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
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Proceedings ArticleDOI
Data Warehouse Educacional: Uma visão sobre a Evasão no Ensino Superior.
Gustavo Alexandre Sousa Santos,Alex Laier Bordignon,Diego B. Haddad,Diego N. Brandão,Luís Tarrataca,Kele Teixeira Belloze +5 more
TL;DR: The presented Data Warehouse allows integrated views that assist in analysis such as distribution of students’ performance coefficient; 2) identification of student profiles and 3) insight into student achievement by locality to assist academic management in identifying patterns that lead to dropout.
Journal ArticleDOI
Sparsity-aware reuse of coefficients normalised least mean squares
TL;DR: A new derivation approach that incorporates both coefficient reusing and norm-constrained adaptation (that penalises non-sparse solutions) is advanced to obtain the desired robustness and high convergence rate in sparse scenarios with low computation burden and reduced number of adjustable parameters.
Journal ArticleDOI
Distributed Adaptive Filtering on Wireless Sensor Networks with Shared Medium Competition
Rafael M. Carmo,Luís Tarrataca,Jefferson Colares,Felipe da Rocha Henriques,Diego B. Haddad,Raphael M. Guedes +5 more
TL;DR: This paper proposes two new diffuse adaptive algorithms, aware of the characteristics of the Carrier Sense Multiple Access with Collision Avoidance protocol, namely: (i) Variant Reuse of Coefficients Least Mean Squares (VRCLMS) algorithm; and the (ii) Reuse-LMS algorithm in the Adapt-Then-Combine (ATC) modality.
Journal ArticleDOI
Exact expectation analysis of the LMS adaptive identification of non-linear systems
TL;DR: This Letter advances an analytic model of the least-mean-square (LMS) learning capabilities when the ideal system is not linear, with the additive noise including non-linear functions of the input samples.
Journal ArticleDOI
On the Skewness of the LMS Adaptive Weights
TL;DR: This brief shows that the skewness of the adaptive weights distribution may present a large deviation from the common Gaussian assumption, especially in the first phase of the learning, and it is demonstrated that the skew may grow without limit even when adaptive weights present convergence in both average and mean square behaviors.