Papers
10
Total Citations
113
H-Index
7
About
Djamel Sadok is a researcher whose work sits at the intersection of robotics, artificial intelligence, and industrial IoT systems. His research focuses primarily on human-robot collaboration, deep learning applications for robotics, and smart factory technologies — areas that are increasingly critical as Industry 4.0 reshapes modern manufacturing and automation. Sadok's most impactful contributions center on making intelligent robotic systems safer and more efficient. His work on collision detection and safety modeling in human-robot collaboration environments, drawing on both deep and machine learning techniques, has garnered significant attention, collectively accumulating dozens of citations across multiple publications. His 2022 paper on FCN-Pose — a pruned and quantized neural network for robot pose estimation on constrained IoT devices — demonstrates a particular strength in bridging the gap between computationally intensive AI models and real-world hardware limitations. Beyond safety systems, Sadok has contributed meaningfully to industrial IoT protocol evaluation and middleware design, helping lay conceptual groundwork for smart factory communication infrastructures. His involvement in applied robotics projects, including autonomous radio base station maintenance systems, showcases a commitment to translating research into practical, deployable solutions. His growing citation record reflects rising recognition within the robotics and industrial AI communities.
Research Focus
Key Achievements
Top Papers
- 1
- 2A middleware for industry17 citations · 2015
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- 6An IoT Protocol Evaluation in a Smart Factory Environment11 citations · 2018
- 7HOSA: An End-to-End Safety System for Human-Robot Interaction10 citations · 2022
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- 9Gripper Design for Radio Base Station Autonomous Maintenance System5 citations · 2021
- 10