A novel adaptive controller, suitable for linear and non-linear systems was developed. The controller is a discrete algorithm suitable for computer implementation and is based on gradient descent adaptation rules. Traditional recursive least squares based algorithms suffer from performance deterioration due to the continuous reduction of a covariance matrix used for adaptation. When this covariance matrix becomes too small, recursive least squares algorithms respond slow to changes in model parameters. Gradient descent adaptation was used to avoid the performance deterioration with time associated with regression based adaptation such as Recursive Least Squares methods. Stability was proven with Lyapunov stability theory, using an error filter designed to fulfill stability requirements. Similarities between the proposed controller with PI control have been found. A framework for on-line tuning was developed using the concept of estimation tracks. Estimation tracks allow the estimation gains to be selected from a finite set of possible values, while meeting Lyapunov stability requirements. The trade-off between sufficient excitation for learning and controller performance, typical for dual adaptive control techniques, are met by properly tuning the adaptation and filter gains to drive the rate of adaptation in response to a fixed excitation signal. Two methods for selecting the estimation track were developed. The first method uses simulations to predict the value of the bicriteria cost function that is a combination of prediction and feedback errors, to generate a performance score for each estimation track. The second method uses a linear matrix inequality formulation to find an upper bound on feedback error within the range of uncertainty of the plant parameters and acceptable reference signals. The linear matrix inequality approach was derived from a robust control approach. Numerical simulations were performed to systematically evaluate the performance and computational burden of configuration parameters, such as the number of estimation tracks used for tuning. Comparisons were performed for both tuning methods with an arbitrarily tuned adaptive controller, with arbitrarily selected tuning parameters as well as a common adaptive control algorithm.

Motor unit number estimation (MUNE) is an electrodiagnostic procedure used to estimate the number of MUs in a muscle. In this thesis, a new MUNE technique, called Automated MPS, has been developed to overcome the shortcomings of two current techniques, namely MPS and MUESA. This method can be summarized as follows. First, a muscle is stimulated with a train of constant intensity current pulses. Depending on various factors, one to three MUs activate probabilistically after each pulse, and several responses are collected. These collected responses should be divided into up to 2^n clusters, such that each cluster represents one possible combination of n Surface-detected Motor Unit Potentials (SMUPs). After clustering the collected responses, the average response of each cluster is calculated, the outliers are excluded, and similar groups are merged together. Then, depending on the number of response set groups, a decomposition technique is applied to the response clusters to obtain the $n$ constituent SMUPs. To estimate the number of MUs, the aforementioned process is repeated several times until enough SMUPs to calculate a reliable mean-SMUP are acquired. The number of MUs can then be determined by dividing the maximal compound muscle action potential (CMAP) size by the mean-SMUP size. The focus of this thesis was on using pattern recognition techniques to detect n SMUPs from a collected set of waveforms. Several experiments were performed using both simulated and real data to evaluate the ability of Automated MPS in finding the constituent SMUPs of a response set. Our experiments showed that performing Automated MPS needs less experience compared with MPS. Moreover, it can deal with more difficult situations and detect more accurate SMUPs compared with MUESA.

This dissertation deals with the channel estimation techniques for orthogonal frequency division multiplexing (OFDM) systems such as in IEEE 802.11. Although there has been a great amount of research in this area, characterization of typical wireless indoor environments and design of channel estimation schemes that are both robust and practical for such channel conditions have not been thoroughly investigated. It is well known that the minimum mean-square-error (MMSE) estimator provides the best mean-square-error (MSE) performance given a priori knowledge of channel statistics and operating signal-to-noise ratio (SNR). However, the channel statistics are usually unknown and the MMSE estimator has too much computational complexity to be realized in practical systems. In this work, we propose two simple channel estimation techniques: one that is based on modifying the channel correlation matrix from the MMSE estimator and the other one with averaging window based on the LS estimates. We also study the characteristics of several realistic indoor channel models that are of potential use for wireless local area networks (LANs). The first method, namely MMSE-exponential-Rhh, does not depend heavily on the channel statistics and yet offer performance improvement compared to that of the LS estimator. The simulation results also show that the second method, namely averaging window (AW) estimator, provides the best performance at moderate SNR range.

Pose estimation is a common problem in computer vision. The pose is the combination of the position and orientation of a particular object relative to some reference coordinate system. The pose estimation problem involves determining the pose of an object from one or multiple images of the object. This problem often arises in the area of robotics. It is necessary to determine the pose of an object before it can be manipulated by the robot. In particular, this research focuses on pose estimation for initialization of position-based visual servoing. A closely related problem is the correspondence problem. This is the problem of finding a set of features from the image of an object that can be identified as the same feature from a model of the object. Solving for pose without known corre- spondence is also refered to as the simultaneous pose and correspondence problem, and it is a lot more difficult than solving for pose with known correspondence. This thesis explores a number of methods to solve the simultaneous pose and correspondence problem, with focuses on a method called SoftPOSIT. It uses the idea that the pose is easily determined if correspondence is known. It first produces an initial guess of the pose and uses it to determine a correspondence. With the correspondence, it determines a new pose. This new pose is assumed to be a better estimate, thus a better correspondence can be determined. The process is repeated until the algorithm converges to a correspondence pose estimate. If this pose estimate is not good enough, the algorithm is restarted with a new initial guess. An improvement is made to this algorithm. An early termination condition is added to detect conditions where the algorithm is unlikely to converge towards a good pose. This leads to an reduction in the runtime by as much as 50% and improvement in the success rate of the algorithm by approximately 5%. The proposed solution is tested and compared with the RANSAC method and simulated annealing in a simulation environment. It is shown that the proposed solution has the potential for use in commercial environments for pose estimation.

This thesis aims to reveal the magnitude of the income elasticity of health expenditure and the impact of non-income determinants of health expenditures in the Canadian Provinces. Health can be seen as a luxury good if the income elasticity exceeds unity and as a necessity good if the income elasticity is below unity. The motivation behind the analysis of the determinants of health spending is to identify the forces that drive the persistent increase in health expenditures in Canada and to explain the disparities in provincial health expenditures, thereby to prescribe sustainable macroeconomic policies regarding health spending. Panel data on real per capita GDP, relative price of health care, the share of publicly funded health expenditure, the share of senior population and life expectancy at birth have been used to investigate the determinants of Canadian real per capita provincial total, private and government health expenditures for the period 1975-2002. Dynamic models of health expenditure are analyzed via Generalized Instrumental Variables and Generalized Method of Moments techniques. Evidence confirms that health is far from being a luxury for Canada and government health expenditures are constrained by the relative prices. Results also cast doubt upon the power of quantitative analysis in explaining the increasing health expenditures.

Mining operations can produce highly localized, low intensity earthquakes that are referred to as microseismic events. Monitoring of microseismic events is useful in predicting and comprehending hazards, and in evaluating the overall performance of a mine design. <p>A robust localization algorithm is used to estimate the source position of the microseismic event by selecting the hypothesized source location that maximizes an energy function generated from the sum of the time--aligned sensor signals. The accuracy of localization for the algorithm characterized by the variance depends in part upon the configuration of sensors. Two algorithms, MAXSRC and MINMAX, are presented that use the variance of localization error, in a particular direction, as a performance measure for a given sensor configuration.<p>The variance of localization error depends, in part, upon the energy spectral density of the microseismic event. The energy spectral density characterization of sensor signals received in two potash mines are presented and compared using two spectral estimation techniques: multitaper estimation and combined time and lag weighting. It is shown that the difference between the the two estimation techniques is negligible. However, the differences between the two mine characterizations, though not large, is significant. An example uses the characterized energy spectral densities to determine the variance of error for a single step localization algorithm.<p>The MAXSRC and MINMAX algorithms are explained. The MAXSRC sensor placement algorithm places a sensor as close as possible to the source position with the maximum variance. The MINMAX sensor placement algorithm minimizes the variance of the source position with the maximum variance after the sensor has been placed. The MAXSRC algorithm is simple and can be solved using an exhaustive search while the MINMAX algorithm uses a genetic algorithm to find a solution. These algorithms are then used in three examples, two of which are simple and synthetic. The other example is from Lanigan Potash Mine. The results show that both sensor placement algorithms produce similar results, with the MINMAX algorithm consistently doing better. The MAXSRC algorithm places a single sensor approximately 100 times faster than the MINMAX algorithm. The example shows that the MAXSRC algorithm has the potential to be an efficient and intuitively simple sensor placement algorithm for mine microseismic event monitoring. The MINMAX algorithm provides, at an increase in computational time, a more robust placement criterion which can be solved adequately using a genetic algorithm.

A networked control system (NCS) is a feedback control system that has its control loop physically connected via real-time communication networks. To meet the demands of `teleautomation', modularity, integrated diagnostics, quick maintenance and decentralization of control, NCSs have received remarkable attention worldwide during the past decade. Yet despite their distinct advantages, NCSs are suffering from network-induced constraints such as time delays and packet dropouts, which may degrade system performance. Therefore, the network-induced constraints should be incorporated into the control design and related studies.<p> For the problem of state estimation in a network environment, we present the strategy of simultaneous input and state estimation to compensate for the effects of unknown input missing. A sub-optimal algorithm is proposed, and the stability properties are proven by analyzing the solution of a Riccati-like equation.<p> Despite its importance, system identification in a network environment has been studied poorly before. To identify the parameters of a system in a network environment, we modify the classical Kalman filter to obtain an algorithm that is capable of handling missing output data caused by the network medium. Convergence properties of the algorithm are established under the stochastic framework.<p> We further develop an adaptive control scheme for networked systems. By employing the proposed output estimator and parameter estimator, the designed adaptive control can track the expected signal. Rigorous convergence analysis of the scheme is performed under the stochastic framework as well.

This paper studies the interval estimation of three discrete distributions: thebinomial distribution, the Poisson distribution and the negative-binomialdistribution. The problem is the chaotic behavior of the coverage probabilityfor the Wald interval. To solve this problem, alternative confidence intervals areintroduced. Coverage probability and expected length are chosen to be thecriteria evaluating the intervals.In this paper, I firstly tested the chaotic behavior of the coverageprobability for the Wald interval, and introduced the alternative confidenceintervals. Then I calculated the coverage probability and expected length forthose intervals, made comparisons and recommended confidence intervals forthe three cases. This paper also discussed the relationship among the threediscrete distributions, and in the end illustrated the applications on binomialand Poisson data with brief examples.

In this thesis, we consider the problem of distributed detection problem in cooperative communication networks when the channel state information (CSI) is unknown. The amplify-and-forward relay strategy is considered in this thesis. Since the CSI is assumed to be unknown to the system, the joint detection and estimation approach is considered in this work. The proposed scheme in this work differs from existing joint detection and estimation schemes in that it utilizes a distributed approach, which exploits node cooperation and achieves a better system performance in cooperative communication networks. Moreover, by contrast to the existing channel estimation and symbol detection schemes, the proposed scheme is mainly developed based on the assumption that the data communication from the source to each relay node is to undergo a correlated fading channel. We derive the joint detection and estimation rules for our problem using the expectation-maximum (EM) algorithm. Simulation results show that the proposed scheme can perform well. Moreover, the obtained results show that the proposed iteration algorithm converges very fast, which implies the proposed scheme can work well in real-time applications.

In the thesis, we present the structures of interacting multiple model (IMM) algorithm-based filter design for real-time motion orientation estimation and tracking by using inertial sensor measurements in three-dimensional space. The major sensor such as gyroscope, though has high-sensitivity characteristics, suffers from bias build-up and error drift over time. The complementary sensors such as accelerometer and magnetometer, on the other hand, have low sensitivity, but do not suffer from bias problems. By using individual inertial and magnetic sensors, measurements of multiple modes can be interactively computed. The IMM based designs show the advantages of weighting individual sensors in different motion states. We propose a signal processing architecture based on the IMM algorithm. It is composed of three parallel Kalman filters (KFs), each deals with measured signals from accelerometer, magnetometer and gyroscope, respectively. The accelerometer cannot effectively sense the rotation around the vertical axis; while the magnetometer can only sense the rotation around vertical axis. Therefore, estimation accuracy with the parallel filtering arrangement of the IMM algorithm-based structure may be affected. A scheme using the residual signal, which is computed in the IMM, provides the information of gyroscope-based KF to the other two filters for feasible calculation of update weights. Related research also usually combined the information of major and complementary sensors in estimator designs. In the literature, existing ¡§Triad¡¨ methods with quaternion-based extended Kalman filter (EKF), process the measurements from major and complementary sensors. To compensate the functions, we propose to use a gyroscope-based EKF and a Triad EKF in forming a parallel multiple model-based structure. The analysis and performance evaluation shows advantages and disadvantages of using EKFs and KFs in IMM-based filtering approachs. Simulation results validate the proposed estimator design concept, and show that the scheme is capable of reducing the overall estimation errors by flexible computation of model weights.