D
Diego B. Haddad
Researcher at Centro Federal de Educação Tecnológica de Minas Gerais
Publications - 90
Citations - 516
Diego B. Haddad is an academic researcher from Centro Federal de Educação Tecnológica de Minas Gerais. The author has contributed to research in topics: Adaptive filter & Least mean squares filter. The author has an hindex of 11, co-authored 80 publications receiving 337 citations. Previous affiliations of Diego B. Haddad include Centro Federal de Educação Tecnológica Celso Suckow da Fonseca & Universidade Tecnológica Federal do Paraná, Medianeira.
Papers
More filters
Journal ArticleDOI
Exact expectation analysis of the deficient-length LMS algorithm
TL;DR: In this article, a sufficient-order adaptive filter analysis is presented, in which the lengths of the unknown plant and the adaptive filter are equal. But the analysis is restricted neither to white nor to Gaussian input signals and is able to provide a proper step size upper bound.
Proceedings ArticleDOI
Sparsity-Aware Distributed Adaptive Filtering Algorithms for Nonlinear System Identification
TL;DR: This work considers a scenario in which several dispersed nodes intend to identify a nonlinear Volterra system, represented by a series that has sparse kernels, with few non-zero coefficients, and proposes distributed and sparsity- aware adaptive filtering algorithms, that aim at identifying such nonlinear system.
Proceedings ArticleDOI
A Variable Step-Size NLMS Algorithm with Adaptive Coefficient Vector Reusing
TL;DR: A new adaptive filtering algorithm is presented that combines both strategies to achieve fast convergence speed and low steady-state misadjustment simultaneously.
Proceedings ArticleDOI
EvolveDTree: Analyzing Student Dropout in Universities.
G. A. S. Santos,Kele Teixeira Belloze,Luís Tarrataca,Diego B. Haddad,Alex Laier Bordignon,Diego N. Brandão +5 more
TL;DR: This work presents a methodology that aims to predict evasion by using machine learning and was able to classify student abandonment with an average f-score and accuracy results above 95%.
Journal ArticleDOI
An Exact Expectation Model for the LMS Tracking Abilities
Thiago T. P. Silva,Pedro Lara,Filipe Igreja,Fernanda D. V. R. Oliveira,Luís Tarrataca,Diego B. Haddad +5 more
TL;DR: This work presents a comprehensive model of the performance of the least mean square algorithm, operating under Markovian time-varying channels, and is able to provide a deterministic theoretical step-size sequence that optimizes algorithmic performance, as well as an accurate step size upper bound that guarantees algorithm stability.