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Title: A neural network model of the primary visual cortex.
Contributors: Alan Spara (author), Charles Brown (Thesis advisor), University of Northern British Columbia (Degree granting institution)
Abstract: Many problems in modern computing require a visual component. That is to say, it is fairly common for applications to have a need to see their environments. These applications will typically employ techniques designed specifically to solve the particular task needed for the application, and have little or no relation to the human visual system. Humans generally do not have difficulty interpreting the world around us. When traveling through known environments, we can easily recognize particular walls, doors and other objects in our view. We are not confused by the huge number factors that can complicate an image. The generalization and robustness of the human system would provide a huge benefit to any system that requires more advanced vision than is capable with the ad-hoc methods developed previously. If the underlying principles that make the human visual system so powerful can be identified and implemented programmatically, then a machine could reap the benefits obtained by humans. The purpose of this thesis is to demonstrate that a visual system modeled after the human visual system will be robust and accurate enough to solve real world problems - and to be useful in a non-trivial application. By developing neural networks that directly model the most primitive image processing cells of the human visual system, a platform can be built on which advanced vision systems can be developed.
Discipline: Computer Science
Content Model: info:fedora/ir:thesisCModel
Date added: 2008

Title: The generalized tutor-student learning algorithm for autonomous mobile robots.
Contributors: Kevin Brammer (author), Charles Brown (Thesis advisor), University of Northern British Columbia (Degree granting institution)
Abstract: No abstract available.
Discipline: Computer Science
Content Model: info:fedora/ir:thesisCModel
Date added: 2006

Title: Approximating the rank of a homomorphism using a Prolog based system
Contributors: Richard Kenneth Little (author), Jennifer Hyndman (Thesis advisor), Charles Brown (Thesis advisor), University of Northern British Columbia (Degree granting institution)
Abstract: A system of Prolog based programs for the purpose of approximating the rank of algebraic operations of finite unary algebras is presented. The rank function is a measure of finite algebras and their algebraic operations. Rank is a recursive function used in universal algebra and was first introduced as a tool for proving strong dualizability. Logic programming. particularly Prolog, is commonly used in natural language processing, an area of study devoted to the use of computers to understand human (natural) languages. One goal of this thesis is to explore a relationship between the fields of Mathematics and Computer Science through the application of logic programming techniques on structures from universal algebra. This thesis is motivated by the idea that when universal algebra is viewed as a language, the ideas of natural language processing can be used to create a computer system which approximates rank. The outcome of the research is a computational model that computes the Kth approximation of rank. A set of Prolog programs that act as useful tools on algebraic structures are created.
Discipline: Interdisciplinary Studies
Content Model: info:fedora/ir:thesisCModel
Date added: 2000

Title: Lumping of atmospheric organic chemical species by machine learning.
Contributors: Pruthvi Polam (author), Margot Mandy (Thesis advisor), Charles Brown (Thesis advisor), University of Northern British Columbia (Degree granting institution)
Abstract: No abstract available.
Discipline: Chemistry and Computer Science
Content Model: info:fedora/ir:thesisCModel
Date added: 2006

Title: A basis for pronominal anaphora resolution using a model of working memory and long-term memory
Contributors: Clifford James Thompson (author), Charles Brown (Thesis advisor), University of Northern British Columbia (Degree granting institution)
Abstract: This thesis presents a new theory of information modelling in natural language processing that attempts to resolve anaphoric references, while also addressing the problem of knowledge complexity. A modular model of semantic representation is introduced that addresses the deficiencies of existing representations, as well as the drawbacks associated with expanding these semantic representations. Rather than using a single semantic representation to model human knowledge and the knowledge within a sentence, the theory proposes a modular, multi-level model which is based around a semantic network. The behaviour of the model uses theories on the nature of working and long-term memory from cognitive psychology. Two methods of artificial neuron activation and decay were implemented - the ACT-R model and the Thompson model. Maximum success rates of 54.10% and 83.61% were achieved for The Three Brothers corpus, and maximum success rates of 56.00% and 86.67% were achieved for the Rumpelstiltskin corpus.
Discipline: Computer Science
Content Model: info:fedora/ir:thesisCModel
Date added: 2006

Title: Improving Sonar Sensor Fidelity in a Robot Simulator.
Contributors: Allan Edward Kranz (author), Charles Brown (Thesis advisor), Liang Chen (Thesis advisor), University of Northern British Columbia (Degree granting institution)
Abstract: It is slow and expensive to develop robot control systems using real robots. Simulation can provide the benefits of lowering the time and cost. In order to take advantage of the benefits of development in a simulator we need high fidelity representations of actual sensors. Sensors do not provide perfect data and simulations that use either perfect models or models that are too simple will not translate well into the real world. This research introduces a sensor model that overcomes some of the existing limitations in current simulations and provides a methodology for developing both new models and corresponding testing regimes. An actual sensor is used in realistic situations to create authentic models that more closely match the performance of the robot in the real world. A simple sonar sensor is tested against three generic obstacles and a realistic software simulation model of its capabilities is created. The Simbad robot simulator is modified to use this model, a testing regime is created to validate the results, and improved performance over the existing model is achieved. --Leaf iii.
Discipline: Computer Science
Content Model: info:fedora/ir:thesisCModel
Date added: 2015

Title: A diverse user model in the context of an intelligent tutoring system.
Contributors: Nathan Kevin Keim (author), Charles Brown (Thesis advisor), University of Northern British Columbia (Degree granting institution)
Abstract: No abstract available.
Discipline: Computer Science
Content Model: info:fedora/ir:thesisCModel
Date added: 2003

Title: Evolving artificial neural network controllers for autonomous agents navigating dynamic environments.
Contributors: Robert A. Lucas (author), Charles Brown (Thesis advisor), University of Northern British Columbia (Degree granting institution)
Abstract: This thesis presents and discusses a potential method for solving the dynamic obstacle avoidance problem using contemporary work with artificial neural networks (ANNs) and genetic algorithms (GAs) in combination with an imitation of a biological genetic process called segmental duplication. ANNs, GAs and segmental duplication are merged in the project to form SDNEAT, a type of evolutionary artificial neural network (EANN) system based on NeuroEvolution of Augmenting Topologies, or NEAT. The system is then used to develop an artificial neural network system that attempts to navigate environments incorporating both static and dynamic obstacles.
Discipline: Computer Science
Content Model: info:fedora/ir:thesisCModel
Date added: 2009

Title: Study of document retrieval using Latent Semantic Indexing (LSI) on a very large data set.
Contributors: A. N. K. Zaman (author), Liang Chen (Thesis advisor), Charles Brown (Thesis advisor), University of Northern British Columbia (Degree granting institution)
Abstract: The primary purpose of an information retrieval system is to retrieve all the relevant documents, which are relevant to the user query. The Latent Semantic Indexing (LSI) based ad hoc document retrieval task investigates the performance of retrieval systems that search a static set of documents using new questions/queries. Performance of LSI has been tested for several smaller datasets (e.g., MED, CISI abstracts etc) however, LSI has not been tested for a large dataset. In this research, we concentrated on the performance of LSI on large dataset. Stop word list and term weighting schemes are two key parameters in the area of information retrieval. We investigated the performance of LSI by using three different set of stop word lists and, also, without removing the stop words from the test collection. We also applied three different term-weighting (raw term frequency, log-entropy, and tf-idf) schemes to measure retrieval performance of LSI. We observed that, firstly, for a LSI based ad hoc information retrieval system, a tailored stop word list must be assembled for every unique large dataset. Secondly, the use of tf-idf term weighting scheme shows better retrieval performance than log-entropy and raw term frequency weighting schemes even when the test collection became large. --P. ii.
Discipline: Computer Science
Content Model: info:fedora/ir:thesisCModel
Date added: 2010

Title: Architecture for automatic poetry generation through pattern recognition
Contributors: Kimberley Scofield (author), Tina Fraser (Thesis advisor), Liang Chen (Thesis advisor), Charles Brown (Thesis advisor), University of Northern British Columbia College of Science and Management (Degree granting institution), David Casperson (Committee member), Robert Budde (Committee member), Han Li (Committee member)
Abstract: Document representation and topic modelling are important problems for artificial intelligence researchers, with applications ranging from education technology to bioinformatics. Many approaches have been proposed, the majority falling broadly into categories of Statistical Analysis and Natural Language Processing (NLP). This thesis proposes an architecture that optimizes a combination of statistical and linguistic analysis in an unsupervised machine learning environment. The proposed architecture is a design for agile, stable, document modelling. By clustering within the statistical inference algorithm, it reduces the computational cost of time and space associated with conventional classifying algorithms such as K-means, increasing the threshold for size and frequency of aggregate data analysis. This translates to an increased stability for evolution of learning. The architecture builds on the concept of socio-linguistic connections as an inherent combination of statistics and linguistics, and employs well-researched concepts of statistical and linguistic analysis, including embedded sub-manifold analysis. ...
Discipline: Interdisciplinary Studies
Content Model: info:fedora/ir:thesisCModel
Date added: 2017

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