This thesis presents a model of fabric bending hysteresis. The hysteresis model is designed to reproduce the fabric bending measurements taken by the Kawabata Evaluation System (KES) and the model parameters can be derived directly from these property measurements. The advantage to using this technique is that it provides the ability to simulate a continuum of property curves. Results of the model and its components are compared and constrasted with experimental results for fabrics composed of different weaves and yarn types. An attempt to incorporate the bending model as part of a fabric drape simulation is also made.

A new approach to local nonlinear image restoration is described, based on approximating functions using a regular grid of points in a many-dimensional space. Symmetry reductions and compression of the sparse grid make it feasible to work with twelve-dimensional grids as large as 22¹². Unlike polynomials and neural networks whose filtering complexity per pixel is linear in the number of filter co-efficients, grid filters have O(1) complexity per pixel. Grid filters require only a single presentation of the training samples, are numerically stable, leave unusual image features unchanged, and are a superset of order statistic filters. Results are presented for additive noise, blurring, and superresolution.

An automatic question answering system must find, within a corpus,short factual answers to questions posed in natural language. The process involves analyzing the question, retrieving information related to the question, and extracting answers from the retrieved information. This thesis presents a novel approach to answer extraction in an automated question answering (QA) system. The answer extraction approach is an extension of the MultiText QA system. This system employs a question analysis component to examine the question and to produce query terms for the retrieval component which extracts several document fragments from the corpus. The answer extraction component selects a few short answers from these fragments. This thesis describes the design and evaluation of the Redundant Inverse Term Frequency (RITF) answer extraction component. The RITF algorithm locates and evaluates words from the passages that are likely to be associated with the answer. Answers are selected by finding short fragments of text that contain the most likely words based on: the frequency of the words in the corpus, the number of fragments in which the word occurs, the rank of the passages as determined by the IR, the distance of the word from the centre of the fragment, and category information found through question analysis. RITF makes a substantial contribution in overall results, nearly doubling the Mean Reciprocal Rank (MRR), a standard measure for evaluating QA systems.

A new approach to local nonlinear image restoration is described, based on approximating functions using a regular grid of points in a many-dimensional space. Symmetry reductions and compression of the sparse grid make it feasible to work with twelve-dimensional grids as large as 22¹². Unlike polynomials and neural networks whose filtering complexity per pixel is linear in the number of filter co-efficients, grid filters have O(1) complexity per pixel. Grid filters require only a single presentation of the training samples, are numerically stable, leave unusual image features unchanged, and are a superset of order statistic filters. Results are presented for additive noise, blurring, and superresolution.

This thesis presents a model of fabric bending hysteresis. The hysteresis model is designed to reproduce the fabric bending measurements taken by the Kawabata Evaluation System (KES) and the model parameters can be derived directly from these property measurements. The advantage to using this technique is that it provides the ability to simulate a continuum of property curves. Results of the model and its components are compared and constrasted with experimental results for fabrics composed of different weaves and yarn types. An attempt to incorporate the bending model as part of a fabric drape simulation is also made.

An automatic question answering system must find, within a corpus,short factual answers to questions posed in natural language. The process involves analyzing the question, retrieving information related to the question, and extracting answers from the retrieved information. This thesis presents a novel approach to answer extraction in an automated question answering (QA) system. The answer extraction approach is an extension of the MultiText QA system. This system employs a question analysis component to examine the question and to produce query terms for the retrieval component which extracts several document fragments from the corpus. The answer extraction component selects a few short answers from these fragments. This thesis describes the design and evaluation of the Redundant Inverse Term Frequency (RITF) answer extraction component. The RITF algorithm locates and evaluates words from the passages that are likely to be associated with the answer. Answers are selected by finding short fragments of text that contain the most likely words based on: the frequency of the words in the corpus, the number of fragments in which the word occurs, the rank of the passages as determined by the IR, the distance of the word from the centre of the fragment, and category information found through question analysis. RITF makes a substantial contribution in overall results, nearly doubling the Mean Reciprocal Rank (MRR), a standard measure for evaluating QA systems.