Neste trabalho, investigam-se técnicas autodidatas baseadas em estatísticas de ordem superior, aplicadas à equalização de canais de comunicação. Inicialmente, obtém-se um intervalo do passo de adaptação que assegura a convergência do algoritmo do Módulo Constante com o gradiente exato. Algoritmos como o CMA (Constant Modulus Algorithm) e o SWA (Shalvi-Weinstein Algorithm) são revisitados e suas capacidades de tracking analisadas, utilizando-se uma relação de conservação de energia. Além disso, é proposto um algoritmo autodidata denominado AC-CMA (Accelerated Constant Modulus Algorithm) que utiliza a segunda derivada (“aceleração”) da estimativa dos coeficientes. Esse algoritmo pode apresentar um compromisso mais favorável entre complexidade computacional e velocidade de convergência que o CMA e o SWA. Esses resultados são estendidos para o caso multiusuário. Através de simulações, os algoritmos são comparados e as análises de convergência e tracking validadas. Considerando o DFE (Decision Feedback Equalizer) no caso monousuário com o critério do módulo constante, é proposto um algoritmo concorrente que evita soluções degeneradas e apresenta um desempenho melhor do que os existentes na literatura. Com o intuito de evitar propagação de erros, é proposta uma estrutura híbrida que utiliza uma rede neural recorrente na malha de realimentação. Resultados de simulações indicam que seu uso pode ser vantajoso para canais lineares e não-lineares. / The equalization of communication channels is addressed by using blind techniques based on higher order statistics. A step-size interval is obtained to ensure the convergence of Steepest-Descent Constant Modulus Algorithm. The Shalvi-Weinstein Algorithm (SWA) and Constant Modulus Algorithm (CMA) are revisited and their tracking capabilities are analyzed by using an energy conservation relation. Moreover, a novel blind algorithm named Accelerated Constant Modulus Algorithm (AC-CMA) is proposed. It adjusts the second derivative (“acceleration”) of the coefficient estimates and presents a more favorable compromise between computational complexity and convergence rate than CMA or SWA. These results are extended to the MIMO (Multiple-Input Multiple-Output) case. By means of simulations, the algorithms are compared and the convergence and tracking analysis are validated. The Decision Feedback Equalizer (DFE) is considered in the SISO (Single-Input Single-Output) case with the Constant Modulus criterion and a concurrent algorithm is proposed. It avoids degenerated solutions and shows better behavior than the others presented in the literature. In order to avoid error propagation, a hybrid DFE is also proposed. It includes a recurrent neural network in the feedback filter and may be advantageously used to equalize linear and nonlinear channels.

É investigado o uso de redes neurais aplicadas à equalização de canais de comunicação, sendo consideradas três tipos de redes: MLP (Multilayer Perceptron), RBF (Radial Basis Function) e RNN (Recurrent Neural Network). Os equalizadores não-lineares baseados nestas redes foram comparados com o equalizador linear transversal e com os equalizadores ótimos segundo os critérios de Bayes e da máxima verossimilhança. Nestas comparações foram utilizados um alfabeto binário e um quaternário transmitidos em modelos de canais cuja resposta ao pulso unitário é finita. Além das versões usuais de equalizadores, foram consideradas versões com realimentação de decisões sempre que isso se mostrou adequado. O treinamento desses equalizadores foi feito de forma supervisionada, ou seja, na fase de treinamento a seqüência de símbolos transmitida era conhecida no receptor. Além disso, foi realizado um estudo comparativo dos algoritmos de treinamento das redes. Neste âmbito, foi obtido um algoritmo do tipo acelerador para o treinamento de redes MLP. Com o intuito de se obter uma estrutura não-linear menos complexa e mais flexível, foi proposto ainda um equalizador híbrido constituído de uma combinação do equalizador linear e da rede RNN que faz uso de realimentação de decisões. Resultados de simulações indicam que o seu uso pode ser vantajoso tanto para canais não-lineares como lineares. / Equalization of communication channels using neural networks is investigated by considering three kinds of networks: MLP (Multilayer Perceptron), RBF (Radial Basis Function) and RNN (Recurrent Neural Network). The performance of the nonlinear equalizers based on these networks are compared with the linear transversal equalizer and the optimal equalizers given by the bayesian and maximum likelihood criteria. Binary and quaternary alphabets are used and transmitted over finite pulse response channel models. Decision feedback is considered whenever it is worthwhile. The training of these equalizers is considered in the supervised form and a comparison of some training algorithms has been performed. In this scope, a new algorithm based on parameter acceleration is introduced for the training of MLP networks. Moreover, a hybrid equalizer composed of a linear transversal equalizer and a RNN network is proposed. It is a simple and flexible nonlinear structure making use of decision feedback. imulation results show that it may be advantageously used to equalize linear and nonlinear channels.

We consider two transceivers equipped with multiple antennas that intend to communicate i.e. both of which transmit and receive data in a TDD fashion. Assuming that the responses of the physical communication channels between these two nodes are linear and reciprocal (time invariant or with very slow time variations), and by exploiting the closed loop conversation between these nodes, we have proposed efficient algorithms allowing to adaptively identify the Best Singular Mode (BSM) of the channel (those algorithms are for training, blind, and semi-blind channel identification). Unlike other proposed algorithms, our proposed adaptive algorithms are robust to noise as the involved step-size allows a trade-off to reduce the impact of the additive noise at the expense of some estimation delay. In practice, however, the reciprocity of the equivalent channels is lost because of the mismatch between the transmit and the receive filters of the communicating nodes. This mismatch causes significant degradation in the performance of the BSM estimation. Therefore, we have also proposed adaptive self-calibrating algorithms (which do not require any additional RF circuitry) that account for such a mismatch. In addition, we have conducted a convergence analysis of the BSM algorithm and extended it to estimate multiple modes simultaneously. Finally, we have also proposed an adaptive, iterative algorithm that is capable of allocating power in such a way that maximizes the capacity of a SISO OFDM communication system. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2010-07-21 16:53:33.077

Les simulations moléculaires sont devenues un outil essentiel en biologie, chimie et physique. Malheureusement, elles restent très coûteuses. Dans cette thèse, nous proposons des algorithmes qui accélèrent les simulations moléculaires en regroupant des particules en plusieurs objets rigides. Nous étudions d’abord plusieurs algorithmes de recherche de voisins dans le cas des grands objets rigides, et démontrons que les algorithmes hiérarchiques permettent d’obtenir des accélérations importantes. En conséquence, nous proposons une technique pour construire une représentation hiérarchique d’un graphe moléculaire arbitraire. Nous démontrons l’usage de cette technique pour la mécanique adaptative en angles de torsion, une méthode de simulation qui décrit les molécules comme des objets rigides articulés. Enfin, nous introduisons ARPS – Adaptively Restrained Particle Simulations (“Simulations de particules restreintes de façon adaptative”) – une méthode mathématiquement fondée capable d’activer et de désactiver les degrés de liberté en position. Nous proposons deux stratégies d’adaptation, et illustrons les avantages de ARPS sur plusieurs exemples. En particulier, nous démontrons comment ARPS permet de choisir finement le compromis entre précision et vitesse, ainsi que de calculer rapidement des proprietésstatiques d’équilibre sur les systèmes moléculaires. / Molecular simulations have become an essential tool in biology, chemistry and physics. Unfortunately, they still remain computationally challenging. In this dissertation, we propose algorithms that accelerate molecular simulations by clustering particles into rigid bodies.We first study several neighbor-search algorithms for large rigid bodies, and show that hierarchy-based algorithms may provide significant speedups. Accordingly, we propose a technique to build a hierarchical representation of an arbitrary molecular graph. We show how this technique can be used in adaptive torsion-angle mechanics, a simulation method that describes molecules as articulated rigid bodies. Finally, we introduce ARPS – Adaptively Restrained Particle Simulations – a mathematically-grounded method able to switch positional degrees of freedom on and off. We propose two switching strategies, and illustrate the advantages of ARPS on various examples. In particular, we show how ARPS allow us to smoothly trade between precision and speed, and to efficiently compute correct static equilibrium properties on molecular systems.

L'essor des nouvelles technologies de télécommunication et de conception des capteurs a fait apparaître un nouveau domaine du traitement du signal : les réseaux de capteurs. Une application clé de ce nouveau domaine est l'estimation à distance : les capteurs acquièrent de l'information et la transmettent à un point distant où l'estimation est faite. Pour relever les nouveaux défis apportés par cette nouvelle approche (contraintes d'énergie, de bande et de complexité), la quantification des mesures est une solution. Ce contexte nous amène à étudier l'estimation à partir de mesures quantifiées. Nous nous concentrons principalement sur le problème d'estimation d'un paramètre de centrage scalaire. Le paramètre est considéré soit constant, soit variable dans le temps et modélisé par un processus de Wiener lent. Nous présentons des algorithmes d'estimation pour résoudre ce problème et, en se basant sur l'analyse de performance, nous montrons l'importance de l'adaptativité de la dynamique de quantification pour l'obtention d'une performance optimale. Nous proposons un schéma adaptatif de faible complexité qui, conjointement, estime le paramètre et met à jour les seuils du quantifieur. L'estimateur atteint de cette façon la performance asymptotique optimale. Avec 4 ou 5 bits de résolution, nous montrons que la performance optimale pour la quantification uniforme est très proche des performances d'estimation à partir de mesures continues. Finalement, nous proposons une approche à haute résolution pour obtenir les seuils de quantification non-uniformes optimaux ainsi qu'une approximation analytique des performances d'estimation. / With recent advances in sensing and communication technology, sensor networks have emerged as a new field in signal processing. One of the applications of his new field is remote estimation, where the sensors gather information and send it to some distant point where estimation is carried out. For overcoming the new design challenges brought by this approach (constrained energy, bandwidth and complexity), quantization of the measurements can be considered. Based on this context, we study the problem of estimation based on quantized measurements. We focus mainly on the scalar location parameter estimation problem, the parameter is considered to be either constant or varying according to a slow Wiener process model. We present estimation algorithms to solve this problem and, based on performance analysis, we show the importance of quantizer range adaptiveness for obtaining optimal performance. We propose a low complexity adaptive scheme that jointly estimates the parameter and updates the quantizer thresholds, achieving in this way asymptotically optimal performance. With only 4 or 5 bits of resolution, the asymptotically optimal performance for uniform quantization is shown to be very close to the continuous measurement estimation performance. Finally, we propose a high resolution approach to obtain an approximation of the optimal nonuniform quantization thresholds for parameter estimation and also to obtain an analytical approximation of the estimation performance based on quantized measurements.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-03-10T12:31:18Z No. of bitstreams: 1 eduardopestanadeaguiar.pdf: 7884545 bytes, checksum: 182caace21281f7afce6554505811116 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-03-13T17:18:31Z (GMT) No. of bitstreams: 1 eduardopestanadeaguiar.pdf: 7884545 bytes, checksum: 182caace21281f7afce6554505811116 (MD5) / Made available in DSpace on 2017-03-13T17:18:31Z (GMT). No. of bitstreams: 1 eduardopestanadeaguiar.pdf: 7884545 bytes, checksum: 182caace21281f7afce6554505811116 (MD5) Previous issue date: 2016-09-26 / - / This thesis presents new fuzzy models applied to classification problems. With this regards, we introduce the use of set-membership concept, derived from the adaptive filter theory, into the training procedure of type-1 and singleton/non-singleton fuzzy logic systems, in order to reduce computational complexity and to increase convergence speed. Also, we present different criteria for using together with set-membership. Furthermore, we discuss the usefulness of delta rule delta, local Lipschitz estimation, variable step size and variable step size adaptive algorithms to yield additional improvement in terms of computational complexity reduction and convergence speed. Another important contribution of this thesis is to address the height type-reduction and to propose a modified version of interval singleton type-2 fuzzy logic system, so−called upper and lower singleton type-2 fuzzy logic system. The obtained results are compared with other models reported in the literature, demonstrating the effectiveness of the proposed classifiers and revealing that the proposals are able to properly handle with uncertainties associated with the measurements and with the data that are used to tune the parameters of the model. Based on data set provided by a Brazilian railway company, the models outlined above are applied in the classification of three possible faults and the normal condition of the switch machine, which is an equipment used for handling railroad switches. Finally, this thesis discusses the use of set-membership concept into the training procedure of an interval and singleton type-2 fuzzy logic system and of an upper and lower singleton type-2 fuzzy logic system, aiming to reduce computational complexity and to increase the convergence speed and the classification ratio. Also, we discuss the adoption of different criteria together with set-membership based-techniques. The performance is based on the data set composed of images provided by the same Brazilian railway company, which covers the four possible rail head defects and the normal condition of the rail head. The reported results show that the proposed models result in improved convergence speed, slightly higher classification ratio and remarkable computation complexity reduction when we limit the number of epochs for training, which may be required due to real time constraint or low computational resource availability.

<p>Multi-input multi-output (MIMO) systems are expected to playa crucial role in future wireless communications and a significant increase of interest in all aspects of MIMO system design has been seen in the past decade. The primary interest of this thesis is in the receiver part of the MIMO system. In this area, continuous interest has been shown in developing blind-adaptive decoding algorithms. While blind decoding algorithms improve data throughput by enabling the system de:signer to replace training symbols with data, they also tend to perform robustly against any environment disturbances, compared to their training-based counterparts. On the other hand, considering the fact that the wireless end user environment is becoming increasingly mobile, adaptive algorithms have the ability to improve the performance of a system regardless of whether it is a blind system or a training-based one. The primary difficulty faced by blind and adaptive algorithms is that they generally are computationally intense. In this thesis, we develop semi-blind and blind decoding algorithms that are adaptive in nature as well as computationally efficient for multi-antenna systems.</p> <p>First, we consider the problem of channel tracking for MIMO communication systems where the MIMO channel is time-varying. We consider a class of MIMO systems where orthogonal space-time block codes (OSTBCs) are used as the underlying space-time coding schemes. For a general MIMO system with any number of transmitting and receiving antenna combinations, a two-step MIMO channel tracking algorithm is proposed. As the first step, Kalman filtering is used to obtain an initial channel estimate for the current block based on the channel estimates obtained for previous blocks. Then, in the second step, the so-obtained initial channel estimate is refined using a decision-directed iterative method. We show that, due to specific properties of orthogonal space-time block codes, both the Kalman filter and the decision-directed algorithm can be significantly simplified. Then, we extend the above receiver for MIMO-OFDM systems and propose a computationally efficient semi-blind receiver for MIMO systems in frequency-selective channels. Further, for the proposed receivers, we have derived theoretical performance analysis in terms of probability of error. Assuming the knowledge of the transmitted symbols for the first block, we have derived the instantaneous signal to interference and noise ratio (SINR) for consecutive transmission blocks in the absence of training, by exploiting Kalman filtering to track the channel in a decision-directed mode. Later, we extend the the theoretical performance limit comparisons for time-domain vs. frequency-domain channel tracking for MIMO-OFDM systems. Further, we study the advantage of adaptive channel tracking algorithms in comptype pilot aided channel estimation schemes in practical MIMO-OFDM systems.</p> <p>After that, an efficient sequential Monte-Carlo (SMC) algorithm is developed for blind detection in MIMO systems where OSTBCs are used as the underlying space-time coding scheme. The proposed algorithm employs Rao-Blackwellization strategy to marginalize out the (unwanted) channel coefficients and uses optimal importance function to generate samples to propagate the posterior distribution. The algorithm is blind in the sense that, unlike the earlier ones, the transmission of training symbols is not required by this scheme. The marginalization involves the computation of (hundreds of) Kalman filters running in parallel resulting in intense computer requirement. We show that, the marginalization step can be significantly simplified for the speci1ied problem under no additional assumptions - resulting in huge computational savings. Further, we extend this result to frequency selective channels and propose a novel and efficient SMC receiver for MIMO-OFDM systems.</p> <p>Finally, a novel adaptive algorithm is presented for directional MIMO systems. Specifically, the problem of direction of arrivall (DOA) tracking of an unknown time-varying number of mobile sources is considered. The challenging part of the problem is the unknown, time-varying number of sources that demand a combination of source enumeration techniques and sequential state estimation methods to track the time-varying number of DOAs. In this thesis, we transform the problem into a novel state-space model, and, by employing probability hypothesis density (PHD) filtering technique, propose a simple algorithm that is able to track the number of sources as well as the corresponding directions of arrivals. In addition to the fact that the proposed algorithm is simple and easier to implement, simulation results show that, the PHD implementation yields superior performance over competing schemes in tracking rapidly varying number of targets.</p> / Doctor of Philosophy (PhD)

Range-based localization is a widely used technique for position estimation where distances are measured to anchors, nodes with known positions, and the position is analytically estimated. It offers the benefits of providing high localization accuracy and involving simple operation over multiple deployments. Examples are the Global Positioning System (GPS) and network-based cellular handset localization. Range-based localization is promising for a range of applications, such as robot deployment in emergency scenarios or monitoring industrial processes. However, the presence of clutter in some of these environments leads to a severe degradation of the localization accuracy due to non-line-of-sight (NLOS) signal propagation. Moreover, current literature in NLOS-mitigation techniques requires that the NLOS distances constitute only a minority of the total number of distances to anchors. The key ideas proposed in the dissertation are: 1) multi-hop localization offers significant advantages over single-hop localization in NLOS-prone environments; and 2) it is possible to further reduce position errors by carefully placing intermediate nodes among the clutter to minimize multi-hop distances between the anchors and the unlocalized node. We demonstrate that shortest path distance (SPD) based multi-hop localization algorithms, namely DV-Distance and MDS-MAP, perform the best among other competing techniques in NLOS-prone settings. However, with random node placement, these algorithms require large node densities to produce high localization accuracy. To tackle this, we show that the strategic placement of a relatively small number of nodes in the clutter can offer significant benefits. We propose two algorithms for node placement: first, the Optimal Placement for DV-Distance (OPDV) focuses on obtaining the optimal positions of the nodes for a known clutter topology; and second, the Adaptive Placement for DV-Distance (APDV) offers a distributed control technique that carefully moves nodes in the monitored area to achieve localization accuracies close to those achieved by OPDV. We evaluate both algorithms via extensive simulations, as well as demonstrate the APDV algorithm on a real robotic hardware platform. We finally demonstrate how the characteristics of the clutter topology influence single-hop and multi-hop distance errors, which in turn, impact the performance of the proposed algorithms.

To simulate real world problems modeled by differential equations, it is often not sufficient to  consider and tackle a single equation. Rather, complex phenomena are modeled by several partial dierential equations that are coupled to each other. For example, a heart beat involve electric activity, mechanics of the movement of the walls and valves, as well as blood fow - a true multiphysics problem. There may also be ordinary differential equations modeling the reactions on a cellular level, and these may act on a much finer scale in both space and time. Determining efficient and accurate simulation tools for such multiscalar multiphysics problems is a challenge. The five scientific papers constituting this thesis investigate and present solutions to issues regarding accurate and efficient simulation using adaptive finite element methods. These include handling local accuracy through submodeling, analyzing error propagation in time-dependent  multiphysics problems, developing efficient algorithms for adaptivity in time and space, and deriving error analysis for coupled PDE-ODE systems. In all these examples, the error is analyzed and controlled using the framework of dual-weighted residuals, and the spatial meshes are handled using octree based data structures. However, few realistic geometries fit such grid and to address this issue a discontinuous Galerkin Nitsche method is presented and analyzed.

Keyword queries are extremely easy for a user to write. They have become a standard way to query for information in web search engines and most other information retrieval systems whose users are usually laypersons and might not have knowledge about the database schema or contained data. As keyword queries do not impose any structural constraints on the retrieved information, the quality of the obtained results is far from perfect. However, one can hardly improve it without changing the ways the queries are asked and the methods the information is stored in the database. The purpose of this thesis is to propose a method to improve the quality of the information retrieving by adding weights to the existing ways of keyword queries asking and information storing in the database. We consider weighted queries on two different data structures: weighted binary relations and weighted multi-labeled trees. We propose adaptive algorithms to solve these queries and prove the measures of the complexity of these algorithms in terms of the high-level operations. We describe how these algorithms can be implemented and derive the upper bounds on their complexity in two specific models of computations: the comparison model and the word-RAM model.