Research

In this paper, published with PhD candidate Daniel G. Tiglea, Prof. Luis Azpicueta-Ruiz, and Prof. Magno Silva, we propose an extension of the sampling algorithm presented in “A Low-Cost Algorithm for Adaptive Sampling and Censoring in Diffusion Networks” to the adaptive kernel diffusion networks based on random Fourier features.

The idea is to locally adapt the number of nodes censored according to the estimation error, reducing the computational complexity in nonlinear scenarios.

The PDF file can be obtained in this page.

Simpósio Brasileiro de Telecomunicações (SBrT) is the most important brazilian symposium on telecommunications and signal processing. In the 2022 edition, that happened from September 25th to 28th, we had 2 papers published:

We propose a normalized least mean squares algorithm with variable step size. Unlike other solutions, it has low computational cost, only three parameters that are simple to choose, and its steady-state performance can be easily predicted.

Recently, we proposed a sampling algorithm for diffusion networks that locally adapts the number of nodes sampled according to the estimation error. In this paper, we extend the results, proposing some improvements to the algorithm.

Simpósio Brasileiro de Telecomunicações (SBrT) is the most important brazilian symposium on telecommunications and signal processing. In the 2021 edition, that happened from September 26th to 29th, we had 4 papers published:

Conference paper presented on the 28th European Signal Processing Conference (EUSIPCO) in which we propose an adaptive sampling method for the diffusion networks.

This paper summarizes the results obtained by Daniel G. Tiglea during the period he was working to obtain the M.S. Degree.

Conference paper presented on the 27th European Signal Processing Conference (EUSIPCO) in which we propose an adaptive sampling method for the diffusion algorithm for adaptively learning from streaming graphs signals.

As an extension to the paper presented at SPS 2017 (in portuguese), we present a study on the use of kernel adaptive filters for channel equalization in a chaos-based communication system.