49 citations · 215 across the 27 of their papers we have counts for
28 papers
A Study of the Framework and Real-World Applications of Language Embedding for 3D Scene Understanding
Mahmoud Chick Zaouali, Todd Charter, Yehor Karpichev +2
Gaussian Splatting has rapidly emerged as a transformative technique for real-time 3D scene representation, offering a highly efficient and expressive alternative to Neural Radianc…
Object Recognition Datasets and Challenges: A Review
Aria Salari, Abtin Djavadifar, Xiangrui Liu +1
Object recognition is among the fundamental tasks in the computer vision applications, paving the path for all other image understanding operations. In every stage of progress in o…
From Flight to Insight: Semantic 3D Reconstruction for Aerial Inspection via Gaussian Splatting and Language-Guided Segmentation
Mahmoud Chick Zaouali, Todd Charter, Homayoun Najjaran
High-fidelity 3D reconstruction is critical for aerial inspection tasks such as infrastructure monitoring, structural assessment, and environmental surveying. While traditional pho…
A Review of AI and Machine Learning Contribution in Predictive Business Process Management (Process Enhancement and Process Improvement Approaches)
Mostafa Abbasi, Rahnuma Islam Nishat, Corey Bond +4
Purpose- The significance of business processes has fostered a close collaboration between academia and industry. Moreover, the business landscape has witnessed continuous transfor…
Ego-Motion Aware Target Prediction Module for Robust Multi-Object Tracking
Navid Mahdian, Mohammad Jani, Amir M. Soufi Enayati +1
Multi-object tracking (MOT) is a prominent task in computer vision with application in autonomous driving, responsible for the simultaneous tracking of multiple object trajectories…
Condition Monitoring with Incomplete Data: An Integrated Variational Autoencoder and Distance Metric Framework
Maryam Ahang, Mostafa Abbasi, Todd Charter +1
Condition monitoring of industrial systems is crucial for ensuring safety and maintenance planning, yet notable challenges arise in real-world settings due to the limited or non-ex…
Extended Reality for Enhanced Human-Robot Collaboration: a Human-in-the-Loop Approach
Yehor Karpichev, Todd Charter, Jayden Hong +4
The rise of automation has provided an opportunity to achieve higher efficiency in manufacturing processes, yet it often compromises the flexibility required to promptly respond to…
A mathematical model for simultaneous personnel shift planning and unrelated parallel machine scheduling
Maziyar Khadivi, Mostafa Abbasi, Todd Charter +1
This paper addresses a production scheduling problem derived from an industrial use case, focusing on unrelated parallel machine scheduling with the personnel availability constrai…
Intelligent Condition Monitoring of Industrial Plants: An Overview of Methodologies and Uncertainty Management Strategies
Maryam Ahang, Todd Charter, Mostafa Abbasi +3
Condition monitoring is essential for ensuring the safety, reliability, and efficiency of modern industrial systems. With the increasing complexity of industrial processes, artific…
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,…