Robust Graph Signal Sampling

Güler, Başak; Jayawant, Ajinkya; Avestimehr, A. Salman; Ortega, Antonio

doi:10.1109/icassp.2019.8682340

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…A later work [6] aimed at finding the smallest possible set for a given MSE bound, while [7] concentrated on the opposite problem, i.e., finding the sampling set of fixed size with the minimum MSE. A modification of [6] that is resilient to packet losses was presented in [8], but the authors still do not take into account the fact that the data stemming from the graph nodes are a time series, while [9] considers the temporal aspect but neglects to consider the fact that the importance of a node depends on the other nodes in the subset.…”

Section: Introductionmentioning

confidence: 99%

Lifetime Maximization of an Internet of Things (IoT) Network Based on Graph Signal Processing

et al. 2021

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The lifetime of an Internet of Things (IoT) system consisting of battery-powered devices can be increased by minimizing the number of transmissions per device while not excessively deteriorating the correctness of the overall IoT monitoring. We propose a graph signal processing based algorithm for partitioning the sensor nodes into disjoint sampling sets. The sets can be sampled on a roundrobin basis and each one contains enough information to reconstruct the entire signal within an acceptable error bound. Simulations on different models of graphs, based on graph theory and on real-world applications, show that our proposal consistently outperforms state-ofthe-art sampling schemes, with no additional computational burden.

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Section: Introductionmentioning

confidence: 99%

Lifetime Maximization of an Internet of Things (IoT) Network Based on Graph Signal Processing

et al. 2021

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“…References [23]- [25] consider the space of bandlimited graph signals (Paley-Wiener spaces) and establish that low-pass graph signals can be perfectly reconstructed from their values on some subsets of vertices (sampling sets). Sampling has received considerable attention in the GSP literature [26]- [53]. These references address down-and up-spectral and vertex sampling, perfect, robust, greedy reconstruction, vertex domain eigenvector free sampling, interpolation of graph signals, sampling set selection, a probabilistic interpretation or a distance-based formulation of sampling, use graph sampling to solve sensor position selection, critical sampling for wavelet filterbanks, sampling of graph signals through successive local aggregations, uncertainty principles, among many other topics.…”

Section: Introductionmentioning

confidence: 99%

Topics in Graph Signal Processing: Convolution and Modulation

Shi

Moura

2019

2019 53rd Asilomar Conference on Signals, Systems, and Computers

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To analyze data supported by arbitrary graphs G, DSP has been extended to Graph Signal Processing (GSP) by redefining traditional DSP concepts like shift, filtering, and Fourier transform among others. This paper revisits modulation, convolution, and sampling of graph signals as appropriate natural extensions of the corresponding DSP concepts. To define these for both the vertex and the graph frequency domains, we associate with generic data graph G and its graph shift A a graph spectral shift M and a spectral graph G s . This leads to a spectral GSP theory that parallels in the graph frequency domain the existing GSP theory in the vertex domain. The paper applies this to design and recovery sampling techniques for data supported by arbitrary directed graphs.

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New Methods for Control System Signal Sampling in Neural Networks of Power Facilities

2020

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The authors of the paper emphasize that when the Nyquist-Shannon sampling theorem is used in practice, there arise several problems, which can be explained only through the use of new methodologies and mathematical models. The review of the researchers' works, as well as the authors' own practical research in the course of processing the statistical sample, which is described by a wave-like sine-cosine function, leads to the conclusion that it is necessary to take into account optimization criteria for high-tech processes and innovative indicators of building functions for the statistical sample, for example, when signals are transmitted and sampled using neural networks at production facilities. In the practice of economic calculations, for example, when making a graphic presentation of trend lines based on the functions built subject to the sampling conditions by the Nyquist theorem, the authors propose to use new methods for approximating piece linear functions, which allow for achieving a smaller error as compared to standard calculation methods. The work resulted in the creation of a neural network regulation algorithm, which will be trained based on the collected data and adapted to a specific type of a boiler unit. Besides, it was established that the task of neural network algorithms in the program is to find the optimal value of the weight coefficient for each argument of the resulting function to obtain the maximum number of predictions of the flare level and the particle burn-up time, which are consistent with reality. The use of these methods for the first time made it possible to significantly reduce the error, which is confirmed not only by calculations, but also by experimental data.

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Robust Graph Signal Sampling

Cited by 4 publications

References 14 publications

Lifetime Maximization of an Internet of Things (IoT) Network Based on Graph Signal Processing

Lifetime Maximization of an Internet of Things (IoT) Network Based on Graph Signal Processing

Topics in Graph Signal Processing: Convolution and Modulation

New Methods for Control System Signal Sampling in Neural Networks of Power Facilities

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