Oliver Lyon

Oliver Lyon

he/him/they/them

Complexity Enthusiast

Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

Application of a Simulation Model to Estimate Treatment Error and Clinical Risk Derived from Point-of-Care International Normalized Ratio Device Analytic Performance

Published in The Journal of Applied Laboratory Medicine, 2019

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Recommended citation: Martha E Lyon, Roona Sinha, Oliver A S Lyon, Andrew W Lyon, Application of a Simulation Model to Estimate Treatment Error and Clinical Risk Derived from Point-of-Care International Normalized Ratio Device Analytic Performance, The Journal of Applied Laboratory Medicine, Volume 2, Issue 1, 1 July 2017, Pages 25–32, https://doi.org/10.1373/jalm.2017.022970
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Simulation Models of Misclassification Error for Single Thresholds of High-Sensitivity Cardiac Troponin I Due to Assay Bias and Imprecision

Published in Clinical Chemistry, 2017

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Recommended citation: Andrew W Lyon, Peter A Kavsak, Oliver A S Lyon, Andrew Worster, Martha E Lyon, Simulation Models of Misclassification Error for Single Thresholds of High-Sensitivity Cardiac Troponin I Due to Assay Bias and Imprecision, Clinical Chemistry, Volume 63, Issue 2, 1 February 2017, Pages 585–592, https://doi.org/10.1373/clinchem.2016.265058
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Conference Papers

Nondeterministic State Complexity of Site-Directed Deletion

Published in Implementation and Application of Automata , 2022

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Recommended citation: Lyon, O.A.S., Salomaa, K. (2022). Nondeterministic State Complexity of Site-Directed Deletion. In: Caron, P., Mignot, L. (eds) Implementation and Application of Automata. CIAA 2022. Lecture Notes in Computer Science, vol 13266. Springer, Cham. https://doi.org/10.1007/978-3-031-07469-1_15
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A Novel Technique Combining Image Processing, Plant Development Properties, and the Hungarian Algorithm, to Improve Leaf Detection in Maize

Published in In the proceedings of Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020

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Recommended citation: Nazifa Khan, Oliver Lyon, Mark Eramian, Ian McQuillan, "A Novel Technique Combining Image Processing, Plant Development Properties, and the Hungarian Algorithm, to Improve Leaf Detection in Maize." In the proceedings of Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020.

Research Theses

Nondeterministic State Complexity of Site-Directed Operations

Published in Queen" s University, 2021

In this thesis, we consider the nondeterministic state complexity of PCR-inspired (polymerase chain reaction) operations. Site-directed operations are used to formally describe the behavior of certain DNA (deoxyribonucleic acid) editing methods which need to identify a subsequence in a host DNA strand prior to editing. These operations can be considered as language operations acting to match patterns between two sets of strings. The site-directed insertion and deletion operations, insert or delete into a host string based on a directing string. The directing string must have a non-empty outfix that matches a substring in the host before operating. Prefix and suffix directed insertion are similar to site-directed insertion except, instead of matching a non-empty outfix, a non-empty prefix or suffix is matched before insertion. We consider the nondeterministic state complexity of site-directed insertion and deletion. Constructing a nondeterministic finite automaton (NFA) for the operation provides an upper bound for the state complexity of the operation. Our construction improves the earlier upper bound in the literature. Existing literature did not give lower bounds for the nondeterministic state complexity of site-directed insertion and deletion. Using the fooling set method we establish lower bounds that are fairly close to the upper bound, albeit the lower bound is not tight

Recommended citation: Oliver Lyon, Kai Salomma, "Nondeterministic State Complexity of Site-Directed Operations." Queen"s University, 2021.

Lindenmayer Systems - Inferring Branching Topology

Published in University of Saskatchewan, 2019

Plants and their structures have been studied for centuries. Many plants develop slowly and follow patterns through out their development. These patterns are often repetitive, or self-nested. To understand these patterns, there have been many tools and algorithms developed.

In the late 1960’s a biologist named Aristid Lindenmayer developed a type of formal language to model the growth of algae. This language would continue to be used for many years in the modeling of plants and other recursive structures. These tools were combined with turtle geometry, which allowed for repetitive sequences of drawing instructions to be built from a formal language to mimic a plants’ structure or pattern.

In 2010, an algorithm called NEST was developed by Chrisophe Godin and Pascal Ferraro to help study the branching structures of plants. This algorithm can be used as a predictor of the original plants’ branching structure. The ability to enter a Lindenmayer language and run a qualitative algorithm could be useful for comparison against real plant data. We expan of thier work, and provide a graphical interface.

Recommended citation: Oliver Lyon, Ian McQuillan, "Lindenmayer Systems - Inferring Branching Topology." University of Saskatchewan, 2019.