collaborators

15 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.CV2026

Enhancing Few-Shot Classification of Benchmark and Disaster Imagery with ABHFA-Net

Gao Yu Lee, Tanmoy Dam, Md Meftahul Ferdaus +2

The rising incidence of natural and human-induced disasters necessitates robust visual recognition systems capable of operating under limited labeled data conditions. However, disa…

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

VeloxNet: Efficient Spatial Gating for Lightweight Embedded Image Classification

Md Meftahul Ferdaus, Elias Ioup, Mahdi Abdelguerfi +4

Deploying deep learning models on embedded devices for tasks such as aerial disaster monitoring and infrastructure inspection requires architectures that balance accuracy with stri…

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…