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Douglas Gomes

Publications and source records attributed to Douglas Gomes.

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Channel Modeling of Single Wire Earth Return Networks for Narrowband Power Line Communication and Sensing: A Field-Validated High-Frequency Digital Twin

Upgrading Single-Wire Earth Return (SWER) networks for smart grid capabilities requires a reliable communications technology. Narrowband Power Line Communication (NB-PLC) is a potential low cost solution. Real-world deployment is challenging due to the severe, frequency-dependent attenuation caused by complex earth-return paths and heterogeneous network infrastructure. To accurately characterize the communication channel, this paper develops a high-frequency (up to 300 kHz) digital twin of an operational SWER network. The digital twin integrates a segment-by-segment transmission line model with Vector Network Analyzer (VNA) measurements of physical grid hardware, replacing standard uniform assumptions with empirical component responses. Parametric sensitivity analysis demonstrates that distributed environmental factors, such as soil moisture and line sag, act as uniform magnitude offsets. Conversely, the conductor's magnetic permeability and local injection-transformer impedances dictate the channel's resonant spectral shape. Furthermore, cross-brand analysis proves that utilizing generic transformer models introduces significant prediction errors, confirming that accurate simulation requires manufacturer- and tap-specific data. Validated against in-situ field measurements from three transmitters, this digital twin replicates the path loss and dominant frequency-selective fading of the physical grid. Yielding a Root Mean Square Error (RMSE) between 4.65 dB and 9.73 dB across the three transmit paths, the model provides a practically reliable framework for deploying NB-PLC across rural SWER infrastructure.

eess.SP

Computer Vision For COVID-19 Control: A Survey

The COVID-19 pandemic has triggered an urgent need to contribute to the fight against an immense threat to the human population. Computer Vision, as a subfield of Artificial Intelligence, has enjoyed recent success in solving various complex problems in health care and has the potential to contribute to the fight of controlling COVID-19. In response to this call, computer vision researchers are putting their knowledge base at work to devise effective ways to counter COVID-19 challenge and serve the global community. New contributions are being shared with every passing day. It motivated us to review the recent work, collect information about available research resources and an indication of future research directions. We want to make it available to computer vision researchers to save precious time. This survey paper is intended to provide a preliminary review of the available literature on the computer vision efforts against COVID-19 pandemic.

eess.IV