Thesis (M.C.S.)--Carleton University, 1988. / Also available in electronic format on the Internet.

Thesis (M. Eng.)--Carleton University, 2001. / Includes bibliographical references (p. 95-97). Also available in electronic format on the Internet.

Thesis (Ph. D.)--University of Hong Kong, 2005.

Thesis (M.S.)--Rochester Institute of Technology, 2006. / Typescript. Includes bibliographical references (leaves 48-51).

We study representation systems for relational data based on relational algebra expressions with unions, products, and singleton relations. Algebraic factorisation using the distributivity of product over union allows succinct representation of many-to-many relationships; further succinctness is brought by sharing repeated subexpressions. We show that these techniques are especially applicable to results of conjunctive queries. In the first part of the dissertation we derive tight asymptotic size bounds for two flavours of factorised representations of results of conjunctive queries. Any conjunctive query is characterised by rational parameters that govern the factorisability of its results independently of the database instance. We relate these parameters to fractional edge covers and fractional hypertree decompositions. Factorisation naturally extends from relational data to its provenance. We characterise conjunctive queries by tight bounds on their readability, which captures how many times each input tuple is used to contribute to an output tuple, and we define syntactically the class of queries with bounded readability. In the second part of the dissertation we describe FDB, a relational database engine that uses factorised representations at the physical layer to reduce data redundancy and boost query performance. We develop algorithms for optimisation and evaluation of queries with selection, projection, join, aggregation and order-by clauses on factorised representations. By introducing novel operators for factorisation restructuring and a new optimisation objective to maintain intermediate and final results succinctly factorised, we allow query evaluation with lower time complexity than on flat relations. Experiments show that for data sets with many-to-many relationships, FDB can outperform relational engines by orders of magnitude.

In the last twenty years, important advances have been made in the field of automatic face processing, given the importance of human faces for personal identification, emotional expression and verbal and non verbal communication. The very first step in a face processing algorithm is the detection of faces; while this is a trivial problem in controlled environments, the detection of faces in real environments is still a challenging task. Until now, the most successful approaches for face detection represent the face as a grey-level pattern, and the problem itself is considered as the classification between "face" and "non-face" patterns. Satisfactory results have been achieved in this area. The main disadvantage is that an exhaustive search has to be done on each image in order to locate the faces. This search normally involves testing every single position on the image at different scales, and although this does not represent an important drawback in off-line face processing systems, in those cases where a real-time response is needed it is still a problem. In the different proposed methods for face detection, the "observer" is a disembodied entity, which holds no relationship with the observed scene. This thesis presents a framework for an efficient location of faces in real scenes, in which, by considering both the observer to be situated in the world, and the relationships that hold between the two, a set of constraints in the search space can be defined. The constraints rely on two main assumptions; first, the observer can purposively interact with the world (i.e. change its position relative to the observed scene) and second, the camera is fully calibrated. The first source constraint is the structural information about the observer environment, represented as a depth map of the scene in front of the camera. From this representation the search space can be constrained in terms of the range of scales where a face might be found as different positions in the image. The second source of constraint is the geometrical relationship between the camera and the scene, which allows us to project a model of the subject into the scene in order to eliminate those areas where faces are unlikely to be found. In order to test the proposed framework, a system based on the premises stated above was constructed. It is based on three different modules: a face/non-face classifier, a depth estimation module and a search module. The classifier is composed of a set of convolutional neural networks (CNN) that were trained to differentiate between face and non-face patterns, the depth estimation modules uses a multilevel algorithm to compute the scene depth map from a sequence of images captured the depth information and the subject model into the image where the search will be performed in order to constrain the search space. Finally, the proposed system was validated by running a set of experiments on the individual modules and then on the whole system.

<p>It is well established that cochlear implants (CIs) are able to provide many users with excellent speech recognition ability in quiet conditions; however, the ability to correctly identify speech in noisy conditions or appreciate music is generally poor for implant users with respect to normal-hearing listeners. This discrepancy has been hypothesized to be in part a function of the relative decrease in spectral information available to implant users (Rubinstein and Turner, 2003; Wilson et al., 2004). One method that has been proposed for increasing the amount of spectral information available to CI users is to include time-varying stimulation rate in addition to changes in the place of stimulation. However, previous implementations of multi-rate strategies have failed to result in an improvement in speech recognition over the clinically available, fixed-rate strategies (Fearn, 2001; Nobbe, 2004). It has been hypothesized that this lack of success was due to a failure to consider the underlying perceptual responses to multi-rate stimulation. </p><p>In this work, psychophysical experiments were implemented with the goal of achieving a better understanding of the interaction of place and rate of stimulation and the effects of duration and context on CI listeners' ability to detect changes in stimulation rate. Results from those experiments were utilized in the implementation of a tuned multi-rate sound processing strategy for implant users in order to potentially ``tune" multi-rate strategies and improve speech recognition performance. </p><p>In an acute study with quiet conditions, speech recognition performance with a tuned multi-rate implementation was better than performance with a clinically available, fixed-rate strategy, although the difference was not statistically significant. These results suggest that utilizing time-varying pulse rates in a subject-specific implementation of a multi-rate algorithm may offer improvements in speech recognition over clinically available strategies. A longitudinal study was also performed to investigate the potential benefit from training to speech recognition. General improvements in speech recognition ability were observed as a function of time; however, final scores with the tuned multi-rate algorithm never surpassed performance with the fixed-rate algorithm for noisy conditions. </p><p>The ability to improve upon speech recognition scores for quiet conditions with respect to the fixed-rate algorithm suggests that using time-varying stimulation rates potentially provides additional, usable information to listeners. However, performance with the fixed-rate algorithm proved to be more robust to noise, even after three weeks of training. This lack of robustness to noise may be in part a result of the frequency estimation technique used in the multi-rate strategy, and thus more sophisticated techniques for real-time frequency estimation should be explored in the future.</p> / Dissertation

Analog and mixed signal device testing is resource intensive due to the spectral and temporal speci cations of the input/output interface signals. These devices and circuits are commonly validated by parametric speci fication tests to ensure compliance with the required performance criteria. Analog signal complexity increases resource requirements for the Automatic Test Equipment (ATE) systems used for commercial testing, making mixed signal testing resource ine cient as compared to digital structural testing. This dissertation proposes and implements a test ecosystem to address these constraints where Built In Self Test (BIST) modules are designed for internal stimulus generation. Data learning and processing algorithms are developed for output response shaping. This modi ed output response is then compared against the established performance matrices to maintain test quality with low cost receiver hardware. BIST modules reduce dependence on ATE resources for stimulus and output observation while improving capability to test multiple devices in parallel. Data analysis algorithms are used to predict specification parameters based on learning methods applied to measurable device parameters. Active hardware resources can be used in conjunction with post processing resources to implement complex speci cation based tests within the hardware limitations. This dissertation reviews the results obtained with the consolidated approach of using BIST, output response analysis and active hardware resources to reduce test cost while maintaining test quality. / text

Based on doctoral research, 1996-2000--Rensselaer Polytechnic Institute. / Description based on print version record. Includes bibliographical references (p. 159-168) and index.

Thesis (Ph. D.)--Carleton University, 2002. / Includes bibliographical references (p. 166-177). Also available in electronic format on the Internet.