Prolonging the battery lifetime of sensors has been one of the most important issues in wireless sensor networks (WSNs). With the development of Wireless Power Transfer (WPT) technology, sensors can be recharged and possibly have infinite lifetime. One common approach to achieving this is having a wireless charging vehicle (WCV) move in the system coverage area and charge sensors nearby when it stops. The duration that the WCV stays at each charging location, the amount of traffic that each sensor carries, and the transmission power of individual sensors are closely related, and their joint optimization affects not only the data transmissions in the WSN but also energy consumption of the system. This problem is formulated as a mixed integer and nonconvex optimization problem. Different from existing work that either solves similar problems using genetic algorithms or considers charging sensors based on clusters, we consider the optimum charging time for each sensor, and solve the joint communication and charging problem optimally. Numerical results demonstrate that our solution can significantly reduce the average power consumption of the system, compared to the cluster-based charging solution. / Thesis / Master of Applied Science (MASc) / In a wireless sensor network (WSN), sensor nodes monitor the physical environment and forward the collected data to a data sink for further processing. Sensors are battery powered and, therefore, prolonging the lifetime of their batteries is critically important. In a rechargeable WSN (RWSN), prolonging the battery lifetime of sensors is achieved through reducing communication energy and recharging the batteries periodically. Reducing the communication energy consumption is done through choosing the best forwarding sensors (i.e., routing) for data collected by each sensor and deciding the transmission power of each sensor (i.e., power allocation). Recharging the batteries is achieved through harvesting energy from external sources. In this thesis, we consider a RWSN that uses wireless power transfer as the energy harvesting technology and jointly optimizes charging and communications in order to minimize the power consumption of the RWSN.

Spoken language processing is one of the oldest and most natural modes of information exchange between humans beings. For centuries, people have tried to develop machines that can understand and produce speech the way humans do so naturally. The biggest problem in our inability to model speech with computer programs and mathematics results from the fact that language is instinctive, whereas, the vocabulary and dialect used in communication are learned. Human beings are genetically equipped with the ability to learn languages, and culture imprints the vocabulary and dialect on each member of society. This thesis examines the role of pattern classification in the recognition of human speech, i.e., machine learning techniques that are currently being applied to the spoken language processing problem. The primary objective of this thesis is to create a network training paradigm that allows for direct training of multi-path models and alleviates the need for complicated systems and training recipes. A traditional trainer uses an expectation maximization (EM)based supervised training framework to estimate the parameters of a spoken language processing system. EM-based parameter estimation for speech recognition is performed using several complicated stages of iterative reestimation. These stages typically are prone to human error. The network training paradigm reduces the complexity of the training process while retaining the robustness of the EM-based supervised training framework. The hypothesis of this thesis is that the network training paradigm can achieve comparable recognition performance to a traditional trainer while alleviating the need for complicated systems and training recipes for spoken language processing systems.

We study the existence of flows in infinite networks and extend basic theorems due to Gale and Hoffman and to Ford and Fulkerson. The classical approach to finite networks uses a constructive combinatorical algorithm that has become known as the labelling algorithm. Our approach to infinite networks involves Hahn--Banach type theorems on the existence of certain linear functionals. Thus the main tools are from the theory of functional and convex analysis. In Chapter II, we discuss sublinear and linear functionals on real vector spaces in the spirit of the work of K"{o}nig. In particular, a generalization of K"{o}nig's minimum theorem is established. Our theory leads to some useful interpolation results. We also establish a variant of the main interpolation theorem in the context of convex cones. We reformulate the results of Ford--Fulkerson and Gale--Hoffman in terms of certain additive and biadditive set functions. In Chapter III, we show that the space of all additive set functions may be canonically identified with the dual space of a space of certain step functions and that the space of all biadditive set functions may be identified with the dual space of a space of certain step functions in two variables. Our work an additive set functions is in the spirit of classical measure theory, while the case of biadditive set functions resembles the theory of product measures. In Chapter IV, we develop an extended version of the Gale--Hoffman theorem on the existence of flows in infinite networks in a setting of measure-theoretic flavor. This general flow theorem is one of our central results. We discuss, as an application of our flow theorem, a Ford--Fulkerson type result on maximal flows and minimal cuts in infinite networks containing sources and sinks. In addition, we present applications to flows in locally finite networks and to the existence of antisymmetric flows under certain natural conditions. We conclude with a discussion of the case of triadditive set functions. In the appendix, we review briefly the classical theory of maximal flows and minimal cuts in networks with finitely many nodes.

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This study examines what growing old names for the older women's sense of self-identity. Some gerontological argue that old age constitutes a period of role loss with detrimental impact on identity. A sizable body of data do not support this assumption. The underlying assumption of this study is that identity emerges and is sustained through social interaction. Thus the research focuses on (1) the meaning of 'being old' as defined by older women and (2) the extent to which a positive identity can be retained through social network involvement with family, friends and community leisure activities. Instead of focusing on what is lost, the emphasis was on determining what role relationships are retained and established in old age, and the extent to which these serve to maintain a positive self-image. The research is based on interviews with 142 women over age 65 living in the town of Bridgewater, Nova Scotia. The principle component of this sample comprised 101 women living in their own home or apartments (i.e. community dwelling). Most of the analysis is based on this sample. However, where comparative analysis is useful, these data are supplemented with information from interviews with 31 residents of senior citizens apartment complexes and 10 residents of nursing homes. The findings indicate that the elderly women studied here generally do not view themselves as 'elderly' or 'old'. Age identity is found to be largely situational and more relevant to some interactions than others. The majority have managed to retain a positive identity which is deeply embedded within their informal role involvements and social network ties. As a result, the loss of more formal role relationships had only limited impact on identity. The findings also indicate that a relatively stable small community appears to offer advantages for identity management in old age. / Thesis / Doctor of Philosophy (PhD)

Hydrometric data provides forcing data inputs to run hydrological models and observed output time-series to facilitate the calibration and validation process. Hydrometric monitoring networks are often designed without considering the innate relationship between data collection, model set-up, and model application. This research compares the relative effectiveness of a previously established model-based network design strategy to a newly proposed method. The traditional design method identifies a set of Pareto-optimal networks using intermediate entropy-based design objectives, facilitated by the dual entropy multi-objective optimization (DEMO) tool, and then applies models as a post-processing mechanism. Streamflow time-series from networks initially identified by DEMO are used to calibrate two semi-distributed rainfall-runoff models. The calibration process enables a reassessment for non-dominance based on the primary network design objectives, which are maximizing model performance at manually defined flood sensitive catchment outlets and minimizing network size. The newly proposed alternative method embeds the hydrological models and their calibration process into the optimization algorithm, resulting in direct optimization based on the primary design objectives. Both techniques were applied to design networks in two large western Canadian watersheds. Bubble maps are presented to illustrate variations in the spatial distribution of optimal solution sets, with respect to both model performance at flood sensitive catchments and individual station selection frequency, for all design scenarios. Results indicate the newly proposed method provides superior results regardless of network size and that trends in the spatial distribution of optimal networks are highly case-specific. The proposed methodology can be readily adapted to address a wide variety of design applications by varying the models and model performance criteria used in the design process. The findings from this research can be used to guide future network design projects when the proposed network is intended to support one or more model-based applications. / Thesis / Master of Civil Engineering (MCE) / Engineers in the field of water resources monitor hydrometric data to maintain a record of historical conditions that can be used to guide future designs and decisions. Hydrometric data are collected from monitoring networks that should be optimally designed to ensure measurements are gathered efficiently. One of the main applications of hydrometric data are to run and calibrate hydrological models. Therefore, hydrological models can be integrated into the network design process to design monitoring networks that enhance model performance while considering the intended model application. This research introduces a new model-based approach for designing streamflow monitoring networks and compares the relative effectiveness of the new technique to previously established methods. Results indicate the new process provides superior results but is also more computationally demanding. The newly proposed methodology is adaptable and can be used to facilitate user-directed designs of hydrometric monitoring networks for a wide variety of engineering applications.

This study explores the factors that support the development of interdisciplinary research connections in a large public research university. Graduate students and faculty from 15 departments (n = 227) responded to an online survey focusing on an individual's openness toward interdisciplinary research, applied epistemological orientation, and potential interdisciplinary collaborations. The findings suggest that the formation of interdisciplinary connections is tied to two main determinants: interdisciplinary openness, which is supported by reporting a more applied epistemological orientation and being a graduate student. The diversity of interdisciplinary connections is influenced by the academic status, with tenured faculty exhibiting the most diversity in connections. Finally, research network analysis suggests that the patterns of interdisciplinary collaborations tend to orient toward collaborations between similar and familiar methodological partners, and not toward collaborations with partners that are wholly unfamiliar in terms of methodology or research focus. / Ph. D.