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
Object Classification in Thermal Images using Convolutional Neural Networks for Search and Rescue Missions with Unmanned Aerial Systems
Christopher Dahlin Rodin,Luciano Netto de Lima,Fabio Augusto de Alcantara Andrade,Diego B. Haddad,Tor Arne Johansen,Rune Storvold +5 more
TL;DR: This paper explores the use of UAS in maritime Search And Rescue (SAR) missions by using experimental data to detect and classify objects at the sea surface by using a Convolutional Neural Network.
Journal ArticleDOI
Autonomous Unmanned Aerial Vehicles in Search and Rescue Missions Using Real-Time Cooperative Model Predictive Control.
Fabio Augusto de Alcantara Andrade,Fabio Augusto de Alcantara Andrade,Anthony Reinier Hovenburg,Luciano Netto de Lima,Christopher Dahlin Rodin,Tor Arne Johansen,Rune Storvold,Carlos Alberto Moraes Correia,Diego B. Haddad +8 more
TL;DR: A real-time path-planning solution using multiple cooperative UAVs for SAR missions is proposed, using the technique of Particle Swarm Optimization to solve a Model Predictive Control (MPC) problem that aims to perform search in a given area of interest, following the directive of international standards of SAR.
Journal ArticleDOI
Robust Acoustic Self-Localization of Mobile Devices
Diego B. Haddad,Wallace A. Martins,Mauricio do V. M. da Costa,Luiz W. P. Biscainho,Leonardo O. Nunes,Bowon Lee +5 more
TL;DR: This work proposes a set of algorithms that enable a mobile device to passively determine its position relative to a known reference with centimeter precision, based exclusively on the capture of acoustic signals emitted by controlled sources around it.
Journal ArticleDOI
Transient analysis of l0-LMS and l0-NLMS algorithms
TL;DR: A stochastic model for both l0-LMS and l 0-NLMS algorithms is proposed, and an accurate transient analysis of these algorithms without requiring the input signal to be white is carried out.
Journal ArticleDOI
Transient and steady-state MSE analysis of the IMPNLMS algorithm
TL;DR: An accurate transient analysis of the improved μ-law proportionate normalized least mean squares (IMPNLMS) algorithm is presented and an estimate of its steady-state MSE is derived, without requiring the assumption of white Gaussian input signals.