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2022
In this PhD thesis, several signal-processing models applied on graphs have been studied. Half-quadratic (HQ) optimization, robust estimators, such as marginal median and M-estimators, l21 norm regularization, block least mean squares have been the fundamental basis of the proposed approaches.
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In this PhD thesis, several signal-processing models applied on graphs have been studied. Half-quadratic (HQ) optimization, robust estimators, such as marginal median and M-estimators, l21 norm regularization, block least mean squares have been the fundamental basis of the proposed approaches.
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Digital signal processing on graphs
Vestnik of Russian New University. Series «Complex systems: models, analysis, management», 2023Рассмотрены понятия цифровой обработки сигналов, которые представлены в виде взвешенных графов, применены различные фильтры для очистки зашумления выборки сигналов в виде сенсорного и кластерного графов. Использованы фильтры на основе метода теплового ядра, алгоритма Таубина и свертки этих алгоритмов.
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Introduction to Graph Signal Processing
2022An intuitive and accessible text explaining the fundamentals and applications of graph signal processing. Requiring only an elementary understanding of linear algebra, it covers both basic and advanced topics, including node domain processing, graph signal frequency, sampling, and graph signal representations, as well as how to choose a graph ...
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Discrete signal processing on graphs: Graph fourier transform
2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013We propose a novel discrete signal processing framework for the representation and analysis of datasets with complex structure. Such datasets arise in many social, economic, biological, and physical networks. Our framework extends traditional discrete signal processing theory to structured datasets by viewing them as signals represented by graphs, so ...
Aliaksei Sandryhaila, Jose M. F. Moura
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Decidable Signal Processing Dataflow Graphs
2018Digital signal processing algorithms can be naturally represented by a dataflow graph where nodes represent function blocks and arcs represent the data dependency between nodes. Among various dataflow models, decidable dataflow models have restricted semantics so that we can determine the execution order of nodes at compile-time and decide if the ...
Soonhoi Ha, Hyunok Oh
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Introduction to Graph Signal Processing
2018Graph signal processing deals with signals whose domain, defined by a graph, is irregular. An overview of basic graph forms and definitions is presented first. Spectral analysis of graphs is discussed next. Some simple forms of processing signal on graphs, like filtering in the vertex and spectral domain, subsampling and interpolation, are given. Graph
Ljubiša Stanković +2 more
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Brain signal analytics from graph signal processing perspective
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017This paper presents methods to analyze functional brain networks and signals from graph spectral perspectives. The notion of frequency and filters recently generalized to irregular graph domains defines brain graph frequencies associated with different levels of spatial smoothness across the brain regions.
Leah Goldsberry +5 more
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Fundamentals of multirate graph signal processing
2015 49th Asilomar Conference on Signals, Systems and Computers, 2015In this work, the fundamental blocks of multirate signal processing on graphs are analyzed. First the decimator is defined, and expander is solved accordingly. Then, noble identities and lazy filter bank for graph signals are constructed. After decimation, the length of the signal changes and the original adjacency matrix is not applicable.
Teke, Oguzhan, Vaidyanathan, P. P.
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SignalP 6.0 predicts all five types of signal peptides using protein language models
Nature Biotechnology, 2022Felix Teufel +2 more
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