Submitted by ANA KARLA PEREIRA RODRIGUES (anakarla_@hotmail.com) on 2017-08-25T12:01:48Z No. of bitstreams: 1 arquivototal.pdf: 1037382 bytes, checksum: d2e1d49848d1cc5fb6843de80b1ff13f (MD5) / Made available in DSpace on 2017-08-25T12:01:48Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 1037382 bytes, checksum: d2e1d49848d1cc5fb6843de80b1ff13f (MD5) Previous issue date: 2016-11-29 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / In this work, we aim to prove the existence of positive solution for some nonlocal elliptic problems via bifurcation theory, more precisely by the Global Bifurcation Theorem due to Rabinowitz, where such problems are related to modeling the behavior of specie in a given environment. / Neste trabalho, temos como objetivo provar a exist^encia de solu c~ao positiva para alguns problemas el pticos n~ao-locais via Teoria de Bifurca c~ao, mais precisamente pelo Teorema Global de Bifurca c~ao devido a Rabinowitz, onde tais problemas est~ao relacionados a modelagem do comportamento de esp ecies num determinado ambiente.

The need for reliable, high-throughput, mobile wireless communication technologies has never been greater as increases in the demand for on-the-go access to information, entertainment, and other electronic services continues. Two such technologies, which are at the forefront of current research efforts, are orthogonal frequency division multiplexing (OFDM) and multiple-input multiple-output (MIMO) systems, their union being known simply as MIMO-OFDM. The successful performance of these technologies depends upon the availability of accurate information concerning the wireless communication channel. In this dissertation, several issues related to quality of this channel state information (CSI) are studied. Specifically, the first part of this dissertation considers the design of optimal pilot signals for OFDM systems. The optimization is addressed via lower bounds on the estimation error variance, which bounds are given by formulations of the Cram'{e}r-Rao bound (CRB). The second part of this dissertation uses the CRB once again, this time as a tool for evaluating the potential performance of MIMO-OFDM channel estimation and prediction. Bounds are found for several parametric time-varying wideband MIMO-OFDM channel models, and numerical evaluations of these bounds are used to illuminate several interesting features regarding the estimation and prediction of MIMO-OFDM channels. The final part of this dissertation considers the problem of MIMO multiplexing using SVD-based methods when only imperfect CSI is available. For this purpose, general per-MIMO-subchannel signal and interference-plus-noise power expressions are derived to quantify the effects of CSI imperfections, and these expressions are then used to find robust MIMO-SVD power and bit allocations which maintain good overall performance in spite of imperfect CSI.

Dans cette thèse, nous nous intéressons à deux méthodes permettant de reconstruire un signal parcimonieux largement sous-échantillonné : l’échantillonnage de signaux à taux d’innovation fini et l’acquisition comprimée.Il a été montré récemment qu’en utilisant un noyau de pré-filtrage adapté, les signaux impulsionnels peuvent être parfaitement reconstruits bien qu’ils soient à bande non-limitée. En présence de bruit, la reconstruction est réalisée par une procédure d’estimation de tous les paramètres du signal d’intérêt. Dans cette thèse, nous considérons premièrement l’estimation des amplitudes et retards paramétrisant une somme finie d'impulsions de Dirac filtrée par un noyau quelconque et deuxièmement l’estimation d’une somme d’impulsions de forme quelconque filtrée par un noyau en somme de sinus cardinaux (SoS). Le noyau SoS est intéressant car il est paramétrable par un jeu de paramètres à valeurs complexes et vérifie les conditions nécessaires à la reconstruction. En se basant sur l’information de Fisher Bayésienne relative aux paramètres d’amplitudes et de retards et sur des outils d’optimisation convexe, nous proposons un nouveau noyau d’échantillonnage.L’acquisition comprimée permet d’échantillonner un signal en-dessous de la fréquence d’échantillonnage de Shannon, si le vecteur à échantillonner peut être approximé comme une combinaison linéaire d’un nombre réduit de vecteurs extraits d’un dictionnaire sur-complet. Malheureusement, dans des conditions réalistes, le dictionnaire (ou base) n’est souvent pas parfaitement connu, et est donc entaché d’une erreur (DB). L’estimation par dictionnaire, se basant sur les mêmes principes, permet d’estimer des paramètres à valeurs continues en les associant selon une grille partitionnant l’espace des paramètres. Généralement, les paramètres ne se trouvent pas sur la grille, ce qui induit un erreur d’estimation même à haut rapport signal sur bruit (RSB). C’est le problème de l’erreur de grille (EG). Dans cette thèse nous étudions les conséquences des modèles d’erreur DB et EG en terme de performances bayésiennes et montrons qu’un biais est introduit même avec une estimation parfaite du support et à haut RSB. La BCRB est dérivée pour les modèles DB et EG non structurés, qui bien qu’ils soient très proches, ne sont pas équivalents en terme de performances. Nous donnons également la borne de Cramér-Rao moyennée (BCRM) dans le cas d’une petite erreur de grille et étudions l’expression analytique de l’erreur quadratique moyenne bayésienne (BEQM) sur l’estimation de l’erreur de grille à haut RSB. Cette dernière est confirmée en pratique dans le contexte de l’estimation de fréquence pour différents algorithmes de reconstruction parcimonieuse.Nous proposons deux nouveaux estimateurs : le Bias-Correction Estimator (BiCE) et l’Off-Grid Error Correction (OGEC) permettant de corriger l'erreur de modèle induite par les erreurs DB et EG, respectivement. Ces deux estimateurs principalement basés sur une projection oblique des mesures sont conçus comme des post-traitements, destinés à réduire le biais d’estimation suite à une pré-estimation effectuée par n’importe quel algorithme de reconstruction parcimonieuse. Les biais et variances théoriques du BiCE et du OGEC sont dérivés afin de caractériser leurs efficacités statistiques.Nous montrons, dans le contexte difficile de l’échantillonnage des signaux impulsionnels à bande non-limitée que ces deux estimateurs permettent de réduire considérablement l’effet de l'erreur de modèle sur les performances d’estimation. Les estimateurs BiCE et OGEC sont tout deux des schémas (i) génériques, car ils peuvent être associés à tout estimateur parcimonieux de la littérature, (ii) rapides, car leur coût de calcul reste faible comparativement au coût des estimateurs parcimonieux, et (iii) ont de bonnes propriétés statistiques. / In this thesis, we are interested in two different low rate sampling schemes that challenge Shannon’s theory: the sampling of finite rate of innovation signals and compressed sensing.Recently it has been shown that using appropriate sampling kernel, finite rate of innovation signals can be perfectly sampled even though they are non-bandlimited. In the presence of noise, reconstruction is achieved by a model-based estimation procedure. In this thesis, we consider the estimation of the amplitudes and delays of a finite stream of Dirac pulses using an arbitrary kernel and the estimation of a finite stream of arbitrary pulses using the Sum of Sincs (SoS) kernel. In both scenarios, we derive the Bayesian Cramér-Rao Bound (BCRB) for the parameters of interest. The SoS kernel is an interesting kernel since it is totally configurable by a vector of weights. In the first scenario, based on convex optimization tools, we propose a new kernel minimizing the BCRB on the delays, while in the second scenario we propose a family of kernels which maximizes the Bayesian Fisher Information, i.e., the total amount of information about each of the parameter in the measures. The advantage of the proposed family is that it can be user-adjusted to favor either of the estimated parameters.Compressed sensing is a promising emerging domain which outperforms the classical limit of the Shannon sampling theory if the measurement vector can be approximated as the linear combination of few basis vectors extracted from a redundant dictionary matrix. Unfortunately, in realistic scenario, the knowledge of this basis or equivalently of the entire dictionary is often uncertain, i.e. corrupted by a Basis Mismatch (BM) error. The related estimation problem is based on the matching of continuous parameters of interest to a discretized parameter set over a regular grid. Generally, the parameters of interest do not lie in this grid and there exists an estimation error even at high Signal to Noise Ratio (SNR). This is the off-grid (OG) problem. The consequence of the BM and the OG mismatch problems is that the estimation accuracy in terms of Bayesian Mean Square Error (BMSE) of popular sparse-based estimators collapses even if the support is perfectly estimated and in the high Signal to Noise Ratio (SNR) regime. This saturation effect considerably limits the effective viability of these estimation schemes.In this thesis, the BCRB is derived for CS model with unstructured BM and OG. We show that even though both problems share a very close formalism, they lead to different performances. In the biased dictionary based estimation context, we propose and study analytical expressions of the Bayesian Mean Square Error (BMSE) on the estimation of the grid error at high SNR. We also show that this class of estimators is efficient and thus reaches the Bayesian Cramér-Rao Bound (BCRB) at high SNR. The proposed results are illustrated in the context of line spectra analysis for several popular sparse estimator. We also study the Expected Cramér-Rao Bound (ECRB) on the estimation of the amplitude for a small OG error and show that it follows well the behavior of practical estimators in a wide SNR range.In the context of BM and OG errors, we propose two new estimation schemes called Bias-Correction Estimator (BiCE) and Off-Grid Error Correction (OGEC) respectively and study their statistical properties in terms of theoretical bias and variances. Both estimators are essentially based on an oblique projection of the measurement vector and act as a post-processing estimation layer for any sparse-based estimator and mitigate considerably the BM (OG respectively) degradation. The proposed estimators are generic since they can be associated to any sparse-based estimator, fast, and have good statistical properties. To illustrate our results and propositions, they are applied in the challenging context of the compressive sampling of finite rate of innovation signals.

Cette thèse concerne l'étude de certains problèmes elliptiques d'ordre 4 et, notamment, des propriétés qualitatives des solutions. Ces problèmes apparaissent dans de nombreux domaines, par exemple dans la théorie des plaques et dans la géométrie conforme, et, comparés à leurs homologues du deuxième ordre, ils présentent des difficultés intrinsèques, surtout liées à l'absence de principe de maximum. Premièrement on étudie la positivité des solutions dans le cas des conditions au bord de Steklov, qui sont intermédiaires entre les conditions de Dirichlet et de Navier. Elles apparaissent naturellement dans l'étude des minimiseurs de la fonctionnelle de Kirchhoff-Love, qui représente l'énergie d'une plaque encastrée soumise à l'action d'une force extérieure, en fonction d'un paramètre $\sigma$. On trouve des conditions suffisantes sur le domaine pour que les minimiseurs de la fonctionnelle soient positifs. De plus, pour ces domaines on étudie une version généralisée de la fonctionnelle. En utilisant des techniques variationnelles, on examine l'existence et la positivité des états fondamentaux, ainsi que leur comportement asymptotique pour les valeurs pertinentes de $\sigma$. Dans la deuxième partie de la thèse on établit des estimations uniformes a priori pour des problèmes semi linéaires du quatrième ordre dans $\mathbb R^4$, et donc avec des non linéarités exponentielles. On considère des conditions au bord soit de Dirichlet soit de Navier et on suppose que les non linéarités sont positives et sous-critiques. Nos arguments combinent des estimations uniformes près du bord et une analyse de blow-up. Enfin, en utilisant la théorie du degré, on obtient l'existence d'une solution. / This thesis concerns the study of fourth-order elliptic boundary value problems and, in particular, qualitative properties of solutions. Such problems arise in various fields, from plate theory to conformal geometry and, compared to their second-order counterparts, they present intrinsic difficulties, mainly due to the lack of the maximum principle. In the first part of the thesis, we study the positivity of solutions in case of Steklov boundary conditions, which are intermediate between Dirichlet and Navier boundary conditions. They naturally appear in the study of the minimizers of the Kirchhoff-Love functional, which represents the energy of a hinged thin and loaded plate in dependence of a parameter $\sigma$. We establish sufficient conditions on the domain to obtain the positivity of the minimizers of the functional. Then, for such domains, we study a generalized version of the functional. Using variational techniques, we investigate existence and positivity of the ground states, as well as their asymptotic behaviour for the relevant values of $\sigma$. In the second part of the thesis we establish uniform a-priori bounds for a class of fourth-order semi linear problems in $\mathbb R^4$, and thus with exponential non linearities. We considered both Dirichlet and Navier boundary conditions and we suppose our non linearities positive and subcritical. Our arguments combine uniform estimates near the boundary and a blow-up analysis. Finally, by means of the degree theory, we obtain the existence of a positive solution.

Stochastic User Equilibrium (SUE) models allow the representation of the perceptual and preferential differences that exist when drivers compare alternative routes through a transportation network. However, as an effect of the used choice models, conventional applications of SUE are based on the assumption that all available routes have a positive probability of being chosen, however unattractive. In this paper, a novel choice model, the Bounded Choice Model (BCM), is presented along with network conditions for a corresponding Bounded SUE. The model integrates an exogenously-defined bound on the random utility of the set of paths that are used at equilibrium, within a Random Utility Theory (RUT) framework. The model predicts which routes are used and unused (the choice sets are equilibrated), while still ensuring that the distribution of flows on used routes accords to a Discrete Choice Model. Importantly, conditions to guarantee existence and uniqueness of the Bounded SUE are shown. Also, a corresponding solution algorithm is proposed and numerical results are reported by applying this to the Sioux Falls network.

Linear time-invariant models are widely used in the control community. They often serve as approximations of nonlinear systems. For control purposes linear approximations are often good enough since feedback control systems are inherently robust to model errors. In this thesis some of the possibilities for linear time-varying modeling are studied. In the thesis it is shown that the balanced truncation procedure can be applied to reduce the order of linear time-varying systems. Many of the attractive properties of balanced truncation for time-invariant systems can be generalized into the time-varying framework. For example, it is shown that a truncated input-output stable system will be input-output stable, and computable simple worst-case error bounds are derived. The method is illustrated with model reduction of a nonlinear diesel exhaust catalyst model. It is also shown that linear time-periodic models can be used for analysis of systems with power converters. Power converters produce harmonics in the power grids and give frequency coupling that cannot be modeled with standard time-invariant linear models. With time-periodic models we can visualize the coupling and also use all the available tools for linear time-varying systems, such as balanced truncation. The method is illustrated on inverter locomotives. / QC 20120208

Cette thèse porte sur des minorants pour des mesures de complexité liées à des sous-classes de la classe P de langages pouvant être décidés en temps polynomial par des machines de Turing. Nous considérons des modèles de calcul non uniformes tels que les programmes sur monoïdes et les programmes de branchement. Notre première contribution est un traitement abstrait de la méthode de Nečiporuk pour prouver des minorants, indépendamment de toute mesure de complexité spécifique. Cette méthode donne toujours les meilleurs minorants connus pour des mesures telles que la taille des programmes de branchements déterministes et non déterministes ou des formules avec des opérateurs booléens binaires arbitraires ; nous donnons une formulation abstraite de la méthode et utilisons ce cadre pour démontrer des limites au meilleur minorant obtenable en utilisant cette méthode pour plusieurs mesures de complexité. Par là, nous confirmons, dans ce cadre légèrement plus général, des résultats de limitation précédemment connus et exhibons de nouveaux résultats de limitation pour des mesures de complexité auxquelles la méthode de Nečiporuk n'avait jamais été appliquée. Notre seconde contribution est une meilleure compréhension de la puissance calculatoire des programmes sur monoïdes issus de petites variétés de monoïdes finis. Les programmes sur monoïdes furent introduits à la fin des années 1980 par Barrington et Thérien pour généraliser la reconnaissance par morphismes et ainsi obtenir une caractérisation en termes de semi-groupes finis de NC^1 et de ses sous-classes. Étant donné une variété V de monoïdes finis, on considère la classe P(V) de langages reconnus par une suite de programmes de longueur polynomiale sur un monoïde de V : lorsque l'on fait varier V parmi toutes les variétés de monoïdes finis, on obtient différentes sous-classes de NC^1, par exemple AC^0, ACC^0 et NC^1 quand V est respectivement la variété de tous les monoïdes apériodiques finis, résolubles finis et finis. Nous introduisons une nouvelle notion de docilité pour les variétés de monoïdes finis, renforçant une notion de Péladeau. L'intérêt principal de cette notion est que quand une variété V de monoïdes finis est docile, nous avons que P(V) contient seulement des langages réguliers qui sont quasi reconnus par morphisme par des monoïdes de V. De nombreuses questions ouvertes à propos de la structure interne de NC^1 seraient réglées en montrant qu'une variété de monoïdes finis appropriée est docile, et, dans cette thèse, nous débutons modestement une étude exhaustive de quelles variétés de monoïdes finis sont dociles. Plus précisément, nous portons notre attention sur deux petites variétés de monoïdes apériodiques finis bien connues : DA et J. D'une part, nous montrons que DA est docile en utilisant des arguments de théorie des semi-groupes finis. Cela nous permet de dériver une caractérisation algébrique exacte de la classe des langages réguliers dans P(DA). D'autre part, nous montrons que J n'est pas docile. Pour faire cela, nous présentons une astuce par laquelle des programmes sur monoïdes de J peuvent reconnaître beaucoup plus de langages réguliers que seulement ceux qui sont quasi reconnus par morphisme par des monoïdes de J. Cela nous amène à conjecturer une caractérisation algébrique exacte de la classe de langages réguliers dans P(J), et nous exposons quelques résultats partiels appuyant cette conjecture. Pour chacune des variétés DA et J, nous exhibons également une hiérarchie basée sur la longueur des programmes à l'intérieur de la classe des langages reconnus par programmes sur monoïdes de la variété, améliorant par là les résultats de Tesson et Thérien sur la propriété de longueur polynomiale pour les monoïdes de ces variétés. / This thesis deals with lower bounds for complexity measures related to subclasses of the class P of languages that can be decided by Turing machines in polynomial time. We consider non-uniform computational models like programs over monoids and branching programs.Our first contribution is an abstract, measure-independent treatment of Nečiporuk's method for proving lower bounds. This method still gives the best lower bounds known on measures such as the size of deterministic and non-deterministic branching programs or formulae{} with arbitrary binary Boolean operators; we give an abstract formulation of the method and use this framework to prove limits on the best lower bounds obtainable using this method for several complexity measures. We thereby confirm previously known limitation results in this slightly more general framework and showcase new limitation results for complexity measures to which Nečiporuk's method had never been applied.Our second contribution is a better understanding of the computational power of programs over monoids taken from small varieties of finite monoids. Programs over monoids were introduced in the late 1980s by Barrington and Thérien as a way to generalise recognition by morphisms so as to obtain a finite-semigroup-theoretic characterisation of NC^1 and its subclasses. Given a variety V of finite monoids, one considers the class P(V) of languages recognised by a sequence of polynomial-length programs over a monoid from V: as V ranges over all varieties of finite monoids, one obtains different subclasses of NC^1, for instance AC^0, ACC^0 and NC^1 when V respectively is the variety of all finite aperiodic, finite solvable and finite monoids. We introduce a new notion of tameness for varieties of finite monoids, strengthening a notion of Péladeau. The main interest of this notion is that when a variety V of finite monoids is tame, we have that P(V) does only contain regular languages that are quasi morphism-recognised by monoids from V. Many open questions about the internal structure of NC^1 would be settled by showing that some appropriate variety of finite monoids is tame, and, in this thesis, we modestly start an exhaustive study of which varieties of finite monoids are tame. More precisely, we focus on two well-known small varieties of finite aperiodic monoids: DA and J. On the one hand, we show that DA is tame using finite-semigroup-theoretic arguments. This allows us to derive an exact algebraic characterisation of the class of regular languages in P(DA). On the other hand, we show that J is not tame. To do this, we present a trick by which programs over monoids from J can recognise much more regular languages than only those that are quasi morphism-recognised by monoids from J. This brings us to conjecture an exact algebraic characterisation of the class of regular languages in P(J), and we lay out some partial results that support this conjecture. For each of the varieties DA and J, we also exhibit a program-length-based hierarchy within the class of languages recognised by programs over monoids from the variety, refining Tesson and Thérien's results on the polynomial-length property for monoids from those varieties.

This paper examines the effects of the exchange rate on bilateral industry trade in Sweden's trade with the Euro area. This is done by examining whether the J-curve effect exists using quarterly data from 1995 until 2022. Since becoming floating in the 1990s, the Swedish Krona has weakened significantly and recently, the discussion about the weakness of the Swedish Krona has gained renewed attention. Since Sweden is a small and open economy highly dependent on international trade, changes in the exchange rate may have large effects on the Swedish economy. The J-curve effect implies that the trade balance following a depreciation may initially worsen before later improving. The ARDL-approach is employed to obtain both short- and long-run effects of a depreciation on Swedish trade balance. In the 66 industries studied, little support can be found for a J-curve effect in Sweden's trade with the Euro area. Although 27 industries present short-run effects of a depreciation only five lasts until the long-run. Similarly, the results indicate that industries with a lower share of foreign inputs in their exports are affected more favourable than those with a higher share in the short run. No such results are found in the long run.

With diminishing returns in performance with scaling of traditional transistor devices, there is a growing need to understand and improve potential replacements technologies. Sufficient reliability has not been established in these devices and additional redundancy through use of fault tolerance and error correction codes are necessary. There is a price to pay in terms of energy and area, with this additional redundancy. It is of utmost importance to determine this energy cost and relate it to the increased reliability offered by the use of error correction codes. In this thesis, we have determined the lower bound for energy dissipation associated with error correction using a linear (n,k) block code. The bound obtained is implementation independent and is derived from fundamental considerations and it allows for quantum effects in the channel and decoder. We have also developed information theoretic efficacy measures that can quantify the performance of the error correction and their relationship to the corresponding energy cost.

<div>Structured prediction consists of receiving a structured input and producing a combinatorial structure such as trees, clusters, networks, sequences, permutations, among others. From the computational viewpoint, structured prediction is in general considered <i>intractable</i> because of the size of the output space being exponential in the input size. For instance, in image segmentation tasks, the number of admissible segments is exponential in the number of pixels. A second factor is the combination of the input dimensionality along with the amount of data under availability. In structured prediction it is common to have the input live in a high-dimensional space, which involves to jointly reason about thousands or millions of variables, and at the same time contend with limited amount of data. Thus, learning and inference methods with strong computational and statistical guarantees are desired. The focus of our research is then to propose <i>principled methods</i> for structured prediction that are both polynomial time, i.e., <i>computationally efficient</i>, and require a polynomial number of data samples, i.e., <i>statistically efficient</i>.</div><div><br></div><div>The main contributions of this thesis are as follows:</div><div><br></div><div>(i) We develop an efficient and principled learning method of latent variable models for structured prediction under Gaussian perturbations. We derive a Rademacher-based generalization bound and argue that the use of non-convex formulations in learning latent-variable models leads to tighter bounds of the Gibbs decoder distortion.</div><div><br></div><div>(ii) We study the fundamental limits of structured prediction, i.e., we characterize the necessary sample complexity for learning factor graph models in the context of structured prediction. In particular, we show that the finiteness of our novel MaxPair-dimension is necessary for learning. Lastly, we show a connection between the MaxPair-dimension and the VC-dimension---which allows for using existing results on VC-dimension to calculate the MaxPair-dimension.</div><div><br></div><div>(iii) We analyze a generative model based on connected graphs, and find the structural conditions of the graph that allow for the exact recovery of the node labels. In particular, we show that exact recovery is realizable in polynomial time for a large class of graphs. Our analysis is based on convex relaxations, where we thoroughly analyze a semidefinite program and a degree-4 sum-of-squares program. Finally, we extend this model to consider linear constraints (e.g., fairness), and formally explain the effect of the added constraints on the probability of exact recovery.</div><div><br></div>