There is a need for generalised definitions of electrical powers to provide a simultaneous common base for measurement, compensation, power quality and identification of source of distortion. The major problem area today is definitions of powers in the presence of harmonics and nonlinear loads in the electrical power system. In such a scenario, there is a problem to accurately measure especially reactive (nonactive) power. This is important for accurate energy billing. Another important area is the mitigation equipment used to remove unwanted polluting quantities from the power system. Definitions of powers have an important role to play in providing the correct information for the optimal design and performance of such equipment. Evaluation of the quality of the power system to enable appropriate allocation costs to those causing deterioration in the power quality also cannot be discounted. To enable this cost allocation, there is a need to identify the polluters and the definitions should indicate degradation in power quality as well as identify the source of this degradation. Finally, it would be very useful if the definitions could also be used to perform a general analysis of the power system. This thesis commenced with investigation of the problem with an in-depth study of the existing definitions, and what other researchers have indicated about this problem, from the definitions perspective. The issues identified with current definitions are that some definitions do not possess the attributes that are related to source-load properties, and others are based on mathematical consideration and lack physical meaning. One issue in measurement of nonactive power is its nature of having zero average value. Another contributing factor is that the presence of source impedance is neglected in definitions. The use of RMS quantities to determine powers, especially instantaneous powers, in the presence of multi-frequency voltages and currents also contributes to the problem. Additionally, RMS based definitions are based on heating effect while not all sourceload relationships are totally of a heating nature. The RMS based definitions also do not satisfy the energy conservation principle. Another issue is that though harmonic currents are used, current definitions still utilise the RMS value of the voltage wave thus losing harmonic information. The solution is to decompose, as accurately as possible, the total instantaneous power into active and nonactive components utilising DC, fundamental and harmonics of voltage and current as well as being based on the power system properties. To enable this, the load model must closely represent the reality. This thesis presents the new instantaneous power definitions to achieve this. In addition to the fundamental, five sub-components for each of the active and nonactive parts are defined. The definitions are based on both the voltage and current DC, fundamental and harmonic components thus retaining harmonic information. Thus these definitions are not only mathematically based but also have a direct relationship with the load. The definitions do not make the assumption of zero source impedance. With good knowledge of the time profile of active and nonactive power components, an accurate time-domain measurement of the active and nonactive power is achieved. The components of powers introduced in the proposed definitions can be utilised to gauge power quality, to identify the source of distortion and to achieve optimal compensation. Based on the new instantaneous power definitions, the definitions for average values of the powers are also proposed. The recognition of positive going and negative going parts of the nonactive power waveform in defining the average nonactive power alleviates the problem of the “zero average nature” of nonactive power. It also retains energy information and satisfies the principle of energy conservation. The new definitions are evaluated for linear and non-linear loads in the presence of harmonics using benchmark case studies. Evaluation results demonstrate good performance of the proposed definitions. The practical applications of the definitions are explored with a number of examples from the areas of measurement of power and energy, compensation, detection of source of distortion and power quality. An application example showing the capability of the definitions in general analysis of a system is also presented. Good and useful results are obtained for all these examples. The proposed definitions are implemented on prototype systems with digital signal processors to demonstrate their practical usability. The proposed definitions are shown to be consistent with the traditional definitions under the conventional sinusoidal conditions, and their relationships to the commonly used existing definitions are also revealed.

In response to energy crisis and power quality concerns, three different methodologies to integrate the concept of active filtering into the alternators are proposed. Wind energy, due to its free availability and its clean and renewable character, ranks as the most promising renewable energy resource that could play a key role in solving the worldwide energy crisis. An Integrated Doubly-fed Electric Alternator/Active filter (IDEA) for wind energy conversion systems is proposed. The proposed IDEA is capable of simultaneous capturing maximum power of wind energy and improving power quality, which are achieved by canceling the most significant and troublesome harmonics of the utility grid and power factor correction and reactive power compensation in the grid. The back-to-back current regulated power converters are employed to excite the rotor of IDEA. The control strategy of rotor-side power converter is based on position sensoreless field oriented control method with higher power density. Analysis and experimental results are presented to demonstrate the effectiveness of the proposed IDEA. In next step, an integrated synchronous machine/active filter is discussed. The proposed technology is essentially a rotating synchronous machine with suitable modification to its field excitation circuit to allow dc and ac excitations. It is shown that by controlling the ac excitation, the 5th and 7th harmonics currents of the utility are compensated. The proposed method is cost effective because it can be applied to existing standby generators in commercial and industrial plants with minimal modification to the excitation circuits. To boost the gain of harmonic compensatory, an advanced electric machine is proposed. An Asymmetric Airgap Concentrated Winding Synchronous Machine (AACWSM) with ac and dc excitation was designed and employed. It is shown that the AACWSM with its unique design, in addition to power generation capability, could be used to compensate the most dominant current harmonics of the utility. The proposed AACWSM can compensate for the 5th and 7th harmonics currents in the grid by controlling the ac field excitation. In addition, the 11th and 13th harmonics currents are also significantly reduced. This system can be used at medium and low voltages for generation or motoring mode of operation.

Active transportation (AT) is a form of physical activity involving human-powered transportation (e.g., walking) and is associated with health benefits. However, the majority of Canadians do not use AT. Although environmental factors, such as proximity, are correlated with AT, interventions to change such factors have been ineffective. According to social cognitive theory, both environmental and personal factors (i.e., social and spatial cognitions) may influence motivated behaviour. The social cognitions of interest in the present study included self-regulatory efficacy to schedule (i.e., confidence to regularly schedule walking for AT), and to overcome barriers (i.e., confidence to cope with barriers to walking for AT). Spatial cognitions included distance and travel time cognitions. The purpose of the study was to examine whether social cognitive personal factors (i.e., scheduling self-efficacy, barriers self-efficacy), spatial cognitive personal factors (i.e., distance and travel time cognitions), and an environmental factor (i.e., proximity) were associated with walking for AT to/from a university campus over a two-week period in a convenience sample of adults. Participants in this prospective observational study were a convenience sample of 105 students, faculty, and staff at a western Canadian university, who ranged in age from 17 to 55 years (M = 24.62 years, SD = 8.15). Participants completed three online surveys over a two-week period. Social cognitions for the following two-week period and spatial cognitions were assessed at Time 1. Recall of walking for AT to/from a university campus in the previous week was assessed at Time 2 and Time 3. Total walking for AT to/from campus over the two-week period was the outcome variable. The overall hierarchical multiple regression model predicting AT from the social and spatial cognitions and proximity was significant (R2adjusted = .53; p < .01). As hypothesized, scheduling (ßstd = .44, p < .01) and barriers (ßstd = .23, p < .05) self-efficacy were associated with AT. Scheduling self-efficacy was the strongest predictor. Contrary to hypotheses, distance and travel time cognitions and proximity were not significant (ps > .05). Social cognitions, particularly self-regulatory efficacy to schedule, and efficacy to overcome barriers, may play an important role in individuals use of walking for AT to/from a university campus. Future research should continue to examine social cognitive-theory based personal and environmental predictors of AT, such as self-regulatory efficacy to goal set, outcome expectations, the weather and residential density, to better understand potential determinants of this health-promoting type of physical activity.

The accurate detection of static object boundaries such as contours or surfaces and dynamic tunnels of moving objects via deformable models is an ongoing research topic in computer vision. Most deformable models attempt to converge towards a desired solution by minimizing the sum of internal (prior) and external (measurement) energy terms. Such an approach is elegant, but frequently mis-converges in the presence of noise or complex boundaries and typically requires careful semi-dependent parameter tuning and initialization. Furthermore, current deformable model based approaches are computationally demanding which precludes real-time use. To address these limitations, a decoupled deformable model (DDM) is developed which optimizes the two energy terms separately. Essentially, the DDM consists of a measurement update step, employing a Hidden Markov Model (HMM) and Maximum Likelihood (ML) estimator, followed by a separate prior step, which modifies the updated deformable model based on the relative strengths of the measurement uncertainty and the non-stationary prior. The non-stationary prior is generated by using a curvature guided importance sampling method to capture high curvature regions. By separating the measurement and prior steps, the algorithm is less likely to mis-converge; furthermore, the use of a non-iterative ML estimator allows the method to converge more rapidly than energy-based iterative solvers. The full functionality of the DDM is developed in three phases. First, a DDM in 2D called the decoupled active contour (DAC) is developed to accurately identify the boundary of a 2D object in the presence of noise and background clutter. To carry out this task, the DAC employs the Viterbi algorithm as a truncated ML estimator, curvature guided importance sampling as a non-stationary prior generator, and a linear Bayesian estimator to fuse the non-stationary prior with the measurements. Experimental results clearly demonstrate that the DAC is robust to noise, can capture regions of very high curvature, and exhibits limited dependence on contour initialization or parameter settings. Compared to three other published methods and across many images, the DAC is found to be faster and to offer consistently accurate boundary identification. Second, a fast decoupled active contour (FDAC) is proposed to accelerate the convergence rate and the scalability of the DAC without sacrificing the accuracy by employing computationally efficient and scalable techniques to solve the three primary steps of DAC. The computational advantage of the FDAC is demonstrated both experimentally and analytically compared to three computationally efficient methods using illustrative examples. Finally, an extension of the FDAC from 2D to 3D called a decoupled active surface (DAS) is developed to precisely identify the surface of a volumetric 3D image and the tunnel of a moving 2D object. To achieve the objectives of the DAS, the concepts of the FDAC are extended to 3D by using a specialized 3D deformable model representation scheme and a computationally and storage efficient estimation scheme. The performance of the DAS is demonstrated using several natural and synthetic volumetric images and a sequence of moving objects.

Active transportation (AT) is a form of physical activity involving human-powered transportation (e.g., walking) and is associated with health benefits. However, the majority of Canadians do not use AT. Although environmental factors, such as proximity, are correlated with AT, interventions to change such factors have been ineffective. According to social cognitive theory, both environmental and personal factors (i.e., social and spatial cognitions) may influence motivated behaviour. The social cognitions of interest in the present study included self-regulatory efficacy to schedule (i.e., confidence to regularly schedule walking for AT), and to overcome barriers (i.e., confidence to cope with barriers to walking for AT). Spatial cognitions included distance and travel time cognitions. The purpose of the study was to examine whether social cognitive personal factors (i.e., scheduling self-efficacy, barriers self-efficacy), spatial cognitive personal factors (i.e., distance and travel time cognitions), and an environmental factor (i.e., proximity) were associated with walking for AT to/from a university campus over a two-week period in a convenience sample of adults. Participants in this prospective observational study were a convenience sample of 105 students, faculty, and staff at a western Canadian university, who ranged in age from 17 to 55 years (M = 24.62 years, SD = 8.15). Participants completed three online surveys over a two-week period. Social cognitions for the following two-week period and spatial cognitions were assessed at Time 1. Recall of walking for AT to/from a university campus in the previous week was assessed at Time 2 and Time 3. Total walking for AT to/from campus over the two-week period was the outcome variable. The overall hierarchical multiple regression model predicting AT from the social and spatial cognitions and proximity was significant (R2adjusted = .53; p < .01). As hypothesized, scheduling (ßstd = .44, p < .01) and barriers (ßstd = .23, p < .05) self-efficacy were associated with AT. Scheduling self-efficacy was the strongest predictor. Contrary to hypotheses, distance and travel time cognitions and proximity were not significant (ps > .05). Social cognitions, particularly self-regulatory efficacy to schedule, and efficacy to overcome barriers, may play an important role in individuals use of walking for AT to/from a university campus. Future research should continue to examine social cognitive-theory based personal and environmental predictors of AT, such as self-regulatory efficacy to goal set, outcome expectations, the weather and residential density, to better understand potential determinants of this health-promoting type of physical activity.

Modern distributed applications utilize a rich variety of distributed services. Due to the computation-centric notions of modern machines, application-level implementations of these services are problematic for applications requiring high data transfer rates, for reasons that include the inability of modern architectures to efficiently execute computations with communication. Conversely,network-level implementations of services are limited due to the network's inability to interpret application-level data or execute application-level operations on such data. The emergence of programmable network processors capable of high-rate data transfers, with flexible interfaces for external reconfiguration, has created new possibilities for movement of processing into the network infrastructure. This thesis explores the extent to which programmable network processors can be used in conjunction with standard host nodes, to form enhanced computational host-ANP (Attached Network Processor) platforms that can deliver increased efficiency for variety of applications and services. The main contributions of this research are the creation of SPLITS, a Software architecture for Programmable LIghtweighT Stream handling, and its key abstraction stream handlers. SPLITS enables the dynamic configuration of data paths through the host-ANP nodes, and the dynamic creation, deployment and reconfiguration of application-level processing applied along these paths. With SPLITS, application-specific services can be dynamically mapped to the host, ANP, or both, to best exploit their joint capabilities. The basic abstraction used by SPLITS to represent instances of application-specific activities are stream handlers - parameterizable, lightweight, computation units that operate on data headers as well as application-level content. Experimental results demonstrate performance gains of executing various application-level services on ANPs, and demonstrate the importance of the SPLITS host-ANP nodes to support dynamically reconfigurable services, and to deal with the resource limitations on the ANPs.

Due to the drastic development of semiconductor, nonlinear loads are widely used in high-power applications, which results in harmonic distortion of current and voltage in the power system. Installation of passive filter is one of the conventional solutions to harmonic distortion. But line impedance, load inductors and/or filter components may result in harmonic resonance, which amplifies the harmonic components, and worsens the harmonic distortion and power quality. This thesis proposed a control algorithm of shunt hybrid active filter to suppress the harmonics and prevent harmonic resonance in industrial facilities. The hybrid active filter is composed of an active filter and a seventh-harmonic frequency tuned passive filter. The hybrid active filter functions as damping conductance for harmonic frequencies. A dynamical tuning control is realized to adjust the damping conductance for maintaining the voltage harmonic distortion. The suppressed harmonic distortion is conformed to the harmonics limitation, such as IEEE std. 519-1992. The capacitors of the hybrid filter sustain fundamental grid voltage and allow the inverter to operate in lower kVA rating. In addition, a dc bus controller was designed to hold the capacitor voltage by controlling the fundamental leading current. The simulations and laboratory results are provided to verify the effectiveness on suppressing harmonic resonance.

In response to energy crisis and power quality concerns, three different methodologies to integrate the concept of active filtering into the alternators are proposed. Wind energy, due to its free availability and its clean and renewable character, ranks as the most promising renewable energy resource that could play a key role in solving the worldwide energy crisis. An Integrated Doubly-fed Electric Alternator/Active filter (IDEA) for wind energy conversion systems is proposed. The proposed IDEA is capable of simultaneous capturing maximum power of wind energy and improving power quality, which are achieved by canceling the most significant and troublesome harmonics of the utility grid and power factor correction and reactive power compensation in the grid. The back-to-back current regulated power converters are employed to excite the rotor of IDEA. The control strategy of rotor-side power converter is based on position sensoreless field oriented control method with higher power density. Analysis and experimental results are presented to demonstrate the effectiveness of the proposed IDEA. In next step, an integrated synchronous machine/active filter is discussed. The proposed technology is essentially a rotating synchronous machine with suitable modification to its field excitation circuit to allow dc and ac excitations. It is shown that by controlling the ac excitation, the 5th and 7th harmonics currents of the utility are compensated. The proposed method is cost effective because it can be applied to existing standby generators in commercial and industrial plants with minimal modification to the excitation circuits. To boost the gain of harmonic compensatory, an advanced electric machine is proposed. An Asymmetric Airgap Concentrated Winding Synchronous Machine (AACWSM) with ac and dc excitation was designed and employed. It is shown that the AACWSM with its unique design, in addition to power generation capability, could be used to compensate the most dominant current harmonics of the utility. The proposed AACWSM can compensate for the 5th and 7th harmonics currents in the grid by controlling the ac field excitation. In addition, the 11th and 13th harmonics currents are also significantly reduced. This system can be used at medium and low voltages for generation or motoring mode of operation.

The accurate detection of static object boundaries such as contours or surfaces and dynamic tunnels of moving objects via deformable models is an ongoing research topic in computer vision. Most deformable models attempt to converge towards a desired solution by minimizing the sum of internal (prior) and external (measurement) energy terms. Such an approach is elegant, but frequently mis-converges in the presence of noise or complex boundaries and typically requires careful semi-dependent parameter tuning and initialization. Furthermore, current deformable model based approaches are computationally demanding which precludes real-time use. To address these limitations, a decoupled deformable model (DDM) is developed which optimizes the two energy terms separately. Essentially, the DDM consists of a measurement update step, employing a Hidden Markov Model (HMM) and Maximum Likelihood (ML) estimator, followed by a separate prior step, which modifies the updated deformable model based on the relative strengths of the measurement uncertainty and the non-stationary prior. The non-stationary prior is generated by using a curvature guided importance sampling method to capture high curvature regions. By separating the measurement and prior steps, the algorithm is less likely to mis-converge; furthermore, the use of a non-iterative ML estimator allows the method to converge more rapidly than energy-based iterative solvers. The full functionality of the DDM is developed in three phases. First, a DDM in 2D called the decoupled active contour (DAC) is developed to accurately identify the boundary of a 2D object in the presence of noise and background clutter. To carry out this task, the DAC employs the Viterbi algorithm as a truncated ML estimator, curvature guided importance sampling as a non-stationary prior generator, and a linear Bayesian estimator to fuse the non-stationary prior with the measurements. Experimental results clearly demonstrate that the DAC is robust to noise, can capture regions of very high curvature, and exhibits limited dependence on contour initialization or parameter settings. Compared to three other published methods and across many images, the DAC is found to be faster and to offer consistently accurate boundary identification. Second, a fast decoupled active contour (FDAC) is proposed to accelerate the convergence rate and the scalability of the DAC without sacrificing the accuracy by employing computationally efficient and scalable techniques to solve the three primary steps of DAC. The computational advantage of the FDAC is demonstrated both experimentally and analytically compared to three computationally efficient methods using illustrative examples. Finally, an extension of the FDAC from 2D to 3D called a decoupled active surface (DAS) is developed to precisely identify the surface of a volumetric 3D image and the tunnel of a moving 2D object. To achieve the objectives of the DAS, the concepts of the FDAC are extended to 3D by using a specialized 3D deformable model representation scheme and a computationally and storage efficient estimation scheme. The performance of the DAS is demonstrated using several natural and synthetic volumetric images and a sequence of moving objects.

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