This Compound Gauss-Markov image model has been proven helpful in image restoration. In this model, a pixel in the image random field is determined by the surrounding pixels according to a predetermined line field. In this thesis, we restored the noisy image based upon the traditional Compound Gauss-Markov image field without the constraint of the model parameters introduced in the original work. The image is restored in two steps iteratively: restoring the line field by the assumed image field and restoring the image field by the just computed line field. Two methods are proposed to replace the traditional method in solving for the line field. They are probability method and vector method. In probability method, we break away from the limitation of the energy function Vcl(L) and the mystical system parameters Ckll(m,n) and£mw2. In vector method, the line field appears more reasonable than the original method. The image restored by our methods has a similar visual quality but a better numerical value than the original method.

SANTOS, D. M. S. Seleção de símbolos piloto em sistemas de comunicação sem fio. 2016. 65 f. Dissertação (Mestrado em Engenharia de Teleinformática) – Centro de Tecnologia, Universidade Federal do Ceará, Fortaleza, 2016. / Submitted by Hohana Sanders (hohanasanders@hotmail.com) on 2016-11-03T10:53:31Z No. of bitstreams: 1 2016_dis_dmssantos.pdf: 2108362 bytes, checksum: 3d19fb1e9ea4fccac493580cb117ccb2 (MD5) / Approved for entry into archive by Marlene Sousa (mmarlene@ufc.br) on 2016-11-16T16:53:45Z (GMT) No. of bitstreams: 1 2016_dis_dmssantos.pdf: 2108362 bytes, checksum: 3d19fb1e9ea4fccac493580cb117ccb2 (MD5) / Made available in DSpace on 2016-11-16T16:53:45Z (GMT). No. of bitstreams: 1 2016_dis_dmssantos.pdf: 2108362 bytes, checksum: 3d19fb1e9ea4fccac493580cb117ccb2 (MD5) Previous issue date: 2016-07-19 / In order to achieve gains on the transmission capacity with lower error probability so the current requirements of mobile communication applications can be met, the way of how data is processed is crucial to improve system performance. In order to improve the quality of the transmission in multi-antenna systems, this work uses techniques of preprocessing of the transmitted signal to improve the system performance measured by the SNR (Signal to Noise Ratio) under a space-time transmit antenna array channel model, where the temporal dynamics of the channel is modeled by a Gauss-Markov process and the spatial correlation by a Kronecker model. Based on the statistical properties of the channel, we use the optimal linear algorithm, also known as a Kalman filter, associated with the transmitted pilot symbols for its estimation. From several sequences of defined pilot symbols, this work proposes an algorithm capable of selecting the best sequences of pilot symbols that maximize the received SNR. In the numerical simulations, we analyze the performance of the proposed method for pilot symbols selection and, as benchmark, the performance of the method of random pilot symbols selection. The results show the proposed method outperforms the random selection one. / Com o objetivo de se alcançar ganhos na capacidade de transmissão com menor probabilidade de erro para atender as atuais aplicações de comunicação móveis, o modo de tratamento dos dados é fundamental para a melhoria do desempenho do sistema. A fim de melhorar a qualidade de transmissão em sistemas de múltiplas antenas, este trabalho faz uso de técnicas de pré-processamento do sinal transmitido de forma a melhorar o desempenho do sistema, medido pela métrica da SNR (do inglês, Signal to Noise Ratio) sob um modelo de canal de arranjo de antenas transmissoras espaço-temporal, onde a dinâmica temporal do canal é modelada por um processo de Gauss-Markov e a correlação espacial por um modelo de Kronecker. Com base nas propriedades estatísticas do canal, faz-se sua estimação pelo algoritmo linear ótimo, também conhecido como filtro de Kalman, associado com os símbolos piloto transmitidos. A partir de várias sequências de símbolos pilotos definidas em um conjunto de palavras códigos, esta dissertação propõe um algoritmo capaz de selecionar as melhores sequências de símbolos pilotos que maximizam a SNR recebida. Nas simulações computacionais, são analisados o desempenho do método proposto de seleção de símbolos piloto e, como um referencial de comparação, o desempenho do método padrão de símbolos piloto escolhidos de maneira aleatória. Os resultados numéricos mostram que o método proposto tem desempenho de SNR recebida melhor do que o método de seleção aleatória.

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Ce travail concerne l'imagerie micro-onde en vue d'application à l'imagerie biomédicale. Cette technique d'imagerie a pour objectif de retrouver la distribution des propriétés diélectriques internes (permittivité diélectrique et conductivité) d'un objet inconnu illuminé par une onde interrogatrice connue à partir des mesures du champ électrique dit diffracté résultant de leur interaction. Un tel problème constitue un problème dit inverse par opposition au problème direct associé qui consiste à calculer le champ diffracté, l'onde interrogatrice et l'objet étant alors connus.La résolution du problème inverse nécessite la construction préalable du modèle direct associé. Celui-ci est ici basé sur une représentation intégrale de domaine des champs électriques donnant naissance à deux équations intégrales couplées dont les contreparties discrètes sont obtenues à l'aide de la méthode des moments. En ce qui concerne le problème inverse, hormis le fait que les équations physiques qui interviennent dans sa modélisation directe le rendent non-linéaire, il est également mathématiquement mal posé au sens de Hadamard, ce qui signifie que les conditions d'existence, d'unicité et de stabilité de la solution ne sont pas simultanément garanties. La résolution d'un tel problème nécessite sa régularisation préalable qui consiste généralement en l'introduction d'information a priori sur la solution recherchée. Cette résolution est effectuée, ici, dans un cadre probabiliste bayésien où l'on introduit une connaissance a priori adaptée à l'objet sous test et qui consiste à considérer ce dernier comme étant composé d'un nombre fini de matériaux homogènes distribués dans des régions compactes. Cet information est introduite par le biais d'un modèle de « Gauss-Markov-Potts ». De plus, le calcul bayésien nous donne la distribution a posteriori de toutes les inconnues connaissant l'a priori et l'objet. On s'attache ensuite à déterminer les estimateurs a posteriori via des méthodes d'approximation variationnelles et à reconstruire ainsi l'image de l'objet recherché. Les principales contributions de ce travail sont d'ordre méthodologique et algorithmique. Elles sont illustrées par une application de l'imagerie micro-onde à la détection du cancer du sein. Cette dernière constitue en soi un point très important et original de la thèse. En effet, la détection du cancer su sein en imagerie micro-onde est une alternative très intéressante à la mammographie par rayons X, mais n'en est encore qu'à un stade exploratoire. / This work concerns the problem of microwave tomography for application to biomedical imaging. The aim is to retreive both permittivity and conductivity of an unknown object from measurements of the scattered field that results from its interaction with a known interrogating wave. Such a problem is said to be inverse opposed to the associated forward problem that consists in calculating the scattered field while the interrogating wave and the object are known. The resolution of the inverse problem requires the prior construction of the associated forward model. This latter is based on an integral representation of the electric field resulting in two coupled integral equations whose discrete counterparts are obtained by means of the method of moments.Regarding the inverse problem, in addition to the fact that the physical equations involved in the forward modeling make it nonlinear, it is also mathematically ill-posed in the sense of Hadamard, which means that the conditions of existence, uniqueness and stability of the solution are not simultaneously guaranteed. Hence, solving this problem requires its prior regularization which usually involves the introduction of a priori information on the sought solution. This resolution is done here in a Bayesian probabilistic framework where we introduced a priori knowledge appropriate to the sought object by considering it to be composed of a finite number of homogeneous materials distributed in compact and homogeneous regions. This information is introduced through a "Gauss-Markov-Potts" model. In addition, the Bayesian computation gives the posterior distribution of all the unknowns, knowing the a priori and the object. We proceed then to identify the posterior estimators via variational approximation methods and thereby to reconstruct the image of the desired object.The main contributions of this work are methodological and algorithmic. They are illustrated by an application of microwave imaging to breast cancer detection. The latter is in itself a very important and original aspect of the thesis. Indeed, the detection of breast cancer using microwave imaging is a very interesting alternative to X-ray mammography, but it is still at an exploratory stage.

Bundle Adjustment is a common fine-tuning step used in photogrammetry. It uses different types of parameters, some of which can be considered to be almost linear while others can be considered to be highly nonlinear, e.g. the rotational parameters. However, in the Bundle Adjustment process all parameters are treated equal. In concert with a poor initial estimate, this might cause Bundle Adjustment to diverge. In this report, two novel methods based on the damped Gauss-Newton with Armijo linesearch, modified by giving rotational parameters a special treatment, are tested. These methods, Clamped Alpha and Linear Exponential Search, are compared to Gauss-Newton with Armijo linesearch, as well as to the undamped Gauss-Newton method, also known as the Gauss-Markov method. Parameter sweeps over different perturbation levels for the angular parameters show that each of the three damped methods outperform the Gauss-Newton method. Notably, the Clamped Alpha method also outperforms the other two damped methods, with as much as 16 times as many convergent cases for a given perturbation level. Meanwhile, the average number of iterations is increased by only 1.8 times that of the Gauss-Newton with Armijo linesearch. The results add to existing research arguing for the use of damped methods in Bundle Adjustment. In particular, the simple and cheap Clamped Alpha method is potentially attractive for problems where the uncertainty of the camera angles is significant. While the Clamped Alpha method show promising results, it should be noted that the experiments in this study are on synthetic data. In order to solidify these results, further investigations into the performance of Clamped Alpha using real-world data should be conducted.

We investigate extreme studentized and normalized residuals as test statistics for outlier detection in the Gauss-Markov model possibly not of full rank. We show how critical values (quantile values) of such test statistics are derived from the probability distribution of a single studentized or normalized residual by dividing the level of error probability by the number of residuals. This derivation neglects dependencies between the residuals. We suggest improving this by a procedure based on the Monte Carlo method for the numerical computation of such critical values up to arbitrary precision. Results for free leveling networks reveal significant differences to the values used so far. We also show how to compute those critical values for non‐normal error distributions. The results prove that the critical values are very sensitive to the type of error distribution. / Wir untersuchen extreme studentisierte und normierte Verbesserungen als Teststatistik für die Ausreißererkennung im Gauß-Markov-Modell von möglicherweise nicht vollem Rang. Wir zeigen, wie kritische Werte (Quantilwerte) solcher Teststatistiken von der Wahrscheinlichkeitsverteilung einer einzelnen studentisierten oder normierten Verbesserung abgeleitet werden, indem die Irrtumswahrscheinlichkeit durch die Anzahl der Verbesserungen dividiert wird. Diese Ableitung vernachlässigt Abhängigkeiten zwischen den Verbesserungen. Wir schlagen vor, diese Prozedur durch Einsatz der Monte-Carlo-Methode zur Berechnung solcher kritischen Werte bis zu beliebiger Genauigkeit zu verbessern. Ergebnisse für freie Höhennetze zeigen signifikante Differenzen zu den bisher benutzten Werten. Wir zeigen auch, wie man solche Werte für nicht-normale Fehlerverteilungen berechnet. Die Ergebnisse zeigen, dass die kritischen Werte sehr empfindlich auf den Typ der Fehlerverteilung reagieren.

We investigate extreme studentized and normalized residuals as test statistics for outlier detection in the Gauss-Markov model possibly not of full rank. We show how critical values (quantile values) of such test statistics are derived from the probability distribution of a single studentized or normalized residual by dividing the level of error probability by the number of residuals. This derivation neglects dependencies between the residuals. We suggest improving this by a procedure based on the Monte Carlo method for the numerical computation of such critical values up to arbitrary precision. Results for free leveling networks reveal significant differences to the values used so far. We also show how to compute those critical values for non‐normal error distributions. The results prove that the critical values are very sensitive to the type of error distribution. / Wir untersuchen extreme studentisierte und normierte Verbesserungen als Teststatistik für die Ausreißererkennung im Gauß-Markov-Modell von möglicherweise nicht vollem Rang. Wir zeigen, wie kritische Werte (Quantilwerte) solcher Teststatistiken von der Wahrscheinlichkeitsverteilung einer einzelnen studentisierten oder normierten Verbesserung abgeleitet werden, indem die Irrtumswahrscheinlichkeit durch die Anzahl der Verbesserungen dividiert wird. Diese Ableitung vernachlässigt Abhängigkeiten zwischen den Verbesserungen. Wir schlagen vor, diese Prozedur durch Einsatz der Monte-Carlo-Methode zur Berechnung solcher kritischen Werte bis zu beliebiger Genauigkeit zu verbessern. Ergebnisse für freie Höhennetze zeigen signifikante Differenzen zu den bisher benutzten Werten. Wir zeigen auch, wie man solche Werte für nicht-normale Fehlerverteilungen berechnet. Die Ergebnisse zeigen, dass die kritischen Werte sehr empfindlich auf den Typ der Fehlerverteilung reagieren.

<p>Video coding schemes designed based on sequential or predictive coding models are vulnerable to the loss of encoded frames at the decoder end. Motivated by this observation, in this thesis we propose two new coding models: robust sequential coding and robust predictive coding. For the Gauss-Markov source with the mean squared error distortion measure, we characterize certain supporting hyperplanes of the rate region of these two coding problems. The proof is divided into three steps: 1) it is shown that each supporting hyperplane of the rate region of Gaussian robust sequential coding admits a max-min lower bound; 2) the corresponding min-max upper bound is shown to be achievable by a robust predictive coding scheme; 3) a saddle point analysis proves that the max-min lower bound coincides with the min-max upper bound. Furthermore, it is shown that the proposed robust predictive coding scheme can be implemented using a successive quantization system. Theoretical and experimental results indicate that this scheme has a desirable \self-recovery" property. Our investigation also reveals an information-theoretic minimax theorem and the associated extremal inequalities.</p> / Doctor of Philosophy (PhD)

<p>U doktorskoj disertaciji je prikazan postupak optimizacije podzemne mreže<br />za potrebe izgradnje beogradskog metroa. U postupku optimizacije kori&scaron;ćen<br />je metod prethodne ocene tačnosti. Na osnovu građevinskih standarda<br />izvr&scaron;en je proračun zahtevane tačnosti proboja tunela, kao osnovnog<br />kriterijuma tačnosti za razvijanje podzemne tunelske mreže. U postupku<br />optimizacije analizirani su različiti planovi opažanja, kao i dobijeni rezultati<br />prethodne analize za svaki plan pojedinačno. Na osnovu zadatog kriterijuma<br />maksimalne poprečene gre&scaron;ke proboja tunela usvojen je konačan plan<br />opažanja.</p> / <p>The docotoral thesis presents an optimization method of the underground<br />network for the construction of the Belgrade metro. In the process of<br />optimization, method of preanalysis has been used. Based on the<br />construction standards, the calculation of the required breakthrough<br />accuracy, as the fundamental criteria of accuracy for the development of the<br />underground tunnel network, has been made. In the process of optimization<br />different plans of observations have been analyzed, as well as the results<br />obtained from the preanalysis for each plan individually. Based on the<br />required criteria of maximal transverse error of the tunnel breakthrough, the<br />final plan of observations has been adopted.</p>

Dans ce travail, nous nous intéressons à l'imagerie de diffraction dans des configurations à deux ou trois dimensions avec pour objectif la reconstruction d'une image (fonction contraste) d'un objet inconnu à l'aide de plusieurs mesures du champ qu'il diffracte. Ce champ résulte de l'interaction entre l'objet et un champ incident connu dont la direction de propagation et la fréquence peuvent varier. La difficulté de ce problème réside dans la non-linéarité du modèle direct et le caractère mal posé du problème inverse qui nécessite l'introduction d'une information a priori (régularisation). Pour cela, nous utilisons une approche bayésienne avec une estimation conjointe du contraste de l'objet, des courants induits et des autres paramètres du modèle. Le modèle direct est décrit par deux équations intégrales couplées exprimant les champs électriques observé et existant à l'intérieur de l'objet, dont les versions discrètes sont obtenues à l'aide de la méthode des moments. Pour l'inversion, l'approche bayésienne permet de modéliser notre connaissance a priori sur l'objet sous forme probabiliste. Les objets que nous étudions ici sont connus pour être constitués d'un nombre fini de matériaux homogènes répartis en régions compactes. Cette information a priori est introduite dans l'algorithme d'inversion à l'aide d'un mélange de gaussiennes, où chaque gaussienne représente une classe de matériaux, tandis que la compacité des régions est prise en compte au travers d'un modèle de Markov caché. La nature non linéaire du modèle direct et l'utilisation de cet a priori nous amènent à des estimateurs qui n'ont pas de formes explicites. Une approximation est donc nécessaire et deux voies sont possibles pour cela: une approche numérique, par exemple MCMC, et une approche analytique comme l'approche bayésienne variationnelle. Nous avons testé ces deux approches qui ont donné de bons résultats de reconstruction par rapport aux méthodes classiques. Cependant, l'approche bayésienne variationnelle permet de gagner énormément en temps de calcul par rapport à la méthode MCMC.