Results 41 to 50 of about 184,341 (273)
Interpolation of Sparse Graph Signals by Sequential Adaptive Thresholds
, 2017 This paper considers the problem of interpolating signals defined on graphs. A major presumption considered by many previous approaches to this problem has been lowpass/ band-limitedness of the underlying graph signal.
Fallah, Maryam +2 more
core +1 more source
The Symmetric Group Defies Strong Fourier Sampling [PDF]
, 2008 The dramatic exponential speedups of quantum algorithms over their best existing classical counterparts were ushered in by the technique of Fourier sampling, introduced by Bernstein and Vazirani and developed by Simon and Shor into an approach to the ...
Moore, Christopher +2 more
core +2 more sources
A FFT-Like MIMO Detection Algorithm
IEEE Access, 2020 This paper studies the multiple-input-multiple-output (MIMO) detection problem. Existing works model the MIMO detection as a tree or factor graph. This work adds a new member called the clique graph model to the MIMO detection graph model family so that ...
Peng Du, Yuan Zhang, Teer Ba
doaj +1 more source
Sampling and Reconstruction of Sparse Signals on Circulant Graphs - An Introduction to Graph-FRI
, 2017 With the objective of employing graphs toward a more generalized theory of signal processing, we present a novel sampling framework for (wavelet-)sparse signals defined on circulant graphs which extends basic properties of Finite Rate of Innovation (FRI)
Dragotti, Pier Luigi +1 more
core +1 more source
De Novo Assembly of Nucleotide Sequences in a Compressed Feature Space [PDF]
, 2017 Sequencing technologies allow for an in-depth analysis of biological species but the size of the generated datasets introduce a number of analytical challenges.
Robertson, David L., Tapinos, Avraam
core +1 more source
Discrete Signal Processing on Graphs: Sampling Theory
, 2015 We propose a sampling theory for signals that are supported on either directed or undirected graphs. The theory follows the same paradigm as classical sampling theory.
Chen, Siheng +3 more
core +1 more source
Multi-dimensional Graph Fourier Transform
, 2017 Many signals on Cartesian product graphs appear in the real world, such as digital images, sensor observation time series, and movie ratings on Netflix. These signals are "multi-dimensional" and have directional characteristics along each factor graph. However, the existing graph Fourier transform does not distinguish these directions, and assigns 1-D ...
Kurokawa, Takashi +2 more
openaire +2 more sources
GSD: An R package for graph signal decomposition
SoftwareXGraph signals residing on the vertices of a graph have recently gained prominence in research of various fields, including neural networks, social networks, traffic patterns, and sensors.
Hyeonglae Cho, Hee-Seok Oh, Donghoh Kim
doaj +1 more source
A Hilbert Space Theory of Generalized Graph Signal Processing
, 2019 Graph signal processing (GSP) has become an important tool in many areas such as image processing, networking learning and analysis of social network data.
Ji, Feng, Tay, Wee Peng
core +1 more source
On Sparse Graph Fourier Transform
, 2018 In this paper, we propose a new regression-based algorithm to compute Graph Fourier Transform (GFT). Our algorithm allows different regularizations to be included when computing the GFT analysis components, so that the resulting components can be tuned for a specific task.
Safavi, Seyed Hamid +3 more
openaire +2 more sources