Murilo T. Silva

About Me

I am a Project Engineer in the Remote Sensing Systems team at C-CORE, where I focus on the intersection of applied electromagnetics, radar systems, and data-driven intelligence. My work involves the end-to-end development of advanced sensing technologies, ranging from high-fidelity EM modeling to the implementation of machine learning architectures for complex signal processing.

I hold a Ph.D. and an M.Eng. in Electrical Engineering from Memorial University of Newfoundland, and a B.Eng. in Industrial Electrical Engineering from the Instituto Federal de Educação, Ciência e Tecnologia da Bahia (IFBA). My doctoral research addressed the limitations of radar cross-section (RCS) modeling by developing a novel approach for calculating electromagnetic scattering over surfaces with large-scale ocean waves. My master’s research proposed a nonlinear extraction method for directional ocean wave spectra from bistatic HF radar data, utilizing Tikhonov regularization in Hilbert spaces to solve complex inverse problems in maritime sensing.

Beyond my research, I am dedicated to professional service and the advancement of engineering excellence, having served as the Communications Officer for the IEEE Newfoundland and Labrador Section from 2019 to 2025. My current research interests include autonomous sensing systems, cognitive radar architectures, and the application of reinforcement learning to electromagnetic spectrum management.

selected publications

IEEE

High-Frequency Radar Cross Section of the Ocean Surface With Arbitrary Roughness Scales: Higher Order Corrections and General Form

Murilo Teixeira Silva, Weimin Huang, and Eric W. Gill

IEEE Transactions on Antennas and Propagation, Oct 2021

Abstract

The present work shows the derivation of general forms for the expressions of the first- and second-order corrections to the radar cross section (RCS) of the ocean surface at electromagnetically high sea states. An analysis of the morphology of the correction terms and their impact on the total RCS of the ocean surface is presented. It is shown that most of the visible impact on the total RCS of the ocean surface is due to first-order hydrodynamic terms and is concentrated between the Bragg peaks. Also, second-order hydrodynamic correction terms can increase the integral over the second-order region outside the dominant Bragg peak by up to 30% at extreme ocean conditions. Although comparison with field data was not conclusive, the presented results can improve the representation of electromagnetically large waves in the total RCS of the ocean surface without dismissing the analysis methods for height-restricted RCSs presented in the literature.
IEEE

High-Frequency Radar Cross Section of the Ocean Surface With Arbitrary Roughness Scales: A Generalized Functions Approach

Murilo Teixeira Silva, Weimin Huang, and Eric W. Gill

IEEE Transactions on Antennas and Propagation, Mar 2021

Abstract

A correction term for electromagnetic scattering from the ocean surface with large roughness scales in the HF band is proposed. A generalized functions’ method has been employed to obtain the operator equation of the electric field over the ocean surface. Using a dipole antenna with a pulsed sinusoidal signal at the transmitter and employing Fourier series and perturbation theory expansions of the ocean surface, three expressions were derived for the electric field up to the second order, with one of them being a correction term for the first order for large roughness scales. Through a cross section analysis, it is observed that the additional term would represent waves with both large heights and wavelengths but small slopes. This allows for the analysis of extreme wave events using HF radar, which was limited by previously derived small heights and small slope methods.
IEEE

Nonlinear Extraction of Directional Ocean Wave Spectrum From Synthetic Bistatic High-Frequency Surface Wave Radar Data

Murilo Teixeira Silva, Reza Shahidi, Eric W. Gill, and 1 more author

IEEE Journal of Oceanic Engineering, Jul 2020

Abstract

This paper proposes the use of a nonlinear inversion technique for the extraction of the directional ocean wave spectrum from bistatic high-frequency surface wave radar (HFSWR) Doppler data. The extraction method is combined with nonlinear regression expressions, solely based on the Doppler data, to retrieve wind speed and direction. Once the initialization parameters have been defined, a blind iterative algorithm based on Tikhonov regularization in Hilbert Scales is used to extract the nondirectional spectrum. The extracted spectrum is then used to determine the directional factor, which is assumed to be described by a cosine-power model. The proposed method yields fairly good results with synthetic noise-contaminated HFSWR data with a priori regularization parameters.
AMS

An Improved Estimation and Gap-Filling Technique for Sea Surface Wind Speeds Using NARX Neural Networks

Murilo Teixeira Silva, Eric W. Gill, and Weimin Huang

Journal of Atmospheric and Oceanic Technology, Jul 2018

Abstract

Abstract This work presents the use of a nonlinear autoregressive neural network to obtain an improved estimate of sea surface winds, taking Placentia Bay, Newfoundland and Labrador, Canada, as a study case. The network inputs and delays were chosen through cross correlation with the target variable. The proposed method was compared with five other wind speed estimation techniques, outperforming them in correlation, precision, accuracy, and bias levels. As an extension, the temporal gap filling of missing wind speed data during a storm has been considered. Data containing a measurement gap from a 40-yr windstorm that hit the same location has been used. The proposed method filled the gaps in the dataset with a high degree of correlation with measurements obtained by surrounding stations. The method presented in this work showed promising results that could be extended to estimate wind speeds in other locations and filling gaps in other datasets.