The multitude of services delivered over the Internet would have been difficult to fathom 40 years ago when much of the initial design was being undertaken. As a consequence, the resulting architecture did not make provisions for differentiating between, and managing the potentially conflicting requirements of different types of services such as real-time voice communication and peer-to-peer file sharing. This shortcoming has resulted in a situation whereby services with conflicting requirements often interfere with each other and ultimately decrease the effectiveness of the Internet as an enabler of new and transformative services. The ability to passively identify different types of Internet traffic then would address this shortcoming and enable effective management of conflicting types of services, in addition to facilitating a better understanding of how the Internet is used in general. Recent attempts at developing such techniques have shown promising results in simulation environments but perform considerably worse when deployed into real-world scenarios. One possible reason for this descrepancy can be attributed to the implicit assumption shared by recent approaches regarding the degree of similarity between the many networks which comprise the Internet. This thesis quantifies the degradation in performance which can be expected when such an assumption is violated as well as demonstrating alternative classification techniques which are less sensitive to such violations.

This thesis investigates the influence of predator movements upon habitat selection and foraging success. It deals with two very distinct datasets one from a marine system, the snow crab (Chionoecetes opilio) fishery, and the second from a freshwater system, an experimental rainbow trout (Oncorhynchus mykiss) aquaculture operation. Deriving a standardized measure of catch from logbook data is important because catch per unit effort (CPUE) is used in fisheries analysis to estimate abundance, but it some cases CPUE is a biased estimate. For the snow crab fishery, a relative abundance measure was developed using fisher movements and logbook data that reflected commercially available biomass and produced an improved relative abundance estimate. Results from the aquaculture dataset indicate that escaped farmed rainbow trout continue to use the cage site when waste feed is available, while native lake trout do not interact with the cage. Once access to waste feed is removed, both lake trout and escaped rainbow trout do not use the cage site. This thesis uses methods to identify patterns and behaviours using movement tracks to increase our understanding of predator habitat usage.

Ce travail de thèse poursuit une perspective double dans l'usage conjoint des méthodes de Monte Carlo séquentielles (MMS) et de l'algorithme Espérance-Maximisation (EM) dans le cadre des modèles de Markov cachés présentant une structure de dépendance markovienne d'ordre supérieur à 1 au niveau de la composante inobservée. Tout d'abord, nous commençons par un exposé succinct de l'assise théorique des deux concepts statistiques à travers les chapitres 1 et 2 qui leurs sont consacrés. Dans un second temps, nous nous intéressons à la mise en pratique simultanée des deux concepts au chapitre 3 et ce dans le cadre usuel où la structure de dépendance est d'ordre 1. L'apport des méthodes MMS dans ce travail réside dans leur capacité à approximer efficacement des fonctionnelles conditionnelles bornées, notamment des quantités de filtrage et de lissage dans un cadre non linéaire et non gaussien. Quant à l'algorithme EM, il est motivé par la présence à la fois de variables observables et inobservables (ou partiellement observées) dans les modèles de Markov Cachés et singulièrement les mdèles de volatilité stochastique étudié. Après avoir présenté aussi bien l'algorithme EM que les méthodes MCs ainsi que quelques unes de leurs propriétés dans les chapitres 1 et 2 respectivement, nous illustrons ces deux outils statistiques au travers de la calibration d'un modèle de volatilité stochastique. Cette application est effectuée pour des taux change ainsi que pour quelques indices boursiers au chapitre 3. Nous concluons ce chapitre sur un léger écart du modèle de volatilité stochastique canonique utilisé ainsi que des simulations de Monte Carlo portant sur le modèle résultant. Enfin, nous nous efforçons dans les chapitres 4 et 5 à fournir les assises théoriques et pratiques de l'extension des méthodes Monte Carlo séquentielles notamment le filtrage et le lissage particulaire lorsque la structure markovienne est plus prononcée. En guise d'illustration, nous donnons l'exemple d'un modèle de volatilité stochastique dégénéré dont une approximation présente une telle propriété de dépendance.

The fast clearing of faults is essential for preventing equipment damage and preserving the stability of the power transmission systems with smaller operating margins. This thesis examined the application of fault generated transients for fast detection and isolation of faults in a transmission system. The basis of the transient based protection scheme developed and implemented in this thesis is the fault current directions identified by a set of relays located at different nodes of the system. The direction of the fault currents relative to a relay location is determined by comparing the signs of the wavelet coefficients of the currents measured in all branches connected to the node. The faulted segment can be identified by combining the fault directions identified at different locations in the system. In order to facilitate this, each relay is linked with the relays located at the adjacent nodes through a telecommunication network. In order to prevent possible malfunctioning of relays due to transients originating from non-fault related events, a transient recognition system to supervise the relays is proposed. The applicability of different classification methods to develop a reliable transient recognition system was examined. A Hidden Markov Model classifier that utilizes the energies associated with the wavelet coefficients of the measured currents as input features was selected as the most suitable solution. Performance of the protection scheme was evaluated using a high voltage transmission system simulated in PSCAD/EMTDC simulation software. The custom models required to simulate the complete protection scheme were implemented in PSCAD/EMTDC. The effects of various factors such as fault impedance, signal noise, fault inception angle and current transformer saturation were investigated. The performance of the protection scheme was also tested with the field recorded signals. Hardware prototypes of the fault direction identification scheme and the transient classification system were implemented and tested under different practical scenarios using input signals generated with a real-time waveform playback instrument. The test results presented in this thesis successfully demonstrate the potential of using transient signals embedded in currents for detection, localization and recognition of faults in transmission networks in a fast and reliable manner.

The automotive industry is growing bigger each year. The central concern for any automotive company is driver and passenger safety. Many automotive companies have developed driver assistance systems, to help the driver and to ensure driver safety. These systems include adaptive cruise control, lane departure warning, lane change assistance, collision avoidance, night vision, automatic parking, traffic sign recognition, and driver fatigue detection. In this thesis, we aim to build a driver fatigue detection system that advances the research in this area. Using vision in detecting driver fatigue is commonly the key part for driver fatigue detection systems. We have decided to investigate different direction. We examine the driver's voice, heart rate, and driving performance to assess fatigue level. The system consists of three main modules: the audio module, the heart rate and other signals module, and the Bayesian network module. The audio module analyzes an audio recording of a driver and tries to estimate the level of fatigue for the driver. A Voice Activity Detection (VAD) module is used to extract driver speech from the audio recording. Mel-Frequency Cepstrum Coefficients, (MFCC) features are extracted from the speech signal, and then Support Vector Machines (SVM) and Hidden Markov Models (HMM) classifiers are used to detect driver fatigue. Both classifiers are tuned for best performance, and the performance of both classifiers is reported and compared. The heart rate and other signals module uses heart rate, steering wheel position, and the positions of the accelerator, brake, and clutch pedals to detect the level of fatigue. These signals' sample rates are then adjusted to match, allowing simple features to be extracted from the signals, and SVM and HMM classifiers are used to detect fatigue level. The performance of both classifiers is reported and compared. Bayesian networks' abilities to capture dependencies and uncertainty make them a sound choice to perform the data fusion. Prior information (Day/Night driving and previous decision) is also incorporated into the network to improve the final decision. The accuracies of the audio and heart rate and other signals modules are used to calculate certain CPTs for the Bayesian network, while the rest of the CPTs are calculated subjectively. The inference queries are calculated using the variable elimination algorithm. For those time steps where the audio module decision is absent, a window is defined and the last decision within this window is used as a current decision. The performance of the system is assessed based on the average accuracy per second. A dataset was built to train and test the system. The experimental results show that the system is very promising. The performance of the system was assessed based on the average accuracy per second; the total accuracy of the system is 90.5%. The system design can be easily improved by easily integrating more modules into the Bayesian network.

This thesis investigates the influence of predator movements upon habitat selection and foraging success. It deals with two very distinct datasets one from a marine system, the snow crab (Chionoecetes opilio) fishery, and the second from a freshwater system, an experimental rainbow trout (Oncorhynchus mykiss) aquaculture operation. Deriving a standardized measure of catch from logbook data is important because catch per unit effort (CPUE) is used in fisheries analysis to estimate abundance, but it some cases CPUE is a biased estimate. For the snow crab fishery, a relative abundance measure was developed using fisher movements and logbook data that reflected commercially available biomass and produced an improved relative abundance estimate. Results from the aquaculture dataset indicate that escaped farmed rainbow trout continue to use the cage site when waste feed is available, while native lake trout do not interact with the cage. Once access to waste feed is removed, both lake trout and escaped rainbow trout do not use the cage site. This thesis uses methods to identify patterns and behaviours using movement tracks to increase our understanding of predator habitat usage.

The fast clearing of faults is essential for preventing equipment damage and preserving the stability of the power transmission systems with smaller operating margins. This thesis examined the application of fault generated transients for fast detection and isolation of faults in a transmission system. The basis of the transient based protection scheme developed and implemented in this thesis is the fault current directions identified by a set of relays located at different nodes of the system. The direction of the fault currents relative to a relay location is determined by comparing the signs of the wavelet coefficients of the currents measured in all branches connected to the node. The faulted segment can be identified by combining the fault directions identified at different locations in the system. In order to facilitate this, each relay is linked with the relays located at the adjacent nodes through a telecommunication network. In order to prevent possible malfunctioning of relays due to transients originating from non-fault related events, a transient recognition system to supervise the relays is proposed. The applicability of different classification methods to develop a reliable transient recognition system was examined. A Hidden Markov Model classifier that utilizes the energies associated with the wavelet coefficients of the measured currents as input features was selected as the most suitable solution. Performance of the protection scheme was evaluated using a high voltage transmission system simulated in PSCAD/EMTDC simulation software. The custom models required to simulate the complete protection scheme were implemented in PSCAD/EMTDC. The effects of various factors such as fault impedance, signal noise, fault inception angle and current transformer saturation were investigated. The performance of the protection scheme was also tested with the field recorded signals. Hardware prototypes of the fault direction identification scheme and the transient classification system were implemented and tested under different practical scenarios using input signals generated with a real-time waveform playback instrument. The test results presented in this thesis successfully demonstrate the potential of using transient signals embedded in currents for detection, localization and recognition of faults in transmission networks in a fast and reliable manner.

Various models driven by a hidden Markov chain in discrete or continuous time are developed to capture the stylised features of market variables whose levels or values constitute as the underliers of financial derivative contracts or investment portfolios. Since the parameters are switching regimes, the changes and developments in the economy as soon as they arise are readily reflected in these models. The change of probability measure technique and the EM algorithm are fundamental techniques utilised in the optimal parameter estimation. Recursive adaptive filters for the state of the Markov chain and other auxiliary processes related to the Markov chain are derived which in turn yield self-tuning dynamic financial models. A hidden Markov model (HMM)-based modelling set-up for commodity prices is developed and the predictability of the gold market under this setting is examined. An Ornstein-Uhlenbeck (OU) model with HMM parameters is proposed and under this set-up, we address two statistical inference issues: the sensitivity of the model to small changes in parameter estimates and the selection of the optimal number of states. The extended OU model is implemented on a data set of 30-day Canadian T-bill yields. An exponential of a Markov-switching OU process plus a compound Poisson process is put forward as a model for the evolution of electricity spot prices. Using a data set compiled by Nord Pool, we illustrate the vast improvements gained in incorporating regimes in the model. A multivariate HMM is employed as a framework in providing the solutions of two asset allocation problems; one involves the mean-variance utility function and the other entails the CVaR constraint. Finally, the valuation of credit default swaps highlights the important considerations necessitated by pricing in a regime-switching environment. Certain numerical schemes are applied to obtain approximations for the default probabilities and swap rates.

Hidden Markov models have become a popular tool for modeling long-term investment guarantees. Many different variations of hidden Markov models have been proposed over the past decades for modeling indexes such as the S&P 500, and they capture the tail risk inherent in the market to varying degrees. However, goodness-of-fit testing, such as residual-based testing, for hidden Markov models is a relatively undeveloped area of research. This work focuses on hidden Markov model assessment, and develops a stochastic approach to deriving a residual set that is ideal for standard residual tests. This result allows hidden-state models to be tested for goodness-of-fit with the well developed testing strategies for single-state models. This work also focuses on parameter uncertainty for the popular long-term equity hidden Markov models. There is a special focus on underlying states that represent lower returns and higher volatility in the market, as these states can have the largest impact on investment guarantee valuation. A Bayesian approach for the hidden Markov models is applied to address the issue of parameter uncertainty and the impact it can have on investment guarantee models. Also in this thesis, the areas of portfolio optimization and portfolio replication under a hidden Markov model setting are further developed. Different strategies for optimization and portfolio hedging under hidden Markov models are presented and compared using real world data. The impact of parameter uncertainty, particularly with model parameters that are connected with higher market volatility, is once again a focus, and the effects of not taking parameter uncertainty into account when optimizing or hedging in a hidden Markov are demonstrated.

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.