Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 14 of 14 for “"Graph framework"”.
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Unified Graph Framework: Optimizing Graph Applications across Novel Architectures
High performance graph applications are crucial in a wide set of domains, but their performance depends heavily on input graph structure, algorithm, and target hardware. Programmers must develop a series of optimizations either on the compiler level, implementing different load balancing or edge …
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Tradespace exploration for space system architectures : a weighted graph framework
… to one another. In this thesis we propose a framework for modeling these relationships based on the evaluation of a pre-existing tradespace for the purpose of analyzing architecture change decisions. This modeling framework is used to discover and evaluate evolutionary pathways through a …
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A gene-space de Bruijn graph framework for improved genotyping of antimicrobial resistance genes
… development and application of a novel de Bruijn graph–based data structure, the gene-space de Bruijn graph (gene DBG), for AMR gene detection. In this graph, the k-mer alphabet is the set of genes in the pan-genome of the bacterial species under analysis. This approach uses an existing tool, …
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A dynamic knowledge graph approach for studying the decarbonisation of power systems
This thesis introduces a dynamic knowledge graph framework, providing a reusable, interoperable, and extensible approach for cross-domain analyses in power system studies, facilitating research on decarbonisation. Domain ontologies were designed to conceptualise the power system and used to create …
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Equalization Using Graphical Models
We examine the use of graphical models for the equalization of digital communication channels with memory. Graphical models provide a framework in which the structure of large systems can be exploited to derive efficient estimation algorithms. Furthermore, properties of a graph on which an …
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Can an Agentic AI System Increase Willingness to Engage with Support Services in Wales?
… and follow-up questions. The system uses Lang Graph’s state graph framework, paired with custom tools and structured input and output via Pydantic models. A novel approach to agentic PDF manipulation is proposed, leveraging deterministic tooling and the large language model’s core strength in …
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Mapping and Localization Using LiDAR Fiducial Markers
… designs. This dissertation proposes a novel framework for mapping and localization using LFMs is proposed to benefit a variety of real-world applications, including the collection of 3D assets and training data for point cloud registration, 3D map merging, Augmented Reality (AR), and many …
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A framework for decision support in systems architecting
… approaches. This thesis provides a computational framework for decision support called the Architecture Decision Graph framework. It supports human decision-making by providing a methodology for generating and analyzing architectures as the result of a set of interrelated decisions. ADG's explicit …
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Estimating Reachability Set Sizes in Dynamic Graphs
Graphs are a commonly used abstraction for diverse kinds of interactions, e.g., on Twitter and Facebook. Different kinds of topological properties of such graphs are computed for gaining insights into their structure. Computing properties of large real networks is computationally very challenging. …
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Graphical model driven methods in adaptive system identification
… the input to the system of interest as a graph-structured random process, it is shown that a large parameter identification problem can be reduced into several smaller pieces, making the overall problem considerably simpler. Algorithms that can leverage this property in order to either …
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Graphical model driven methods in adaptive system identification
… the input to the system of interest as a graph-structured random process, it is shown that a large parameter identification problem can be reduced into several smaller pieces, making the overall problem considerably simpler. Algorithms that can leverage this property in order to either …
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Deep Learning-Enhanced Autonomous Aerial and Ground Robotics Using UWB and Lidar in GNSS-Denied Environments
… these issues by developing a comprehensive framework that merges advanced data collection platforms, deep learning algorithms, and novel fusion methods to enhance UAV positioning accuracy and reliability. A central contribution of this research is the creation of the Q-Drone Ultra-Wideband …
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Multi-modal and inertial sensor solutions for navigation-type factor graphs
… is described by a non-Gaussian factor graph model. Existing inference algorithms in simultaneous localization and mapping assume Gaussian measurement uncertainty, resulting in complex front-end processes that attempt to deal with non-Gaussian measurements. Existing robustness approaches …
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Neural recommender models for sparse and skewed behavioral data
… are not. We develop a self-supervised learning framework where the aggregate co-occurrences guide the recommendation problem while providing room to learn these variations among the item associations. As a result, we improve coverage to ~100% (up from 5%) of the inventory and increase long-tail …