Thesis (Ph. D.)--University of Washington, 2001. / Vita. Includes bibliographical references (leaves 79-92).

Thesis (D. Min.)--Gordon-Conwell Theological Seminary, Boston, MA, 2001. / Abstract and vita. Includes bibliographical references (leaves 136-138).

Thesis (Ph. D.)--University of Oklahoma, 1996. / Includes bibliographical references (leaves 144-153).

Thesis (doctoral)--Universität Kaiserslautern, 2003. / Includes bibliographical references (p. [207]-212) and index.

published_or_final_version / Electrical and Electronic Engineering / Master / Master of Philosophy

Wireless sensor network localization opens the door to many location based applications. In this thesis, some solutions obtained from localization algorithms are investigated. There are two categories of problem on localization. Range-based methods are applied to the situation in which information on the distances between each pair of nodes is available. Algorithms are developed to estimate the location of each sensor in the network. Usually, the distance between each pair of nodes is estimated by the signal strength received between them, and this information is very noisy. Range-free methods, which are also called connectivity-based methods, assume that the distances between any two nodes are unknown but the connectivity information between them is known. If the distance between any two nodes in the network is within a communication range, connectivity between these two nodes is said to be established. In a range-based scenario, with the information of inter-sensor distance measurements as well as the absolute locations of the anchors, the objective is to obtain the location of all the unknown nodes. Two new localization methods based on gradient descent are shown in the thesis. The gradient descent methods would minimize the difference between the measured distances and the distances obtained from the estimated locations. From a comparison with other well-known localization methods, the two newly developed gradient descent algorithms can reach better accuracy at the expense of computational complexity. This is not surprising as the proposed algorithms are iterative in nature. For range-free scenario, a new model utilizing all the information derived from connectivity-based sensor network localization is introduced. Unlike other algorithms which only utilize the information on connections, this model makes use of both information on connections and disconnections between any pair of nodes. The connectivity information between any pair of nodes is modeled as convex and non-convex constraints. The localization problem is solved by an optimization algorithm to obtain a solution that would satisfy all the constraints established in the problem. The simulation has shown that better accuracy is obtained when compared with algorithms developed by other researchers. Another solution for the range-free scenario is obtained with the use of a two-objective evolutionary algorithm called Pareto Archived Evolution Strategy (PAES). In an evolutionary algorithm, the aim is to search for a solution that would satisfy all the convex and non-convex constraints of the problem. The number of wrong connections and the summation of corresponding distances are set as the two objectives. A starting point on the location of the unknown nodes is obtained using a solution from the result of all convex constraints. The final solution can reach the most suitable configuration of the unknown nodes as all the information on the constraints (convex and non-convex) related to connectivity have been used. From the simulation results, a relationship between the communication range and accuracy is obtained. In this thesis, another evolutionary algorithm has been examined to obtain a solution for our problem. The solution is based on a modified differential evolution algorithm with heuristic procedures peculiar to our domain of application. The characteristics of the sensor network localization are thoroughly investigated and utilized to produce corresponding treatment to search for the reasonable node locations. The modified differential evolution algorithm uses a new crossover step that is based on the characteristics of the problem. With the combination of some heuristics, the solution search can move the node to jump out of local minimums more easily, and give better accuracy than current algorithms. In the last part of the thesis, a novel two-level range connectivity-based sensor network localization problem is proposed, which would enrich the connectivity information. In this new problem, the information of the connectivity between any pair of nodes is either strong, weak or zero. Again, a two-objective evolutionary algorithm is used to search for a solution that would satisfy all the convex and non-convex constraints of the problem. Based on simulations on a range of situations, a suitable range value for the second range is found. / published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

Research on psychopathology in children and adolescents has focused on studying the major vulnerability factors for depression and anxiety. Most previous studies have focused exclusively on single domain of the vulnerability factors, such as psychosocial, personality, or cognitive variables. Very few studies have attempted to study the various domains of vulnerability factors in combination. Thus, research data available to date have failed to address the interplay of their roles in anxiety and depression. The present study used a psychometric approach to investigate the roles of major psychosocial, personality, and cognitive vulnerability factors in anxiety and depression, with a particular emphasis on their interrelationships, as well as their primary effects and mediating roles. The psychosocial factors examined in the present study included parenting styles (permissiveness, authoritarianism, and authoritativeness), peer relations (bullying, victimization, and prosocial behaviour), and school climate (teacher support, consistency and clarity of rules and expectations, disciplinary harshness, and safety problems). The personality trait variables included neuroticism and optimism. The cognitive variables included negative automatic thoughts related to anxiety and depression, which is commonly reported by children and adolescents. A battery of self-report measures in Chinese was administered to a sample of 1,164 Hong Kong secondary school students aged 11 to 17. The measures showed good internal consistency, and the factor structures of the translated measures were comparable to the original Western measures. Among the vulnerability factors studied, psychosocial factors including parenting style (authoritarian), peer relations (bullying and victimization), and school climate (teacher support, disciplinary harshness, and safety problems at school) were found to be significant predictors for children’s negative automatic thoughts and negative emotions. As predicted, the personality traits of neuroticism and optimism mediated the effects of the psychosocial factors on negative automatic thoughts. In addition, the results showed that neuroticism was a significant predictor for both depression and anxiety, and that the respective effects were significantly mediated by negative automatic thoughts. Optimism was also a significant predictor of depression and was significantly mediated by negative automatic thoughts. In contrast, optimism did not have a direct effect on anxiety, and its effect was completely mediated by negative automatic thoughts. Structural equation modelling showed that, of the three psychosocial vulnerability factors, parenting styles and school climate had greater effects on depression and anxiety than peer relations. The results from structural equation modelling confirmed the working model, which argues that neuroticism and optimism mediate the effects of psychosocial factors and that neuroticism and optimism are in turn mediated by the negative cognitions associated with depression and anxiety. Overall, the present results suggest the need for future research to examine major domains of vulnerability factors in combination in order to provide a comprehensive understanding of their interplay in anxiety and depression. The present study provides preliminary results that can help to guide future vulnerability research and preventive interventions. / published_or_final_version / Clinical Psychology / Doctoral / Doctor of Psychology

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Several research studies have examined the link between working memory ability and behavior problems in youth. Research suggests that children with working memory deficits demonstrate lower levels of attention and higher levels of hyperactivity, physical aggression, and other behavior problems. The purpose of this research was to determine the effects of developmental trajectories of working memory on the developmental trajectories of behavior problems. Results suggested that developmental increases in working memory did not lead to developmental decreases in behavior problems. Results from this study suggested that internalizing and externalizing behavior problems increase over the course of childhood. Several variables did lead to developmental change in behavior problems in children. Children who had lower initial levels of working memory increased in internalizing behaviors less than children with higher initial working memory ability. Also, high socioeconomic status led to smaller increases in internalizing and externalizing behavior, high Peabody Picture Vocabulary Test (PPVT) scores led to larger increases in internalizing and externalizing behavior, and high PPVT scores led to larger decreases in inattentive and hyperactive behavior. Results are discussed in reference to current theories about working memory and behavior problems. / text