Shadi Atalla
Papers
2
Total Citations
6
H-Index
2
About
Shadi Atalla is a researcher whose work bridges precision manufacturing and intelligent control systems, with a focus on optimizing industrial and robotic processes. In his notable 2018 study on Computer Numerical Control-PCB drilling machines, Atalla tackled a critical inefficiency in printed circuit board fabrication: suboptimal drill path planning. By proposing an efficient routing algorithm, his work directly addressed time and energy waste in manufacturing, offering a practical solution that has garnered 4 citations for its applied impact. More recently, Atalla has ventured into the complex dynamics of quadrotor systems. His 2023 paper employs NARX (Nonlinear AutoRegressive with eXogenous inputs) neural networks to model the inherently nonlinear, underactuated behavior of quadrotors—a challenge central to modern drone control. This work, with 2 citations, demonstrates his ability to integrate advanced machine learning with real-world engineering problems. Atalla’s contributions reflect a dual commitment: enhancing automation efficiency in traditional manufacturing and advancing the modeling techniques needed for next-generation autonomous aerial vehicles. His research stands as a valuable resource for students and engineers seeking to optimize both industrial machinery and robotic flight systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2