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
Transient Analysis of the Block Least Mean Squares Algorithm
Thiago R. P. Gonzaga,Valmir Dos Santos Nogueira Junior,Ana L. F. de Barros,Felipe da Rocha Henriques,Diego B. Haddad +4 more
TL;DR: A stochastic model is advanced that is able to predict the learning capabilities of time-domain block extensions of adaptive filtering algorithms, and demonstrates that their behaviour is not governed by trivial generalizations of the rules presented by standard implementations.
Journal ArticleDOI
Improved sparsity‐aware NSAF‐SF adaptive algorithm
TL;DR: A deterministic local optimisation approach with affine constraints whose result leads to an enhanced NSAF-SF updating mechanism and a performance improvement for both transient and steady-state regions.
Evaluation of Techniques for Blind Sources Separation in the Identification of Musical Instruments
TL;DR: A comparative analysis of some methods of blind source separation and their respective capabilities of serving as a tool accessory to a system of automatic recognition of musical instruments from polyphonic signals is made.
Journal ArticleDOI
Diesel Engine Fault Prediction Using Artificial Intelligence Regression Methods
Denys Pestana Viana,Dionisio Henrique Carvalho de Sá Só Martins,Amaro A. de Lima,Fabrício Lopes e Silva,Milena F. Pinto,Ricardo H. R. Gutiérrez,Ulisses A. Monteiro,L. A. Vaz,Thiago de M. Prego,Luís Tarrataca,Diego B. Haddad +10 more
TL;DR: In this article , the absolute value of the failure severity using random forest (RF) and multilayer perceptron (MLP) neural networks was evaluated for diesel engine prognosis.
Journal ArticleDOI
ℓ 2 -norm feature least mean square algorithm
TL;DR: In this paper, a new least mean square (LMS) based adaptive filter is proposed to exploit the hidden sparsity of the system that the adaptive filter intends to estimate, which minimizes the l 2 -norm of a linear transformation of the coefficient vector using the minimum distortion principle.