abstract: Adaptive processing and classification of electrocardiogram (ECG) signals are important in eliminating the strenuous process of manually annotating ECG recordings for clinical use. Such algorithms require robust models whose parameters can adequately describe the ECG signals. Although different dynamic statistical models describing ECG signals currently exist, they depend considerably on a priori information and user-specified model parameters. Also, ECG beat morphologies, which vary greatly across patients and disease states, cannot be uniquely characterized by a single model. In this work, sequential Bayesian based methods are used to appropriately model and adaptively select the corresponding model parameters of ECG signals. An adaptive framework based on a sequential Bayesian tracking method is proposed to adaptively select the cardiac parameters that minimize the estimation error, thus precluding the need for pre-processing. Simulations using real ECG data from the online Physionet database demonstrate the improvement in performance of the proposed algorithm in accurately estimating critical heart disease parameters. In addition, two new approaches to ECG modeling are presented using the interacting multiple model and the sequential Markov chain Monte Carlo technique with adaptive model selection. Both these methods can adaptively choose between different models for various ECG beat morphologies without requiring prior ECG information, as demonstrated by using real ECG signals. A supervised Bayesian maximum-likelihood (ML) based classifier uses the estimated model parameters to classify different types of cardiac arrhythmias. However, the non-availability of sufficient amounts of representative training data and the large inter-patient variability pose a challenge to the existing supervised learning algorithms, resulting in a poor classification performance. In addition, recently developed unsupervised learning methods require a priori knowledge on the number of diseases to cluster the ECG data, which often evolves over time. In order to address these issues, an adaptive learning ECG classification method that uses Dirichlet process Gaussian mixture models is proposed. This approach does not place any restriction on the number of disease classes, nor does it require any training data. This algorithm is adapted to be patient-specific by labeling or identifying the generated mixtures using the Bayesian ML method, assuming the availability of labeled training data. / Dissertation/Thesis / Ph.D. Electrical Engineering 2012

This thesis focuses on making improvements to time-delayed teleoperation systems, with both direct and semi-autonomous haptic control, by addressing the challenges associated with force-position (F-P) predictive architectures. As the time delay from the communication channel increases, system stability and performance degrade. Previously, solutions focused on communication channel stability and environment force estimation methods that primarily rely on linearization of the Hunt-Crossley (HC) contact model. These result in a loss of transparency in the system and limiting use cases from linearization assumptions. Moreover, semi-autonomous solutions aimed at decreasing user effort and automating subtasks, such as obstacle avoidance and user guidance, require training or singularly focus on joint space tasks. This work addresses the shortcomings of the aforementioned methods by refocusing on system components to achieve more favorable dynamics during environment contact with the use of a series elastic actuator (SEA), investigating alternative HC parameter estimation techniques, and synthesizing an assistive semi-autonomous control framework that predicts user intention recognition and automates gross motion tasks. Experimental results with a remote SEA demonstrate improved performance with stiff environments in delays of up to two seconds round trip time. The coupling of the force and position through the actuator along with simultaneous sensing capabilities also show robustness for contact with soft environments. Further improvements with soft environment contact are achieved through HC parameter estimation, with smooth parameter update switching using a Sigmoid function. A novel application of Chebyshev polynomial approximation for adaptive parameter estimation of the HC model was also proposed. This approach provides control via backstepping with adaptive parameter estimation using Lyapunov methods. Additionally, this method reduces excitation requirements by using nonlinear swapping and the data accumulation concept to guarantee parameter convergence. A simulated teleoperation system demonstrates the effectiveness of this approach and initial results from experiment show promise for this approach in practice. Finally, a user study involving a pick and place task produced favorable results for the proposed semi-autonomous framework which significantly reduced task completion times. / Master of Science / Teleoperated systems are powerful solutions for remotely executing tasks in situations where autonomous solutions are not robust enough and/or user knowledge is desired for a task. However, teleoperation performance and stability is degraded by delays in the communication channel. A common way to deal with time delay is to use a predictive controller on the local side to cancel out the delay by knowing the remote side dynamics. Previous approaches have focused on stabilizing the communication channel or the use of estimators and observers to better capture the remote side dynamics. The drawback of these approaches is that they achieve stability at the expense of system transparency, leading to divergence in the force and position matching between the master and remote side. Many of the methods for environment force estimation involves linearizing contact models, creating limitations in their application. Moreover, semi-autonomous solutions aimed at decreasing user effort and automating subtasks such as obstacle avoidance and user guidance require training data sets for the algorithm or only focus individually on joint space tasks. This thesis addresses the shortcomings of the aforementioned methods by refocusing on system components to achieve more favorable dynamics using a series elastic actuator (SEA) while interacting with the environment, investigating nonlinear and linear contact model estimation methods for identifying parameters of the Hunt-Crossley (HC) model, and synthesising an assistive semi-autonomous control framework that predicts user intention for task execution. Experimental results for the use of an SEA demonstrate improved performance with stiff environments in delays of up to two seconds round trip time (RTT). The coupling of the force and position through the actuator along with simultaneous sensing capabilities also showed robustness for contact with soft environments. Various estimation methods for HC parameter identification was investigated to improve the local side model. A novel application of Chebyshev polynomial approximation of the HC model with adaptive parameter estimation was also proposed to provide control along with decreasing the excitation requirements by using backsteping control with nonlinear swapping and the data accumulation concept. A simulated teleoperation system demonstrated the effectiveness of this approach with a smooth paramater update transition. Initial results from experiment also show promise for this approach in practice. Finally, a user study involving a pick and place task produced favorable results for the proposed semi-autonomous framework which significantly reduced task completion times.

The polynomial-chaos expansion (PCE) approach to modeling provides an estimate of the probabilistic response of a dynamic system with uncertainty in the system parameters. A novel adaptive parameter estimation method exploiting the polynomial-chaos representation of a general quarter-car model is presented. Because the uncertainty was assumed to be concentrated in the sprung mass parameter, a novel pseudo mass matrix was developed for generating the state-space PCE model. In order to implement the PCE model in a real-time adaptation routine, a novel technique for representing PCE output equations was also developed. A simple parameter estimation law based on the output error between measured accelerations and PCE acceleration estimates was developed and evaluated through simulation and experiment. Simulation results of the novel adaptation algorithm demonstrate the estimation convergence properties as well as its limitations. The simulation results are further verified by a real-time experimental implementation on a quarter-car test rig. This work presents the first truly real-time implementation of a PCE model. The experimental real-time implementation of the novel adaptive PCE estimation method shows promising results by its ability to converge and maintain a stable estimate of the unknown parameter. / Master of Science

[pt] Esta tese apresenta novas estruturas e algoritmos para detecção multiusuário e supressão de interferência em sistemas DS-CDMA. São investigadas estruturas baseadas em redes neurais recorrentes para projeto de receptores com decisão realimentada e desenvolvidos algoritmos adaptativos para combater a interferência de múltiplo acesso e a interferência entre símbolos. Novos algoritmos baseados na minimização da taxa de erro de bits são examinados e generalizados para esquemas de detecção com cancelamento de interferência. Para situações onde uma seqüência de treinamento não é disponibilizada, é considerado um novo critério de projeto às cegas de receptores com restrições lineares baseado na função custo módulo constante. Algoritmos adaptativos às cegas baseados neste novo critério são usados para estimar os parâmetros de um receptor linear e do canal de comunicações. São também desenvolvidos novos mecanismos às cegas de ajuste do passo para algoritmos do tipo gradiente estocástico em receptores lineares com base no critério de mínima variância com restrições. Com base nos critérios de mínima variância e módulo constante com restrições, são desenvolvidos critérios de projeto às cegas para receptores com decisão realimentada e propostos algoritmos adaptativos para essas estruturas. Um novo esquema de cancelamento sucessivo de interferência baseado no conceito de arbitragem é proposto e incorporado a uma estrutura de recepção com decisão realimentada para o enlace reverso. Em seguida, o novo esquema de cancelamento de interferência é combinado com uma estrutura iterativa que emprega múltiplos estágios, resultando em melhores estimativas do receptor e um desempenho uniforme para os usuários. Finalmente, são apresentadas novas estruturas de recepção com posto reduzido, baseadas em filtros FIR interpolados e interpoladores variantes no tempo, e desenvolvidos algoritmos adaptativos às cegas e supervisionados para o novo esquema. / [en] This thesis presents new structures and algorithms for multiuser detection and interference suppression in DS-CDMA systems. Structures based on recurrent neural networks are investigated for decision feedback receivers and adaptive algorithms are developed for combatting multiple access interference and intersymbol interference. New algorithms based on the minimization of the bit error rate are examined and generalized for detection schemes with interference cancellation. For situations where a training sequence is not available, a new blind criterion, based on the constant modulus cost function with linear constraints is considered. Based on this novel criterion, blind adaptive algorithms are used for estimating the parameters of linear receivers and the channel. New blind adaptive mechanisms for adjusting the step size of stochastic gradient algorithms, using the constrained minimum variance criterion, are also presented for estimating the parameters of linear receivers and the channel. Based on constrained minimum variance and constrained constant modulus criteria, the blind design of decision feedback structures is considered and blind adaptive algorithms are derived. A new successive interference cancellation scheme using the concept of arbitration is proposed and incorporated within a decision feedback structure for uplink scenarios. Then, the new interference cancellation scheme is combined with an iterative structure that employs multiple stages, resulting in improved receiver estimates and providing uniform performance over the users. Finally, novel reduced-rank receiver structures, based on interpolated FIR filters with time-varying interpolators, are presented and blind and supervised adaptive algorithms are developed for this new scheme.