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X. L. Travassos
Researcher at Universidade Federal de Santa Catarina
Publications - 26
Citations - 255
X. L. Travassos is an academic researcher from Universidade Federal de Santa Catarina. The author has contributed to research in topics: Antenna (radio) & Ground-penetrating radar. The author has an hindex of 8, co-authored 26 publications receiving 183 citations. Previous affiliations of X. L. Travassos include SENAI & École centrale de Lyon.
Papers
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Artificial Neural Networks and Machine Learning techniques applied to Ground Penetrating Radar: A review
TL;DR: This work reviews the use of Artificial Neural Networks and Machine Learning for data interpretation of Ground Penetrating Radar surveys and shows that these computational techniques have progressed GPR forward from locating and testing to imaging and diagnosis approaches.
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Characterization of Inclusions in a Nonhomogeneous GPR Problem by Artificial Neural Networks
TL;DR: This paper aims at detecting and characterizing inclusions in concrete structures by inverting ground-penetrating radar (GPR) data using the principal component analysis (PCA).
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A Review of Ground Penetrating Radar Antenna Design and Optimization
TL;DR: In this article, the fundamental GPR system theory is discussed in order to discuss procedures for improving antenna design and optimization for ground penetrating radar (GPR) applications, and procedures for improved antenna design for GPR applications are discussed.
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Inverse Algorithms for the GPR Assessment of Concrete Structures
TL;DR: A two phase algorithm that combines matched-filter-based reverse-time (MFBRT) migration algorithm with the particle swarm optimization (PSO) is employed to characterize inclusions in concrete structures, including the number of inclusions, their geometries, and electromagnetic properties.
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Mixed assembly line rebalancing: a binary integer approach applied to real world problems in the automotive industry
TL;DR: In this article, a case study involving a problem of rebalancing of automotive assembly line in an environment of arbitrarily mixed models of products, also known as mix, is presented.