Includes bibliographical references.

Thesis (Ph. D.)--University of Washington, 2001. / Vita. Includes bibliographical references (leaves 56-59).

Thesis (Ph. D.)--Ohio State University, 1970. / Includes bibliographical references (leaves 185-187). Available online via OhioLINK's ETD Center.

Includes bibliographical references (p. 185-196).

No description available.

Nearest Neighbor (NN in short) queries are important in emerging applications, such as wireless networks, location-based services, and data stream applications, where the data obtained are often imprecise. The imprecision or imperfection of the data sources is modeled by uncertain data in recent research works. Handling uncertainty is important because this issue affects the quality of query answers. Although queries on uncertain data are useful, evaluating the queries on them can be costly, in terms of I/O or computational efficiency. In this thesis, we study how to efficiently evaluate NN queries on uncertain data. Given a query point q and a set of uncertain objects O, the possible nearest neighbor query returns a set of candidates which have non-zero probabilities to be the query answer. It is also interesting to ask \which region has the same set of possible nearest neighbors", and \which region has one specific object as its possible nearest neighbor". To reveal the relationship between the query space and nearest neighbor answers, we propose the UV-diagram, where the query space is split into disjoint partitions, such that each partition is associated with a set of objects. If a query point is located inside the partition, its possible nearest neighbors could be directly retrieved. However, the number of such partitions is exponential and the construction effort can be expensive. To tackle this problem, we propose an alternative concept, called UV-cell, and efficient algorithms for constructing it. The UV-cell has an irregular shape, which incurs difficulties in storage, maintenance, and query evaluation. We design an index structure, called UV-index, which is an approximated version of the UV-diagram. Extensive experiments show that the UV-index could efficiently answer different variants of NN queries, such as Probabilistic Nearest Neighbor Queries, Continuous Probabilistic Nearest Neighbor Queries. Another problem studied in this thesis is the trajectory nearest neighbor query. Here the query point is restricted to a pre-known trajectory. In applications (e.g. monitoring potential threats along a flight/vessel's trajectory), it is useful to derive nearest neighbors for all points on the query trajectory. Simple solutions, such as sampling or approximating the locations of uncertain objects as points, fails to achieve a good query quality. To handle this problem, we design efficient algorithms and optimization methods for this query. Experiments show that our solution can efficiently and accurately answer this query. Our solution is also scalable to large datasets and long trajectories. / published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy

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A number of techniques have been developed over the last forty years which attempt to solve the problem of recognizing human speech by machine. Although the general problem of unconstrained, speaker independent connected speech recognition is still not solved, some of the methods have demonstrated varying degrees of success on a number of constrained speech recognition tasks. Human speech communication is considered to take place on a number of levels from the acoustic signal through to higher linguistic and semantic levels. At the acoustic level, the recognition process can be divided into time-alignment (the removal of global and local timing differences between the unknown input speech and the stored reference templates) and referencete mplate matching. Little attention seems to have been given to the effective use of acoustic level contextual information to improve the performance of these tasks. In this thesis, a new template matching scheme is developed which addresses this issue and successfully allows the utilization of acoustic level context. The method, based on Bayesian decision theory, is a dynamic time warping approach which incorporates statistical dependencies in matching errors between frames along the entire length of the reference template. In addition, the method includes a speaker compensation technique operating simultaneously. Implementation is carried out using the highly efficient branch and bound algorithm. Speech model storage requirements are quite small as a result of an elegant feature of the recursive matching criterion. Furthermore, a novel method for inferencing the special speech models is introduced. The new method is tested on data drawn from nearly 8000 utterances of the 26 letters of the British English Alphabet spoken by 104 speakers, split almost equally between male and female speakers. Experiments show that the new approach is a powerful acoustic level speech recognizer achieving up to 34% better recognition performance when compared with a conventional method based on the dynamic programming algorithm.

No description available.

No description available.