M.A. Rashid Sarkar
Papers
1
Total Citations
2
H-Index
1
About
M.A. Rashid Sarkar is a researcher whose work centers on the intersection of robotics, automation, and intelligent control systems. His primary research areas include gesture-based human-robot interaction, neural network applications in industrial automation, and low-cost robotic design. His most notable contribution, the 2017 paper "Design of a gesture controlled robotic gripper arm using neural networks," presents an innovative, cost-effective approach to industrial automation by integrating image processing and neural networks for gesture command control. This work demonstrates Sarkar's commitment to making advanced robotic systems more accessible and efficient for real-world manufacturing environments. While his citation count of 2 reflects a focused, early-stage impact, the practical significance of his design—combining visual feedback with neural network-based gesture recognition—positions him as a contributor to the growing field of intuitive human-machine interfaces. Sarkar's research holds particular relevance for students and engineers interested in bridging the gap between complex AI algorithms and tangible, deployable automation solutions.
Research Focus
Key Achievements
Top Papers
- 1Design of a gesture controlled robotic gripper arm using neural networks2 citations · 2017