collaborators

10 papers

cs.LG2026

Learned Response-Field Inertia Operator for HEC-RAS 2D Water-Surface Elevation Prediction

Edward Holmberg, Elias Ioup, Md Meftahul Ferdaus +2

This article presents a cross-dataset evaluation of learned native-cell surrogate models for solver-consistent water-surface elevation (WSE) prediction in HEC-RAS 2D. To avoid rast…

cs.LG2026

Bridging Spectral Operator Learning and U-Net Hierarchies: SpectraNet for Stable Autoregressive PDE Surrogates

Enrique Hernández Noguera, Md Meftahul Ferdaus, Elias Ioup +2

Neural operators for time-dependent PDEs face a structural tension: spectral architectures (FNO and descendants) inherit exponential rollout-error growth from their one-step Lipsch…

cs.CV2026

EfficientPENet: Real-Time Depth Completion from Sparse LiDAR via Lightweight Multi-Modal Fusion

Johny J. Lopez, Md Meftahul Ferdaus, Mahdi Abdelguerfi +4

Depth completion from sparse LiDAR measurements and corresponding RGB images is a prerequisite for accurate 3D perception in robotic systems. Existing methods achieve high accuracy…

cs.CV2026

DeltaSeg: Tiered Attention and Deep Delta Learning for Multi-Class Structural Defect Segmentation

Enrique Hernandez Noguera, Md Meftahul Ferdaus, Elias Ioup +1

Automated segmentation of structural defects from visual inspection imagery remains challenging due to the diversity of damage types, extreme class imbalance, and the need for prec…

cs.CV2026

Edge-Optimized Vision-Language Models for Underground Infrastructure Assessment

Johny J. Lopez, Md Meftahul Ferdaus, Mahdi Abdelguerfi

Autonomous inspection of underground infrastructure, such as sewer and culvert systems, is critical to public safety and urban sustainability. Although robotic platforms equipped w…

cs.CV2025

KARMA: Efficient Structural Defect Segmentation via Kolmogorov-Arnold Representation Learning

Md Meftahul Ferdaus, Mahdi Abdelguerfi, Elias Ioup +3

Semantic segmentation of structural defects in civil infrastructure remains challenging due to variable defect appearances, harsh imaging conditions, and significant class imbalanc…