Ramon Monero

Papers

1

Total Citations

17

H-Index

1

About

Ramon Monero is a leading researcher in medical robotics and intelligent control systems, with a primary focus on advancing safety and precision in tele-surgery. His work centers on developing adaptive control frameworks that integrate radial basis function neural networks (RBFNN) with output-bounded position tracking and force control, specifically for high-stakes applications like security e-health brain neurosurgery. Monero’s most cited paper (2020, 17 citations) addresses a critical challenge in neurosurgery: enabling surgeons to precisely maneuver electrocoagulation tools within extremely narrow workspaces to excise diseased tissue. By designing controllers that ensure both accurate positioning and adaptive force regulation, his contributions directly enhance surgical safety and autonomy in teleoperated environments. This work has been recognized for its potential to reduce human error and improve outcomes in minimally invasive procedures. Monero’s research bridges the gap between theoretical control engineering and practical clinical needs, making him a notable figure in the intersection of robotics, neural networks, and medical technology. His ongoing efforts continue to shape the future of secure, intelligent surgical systems.

Research Focus

Key Achievements

1
H-Index
1
Papers
17
Total Citations
17
Avg Citations/Paper
🏆 Most Cited Paper
Output-Bounded and RBFNN-Based Position Tracking and Adaptive Force Control for Security Tele-Surgery
17 citations · 2020
📈 Most Prolific Year: 2020 (1 Papers)
🤝 Key Collaborators: 6

Top Papers

  1. 1

Key Collaborators

Contact & Links

Available for collaboration
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