Advanced search
Start date
Betweenand

Adaptive filters and machine learning: applications on image, communications, and speech

Grant number: 17/20378-9
Support type:Regular Research Grants
Duration: February 01, 2018 - January 31, 2020
Field of knowledge:Engineering - Electrical Engineering - Telecommunications
Principal researcher:Magno Teófilo Madeira da Silva
Grantee:Magno Teófilo Madeira da Silva
Home Institution: Escola Politécnica (EP). Universidade de São Paulo (USP). São Paulo , SP, Brazil
Assoc. researchers: Renato Candido

Abstract

The equalization of communication channels is a subject very explored in the literature. However, when it is applied to chaos-based communication systems has many open questions. The aim of this project is to find ways to make the performance of these systems closer to that of conventional communication systems. A scheme that allows the switching between these two systems will be studied.We will address other applications that use adaptive filters such as blind image restoration by means of equalization algorithms, such as the convex combination between a blind algorithm and an algorithm in the decision directed mode.The combination will also be used to improve multikernel adaptive filtering. Finally, we intend to study machine learning techniques and apply them to nonlinear problems such as the voice activity detection. (AU)

Articles published in Agência FAPESP Newsletter about the research grant:
Articles published in other media outlets (0 total):
More itemsLess items
VEICULO: TITULO (DATA)
VEICULO: TITULO (DATA)

Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
TIGLEA, DANIEL G.; CANDIDO, RENATO; SILVA, MAGNO T. M. A Low-Cost Algorithm for Adaptive Sampling and Censoring in Diffusion Networks. IEEE TRANSACTIONS ON SIGNAL PROCESSING, v. 69, p. 58-72, 2021. Web of Science Citations: 0.
BUENO, ANDRE A.; SILVA, MAGNO T. M. Gram-Schmidt-Based Sparsification for Kernel Dictionary. IEEE SIGNAL PROCESSING LETTERS, v. 27, p. 1130-1134, 2020. Web of Science Citations: 0.
PAVAN, FLAVIO R. M.; SILVA, MAGNO T. M.; MIRANDA, MARIA D. Performance analysis of the multiuser Shalvi-Weinstein algorithm. Signal Processing, v. 163, p. 153-165, OCT 2019. Web of Science Citations: 0.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.