Multipath reduction from GPS double differences using wavelets: How far can we go?

Wavelets are being extensively used in Geodetic applications. In this paper, the Multi-Resolution Analysis (MRA) using wavelets is applied to pseudorange and carrier phase GPS double differences (DDs) in order to reduce multipath effects. The wavelets were already applied to GPS carrier phase DDs, but some questions remain: How good can be the results, and are all multipath effects reduced? The answers to these questions are discussed in this paper. Thus, the wavelet transform is used to decompose the DD signals, splitting them in lower resolution components. After the decomposition process, the wavelet shrinkage is performed by thresholding to eliminate the components relative to multipath effects. Then, the DD observation can be reconstructed. This new DD signal is used to perform the baseline processing. The daily multipath repeatability was verified. With the application of the proposed approach, the results showed that the reliability of the ambiguity resolution and accuracy of the results improved when compared with the standard procedure. Furthermore, the method showed to be very efficient computationally, because, it is not noticed, at practical level, difference in the time span between the processing with and without application of the proposed method. However, only the high frequency multipath was eliminated.

Satisfação das mulheres em relação à coleta do papanicolaou na estratégia de saúde da família

Pós-graduação em Enfermagem (mestrado profissional) - FMB

Strain rates estimated by geodetic observations in the Borborema Province, Brazil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Posicionamento por ponto preciso com solução de ambiguidades inteiras no contexto de rede GPS

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Image Reconstruction Using Interval Simulated Annealing in Electrical Impedance Tomography

Electrical impedance tomography (EIT) is an imaging technique that attempts to reconstruct the impedance distribution inside an object from the impedance between electrodes placed on the object surface. The EIT reconstruction problem can be approached as a nonlinear nonconvex optimization problem in which one tries to maximize the matching between a simulated impedance problem and the observed data. This nonlinear optimization problem is often ill-posed, and not very suited to methods that evaluate derivatives of the objective function. It may be approached by simulated annealing (SA), but at a large computational cost due to the expensive evaluation process of the objective function, which involves a full simulation of the impedance problem at each iteration. A variation of SA is proposed in which the objective function is evaluated only partially, while ensuring boundaries on the behavior of the modified algorithm.

Horn-schunck optical flow with a multi-scale strategy

[EN] The seminal work of Horn and Schunck [8] is the first variational method for optical flow estimation. It introduced a novel framework where the optical flow is computed as the solution of a minimization problem. From the assumption that pixel intensities do not change over time, the optical flow constraint equation is derived. This equation relates the optical flow with the derivatives of the image. There are infinitely many vector fields that satisfy the optical flow constraint, thus the problem is ill-posed. To overcome this problem, Horn and Schunck introduced an additional regularity condition that restricts the possible solutions. Their method minimizes both the optical flow constraint and the magnitude of the variations of the flow field, producing smooth vector fields. One of the limitations of this method is that, typically, it can only estimate small motions. In the presence of large displacements, this method fails when the gradient of the image is not smooth enough. In this work, we describe an implementation of the original Horn and Schunck method and also introduce a multi-scale strategy in order to deal with larger displacements. For this multi-scale strategy, we create a pyramidal structure of downsampled images and change the optical flow constraint equation with a nonlinear formulation. In order to tackle this nonlinear formula, we linearize it and solve the method iteratively in each scale. In this sense, there are two common approaches: one that computes the motion increment in the iterations, like in ; or the one we follow, that computes the full flow during the iterations, like in. The solutions are incrementally refined ower the scales. This pyramidal structure is a standard tool in many optical flow methods.

Discontinuity preserving in TV-L1 optical flow methods

[EN] We analyze the discontinuity preserving problem in TV-L1 optical flow methods. This type of methods typically creates rounded effects at flow boundaries, which usually do not coincide with object contours. A simple strategy to overcome this problem consists in inhibiting the diffusion at high image gradients. In this work, we first introduce a general framework for TV regularizers in optical flow and relate it with some standard approaches. Our survey takes into account several methods that use decreasing functions for mitigating the diffusion at image contours. Consequently, this kind of strategies may produce instabilities in the estimation of the optical flows. Hence, we study the problem of instabilities and show that it actually arises from an ill-posed formulation. From this study, it is possible to come across with different schemes to solve this problem. One of these consists in separating the pure TV process from the mitigating strategy. This has been used in another work and we demonstrate here that it has a good performance. Furthermore, we propose two alternatives to avoid the instability problems: (i) we study a fully automatic approach that solves the problem based on the information of the whole image; (ii) we derive a semi-automatic approach that takes into account the image gradients in a close neighborhood adapting the parameter in each position. In the experimental results, we present a detailed study and comparison between the different alternatives. These methods provide very good results, especially for sequences with a few dominant gradients. Additionally, a surprising effect of these approaches is that they can cope with occlusions. This can be easily achieved by using strong regularizations and high penalizations at image contours.

Entwicklung und Tests von Algorithmen für die Impedanztomographie

Die Elektrische Impedanztomographie soll als kostengünstige und nebenwirkungsfreie Tomographiemethode in der medizinischen Diagnostik, z. B. in der Mammographie dienen. Mit der EIT läßt sich Krebsgewebe von gesundem Gewebe unterscheiden, da es eine signifikant erhöhte Leitfähigkeit aufweist. Damit kann die EIT als Ergänzung zu den klassischen Diagnoseverfahren dienen. So ist z.B. bei jungen Frauen mit einem dichteren Fettgewebe die Identifizierung eines Mammakarzinoms mit der Röntgentomographie nicht immer möglich. Ziel dieser Arbeit war es, einen Prototypen für die Impedanztomographie zu entwickeln und mögliche Anwendungen zu testen. Der Tomograph ist in Zusammenarbeit mit Dr. K.H.Georgi gebaut worden. Der Tomograph erlaubt es niederohmige, Wechselströme an Elektroden auf der Körperoberfläche einzuspeisen. Die Potentiale können an diesen Elektroden programmierbar vorgegeben werden. Weitere hochohmige Elektroden dienen zur Potentialmessung. Um den Hautwiderstand zu überbrücken, werden Wechselstromfrequenzen von 20-100 kHz eingesetzt. Mit der Möglichkeit der Messung von Strom und Potential auf unterschiedlichen Elektroden kann man das Problem des nur ungenau bekannten Hautwiderstandes umgehen. Prinzipiell ist es mit dem Mainzer EIT System möglich, 100 Messungen in der Sekunde durchzuführen. Auf der Basis von mit dem Mainzer EIT gewonnenen Daten sollten unterschiedliche Rekonstruktionsalgorithmen getestet und weiterentwickelt werden. In der Vergangenheit sind verschiedene Rekonstruktionsalgorithmen für das mathematisch schlecht gestellte EIT Problem betrachtet worden. Sie beruhen im Wesentlichen auf zwei Strategien: Die Linearisierung und iterative Lösung des Problems und Gebietserkennungsmethoden. Die iterativen Verfahren wurden von mir dahingehend modifiziert, dass Leitfähigkeitserhöhungen und Leitfähigkeitserniedrigungen gleichberechtigt behandelt werden können. Für den modifizierten Algorithmus wurden zwei verschiedene Rekonstruktionsalgorithmen programmiert und mit synthetischen Daten getestet. Zum einen die Rekonstruktion über die approximative Inverse, zum anderen eine Rekonstruktion mit einer Diskretisierung. Speziell für die Rekonstruktion mittels Diskretisierung wurde eine Methode entwickelt, mit der zusätzliche Informationen in der Rekonstruktion berücksichtigt werden können, was zu einer Verbesserung der Rekonstruktion beiträgt. Der Gebietserkennungsalgorithmus kann diese Zusatzinformationen liefern. In der Arbeit wurde ein neueres Verfahren für die Gebietserkennung derart modifiziert, dass eine Rekonstruktion auch für getrennte Strom- und Spannungselektroden möglich wurde. Mit Hilfe von Differenzdaten lassen sich ausgezeichnete Rekonstruktionen erzielen. Für die medizinischen Anwendungen sind aber Absolutmessungen nötig, d.h. ohne Leermessung. Der erwartende Effekt einer Inhomogenität in der Leitfähigkeit ist sehr klein und als Differenz zweier grosser Zahlen sehr schwierig zu bestimmen. Die entwickelten Algorithmen kommen auch gut mit Absolutdaten zurecht.

Multiscale methods for shape constraints in deconvolution: Confidence statements for qualitative features

We derive multiscale statistics for deconvolution in order to detect qualitative features of the unknown density. An important example covered within this framework is to test for local monotonicity on all scales simultaneously. We investigate the moderately ill-posed setting, where the Fourier transform of the error density in the deconvolution model is of polynomial decay. For multiscale testing, we consider a calibration, motivated by the modulus of continuity of Brownian motion. We investigate the performance of our results from both the theoretical and simulation based point of view. A major consequence of our work is that the detection of qualitative features of a density in a deconvolution problem is a doable task, although the minimax rates for pointwise estimation are very slow.

Using distributions as statistical evidence in well-structured and ill-structured problems

Research has suggested that understanding in well-structured settings often does not transfer to the everyday, less-structured problems encountered outside of school. Little is known, beyond anecdotal evidence, about how teachers' consideration of distributions as evidence in well-structured settings compares with their use in ill-structured problem contexts. A qualitative study of preservice secondary teachers examined their use of distributions as evidence in four tasks of varying complexity and ill-structuredness. Results suggest that teachers' incorporation of distributions in well-structured settings does not imply that they will be incorporated in less structured problems (and vice-versa). Implications for research and teaching are discussed.

Probability distribution modelling to improve stability in nonlinear MIMO control

We consider the direct adaptive inverse control of nonlinear multivariable systems with different delays between every input-output pair. In direct adaptive inverse control, the inverse mapping is learned from examples of input-output pairs. This makes the obtained controller sub optimal, since the network may have to learn the response of the plant over a larger operational range than necessary. Moreover, in certain applications, the control problem can be redundant, implying that the inverse problem is ill posed. In this paper we propose a new algorithm which allows estimating and exploiting uncertainty in nonlinear multivariable control systems. This approach allows us to model strongly non-Gaussian distribution of control signals as well as processes with hysteresis. The proposed algorithm circumvents the dynamic programming problem by using the predicted neural network uncertainty to localise the possible control solutions to consider.

A procedure for determining a spacewise dependent heat source and the initial temperature

The inverse problem of determining a spacewise dependent heat source, together with the initial temperature for the parabolic heat equation, using the usual conditions of the direct problem and information from two supplementary temperature measurements at different instants of time is studied. These spacewise dependent temperature measurements ensure that this inverse problem has a unique solution, despite the solution being unstable, hence the problem is ill-posed. We propose an iterative algorithm for the stable reconstruction of both the initial data and the source based on a sequence of well-posed direct problems for the parabolic heat equation, which are solved at each iteration step using the boundary element method. The instability is overcome by stopping the iterations at the first iteration for which the discrepancy principle is satisfied. Numerical results are presented for a typical benchmark test example, which has the input measured data perturbed by increasing amounts of random noise. The numerical results show that the proposed procedure gives accurate numerical approximations in relatively few iterations.

Boundary integral equations for acoustical inverse sound-soft scattering

The shape of a plane acoustical sound-soft obstacle is detected from knowledge of the far field pattern for one time-harmonic incident field. Two methods based on solving a system of integral equations for the incoming wave and the far field pattern are investigated. Properties of the integral operators required in order to apply regularization, i.e. injectivity and denseness of the range, are proved.

A variational method for identifying a spacewise-dependent heat source

The inverse problem of determining a spacewise-dependent heat source for the parabolic heat equation using the usual conditions of the direct problem and information from one supplementary temperature measurement at a given instant of time is studied. This spacewise-dependent temperature measurement ensures that this inverse problem has a unique solution, but the solution is unstable and hence the problem is ill-posed. We propose a variational conjugate gradient-type iterative algorithm for the stable reconstruction of the heat source based on a sequence of well-posed direct problems for the parabolic heat equation which are solved at each iteration step using the boundary element method. The instability is overcome by stopping the iterative procedure at the first iteration for which the discrepancy principle is satisfied. Numerical results are presented which have the input measured data perturbed by increasing amounts of random noise. The numerical results show that the proposed procedure yields stable and accurate numerical approximations after only a few iterations.

Determination of a spacewise dependent heat source

This paper investigates the inverse problem of determining a spacewise dependent heat source in the parabolic heat equation using the usual conditions of the direct problem and information from a supplementary temperature measurement at a given single instant of time. The spacewise dependent temperature measurement ensures that the inverse problem has a unique solution, but this solution is unstable, hence the problem is ill-posed. For this inverse problem, we propose an iterative algorithm based on a sequence of well-posed direct problems which are solved at each iteration step using the boundary element method (BEM). The instability is overcome by stopping the iterations at the first iteration for which the discrepancy principle is satisfied. Numerical results are presented for various typical benchmark test examples which have the input measured data perturbed by increasing amounts of random noise.