André Coelho

Paulo Furtado Correia, André Coelho, Manuel Ricardo

Smart-port wireless networks suffer from dynamic radio blockage caused by container stacks and industrial structures, challenging efficient mobile integrated access and backhaul (MIAB) deployment. Existing approaches rely on obstacle maps, geometry information, or computationally intensive propagation models that limit adaptability. This paper presents DOCKING, a radio environment map (REM)-driven framework that converts sparse radio measurements into optimization-ready obstacle representations for MIAB deployment. The framework infers propagation-relevant obstacle abstractions from reconstructed REMs, eliminating the need for obstacle-geometry databases while relying only on known network parameters and sparse measurements. Reference signal received power (RSRP) and signal-to-interference-plus-noise ratio (SINR) observations are reconstructed using Ordinary Kriging (OKG), and dominant attenuation regions are approximated by compact cuboidal blockage models. The inferred geometry feeds a backhaul-aware optimization that determines MIAB placement, user equipment (UE) association, and backhaul selection. Under realistic smart-port conditions, REM reconstruction achieves prediction errors below 3 dB at the 90th percentile using only 15% spatial sampling, while obstacle characterization exceeds 85% true-positive coverage. Capacity gains reach 150% in sparse deployments, and a fast Genetic Algorithm converges within 5-15 s per network snapshot. A field campaign using real measurements validates the workflow, showing throughput trends consistent with optimization predictions. Results demonstrate that sparse radio measurements provide sufficient environmental awareness for practical obstacle-aware MIAB deployment in obstruction-prone industrial environments.

Diogo Lavado, Ricardo Santos, Andre Coelho et al.

Ensuring the safety and reliability of power grids is critical as global energy demands continue to rise. Traditional inspection methods, such as manual observations or helicopter surveys, are resource-intensive and lack scalability. This paper explores the use of 3D computer vision to automate power grid inspections, utilizing the TS40K dataset -- a high-density, annotated collection of 3D LiDAR point clouds. By concentrating on 3D semantic segmentation, our approach addresses challenges like class imbalance and noisy data to enhance the detection of critical grid components such as power lines and towers. The benchmark results indicate significant performance improvements, with IoU scores reaching 95.53% for the detection of power lines using transformer-based models. Our findings illustrate the potential for integrating ML into grid maintenance workflows, increasing efficiency and enabling proactive risk management strategies.