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 9 of 9 for “"Graph Signal Processing"”.
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Learning Sparse Graph Laplacian with K Eigenvector Prior via Iterative GLASSO and Projection
Learning a suitable graph is an important precursor to many graph signal processing (GSP) tasks, such as graph signal compression and denoising. Previous graph learning algorithms either make assumptions on graph connectivity (e.g., graph sparsity), or make individual edge weight assumptions such …
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Graph Learning and Optimization for Irregular-Structured Signal Processing
Graph Signal Processing (GSP) extends harmonic analysis tools, such as Fourier transforms and wavelets, to discrete signals defined on finite graphs, enabling tasks like signal denoising, prediction, and interpolation on irregular domains. A critical first step in GSP is to learn an appropriate …
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Modeling and Control of Networked Systems: Applications to Air Transportation
… we use ideas from switched-systems theory, graph signal processing, and machine learning to develop tools that overcome some of these limitations. The key idea behind our modeling approach is to (i) simplify the complex network interactions by identifying a small, finite set of …
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Generalização de transformadas do cosseno baseada em rotações : contribuições teóricas e cenários de aplicação
… de sinais sobre grafos (GSP, do inglês graph signal processing), que estende a teoria clássica de processamento de sinais para o domínio dos grafos. Também em GSP uma transformada de Fourier foi definida, a transformada de Fourier sobre grafos (GFT, do inglês graph Fourier transform), …
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Spectral Models for Air Transportation Networks
… to power outages, these events, even geographically-localized ones, often result in widespread disruptions across the air transportation network. In order to engineer resilience and design better proactive mitigation strategies, it is important to identify, characterize, and control the …
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STREETS: a benchmark dataset for suburban traffic forecasting
… current datasets lack a coherent traffic network graph to describe the relationship between sensors. The datasets that do provide a graph depict traffic flow in urban population centers or highway systems and use costly sensors like induction loops. These contexts differ from that of a suburban …
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Inference of multiple sparse networks in the presence of hidden nodes
… problem, we assume that there exist sets of graph signals that are stationary on the networks, which provides a global relationship between the observations and the network topologies such that we may characterize the effect of the hidden nodes. Under the assumptions that signals are …
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Inferring Undirected and Causally Directed Graph Structures from Multivariate Time Series
… primates using a novel variation of undirected graph learning based on smoothness prior. In Part Two, we define and implement a novel spatiotemporal graph (STG) model for inferring causally directed graphs. Analysis of brain connectivity networks has a potential to advance our understanding of …
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Signal representations: from images to irregular-domain signals
… play a vital role in many problems in signal processing and related fields, ranging from signal compression and denoising to inverse problems. This thesis studies the design and applications of various representation systems for several classes of signals including images and signals on …