Muhammad Ariff Baharudin
University of Technology Malaysia, Shibaura Institute of Technology
Papers
2
Total Citations
8
H-Index
2
About
Muhammad Ariff Baharudin is a researcher at the forefront of intelligent systems, with key contributions spanning indoor positioning, robotics, and artificial intelligence. His work addresses critical challenges in enabling machines to perceive and interact with complex, dynamic environments. Notably, his 2024 paper on "Accurate Multiclass NLOS Channels Identification in UWB Indoor Positioning System-Based Deep Neural Network" tackles a fundamental problem in ultra-wideband localization—distinguishing line-of-sight from non-line-of-sight propagation for precise distance measurement. This work, already garnering 4 citations, demonstrates his ability to apply deep learning to real-world sensing challenges. Earlier, Baharudin explored the intersection of robotics and commonsense reasoning in his 2013 paper on "Commonsense knowledge extraction for Tidy-up robotic service in domestic environments," which proposed methods for automatically building knowledge bases that allow robots to understand human commands in daily life scenarios. This foundational work, also with 4 citations, highlights his long-standing interest in bridging human-robot communication gaps. Through these contributions, Baharudin is helping to create more accurate, intelligent, and context-aware systems for indoor navigation and domestic robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2