13 citations · 21 across the 14 of their papers we have counts for
17 papers
DU-NO: A Parameter-Efficient Double U-Shaped Neural Operator for Phase-Resolving Wave Modeling
Enrique Hernandez Noguera, Md Meftahul Ferdaus, Nathan Cooper +2
Phase-resolving wave models such as FUNWAVE-TVD are the accuracy standard for nearshore dynamics, resolving the shoaling, refraction, and breaking of individual waves, but their co…
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…
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…
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…
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…
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…
Attention-Enhanced Prototypical Learning for Few-Shot Infrastructure Defect Segmentation
Christina Thrainer, Md Meftahul Ferdaus, Mahdi Abdelguerfi +4
Few-shot semantic segmentation is vital for deep learning-based infrastructure inspection applications, where labeled training examples are scarce and expensive. Although existing…
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…
Few-Shot Learning in Video and 3D Object Detection: A Survey
Md Meftahul Ferdaus, Kendall N. Niles, Joe Tom +2
Few-shot learning (FSL) enables object detection models to recognize novel classes given only a few annotated examples, thereby reducing expensive manual data labeling. This survey…
Accelerating HEC-RAS: A Recurrent Neural Operator for Rapid River Forecasting
Edward Holmberg, Pujan Pokhrel, Maximilian Zoch +9
Physics-based solvers like HEC-RAS provide high-fidelity river forecasts but are too computationally intensive for on-the-fly decision-making during flood events. The central chall…
FORTRESS: Function-composition Optimized Real-Time Resilient Structural Segmentation via Kolmogorov-Arnold Enhanced Spatial Attention Networks
Christina Thrainer, Md Meftahul Ferdaus, Mahdi Abdelguerfi +4
Automated structural defect segmentation in civil infrastructure faces a critical challenge: achieving high accuracy while maintaining computational efficiency for real-time deploy…
KANICE: Kolmogorov-Arnold Networks with Interactive Convolutional Elements
Md Meftahul Ferdaus, Mahdi Abdelguerfi, Elias Ioup +4
We introduce KANICE (Kolmogorov-Arnold Networks with Interactive Convolutional Elements), a novel neural architecture that combines Convolutional Neural Networks (CNNs) with Kolmog…
Imbalance-Aware Culvert-Sewer Defect Segmentation Using an Enhanced Feature Pyramid Network
Rasha Alshawi, Md Meftahul Ferdaus, Mahdi Abdelguerfi +3
Imbalanced datasets are a significant challenge in real-world scenarios. They lead to models that underperform on underrepresented classes, which is a critical issue in infrastruct…
SHARP-Net: A Refined Pyramid Network for Deficiency Segmentation in Culverts and Sewer Pipes
Rasha Alshawi, Md Meftahul Ferdaus, Md Tamjidul Hoque +4
This paper introduces Semantic Haar-Adaptive Refined Pyramid Network (SHARP-Net), a novel architecture for semantic segmentation. SHARP-Net integrates a bottom-up pathway featuring…
Towards Trustworthy AI: A Review of Ethical and Robust Large Language Models
Md Meftahul Ferdaus, Mahdi Abdelguerfi, Elias Ioup +3
The rapid progress in Large Language Models (LLMs) could transform many fields, but their fast development creates significant challenges for oversight, ethical creation, and build…
Dual Attention U-Net with Feature Infusion: Pushing the Boundaries of Multiclass Defect Segmentation
Rasha Alshawi, Md Tamjidul Hoque, Md Meftahul Ferdaus +7
The proposed architecture, Dual Attentive U-Net with Feature Infusion (DAU-FI Net), addresses challenges in semantic segmentation, particularly on multiclass imbalanced datasets wi…
Dual-Stream Attention Transformers for Sewer Defect Classification
Abdullah Al Redwan Newaz, Mahdi Abdeldguerfi, Kendall N. Niles +1
We propose a dual-stream multi-scale vision transformer (DS-MSHViT) architecture that processes RGB and optical flow inputs for efficient sewer defect classification. Unlike existi…