Duration models are often concerned with time intervals between trades, longer durations indicating a lack of trading activities. In this thesis, we study parameter estimation for the Autoregressive Conditional Duration (ACD) and Stochastic Conditional Duration (SCD) models. Maximum likelihood methods can usually be used in the case of ACD models. However, the SCD models are based on the assumption that durations are generated by a dynamic stochastic latent variable which is often perturbed by Exponential, Weibull, Gamma or Log-Normal distributed innovations. This makes the use of maximum likelihood methods difficult. One alternative method of parameter estimation, in this case, consists in using quasi-maximum likelihood after transforming the original nonlinear model into a state-space model and using the Kalman filter, a similar filtering scheme or the Generalized Method of Moments (GMM). We use the nonlinear filter and GMM method to analyze the Quadratic Stochastic Conditional duration model as well.

The use of MEMS-based inertial sensors for a relative positioning estimation system (RPES) was investigated. A number of data acquisition and processing techniques are developed and tested, to determine which one would provide the best performance of the proposed method. Because inertial-based sensors don’t rely on other references to calibrate their position and orientation, there is a steady accumulation of errors over time. The errors are caused by various sources of noise such as temperature and vibration, and the errors are significant. This work investigates various methods to increase the signalto- noise ratio, in order to develop the best possible RPES method. The main areas of this work are as follows: (i) The proposed RPES application imposes specific boundary conditions to the signal processing, to increase the accuracy. (ii) We propose that using redundant inertial rate sensors would give a better performance over a single rate sensor. (iii) We investigate three Kalman filter algorithms to accommodate different combinations of sensors: Parallel sensors arrangement, Series sensors arrangement, and compression arrangement. In implementing these three areas, the results show that there is much better improvement in the data in comparison to using regular averaging techniques. / Graduate

Today's automotive industry is undergoing significant changes in technology due to economic, political and environmental pressures. The shift from conventional internal combustion vehicles to hybrid and plug in hybrid electric vehicles brings with it a new host of technical challenges. As the vehicles become more electrified, and the batteries become larger, there are many difficulties facing the battery integration including both embedded control and supervisory control. A very important aspect of Li-Ion battery integration is the state estimation of the battery. State estimation can include multiple states, however the two most important are the state of charge and state of health of the battery. Determining an accurate state of charge estimation of a battery has been an important part of consumer electronics for years now [1]. In small portable electronics, the state of charge of the battery is used to determine the time remaining on the current battery charge. Although difficult, the estimation is simplified by the relatively low charge and discharge currents (approximately + 3C) of the devices and the non-dynamic duty cycle. Hybrid vehicle battery packs can reach much higher charge and discharge currents (+ 20C) [2]. This higher current combined with a very dynamic duty cycle, large changes in temperature, longer periods without usage and long life requirements make state of charge estimation in Hybrid Electric Vehicles (HEV) much more difficult. There have been a host of methods employed by various previous authors. One of the most important factors in state of charge estimation is having an accurate estimation of the actual capacity (depending on state of health) of the battery at any time [3]. Without having an understanding of the state of health of the battery, the state of charge estimation can vary greatly. This paper proposes a state of charge and state of health estimation based on a dual Extended Kalman Filter (EKF). Employing an EKF for the state estimation of the battery pack not only allows for enhanced accuracy of the estimation but allows the control engineer to develop vehicle performance criteria based not only on the state of charge estimation, but also the state of health.

Over the course of the past several decades the benefits of redeveloping brownfields have been widely recognized. Actions have been taken to foster sustainable redevelopment of brownfields by government, policy makers and stakeholders across the world. However, redevelopments encounter great challenges and risks related to environmental and non-environmental issues. In this work, we intend to build a comprehensive and practical framework to evaluate the hydrogeological and financial risks involved during redevelopment and to ensure developers reserve sufficient capital to cover unexpected future costs within the guarantee period. Punitive damages, which contribute to these costs, are in this thesis solely associated with the cost of repossessing a house within a development should the indoor air concentration of TCE exceed the regulatory limit at a later time. The uncertainties associated with brownfield remediation have been among the barriers to brownfield redevelopment. This is mainly caused by the lack of knowledge about a site’s environmental condition. In order to alleviate uncertainties and to better understand the contaminant transport process in the subsurface, numerical simulations have been conducted to investigate the role of controlling parameters in determining the fate and transport of volatile organic compounds originating from a NAPL source zone located below the water table in the subsurface. In the first part of this thesis, the numerical model CompFlow Bio is used on a hypothesized three-dimensional problem geometry where multiple residential dwellings are built. The simulations indicate that uncertainty in the simulated indoor air concentration is sensitive to heterogeneity in the permeability structure of a stratigraphically continuous aquifer with uncertainty defined as the probability of exceeding a regulatory limit. Houses which are laterally offset from the groundwater plume are less affected by vapour intrusion due to limited transverse horizontal flux of TCE within the groundwater plume in agreement with the ASTM (2008) guidance. Within this uncertainty framework, we show that the Johnson and Ettinger (1991) model generates overly-conservative results and contributes to the exclusion zone being much further away from the groundwater plume relative to either CompFlow Bio or ASTM (2008). The probability of failure (or the probability of exceedence of the regulatory limit) is defined and calculated for further study. Due to uncertainties resulting from parameter estimation and model prediction, a methodology is introduced to incorporate field measurements into the initial estimates from the numerical model in order to improve prediction accuracy. The principle idea of this methodology is to combine the geostatistical tool kriging with the statistical data assimilation method Kalman filter to evaluate the worth and effectiveness of data in a quantitative way in order to select an optimal sampling scenario. This methodology is also used to infer whether one of the houses located adjacent to affected houses has indoor air problems based on the measurements subject to the observation that the affected house is monitored and has problems and developers have liability if a problem occurs. In this part of the study, different sampling scenarios are set up in terms of permeability (1 – 80 boreholes) and soil gas concentration (2, 4 and 7 samples) and three metrics are defined and computed as a criterion for comparison. Financing brownfield redevelopment is often viewed as a major barrier to the development process mainly due to risks and liabilities associated with brownfields. The common way of managing the risk is to transfer it to insurers by purchasing insurance coverage. This work provides two different strategies to price the risk, which is equivalent to an insurance premium. It is intended to give an instructive insight into project planning and feasibility studies during the decision-making process of a brownfield project. The two strategies of risk capital valuation are an actuarial premium calculation principle and a martingale premium calculation principle accounting for the hydrogeological and financial uncertainties faced in a project. The data used for valuation are the posterior estimates of data assimilation obtained from the results of different sampling scenarios. The cost-benefit-risk analysis is employed as a basis to construct the objective function in order to find the least cost among sampling scenarios for the project. As a result, it shows that drilling seven boreholes to extract permeability data and taking soil gas samplings in four locations or seven locations alternatively give the minimum total cost. Sensitivity analysis of some influential parameters (the safety loading factors and the possible methods to calculate the probability of failure) is performed to determine their roles of importance in the risk capital valuation. This framework can be applied to provide guidance for other risk-based environmental projects.

Current physiotherapy practice relies on visual observation of the patient for diagnosis and assessment. The assessment process can potentially be automated to improve accuracy and reliability. This thesis proposes a method to recover patient joint angles and automatically extract movement profiles utilizing small and lightweight body-worn sensors. Joint angles are estimated from sensor measurements via the extended Kalman filter (EKF). Constant-acceleration kinematics is employed as the state evolution model. The forward kinematics of the body is utilized as the measurement model. The state and measurement models are used to estimate the position, velocity and acceleration of each joint, updated based on the sensor inputs from inertial measurement units (IMUs). Additional joint limit constraints are imposed to reduce drift, and an automated approach is developed for estimating and adapting the process noise during on-line estimation. Once joint angles are determined, the exercise data is segmented to identify each of the repetitions. This process of identifying when a particular repetition begins and ends allows the physiotherapist to obtain useful metrics such as the number of repetitions performed, or the time required to complete each repetition. A feature-guided hidden Markov model (HMM) based algorithm is developed for performing the segmentation. In a sequence of unlabelled data, motion segment candidates are found by scanning the data for velocity-based features, such as velocity peaks and zero crossings, which match the pre-determined motion templates. These segment potentials are passed into the HMM for template matching. This two-tier approach combines the speed of a velocity feature based approach, which only requires the data to be differentiated, with the accuracy of the more computationally-heavy HMM, allowing for fast and accurate segmentation. The proposed algorithms were verified experimentally on a dataset consisting of 20 healthy subjects performing rehabilitation exercises. The movement data was collected by IMUs strapped onto the hip, thigh and calf. The joint angle estimation system achieves an overall average RMS error of 4.27 cm, when compared against motion capture data. The segmentation algorithm reports 78% accuracy when the template training data comes from the same participant, and 74% for a generic template.

In the control of batch and continuous distillation columns, one of the most challenging problem is the difficulty in measuring compositions. This problem can be handled by estimating the compositions from readily available online temperature measurements using a state observer. The aim of this study is to design a state observer that estimates the product composition in a multicomponent batch distillation column (MBDC) from the temperature measurements and to test this observer using a batch column simulation. To achieve this, first a model for MBDC is prepared and compared with the data from literature where a case column is utilized. After checking the validity of the simulation package, it is used as a fictitious process for the performance evaluations. In the second phase of the study, an extended Kalman Filter (EKF) is designed by utilizing a simplified model of MBDC and it is implemented for performance investigation on the case column with 8 trays separating the mixture of cyclohexane, n-heptane and toluene. The simplified model utilized in EKF results in response, which have some deviation with rigorous model, mainly due to the simplification of vapor-liquid equilibrium relationship. In the performance evaluation, the tuning parameters of EKF / the diagonal terms of process noise covariance matrix and the diagonal terms of measurement model noise covariance matrix are changed in the range of 50&iexcl / 1x10&iexcl / 7 and 0:5&iexcl / 5x108 and the optimum values are found as 0:00001 and 5000, respectively. The effect of number of measurement points is also investigated with a result of number of component measurements. The effect of measurement period value is also studied and found that it has a major effect on the performance which has to be determined by the available computational facilities. The control of the column is done by utilizing the designed EKF estimator and the estimator is successfully used in controlling the product purities in MBDC under variable reflux-ratio operation.

In this thesis, the target tracking problem with input estimation is investigated. The estimation performance of the optimum decoding based smoothing algorithm and a target tracking scheme based on the Kalman filter is compared by performing simulations. The advantages and the disadvantages of these algorithms are presented.

This thesis focuses on 3D marker tracking for human gait analysis. In KISS Gait Analysis System at METU, a subject&#039 / s gait is recorded with 6 cameras while 13 reflective markers are attached at appropriate locations on his/her legs and feet. These images are processed to extract 2 dimensional (2D) coordinates of the markers in each camera. The 3 dimensional (3D) coordinates of the markers are obtained by processing the 2D coordinates of the markers with linearization and calibration algorithms. Then 3D trajectories of the markers are formed using the 3D coordinates of the markers. In this study, software which takes the 2D coordinates of markers in each camera and processes them to form the 3D trajectories of the markers is developed. Kalman Filter is used in formation of 3D trajectories. The results are found to be satisfactory.

This study aims to investigate different orbit determination algorithms for the first Turkish remote sensing satellite, BiLSAT-1. The micro-satellite carries an onboard GPS receiver. Pseudorange measurements simulated from the position and velocity data supplied by T&Uuml / BiTAK-BiLTEN are used for the implementation of different orbit determination algorithms concluding to an estimate of the satellite&rsquo / s state. Satellite&rsquo / s position, velocity components and the GPS receiver&rsquo / s clock bias are selected as the state parameters to be estimated. Kalman filter algorithms are used for the estimation of these state parameters. The modeled affecting force components include / geopotential and atmospheric drag. The global gravity models EGM96 and EIGEN-CG03C have been utilized together with Harris Priester atmospheric density model for the force modeling. The effect of the changes during the implementation of the force models, numerical integration, and estimation algorithms are investigated. Software has been developed using MATLAB programming language for the implementation of all algorithms performed in this study for orbit determination.

Mapping stationary objects and tracking moving targets are essential for many autonomous functions in vehicles. In order to compute the map and track estimates, sensor measurements from radar, laser and camera are used together with the standard proprioceptive sensors present in a car. By fusing information from different types of sensors, the accuracy and robustness of the estimates can be increased. Different types of maps are discussed and compared in the thesis. In particular, road maps make use of the fact that roads are highly structured, which allows relatively simple and powerful models to be employed. It is shown how the information of the lane markings, obtained by a front looking camera, can be fused with inertial measurement of the vehicle motion and radar measurements of vehicles ahead to compute a more accurate and robust road geometry estimate. Further, it is shown how radar measurements of stationary targets can be used to estimate the road edges, modeled as polynomials and tracked as extended targets. Recent advances in the field of multiple target tracking lead to the use of finite set statistics (FISST) in a set theoretic approach, where the targets and the measurements are treated as random finite sets (RFS). The first order moment of a RFS is called probability hypothesis density (PHD), and it is propagated in time with a PHD filter. In this thesis, the PHD filter is applied to radar data for constructing a parsimonious representation of the map of the stationary objects around the vehicle. Two original contributions, which exploit the inherent structure in the map, are proposed. A data clustering algorithm is suggested to structure the description of the prior and considerably improving the update in the PHD filter. Improvements in the merging step further simplify the map representation. When it comes to tracking moving targets, the focus of this thesis is on extended targets, i.e., targets which potentially may give rise to more than one measurement per time step. An implementation of the PHD filter, which was proposed to handle data obtained from extended targets, is presented. An approximation is proposed in order to limit the number of hypotheses. Further, a framework to track the size and shape of a target is introduced. The method is based on measurement generating points on the surface of the target, which are modeled by an RFS. Finally, an efficient and novel Bayesian method is proposed for approximating the tire radii of a vehicle based on particle filters and the marginalization concept. This is done under the assumption that a change in the tire radius is caused by a change in tire pressure, thus obtaining an indirect tire pressure monitoring system. The approaches presented in this thesis have all been evaluated on real data from both freeways and rural roads in Sweden. / SEFS -- IVSS / VR - ETT