Qadri Hamarsheh
Papers
2
Total Citations
6
H-Index
2
About
Qadri Hamarsheh is a researcher whose work sits at the intersection of robotics, signal processing, and intelligent control systems. His key research areas include mobile robotics, industrial manipulator kinematics, and advanced signal transformation techniques for performance optimization. Hamarsheh’s contributions are marked by innovative approaches to solving complex engineering challenges. Notably, his 2014 paper on “PAPR reduction based on entropy wavelet transform for Sniffer Mobile Robot” introduced a novel method for reducing peak-to-average power ratio in robotic communication systems, a critical factor for autonomous navigation and sensor reliability. More recently, his 2023 work on “An Artificial Neural Network Approach in Solving Inverse Kinematics of a 6 DOF KUKA Industrial Robot” demonstrates a practical application of machine learning to overcome the computationally intensive inverse kinematics problem—a fundamental hurdle in robotic arm control. By providing two distinct neural network models, this study offers efficient, real-time solutions for industrial automation. Though his citation counts are still growing, Hamarsheh’s work represents a meaningful step toward integrating AI with traditional robotics, making him a researcher to watch in the evolving landscape of intelligent autonomous systems.
Research Focus
Key Achievements
Top Papers
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