49 citations · 215 across the 27 of their papers we have counts for
17 papers · 1 filter
Empirical Validation of Conformal Prediction for Trustworthy Skin Lesions Classification
Jamil Fayyad, Shadi Alijani, Homayoun Najjaran
Background and objective: Uncertainty quantification is a pivotal field that contributes to realizing reliable and robust systems. It becomes instrumental in fortifying safe decisi…
Mixing Solutions in Bitcoin and Ethereum Ecosystems: A Review and Tutorial
Alireza Arbabi, Ardeshir Shojaeinasab, Behnam Bahrak +1
This manuscript presents an exhaustive review of blockchain-based mixing services, aiming to fill the existing gap between academic innovations and real-world implementations. Star…
Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions
Maziyar Khadivi, Todd Charter, Marjan Yaghoubi +4
Machine scheduling aims to optimize job assignments to machines while adhering to manufacturing rules and job specifications. This optimization leads to reduced operational costs,…
Gap and Overlap Detection in Automated Fiber Placement
Assef Ghamisi, Homayoun Najjaran
The identification and correction of manufacturing defects, particularly gaps and overlaps, are crucial for ensuring high-quality composite parts produced through Automated Fiber P…
A Transformer-based Framework For Multi-variate Time Series: A Remaining Useful Life Prediction Use Case
Oluwaseyi Ogunfowora, Homayoun Najjaran
In recent times, Large Language Models (LLMs) have captured a global spotlight and revolutionized the field of Natural Language Processing. One of the factors attributed to the eff…
Anomaly Detection in Automated Fibre Placement: Learning with Data Limitations
Assef Ghamisi, Todd Charter, Li Ji +3
Conventional defect detection systems in Automated Fibre Placement (AFP) typically rely on end-to-end supervised learning, necessitating a substantial number of labelled defective…
Learning Team-Based Navigation: A Review of Deep Reinforcement Learning Techniques for Multi-Agent Pathfinding
Jaehoon Chung, Jamil Fayyad, Younes Al Younes +1
Multi-agent pathfinding (MAPF) is a critical field in many large-scale robotic applications, often being the fundamental step in multi-agent systems. The increasing complexity of M…
Model Compression Methods for YOLOv5: A Review
Mohammad Jani, Jamil Fayyad, Younes Al-Younes +1
Over the past few years, extensive research has been devoted to enhancing YOLO object detectors. Since its introduction, eight major versions of YOLO have been introduced with the…
Systematic Adaptation of Communication-focused Machine Learning Models from Real to Virtual Environments for Human-Robot Collaboration
Debasmita Mukherjee, Ritwik Singhai, Homayoun Najjaran
Virtual reality has proved to be useful in applications in several fields ranging from gaming, medicine, and training to development of interfaces that enable human-robot collabora…
Reinforcement and Deep Reinforcement Learning-based Solutions for Machine Maintenance Planning, Scheduling Policies, and Optimization
Oluwaseyi Ogunfowora, Homayoun Najjaran
Systems and machines undergo various failure modes that result in machine health degradation, so maintenance actions are required to restore them back to a state where they can per…
Using Implicit Behavior Cloning and Dynamic Movement Primitive to Facilitate Reinforcement Learning for Robot Motion Planning
Zengjie Zhang, Jayden Hong, Amir Soufi Enayati +1
Reinforcement learning (RL) for motion planning of multi-degree-of-freedom robots still suffers from low efficiency in terms of slow training speed and poor generalizability. In th…
Bag of Views: An Appearance-based Approach to Next-Best-View Planning for 3D Reconstruction
Sara Hatami Gazani, Matthew Tucsok, Iraj Mantegh +1
UAV-based intelligent data acquisition for 3D reconstruction and monitoring of infrastructure has experienced an increasing surge of interest due to recent advancements in image pr…
Object Semantics Give Us the Depth We Need: Multi-task Approach to Aerial Depth Completion
Sara Hatami Gazani, Fardad Dadboud, Miodrag Bolic +2
Depth completion and object detection are two crucial tasks often used for aerial 3D mapping, path planning, and collision avoidance of Uncrewed Aerial Vehicles (UAVs). Common solu…
Human-Robot Skill Transfer with Enhanced Compliance via Dynamic Movement Primitives
Jayden Hong, Zengjie Zhang, Amir M. Soufi Enayati +1
Finding an efficient way to adapt robot trajectory is a priority to improve overall performance of robots. One approach for trajectory planning is through transferring human-like s…
Facilitating Sim-to-real by Intrinsic Stochasticity of Real-Time Simulation in Reinforcement Learning for Robot Manipulation
Ram Dershan, Amir M. Soufi Enayati, Zengjie Zhang +2
Simulation is essential to reinforcement learning (RL) before implementation in the real world, especially for safety-critical applications like robot manipulation. Conventionally,…
Sample-Efficient Reinforcement Learning with Symmetry-Guided Demonstrations for Robotic Manipulation
Amir M. Soufi Enayati, Zengjie Zhang, Kashish Gupta +1
Reinforcement learning (RL) suffers from low sample efficiency, particularly in high-dimensional continuous state-action spaces of complex robotic manipulation tasks. RL performanc…
End-to-end deep learning-based framework for path planning and collision checking: bin picking application
Mehran Ghafarian Tamizi, Homayoun Honari, Aleksey Nozdryn-Plotnicki +1
Real-time and efficient path planning is critical for all robotic systems. In particular, it is of greater importance for industrial robots since the overall planning and execution…