no code implementations • 26 Mar 2024 • Daniel G. Tiglea, Renato Candido, Magno T. M. Silva
In particular, we consider in our theoretical analysis the diffusion least-mean-squares algorithm in a scenario in which the nodes are randomly sampled.
1 code implementation • 12 Jan 2024 • Vinícius S. Borges, Magno T. M. Silva, Marcio Eisencraft
When chaotic signals are used in practical communication systems, it is essential to control and eventually limit the spectral bandwidth occupied by these signals.
no code implementations • 22 Dec 2021 • Jerónimo Arenas-García, Luis A. Azpicueta-Ruiz, Magno T. M. Silva, Vitor H. Nascimento, Ali H. Sayed
Adaptive filters are at the core of many signal processing applications, ranging from acoustic noise supression to echo cancelation, array beamforming, channel equalization, to more recent sensor network applications in surveillance, target localization, and tracking.
no code implementations • 5 Aug 2020 • Daniel G. Tiglea, Renato Candido, Magno T. M. Silva
Recently, graph signal processing has risen to prominence, and adaptive distributed solutions have also been proposed in the area.
no code implementations • 13 Jul 2020 • Daniel Gilio Tiglea, Renato Candido, Magno T. M. Silva
In this paper, we propose a sampling mechanism for adaptive diffusion networks that adaptively changes the amount of sampled nodes based on mean-squared error in the neighborhood of each node.
no code implementations • 8 Apr 2015 • Jesus Fernandez-Bes, Jerónimo Arenas-García, Magno T. M. Silva, Luis A. Azpicueta-Ruiz
In this paper, we deal with distributed estimation problems in diffusion networks with heterogeneous nodes, i. e., nodes that either implement different adaptive rules or differ in some other aspect such as the filter structure or length, or step size.
