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R-Jay S. Relano

Publications -  18
Citations -  39

R-Jay S. Relano is an academic researcher. The author has contributed to research in topics: Computer science & Engineering. The author has an hindex of 2, co-authored 18 publications receiving 39 citations.

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Proceedings ArticleDOI

Optimization of Subsurface Imaging Antenna Capacitance through Geometry Modeling using Archimedes, Lichtenberg and Henry Gas Solubility Metaheuristics

TL;DR: In this article , the authors developed a new technique for plate-wire antenna capacitance optimization through equatorial dipole-dipole antenna geometry modeling using genetic programming (GP) integrated with metaheuristic methods, namely Archimedes optimization algorithm (AOA), Lichtenberg algorithm (LA), and Henry gas solubility optimization (HGSO).
Proceedings ArticleDOI

Systematic Analysis and Proposed AI-based Technique for Attenuating Inductive and Capacitive Parasitics in Low and Very Low Frequency Antennas

TL;DR: In this paper , the authors discuss the possible impacts of parasitic inductance and capacitance on the performance of low and very low-frequency antennas, and the collection of various optimization methods as well as the tools and software used in the mitigation of parasitic elements in an electronics system found in different research publications and journals.
Proceedings ArticleDOI

Optimizing Low Power Near L-band Capacitive Resistive Antenna Design for in Silico Plant Root Tomography Based on Genetic Big Bang-Big Crunch

TL;DR: In this article , a low power near L-band capacitive resistive antenna system for in silico maize root tomography optimized using three novel advanced evolutionary computing, namely, Genetic Particle Collision Algorithm (gPCA), Genetic Integrated Radiation Algorithm(gIRA), and Genetic Big Bang-Big Crunch Algorithm.
Journal ArticleDOI

SpeedX: Smart Speed Controller Model of Towed Subterranean Imaging System for Resistivity Data Distortion Reduction Using Computational Intelligence

TL;DR: In this article , the authors developed prediction models using different computational intelligence such as multigene symbolic regression genetic programming (MSRGP), regression-based decision tree (RTree), and feed forward neural network (FFNN) that will result in a smart speed controller system that can maintain the constant speed of the towed underground system.