The road surface friction is an important characteristic that must be measured accurately to navigate vehicles effectively under different conditions. This parameter is very difficult to estimate correctly as it can take up a value from a broad spectrum of possibilities and the knowledge of this characteristic is of utmost significance in modern day automotive applications. The possible real-time knowledge of friction opens a new range of improvements to the active safety systems such as the Electronic Stability Control (ESC) and Anti-lock Braking Systems (ABS) in addition to providing computerized support to safety applications. The aim of the research is to take an engineering approach to the problem and design a simple and a robust algorithm that can be implemented in any automotive application of choice. After integrating the load transfer model with the four wheel vehicle model, the Dugoff tire models are combined with the aforementioned model to represent the plant model. Using the plant model to design an emulator, the sensor measurements are created and these measurements are then used by a non linear estimator such as the Unscented Kalman Filter to predict the forces at the tires. Friction is then calculated for every iteration and then passed back into the loop.In the end, a comparison of different design methodologies, implementation techniques and performance along with design decisions are discussed so that the current work can be implemented on a real-time controller. In addition to this, a section is dedicated towards highlighting the difference that prior friction information has on the stopping distance of a vehicle. For this purpose, a demonstration is made by creating an ABS control system that uses the predicted friction information and the performance improvement is documented. / Master of Science / The goal of the research is to identify methods in which the road surface friction can be detected by the on board computers present on modern day cars. Drivers have the ability to determine the grip on the road surface through various mechanisms, for instance if a driver sees a patch of ice on the road when driving, their normal response is to take the foot off the gas and drive without giving much steering input to avoid a slide. Another input that the driver can use to assess the grip is through the 'steering feel', which is the ability to differentiate different driving conditions through the force feedback from the steering wheel. There have been numerous approaches to help teach the computer to detect these road conditions so that it can operate other computerized systems such as the ABS(Anti-lock Braking System) and ESC( Electronic Stability Control) programs with better accuracy. This work is an attempt to contribute to this vital area of study. At the end of the study, an algorithm to predict the dynamic estimate of friction has been developed and the improvement in the performance of the Anti-lock braking system using this friction estimate has been demonstrated

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According to the model free approach assumption four protein dynamic related parameters, the slow and fast local motion of the NH vector, the generalized order parameter, and the 15N shielding anisotropy can be estimated at each residue by the spectral density functions at the resonant frequencies of N (omega_N) and H (omega_H). In this work, we study the linear and nonlinear estimations of the aforementioned parameters of the two proteins C12A-p8^MTCPI and Pilin from strain K122-4. The principal components of the four parameters of C12A-p8^MTCPI are used to cluster the residues. The results show that the principle components provide useful information about the secondary structure of the protein. Finally, we propose a practical method to examine the model free assumption by characterizing the distribution of the transverse rate R_2 in multifield.

The development of navigation filters that make use of robust estimation techniques is important due to the sensitivity of the typical minimum L2 norm techniques, such as the Kalman filter, to deviations in the assumed underlying probability distribution. In particular, those distributions with thicker tails than the Gaussian distribution can give rise to erratic filter performance and inconsistency of results. This dissertation discusses the development of an adaptive discrete-time robust nonlinear filtering technique based on a recursive form of Huber's mixed minimum L1/L2 norm approach to estimation, which is robust with respect to deviations from the assumed Gaussian error probability distributions inherent to the Kalman filter. This mixed norm approach is applied to a type of Sigma-Point Kalman filter, known as the Divided Difference Filter, which can capture second-order effects of nonlinearities in the system and measurement dynamics. Additionally, if these assumed parameters of the distribution differ greatly from the true parameters, then the filter can exhibit large errors and possibly divergence in nonlinear problems. This behavior is possible even if the true error distributions are Gaussian. To remedy these problems, adaptive filtering techniques have been introduced in order to automatically tune the Kalman filter by estimating the measurement and process noise covariances, however these techniques can also be highly sensitive to the nature of the underlying error distributions. The Huber-based formulations of the filtering problem also make some assumptions regarding the distribution, namely the approach considers a class of contaminated densities in the neighborhood of the Gaussian density. Essentially the method assumes that the statistics of the main Gaussian density are known, as well as the ratio or percentage of the contamination. The technique can be improved upon by the introduction of a method to adaptively estimate the noise statistics along with the state and state error covariance matrix. One technique in common use for adaptively estimating the noise statistics in real-time filtering applications is known as covariance matching. The covariance matching technique is an intuitively appealing approach in which the measurement noise and process noise covariances are determined in such a way that the true residual covariance matches the theoretically predicted covariance. The true residual covariance is approximated in real time using the sample covariance, over some finite buffer of stored residuals. The drawback to this approach is that the presence of outliers and non-Gaussianity can create problems of robustness with the use of the covariance matching technique. Therefore some additional steps must be taken to identify the outliers before forming the covariance estimates. In this dissertation, an adaptive scheme is proposed whereby the filter can estimate the process noise and measurement noise covariance matrices along with the state estimate and state estimate error covariance matrix. The adaptation technique adopts a robust approach to estimating these covariances that can resist the effects of outliers. The particular outlier identification method employed in this paper is based on quantities known as projection statistics, which utilize the sample median and median absolute deviation, and as a result are highly effective technique for multivariate outlier identification. These projection statistics are then employed as weights in the covariance matching procedure in order to reduce the influence of the outliers. The hybrid robust/adaptive nonlinear filtering methods introduced in this dissertation are applied to the problem of 6-DOF rendezvous navigation in elliptical orbit. The full nonlinear equations of relative motion are formulated in spherical coordinates centered on the target orbit. A relatively simple control law based on feedback linearization is used to track a desired rendezvous trajectory. The attitude dynamics are parameterized using Modified Rodrigues Parameters, which are advantageous for both control law development and estimation since they constitute a minimal 3-parameter attitude description. A switching technique which exploits the stereographic projection properties of the MRP coordinate is utilized to avoid singularities which inevitably arise in minimal attitude descriptions. This dissertation also introduces the proper covariance transformations associated with the singularity avoidance switching technique. An attitude control law based on backstepping is employed to track the target vehicle. A sensor suite consisting of a generic lidar or optical sensor, an Inertial Measurement Unit, consisting of accelerometers and gyroscopes, a star tracker, and a horizon sensor are utilized to provide measurement data to the navigation filters so that the chaser vehicle can estimate its relative state during the rendezvous maneuver. Several filters are implemented for comparison, including the Extended Kalman Filter, First and Second-Order Divided Difference Filters and Huber-based generalizations of these filters that include adaptive techniques for estimating the noise covariances. Monte-Carlo simulations are presented which include both Gaussian and non-Gaussian errors, including mismatches in the assumed noise covariances in the navigation filters in order to illustrate the benefits of the robust/adaptive nonlinear filters. Additionally, computational burdens of the various filters is compared. / Ph. D.

The autocorrelation function is a commonly used tool in statistical time series analysis. Under the assumption of Gaussianity, the sample autocorrelation function is the standard method used to estimate this function given a finite number of observations. Non-Gaussian, impulsive observation noise following probability density functions with thick tails, which often occurs in practice, can bias this estimator, rendering classical time series analysis methods ineffective. This work examines the robustness of two estimators of correlation based on memoryless nonlinear functions of observations, the Phase-Phase Correlator (PPC) and the Median- of-Ratios Estimator (MRE), which are applicable to complex-valued Gaussian random pro- cesses. These estimators are very fast and easy to implement in current processors. We show that these estimators are robust from a bias perspective when complex-valued Gaussian pro- cesses are contaminated with impulsive noise at the expense of statistical efficiency at the assumed Gaussian distribution. Additionally, iterative versions of these estimators named the IMRE and IPPC are developed, realizing an improved bias performance over their non- iterative counterparts and the well-known robust Schweppe-type Generalized M-estimator utilizing a Huber cost function (SHGM). An impulsive noise suppression technique is developed using basis pursuit and a priori atom weighting derived from the newly developed iterative estimators. This new technique is proposed as an alternative to the robust filter cleaner, a Kalman filter-like approach that relies on linear prediction residuals to identity and replace corrupted observations. It does not have the same initialization issues as the robust filter cleaner. Robust spectral estimation methods are developed using these new estimators and impulsive noise suppression techniques. Results are obtained for synthetic complex-valued Guassian processes and real-world digital television signals collected using a software defined radio. / Ph. D.

Le codage vidéo distribué (DVC) est une technique récemment proposée dans le cadre du codage vidéo, et qui convient surtout à une nouvelle classe d'applications telles que la surveillance vidéo sans fil, les réseaux de capteurs multimédia, et les téléphones mobiles. En DVC, une information adjacente (SI) est estimée au décodeur en se basant sur les trames décodées disponibles, et utilisée pour le décodage et la reconstruction des autres trames. Dans cette thèse, nous proposons de nouvelles techniques qui permettent d'améliorer la qualité de la SI. Tout d'abord, le raffinement itératif de la SI est réalisé après le décodage de chaque sous-bande DCT. Ensuite, une nouvelle méthode de génération de la SI est proposée, qui utilise l'estimation des vecteurs de mouvement dans les deux sens et le raffinement Quad-tree. Ensuite, de nouvelles approches sont proposées afin de combiner les estimations globale et locale en utilisant les différences entre les blocs correspondants et la technique SVM. En plus, des algorithmes sont proposés pour améliorer la fusion au cours du décodage. En outre, les objets segmentés des trames de référence sont utilisés dans la fusion, en utilisant les courbes élastiques et la compensation de mouvement basée-objets. De nombreuses simulations ont été effectuées pour tester les performances des techniques proposés et qui montrent des gains importants par rapport au codeur classique DISCOVER. Par ailleurs, les performances de DVC obtenues en appliquant les algorithmes proposés surpassent celles de H.264/AVC Intra et H.264/AVC No motion pour les séquences testées. En plus, l'écart vis-à-vis de H.264/AVC Inter (IB...IB) est considérablement réduit. / Distributed Video Coding (DVC) is a recently proposed paradigm in video communication, which fits well emerging applications such as wireless video surveillance, multimedia sensor networks, wireless PC camera, and mobile cameras phones. These applications require a low complexity encoding, while possibly affording a high complexity decoding. In DVC, a Side Information (SI) is estimated at the decoder, using the available decoded frames, and used for the decoding and reconstruction of other frames. In this PhD thesis, we propose new techniques in order to improve the quality of the SI. First, successive refinement of the SI is performed after each decoded DCT band. Then, a new scheme for SI generation based on backward, forward motion estimations, and Quad-tree refinement is proposed. Furthermore, new methods for combining global and local motion estimations are proposed, to further improve the SI, using the differences between the corresponding blocks and Support Vector Machine (SVM). In addition, algorithms are proposed to refine the fusion during the decoding process. Furthermore, the foreground objects are used in the combination of the global and local motion estimations, using elastic curves and foreground objects motion compensation. Extensive experiments have been conducted showing that important gains are obtained by the proposed techniques compared to the classical DISCOVER codec. In addition, the performance of DVC applying the proposed algorithms outperforms now the performance of H.264/AVC Intra and H.264/AVC No motion for tested sequences. Besides that, the gap with H.264/AVC in an Inter IB…IB configuration is significantly reduced.

This thesis describes the algorithms and techniques developed for harmonic state estimation and transient state estimation, which can be used to identify the location of disturbance sources in an electrical power system. The previous harmonic state estimation algorithm is extended to include the estimation of time-varying harmonics using an adaptive Kalman filter. The proposed method utilises two covariance noise models to overcome the divergence problem in traditional Kalman filters. Moreover, it does not require an optimal covariance noise matrix of the Kalman filter to be used. The common problems faced in harmonic state estimation applications due to the influence of measurement bad data associated with measurements and the lack of measurement points, hence the system being partially observable, are investigated with reference to the Lower South Island of the New Zealand system. The state estimation technique is also extended to transient state estimation. Two formulation methods are outlined and the development of the proposed methodology is presented. Fault scenarios with reference to the Lower South Island of the New Zealand system are simulated to demonstrate the ability of transient state estimation in estimating the voltages and currents of the unmeasured locations, and applying the estimated results to search for the fault location. The estimation results are compared with PSCAD/EMTDC simulations to justify their accuracy.

In today&amp / #8217 / s life, companies are faced with the problem of providing quality goods and services at competitive prices. Cost estimation is a very important process for a forging company, as each time a quote is sent to a customer, the company earns or loses money depending on the results of the particular estimate. However, determining pricing for customer quotes is a matter of time consuming, detailed number of tasks repeated each time. Cost estimation software might do those tedious calculations and assist the estimator step by step to reach to a cost estimate in relatively short time. In this study, an interactive cost estimation software named Forge Cost Estimator, which performs the early cost estimation for forgings, has been developed. The program is aimed to be used by the cost estimators in hot forging companies. The software has various databases, which include material, forging and machining equipment data. The cost items defined in the forging work breakdown structure can be estimated by using different modules of the software and summed up in an additive structure by using the bottom-up costestimation method. For calculating the forge volume quicker and easier, a new volume estimation system named Easy-Volume, which is based on the volume fragmentation method, is proposed. The software can also guide the user in selecting the convenient forging production line. The software is written in MS Visual Basic 6.0. The developed program has been tested in a forging company and satisfactory cost estimations for several forgings have been achieved.

Dans le monde d’aujourd’hui, il est très fréquent de vouloir modéliser la relation entre deux ou plusieurs variables. Toutefois, plusieurs expériences sont laissées à l’abandon à cause de la présence systématique de données aberrantes. Ce mémoire portera sur les estimateurs robustes permettant de modéliser des séries de données contenant des valeurs aberrantes, nous aidant ainsi à tirer un maximum d’informations de ces expériences. Dans un premier temps, nous présenterons des estimateurs robustes qui nécessitent l’imposition d’un modèle paramétrique. Ensuite, nous traiterons de l’introduction des copules à ces estimateurs robustes. Finalement, nous présenterons des simulations tirées d’une expérience réelle qui consistait à modéliser le vrai poids d’un porc selon le poids mesuré par une balance, développée au centre de recherche et développement de Sherbrooke, dans l’optique d’améliorer les techniques d’alimentation de précision.

Traditional state estimation whereby the state of the system is assessed based on a limited number of measurements is a well established tool for steady-state situations where the frequency of the system is 50 Hz. Previous contributions have looked at extending this concept to the power quality area. This area of research is called Power Quality State Estimation (PQSE) and represents a class of techniques. Under the umbrella of PQSE, the main contribution of this work is taking Transient State Estimation (TSE) on step further. A new three-phase formulation for TSE using the Numerical Integrator Substitution (NIS) will be detailed. NIS approach, also known as Dommel's method, gives a numerical solution to describe the transient behaviour of a dynamic system at discrete time points. The new transient state estimator is implemented and verified by applying the proposed algorithm to a real distribution test system. It's performance and accuracy are investigated in presence of measurement noise, background harmonics, multiple faults, etc. The conducted study has shown this technique has a great potential.