This thesis presents a new approach for selecting a manipulator arm configuration (a pose) in an environment where the positions of the work items are not able to be fully controlled. The approach utilizes a belief formed from a priori knowledge, observations and predictive models to select manipulator poses and motions. Standard methods for manipulator control provide a fully specified Cartesian pose as the input to a robot controller which is assumed to act as an ideal Cartesian motion device. While this approach simplifies the controller and makes it more portable, it is not well suited for less-controlled environments where the work item position or orientation may not be completely observable and where a measure of the accuracy of the available observations is required. The proposed approach suggests selecting a manipulator configuration using two types of rating function. When uncertainty is high, configurations are rated by combining a belief, represented by a probability density function, and a value function in a decision theoretic manner enabling selection of the sensor??s motion based on its probabilistic contribution to information gain. When uncertainty is low the mean or mode of the environment state probability density function is utilized in task specific linear or angular distances constraints to map a configuration to a cost. The contribution of this thesis is in providing two formulations that allow joint configurations to be found using non-linear optimization algorithms. The first formulation shows how task specific linear and angular distance constraints are combined in a cost function to enable a satisfying pose to be selected. The second formulation is based on the probabilistic belief of the predicted environment state. This belief is formed by utilizing a Bayesian estimation framework to combine the a priori knowledge with the output of sensor data processing, a likelihood function over the state space, thereby handling the uncertainty associated with sensing in a less controlled environment. Forward models are used to transform the belief to a predicted state which is utilized in motion selection to provide the benefits of a feedforward control strategy. Extensive numerical analysis of the proposed approach shows that using the fed-forward belief improves tracking performance by up to 19%. It is also shown that motion selection based on the dynamically maintained belief reduces time to target detection by up to 50% compared to two other control approaches. These and other results show how the proposed approach is effectively able to utilize an uncertain environment state belief to select manipulator arm configurations.

This thesis presents a means for representing and computing beliefs in the form of arbitrary probability density functions with a guarantee for the ongoing validity of such beliefs over indefinte time frames. The foremost aspect of this proposal is the introduction of a general, theoretical, solution to the guaranteed state estimation problem from within the recursive Bayesian estimation framework. The solution presented here determines the minimum space required, at each stage of the estimation process, to represent the belief with limited, or no, loss of information. Beyond this purely theoretical aspect, a number of numerical techniques, capable of determining the required space and performing the appropriate spatial reconfiguration, whilst also computing and representing the belief functions, are developed. This includes a new, hybrid particle-element approach to recursive Bayesian estimation. The advantage of spatial reconfiguration as presented here is that it ensures that the belief functions consider all plausible states of the target system, without altering the recursive Bayesian estimation equations used to form those beliefs. Furthermore, spatial reconfiguration as proposed in this dissertation enhances the estimation process since it allows computational resources to be concentrated on only those states considered plausible. Autonomous maritime search and rescue is used as a focus application throughout this dissertation since the searching-and-tracking requirements of the problem involve uncertainty, the use of arbitrary belief functions and dynamic target systems. Nevertheless, the theoretical development in this dissertation has been kept general and independent of an application, and as such the theory and techniques presented here may be applied to any problem involving dynamic Bayesian beliefs. A number of numerical experiments and simulations show the efficacy of the proposed spatially reconfigurable representations, not only in ensuring the validity of the belief functions over indefinite time frames, but also in reducing computation time and improving the accuracy of function approximation. Improvements of an order of magnitude were achieved when compared with traditional, spatially static representations.

<p>This dissertation presents a quantitative study on the relationship between financial intermediation and the macroeconomy of the United States. It consists of two major chapters, with the first chapter studying adverse shocks to interbank market lending, and with the second chapter studying a theoretical model where aggregate balance sheets of the financial and non-financial sectors play a key role in financial intermediation frictions.</p><p>In the first chapter, I empirically investigate a novel macroeconomic shock: the funding liquidity shock. Funding liquidity is defined as the ability of a (financial) institution to raise cash at short notice, with interbank market loans being a very common source of short-term external funding. Using the "TED spread" as a proxy of aggregate funding liquidity for the period from 1971M1 to 2009M9, I first discover that, by using the vector-autoregression approach, an unanticipated adverse TED shock brings significant recessionary effects: industrial production and prices fall, and the unemployment rate rises. The contraction lasts for about twenty months. I also recover the conventional monetary policy shock, the macro impact of which is in line with the results of Christiano et al (1998) and Christiano et al (2005) . I then follow the factor model approach and find that the excess returns of small-firm portfolios are more negatively impacted by an adverse funding liquidity shock. I also present evidence that this shock as a "risk factor" is priced in the cross-section of equity returns. Moreover, a proposed factor model which includes the structural funding liquidity and monetary policy shocks as factors is able to explain the cross-sectional returns of portfolios sorted on size and book-to-market ratio as well as the Fama and French (1993) three-factor model does. Lastly, I present empirical evidence that funding liquidity and market liquidity mutually affect each other.</p><p>I start the second chapter by showing that, in U.S. data, the balance sheet health of the financial sector, as measured by its equity capital and debt level, is a leading indicator of the balance sheet health of the nonfinancial sector. This fact, and the apparent role of the financial sector in the recent global financial crisis, motivate a general equilibrium macroeconomic model featuring the balance sheets of both sectors. I estimate and study a model within the "loanable funds" framework of Holmstrom and Tirole (1997), which introduces a double moral hazard problem in the financial intermediation process. I find that financial frictions modeled within this framework give rise to a shock transmission mechanism quantitatively different from the one that arises with the conventional modeling assumption, in New Keynesian business cycle models, of convex investment adjustment costs. Financial equity capital plays an important role in determining the depth and persistence of declines in output and investment due to negative shocks to the economy. Moreover, I find that shocks to the financial intermediation process cause persistent recessions, and that these shocks explain a significant portion of the variation in investment. The estimated model is also able to replicate some aspects of the cross-correlation structure of the balance sheet variables of the two sectors.</p> / Dissertation

This thesis presents a new approach for selecting a manipulator arm configuration (a pose) in an environment where the positions of the work items are not able to be fully controlled. The approach utilizes a belief formed from a priori knowledge, observations and predictive models to select manipulator poses and motions. Standard methods for manipulator control provide a fully specified Cartesian pose as the input to a robot controller which is assumed to act as an ideal Cartesian motion device. While this approach simplifies the controller and makes it more portable, it is not well suited for less-controlled environments where the work item position or orientation may not be completely observable and where a measure of the accuracy of the available observations is required. The proposed approach suggests selecting a manipulator configuration using two types of rating function. When uncertainty is high, configurations are rated by combining a belief, represented by a probability density function, and a value function in a decision theoretic manner enabling selection of the sensor??s motion based on its probabilistic contribution to information gain. When uncertainty is low the mean or mode of the environment state probability density function is utilized in task specific linear or angular distances constraints to map a configuration to a cost. The contribution of this thesis is in providing two formulations that allow joint configurations to be found using non-linear optimization algorithms. The first formulation shows how task specific linear and angular distance constraints are combined in a cost function to enable a satisfying pose to be selected. The second formulation is based on the probabilistic belief of the predicted environment state. This belief is formed by utilizing a Bayesian estimation framework to combine the a priori knowledge with the output of sensor data processing, a likelihood function over the state space, thereby handling the uncertainty associated with sensing in a less controlled environment. Forward models are used to transform the belief to a predicted state which is utilized in motion selection to provide the benefits of a feedforward control strategy. Extensive numerical analysis of the proposed approach shows that using the fed-forward belief improves tracking performance by up to 19%. It is also shown that motion selection based on the dynamically maintained belief reduces time to target detection by up to 50% compared to two other control approaches. These and other results show how the proposed approach is effectively able to utilize an uncertain environment state belief to select manipulator arm configurations.

This thesis presents a means for representing and computing beliefs in the form of arbitrary probability density functions with a guarantee for the ongoing validity of such beliefs over indefinte time frames. The foremost aspect of this proposal is the introduction of a general, theoretical, solution to the guaranteed state estimation problem from within the recursive Bayesian estimation framework. The solution presented here determines the minimum space required, at each stage of the estimation process, to represent the belief with limited, or no, loss of information. Beyond this purely theoretical aspect, a number of numerical techniques, capable of determining the required space and performing the appropriate spatial reconfiguration, whilst also computing and representing the belief functions, are developed. This includes a new, hybrid particle-element approach to recursive Bayesian estimation. The advantage of spatial reconfiguration as presented here is that it ensures that the belief functions consider all plausible states of the target system, without altering the recursive Bayesian estimation equations used to form those beliefs. Furthermore, spatial reconfiguration as proposed in this dissertation enhances the estimation process since it allows computational resources to be concentrated on only those states considered plausible. Autonomous maritime search and rescue is used as a focus application throughout this dissertation since the searching-and-tracking requirements of the problem involve uncertainty, the use of arbitrary belief functions and dynamic target systems. Nevertheless, the theoretical development in this dissertation has been kept general and independent of an application, and as such the theory and techniques presented here may be applied to any problem involving dynamic Bayesian beliefs. A number of numerical experiments and simulations show the efficacy of the proposed spatially reconfigurable representations, not only in ensuring the validity of the belief functions over indefinite time frames, but also in reducing computation time and improving the accuracy of function approximation. Improvements of an order of magnitude were achieved when compared with traditional, spatially static representations.

Orbit determination has long relied on the use of the Kalman filter, or specifically the extended Kalman filter, as a means of accurately navigating spacecraft. With the advent of cheaper, more powerful computers more accurate techniques such as the particle filter have been utilized. These Bayesian types of filters have in more recent years found their way to other applications. Dr. Furfaro and B. Gaudet have demonstrated the ability of the particle filter to accurately estimate the angular velocity, homogenous density, and rotation angle of a non-uniformly rotating ellipsoid shaped asteroid. This paper extends that work by utilizing a particle filter to accurately estimate the angular velocity and homogenous density of an ellipsoidal asteroid while simultaneously determining the location and mass of a mass concentration modeled as a point mass embedded within the asteroid. This work shows that by taking measurements in several locations around the asteroid, the asteroid's rotation state and mass distribution can be discerned.

Recently, orthogonal frequency-division multiplexing (OFDM) has been adopted for high-speed wireless communications due to its robustness against multipath fading. However, one of the main fundamental drawbacks of OFDM systems is the high peak-to-average-power ratio (PAPR). Several techniques have been proposed for PAPR reduction. Most of these techniques require transmitter-based (pre-compensated) processing. On the other hand, receiver-based alternatives would save the power and reduce the transmitter complexity. By keeping this in mind, a possible approach is to limit the amplitude of the OFDM signal to a predetermined threshold and equivalently a sparse clipping signal is added. Then, estimating this clipping signal at the receiver to recover the original signal. In this work, we propose a Bayesian receiver-based low-complexity clipping signal recovery method for PAPR reduction. The method is able to i) effectively reduce the PAPR via simple clipping scheme at the transmitter side, ii) use Bayesian recovery algorithm to reconstruct the clipping signal at the receiver side by measuring part of subcarriers, iii) perform well in the absence of statistical information about the signal (e.g. clipping level) and the noise (e.g. noise variance), and at the same time iv is energy efficient due to its low complexity. Specifically, the proposed recovery technique is implemented in data-aided based. The data-aided method collects clipping information by measuring reliable 
data subcarriers, thus makes full use of spectrum for data transmission without the need for tone reservation. The study is extended further to discuss how to improve the recovery of the clipping signal utilizing some features of practical OFDM systems i.e., the oversampling and the presence of multiple receivers. Simulation results demonstrate the superiority of the proposed technique over other recovery algorithms. The overall objective is to show that the receiver-based Bayesian technique is highly recommended to be an effective and practical alternative to state-of-art PAPR reduction techniques.

Sparse signals are abundant among both natural and man-made signals. Sparsity implies that the signal essentially resides in a small dimensional subspace. The sparsity of the signal can be exploited to improve its recovery from limited and noisy observations. Traditional estimation algorithms generally lack the ability to take advantage of signal sparsity. This dissertation considers several problems in the areas of biomedical engineering and geosciences with the aim of enhancing the recovery of information by exploiting the underlying sparsity in the problem. The objective is to overcome the fundamental bottlenecks, both in terms of estimation accuracies and required computational resources. In the first part of dissertation, we present a high precision technique for the monitoring of human respiratory movements by exploiting the sparsity of wireless ultra-wideband signals. The proposed technique provides a novel methodology of overcoming the Nyquist sampling constraint and enables robust performance in the presence of noise and interferences. We also present a comprehensive framework for the important problem of extracting the fetal electrocardiogram (ECG) signals from abdominal ECG recordings of pregnant women. The multiple measurement vectors approach utilized for this purpose provides an efficient mechanism of exploiting the common structure of ECG signals, when represented in sparse transform domains, and allows leveraging information from multiple ECG electrodes under a joint estimation formulation. In the second part of dissertation, we adopt sparse signal processing principles for improved information recovery in large-scale subsurface reservoir characterization problems. We propose multiple new algorithms for sparse representation of the subsurface geological structures, incorporation of useful prior information in the estimation process, and for reducing computational complexities of the problem. The techniques presented here enable significantly enhanced imaging of the subsurface earth and result in substantial savings in terms of convergence time, leading to optimized placement of oil wells. This dissertation demonstrates through detailed experimental analysis that the sparse estimation approach not only enables enhanced information recovery in variety of application areas, but also greatly helps in reducing the computational complexities associated with the problems.

In the aftermath of the recent financial crisis, an environment of historically low interest rates and extensive household indebtedness in the OECD countries have triggered a vivid debate on whether central banks should react to house price fluctuations in their pursuit of monetary policy. In Sweden, a period of low policy rates and house price inflation was halted when the central bank increased the interest rates in 2010. This paper studies whether the Riksbank reacted to house prices in 1993-2013. Using Bayesian methods and quarterly data, I estimate a DSGE model with patient and impatient households, where the central bank reacts to house price inflation. The results suggest that the Riksbank did respond to house prices during the sample period. The findings are robust and plausible from an economic point of view.

The fully Bayesian estimation via the use of Markov chain Monte Carlo (MCMC) techniques has become popular for estimating item response theory (IRT) models. The current development of MCMC includes two major algorithms: Gibbs sampling and the No-U-Turn sampler (NUTS). While the former has been used with fitting various IRT models, the latter is relatively new, calling for the research to compare it with other algorithms. The purpose of the present study is to evaluate the performances of these two emerging MCMC algorithms in estimating two two-parameter logistic (2PL) IRT models, namely, the 2PL unidimensional model and the 2PL multi-unidimensional model under various test situations. Through investigating the accuracy and bias in estimating the model parameters given different test lengths, sample sizes, prior specifications, and/or correlations for these models, the key motivation is to provide researchers and practitioners with general guidelines when it comes to estimating a UIRT model and a multi-unidimensional IRT model. The results from the present study suggest that NUTS is equally effective as Gibbs sampling at parameter estimation under most conditions for the 2PL IRT models. Findings also shed light on the use of the two MCMC algorithms with more complex IRT models.