Results 261 to 270 of about 4,335,273 (293)

Discrete signal processing on graphs: Graph filters

2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013
We propose a novel discrete signal processing framework for structured datasets that arise from social, economic, biological, and physical networks. Our framework extends traditional discrete signal processing theory to datasets with complex structure that can be represented by graphs, so that data elements are indexed by graph nodes and relations ...
Aliaksei Sandryhaila, José M. F. Moura
openaire   +1 more source

Graph Error Effect in Graph Signal Processing

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018
The first step in any graph signal processing (GSP) task is to learn the graph signal representation, i.e., to capture the dependence structure of the data into an adjacency matrix. Indeed, the adjacency matrix is typically not known a priori and has to be learned. However, it is learned with errors.
Vorobyov, Sergiy A., Ollila, Esa, Miettinen, Jari   +3 more
openaire   +3 more sources

Graph Signal Processing

2018
exaly   +2 more sources

Fundamentals of multirate graph signal processing

2015 49th Asilomar Conference on Signals, Systems and Computers, 2015
In 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.
openaire   +3 more sources

Turning Digital Signal Processing into Graph Signal Processing: Overview and Applications

2020 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan), 2020
Digital signal processing on graphs (DSP G ) exploits signals whose domain, represented by a graph, is irregular. In this article, we present an overview and some applications of a graph. We also present some approaches used to process data in graphs, highlighting the application of Fourier transform, frequency analysis, filtering, sampling and data ...
Pierre V. Dantas, Celso B. Carvalho, Waldir S. S. Júnior   +2 more
openaire   +3 more sources

Circulant structures and graph signal processing

2013 IEEE International Conference on Image Processing, 2013
Linear shift-invariant processing of graph signals rests on circulant graphs and filters. The spatial features of circulant structures also permit shift-varying operations such as sampling. Their spectral features-as described by their Graph Fourier Transform profiles-enable novel multiscale signal processing systems and methods. To extend the reach of
Venkatesan N. Ekambaram, Giulia Cecilia Fanti, Babak Ayazifar, Kannan Ramchandran   +3 more
openaire   +2 more sources

Signal processing on graphs

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.
openaire   +1 more source

Signal processing on graphs: Performance of graph structure estimation

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016
A class of models for describing sets of time series generated by interacting agents using directed, weighted graphs is introduced. A computationally tractable algorithm for estimating the graph adjacency matrix of this model from observed time series data is presented.
Jonathan Mei, José M. F. Moura
openaire   +2 more sources

Discrete signal processing on graphs: Graph fourier transform

2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013
We 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, José M. F. Moura
openaire   +1 more source

Simulation of signal flow graphs for signal processing systems

ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005
There are many interacting issues which must be investigated fully before a multiprocessor system for signal processing applications is integrated. Among the most important of these issues are the system architecture, system configuration, and the implementation technology.
T. A. Lanfear, M. G. X. Fernando, Les J. Wu   +2 more
openaire   +2 more sources