Uncalibrated visual servoing (VS) can improve robot performance without needing camera and robot parameters. Multiple cameras improve uncalibrated VS precision, but no works exist simultaneously using more than two cameras. The first data for uncalibrated VS simultaneously using more than two cameras are presented. VS performance is also compared for two different camera models: a high-cost camera and a low-cost camera, the difference being image noise magnitude and focal length. A Kalman filter based control law for uncalibrated VS is introduced and shown to be stable under the assumptions that robot joint level servo control can reach commanded joint offsets and that the servoing path goes through at least one full column rank robot configuration. Adaptive filtering by a covariance matching technique is applied to achieve automatic camera weighting, prioritizing the best available data. A decentralized sensor fusion architecture is utilized to assure continuous servoing with camera occlusion. The decentralized adaptive Kalman filter (DAKF) control law is compared to a classical method, Gauss-Newton, via simulation and experimentation. Numerical results show that DAKF can improve average tracking error for moving targets and convergence time to static targets. DAKF reduces system sensitivity to noise and poor camera placement, yielding smaller outliers than Gauss-Newton. The DAKF system improves visual servoing performance, simplicity, and reliability.

Time Difference Of Arrival (TDOA) based position tracking technique is one of the pinnacles of sports tracking technology. Using radio frequency com-munication, advanced filtering techniques and various computation methods, the position of a moving player in a virtually created sports arena can be iden-tified using MATLAB. It can also be related to player’s movement in real-time. For football in particular, this acts as a powerful tool for coaches to enhanceteam performance. Football clubs can use the player tracking data to boosttheir own team strengths and gain insight into their competing teams as well. This method helps to improve the success rate of Athletes and clubs by analyz-ing the results, which helps in crafting their tactical and strategic approach to game play. The algorithm can also be used to enhance the viewing experienceof audience in the stadium, as well as broadcast.In this thesis work, a typical football field scenario is assumed and an arrayof base stations (BS) are installed along perimeter of the field equidistantly.The player is attached with a radio transmitter which emits radio frequencythroughout the assigned game time. Using the concept of TDOA, the position estimates of the player are generated and the transmitter is tracked contin-uously by the BS. The position estimates are then fed to the Kalman filter, which filters and smoothens the position estimates of the player between the sample points considered. Different paths of the player as straight line, circu-lar, zig-zag paths in the field are animated and the positions of the player are tracked. Based on the error rate of the player’s estimated position, the perfor-mance of the Kalman filter is evaluated. The Kalman filter’s performance is analyzed by varying the number of sample points.

A Tomografia por Impedância elétrica (TIE) tem como objetivo gerar imagens da distribuição de resistividade dentro de um domínio. A TIE injeta correntes em eletrodos alocados na fronteira do domínio e mede potenciais elétricos através dos mesmos eletrodos. A TIE é considerada um problema inverso, não-linear e mal posto. Atualmente, para gerar uma solução do problema inverso, existem duas classes de algoritmos para estimar a distribuição de resistividade no interior do domínio, os que estimam variações da distribuição de resistividade do domínio e os absolutos, que estimam a distribuição de resistividade. Variações da distribuição de resistividade são o resultado da solução de um sistema linear do tipo Ax = b. O objetivo do presente trabalho é avaliar o desempenho da Programação Linear (PL) na solução do sistema linear, avaliar o algoritmo quanto a propaga- ção de erros numéricos e avaliar os efeitos de restringir o espaço solução através de restrições de PL. Os efeitos do uso de Programação Linear é avaliado tanto em métodos que geram imagens de diferenças, como o Matriz de Sensibilidade, como em métodos absolutos, como o Gauss-Newton. Mostra-se neste trabalho que o uso da PL diminui o erro numérico propagado quando comparado ao uso do algoritmo LU Decomposition. Resulta também que reduzir o espaço solução, diretamente através de restrições de PL, melhora a resolução em resistividade e a resolução espacial da imagem quando comparado com o uso de LU Decomposition. / Electrical impedance tomography (EIT) generates images of the resistivity distribution of a domain. The EIT method inject currents through electrodes placed on the boundary of the domain and measures electric potentials through the same electrodes. EIT is considered an inverse problem, non-linear and ill-conditioned. There are two classes of algorithms to estimate the resistivity distribution inside the domain, difference images algorithms, which estimate resistivity distribution variations, and absolute images algorithms, which estimate the resistivity distribution. Resistivity distribution variations are the solution of a linear system, say Ax = b. In this work, the main objective is to evaluate the performance of Linear Programming (LP) solving an EIT linear system from the point of view of the numerical error propagation and the ability to constrain the solution space. The impact of using LP to solve an EIT linear system is evaluated on a difference image algorithm and on an absolute algorithm. This work shows that the use of LP diminishes the numerical error propagation compared to LU Decomposition. It is also shown that constraining the solution space through LP improves the resistivity resolution and the spatial resolution of the images when compared to LU Decomposition.

No description available.

Power-efficient circuits are a vital step in moving towards a greener future. Battery life can get substantially improved by decreasing the amount of power a circuit needs. Lower power also leads to less excess heat generated. Electronics are within everything today – from phones and microwaves to cars! If we want to optimize the electronics to require less power, we need to understand it. In some integrated circuits that utilize bipolar transistors, it has been concluded that the main limitation regarding low-power, high-temperature operations is leakage currents that arise in reverse biased p–n junctions. There is a lack of understanding regarding the magnitude of these leakage currents, especially at higher temperatures. This thesis aims to provide an understanding of the magnitude of the leakage currents in lateral gated PNP bipolar transistors and to provide empirical models of these currents.A discussion of semiconductor physics takes place, explaining how leakage currents arise in reverse-biased pn junctions. Measurements were taken on a chip with the help of different instruments and a relay network that configured the experimental setup into different circuits while measurements were being conducted.It was shown that the leakage currents are clearly exponential to temperature, as was expected. Empirical models are created with the help of the Gauss-Newton linearization method and shown to be of the form<img src="http://www.diva-portal.org/cgi-bin/mimetex.cgi?$y=%20%5Ctheta_1%20%5Cmathrm%7Be%7D%5E%7B%5Ctheta_2%5Cleft(T-%5Ctheta_4%5Cright)%7D+%5Ctheta_3$" data-classname="equation" data-title="" />,where 𝜃 are parameters for the different models.A discussion is held on the impact of the results and how to improve upon them. Numerous sources of error are discussed, and further work is recommended.

<p>We present a detailed study on application of factor<br />graphs and the belief propagation (BP) algorithm to the<br />power system state estimation (SE) problem. We start<br />from the BP solution for the linear DC model, for which<br />we provide a detailed convergence analysis. Using BPbased<br />DC model we propose a fast real-time state<br />estimator for the power system SE. The proposed<br />estimator is easy to distribute and parallelize, thus<br />alleviating computational limitations and allowing for<br />processing measurements in real time. The presented<br />algorithm may run as a continuous process, with each<br />new measurement being seamlessly processed by the<br />distributed state estimator. In contrast to the matrixbased<br />SE methods, the BP approach is robust to illconditioned<br />scenarios caused by significant differences<br />between measurement variances, thus resulting in a<br />solution that eliminates observability analysis. Using the<br />DC model, we numerically demonstrate the performance<br />of the state estimator in a realistic real-time system<br />model with asynchronous measurements. We note that<br />the extension to the non-linear SE is possible within the<br />same framework.<br />Using insights from the DC model, we use two different<br />approaches to derive the BP algorithm for the non-linear<br />model. The first method directly applies BP methodology,<br />however, providing only approximate BP solution for the<br />non-linear model. In the second approach, we make a key<br />further step by providing the solution in which the BP is<br />applied sequentially over the non-linear model, akin to<br />what is done by the Gauss-Newton method. The resulting<br />iterative Gauss-Newton belief propagation (GN-BP)<br />algorithm can be interpreted as a distributed Gauss-<br />Newton method with the same accuracy as the<br />centralized SE, however, introducing a number of<br />advantages of the BP framework. The thesis provides<br />extensive numerical study of the GN-BP algorithm,<br />provides details on its convergence behavior, and gives a<br />number of useful insights for its implementation.<br />Finally, we define the bad data test based on the BP<br />algorithm for the non-linear model. The presented model<br />establishes local criteria to detect and identify bad data<br />measurements. We numerically demonstrate that the<br />BP-based bad data test significantly improves the bad<br />data detection over the largest normalized residual test.</p> / <p>Glavni rezultati ove teze su dizajn i analiza novih<br />algoritama za re&scaron;avanje problema estimacije stanja<br />baziranih na faktor grafovima i &bdquo;Belief Propagation&ldquo; (BP)<br />algoritmu koji se mogu primeniti kao centralizovani ili<br />distribuirani estimatori stanja u elektroenergetskim<br />sistemima. Na samom početku, definisan je postupak za<br />re&scaron;avanje linearnog (DC) problema kori&scaron;ćenjem BP<br />algoritma. Pored samog algoritma data je analiza<br />konvergencije i predloženo je re&scaron;enje za unapređenje<br />konvergencije. Algoritam se može jednostavno<br />distribuirati i paralelizovati, te je pogodan za estimaciju<br />stanja u realnom vremenu, pri čemu se informacije mogu<br />prikupljati na asinhroni način, zaobilazeći neke od<br />postojećih rutina, kao npr. provera observabilnosti<br />sistema. Pro&scaron;irenje algoritma za nelinearnu estimaciju<br />stanja je moguće unutar datog modela.<br />Dalje se predlaže algoritam baziran na probabilističkim<br />grafičkim modelima koji je direktno primenjen na<br />nelinearni problem estimacije stanja, &scaron;to predstavlja<br />logičan korak u tranziciji od linearnog ka nelinearnom<br />modelu. Zbog nelinearnosti funkcija, izrazi za određenu<br />klasu poruka ne mogu se dobiti u zatvorenoj formi, zbog<br />čega rezultujući algoritam predstavlja aproksimativno<br />re&scaron;enje. Nakon toga se predlaže distribuirani Gaus-<br />Njutnov metod baziran na probabilističkim grafičkim<br />modelima i BP algoritmu koji postiže istu tačnost kao i<br />centralizovana verzija Gaus-Njutnovog metoda za<br />estimaciju stanja, te je dat i novi algoritam za otkrivanje<br />nepouzdanih merenja (outliers) prilikom merenja<br />električnih veličina. Predstavljeni algoritam uspostavlja<br />lokalni kriterijum za otkrivanje i identifikaciju<br />nepouzdanih merenja, a numerički je pokazano da<br />algoritam značajno pobolj&scaron;ava detekciju u odnosu na<br />standardne metode.</p>

Conditioning geologic models to production data and assessment of uncertainty is generally done in a Bayesian framework. The current Bayesian approach suffers from three major limitations that make it impractical for field-scale applications. These are: first, the CPU time scaling behavior of the Bayesian inverse problem using the modified Gauss-Newton algorithm with full covariance as regularization behaves quadratically with increasing model size; second, the sensitivity calculation using finite difference as the forward model depends upon the number of model parameters or the number of data points; and third, the high CPU time and memory required for covariance matrix calculation. Different attempts were used to alleviate the third limitation by using analytically-derived stencil, but these are limited to the exponential models only. We propose a fast and robust adaptation of the Bayesian formulation for inverse modeling that overcomes many of the current limitations. First, we use a commercial finite difference simulator, ECLIPSE, as a forward model, which is general and can account for complex physical behavior that dominates most field applications. Second, the production data misfit is represented by a single generalized travel time misfit per well, thus effectively reducing the number of data points into one per well and ensuring the matching of the entire production history. Third, we use both the adjoint method and streamline-based sensitivity method for sensitivity calculations. The adjoint method depends on the number of wells integrated, and generally is of an order of magnitude less than the number of data points or the model parameters. The streamline method is more efficient and faster as it requires only one simulation run per iteration regardless of the number of model parameters or the data points. Fourth, for solving the inverse problem, we utilize an iterative sparse matrix solver, LSQR, along with an approximation of the square root of the inverse of the covariance calculated using a numerically-derived stencil, which is broadly applicable to a wide class of covariance models. Our proposed approach is computationally efficient and, more importantly, the CPU time scales linearly with respect to model size. This makes automatic history matching and uncertainty assessment using a Bayesian framework more feasible for large-scale applications. We demonstrate the power and utility of our approach using synthetic cases and a field example. The field example is from Goldsmith San Andres Unit in West Texas, where we matched 20 years of production history and generated multiple realizations using the Randomized Maximum Likelihood method for uncertainty assessment. Both the adjoint method and the streamline-based sensitivity method are used to illustrate the broad applicability of our approach.

This thesis examines how to find the best fit to a series of data points when curve fitting using power-exponential models. We describe the different numerical methods such as the Gauss-Newton and Levenberg-Marquardt methods to compare them for solving non-linear least squares of curve fitting using different power-exponential functions. In addition, we show the results of numerical experiments that illustrate the effectiveness of this approach.Furthermore, we show its application to the practical problems by using different sets of data such as death rates and rocket-triggered lightning return strokes based on the transmission line model.

This thesis deals with solutions of nonlinear regression problems using R programming language. The introductory theoretical part is devoted to familiarization with the principles of solving nonlinear regression models and of their applications in the program R. In both, theoretical and practical part, the most famous and used differentiator algorithms are presented, particularly the Gauss-Newton's and of the steepest descent method, for estimating the parameters of nonlinear regression. Further, in the practical part, there are some demo solutions of particular tasks using nonlinear regression methods. Overall, a large number of graphs processed by the author is used in this thesis for better comprehension.

K nahrazení člověka při úloze testování dotykových embedded zařízení je zapotřebí vyvinout komplexní automatizovaný robotický systém. Jedním ze zásadních úkolů je tento systém automaticky zkalibrovat. V této práci jsem se zabýval možnými způsoby automatické kalibrace robotického ramene v prostoru ve vztahu k dotykovému zařízení pomocí jedné či více kamer. Následně jsem představil řešení založené na estimaci polohy jedné kamery pomocí iterativních metod jako např. Gauss-Newton nebo Levenberg-Marquardt. Na konci jsem zhodnotil dosaženou přesnost a navrhnul postup pro její zvýšení.