Adaptive filtering and machine learning: applications in diffusion networks, soft ...
Combinations of adaptive filters: low-cost solutions and adaptive networks
Combination of adaptive algorithms and distributed adaptive filtering applied to a...
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| Author(s): |
Total Authors: 3
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| Affiliation: | [1] Univ Sao Paulo, Elect Syst Engn Dept, Escola Politecn, BR-05508010 Sao Paulo, SP - Brazil
Total Affiliations: 1
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| Document type: | Journal article |
| Source: | IEEE TRANSACTIONS ON SIGNAL PROCESSING; v. 69, p. 58-72, 2021. |
| Web of Science Citations: | 0 |
| Abstract | |
Distributed signal processing has attracted widespread attention in the scientific community due to its several advantages over centralized approaches. Recently, graph signal processing has risen to prominence, and adaptive distributed solutions have also been proposed in the area. Both in the classical framework and in graph signal processing, sampling and censoring techniques have been topics of intense research, since the cost associated with measuring, processing and/or transmitting data throughout the entire network may be prohibitive in certain applications. In this paper, we propose a low-cost adaptive mechanism for sampling and censoring over diffusion networks that uses information from more nodes when the error in the network is high and from less nodes otherwise. It presents fast convergence during transient and a significant reduction in computational cost and energy consumption in steady state. As a censoring technique, we show that it is able to noticeably outperform other solutions. We also present a theoretical analysis to give insights about its operation, and to help the choice of suitable values for its parameters. (AU) | |
| FAPESP's process: | 17/20378-9 - Adaptive filters and machine learning: applications on image, communications, and speech |
| Grantee: | Magno Teófilo Madeira da Silva |
| Support Opportunities: | Regular Research Grants |