires SATIE et GREAH et s’inscrivent dans la problématique générale de développement d’outils de modélisation multiphysiques de dispositifs électromagnétiques en vue de leurs dimensionnements optimisés. / In the field of electrical machine modeling, the method that is experiencing great popularity as renowned for the quality of its results is the finite element method. However, computation time becomes important when the finite element models are associated with an optimization and predesign process as part of a complex technical specification sheet. The alternate modeling solution is the lumped parameter models approach. The latter is well suited for the individual physical domains involved in the operation of electrical machines, namely electromagnetic, mechanical and thermal. The latter is well suited for the individual physical domains involved in the operation of electrical machines, namely electromagnetic, mechanical and thermal. Thus, electric machine design routines have been used to determine the properties and performance of the latter under different operating conditions. However, the implementation of these modeling approaches requires significant development time for lack of dedicated tools such as those existing for the finite element method. In the electromagnetic context, the work of this thesis presents a contribution to the reluctance network modeling approach by developing tools allowing their automatic generation. This approach is integrated into a software tool allowing the automated processing of a geometry, providing a precise model in a shorter time than that required for the construction of a dedicated model. The tool, fully developed on MATLAB®, has been called MRNsoftware (for Mesh-based Reluctance Network Software). This dissertation contains four chapters. The first chapter is devoted to a detailed state of the art on reluctance network modeling methods. In the second chapter, we discuss the methodologies implemented based on a conformal mesh of the study space by bidirectional elementary blocks. The non-conformal mesh will be the subject of the third chapter. Magnetic scalar potential interpolation will prove useful to connect the different branches of the block elements at the edge of the non-conformal interfaces. Different mesh patterns of the same structure are tested and the accuracy as well as the evaluation time of the reluctance network models are compared with the finite element reference models. The fourth chapter presents, at first, the graphical interface of the tool. Subsequently, the developed modeling techniques are used to realize the models of the permanent magnet linear machine and the linear wound excitation linear machine. These modeling approaches are the result of the cooperation between SATIE and GREAH laboratories and are part of the general endeavor of developing multiphysics modeling tools for the optimal sizing of electromagnetic devices.