Professor Dr. Khaled Salah, Dean of the College, received Professor Dr. Ehab Khalaf, Professor of Electronics Engineering.

Professor Dr. Khaled Salah, Dean of the College, received Professor Dr. Ehab Khalaf, Professor of Electronics Engineering.

On Tuesday, July 7, 2026, Professor Khaled Salah, Dean of the Faculty of Engineering, received Professor Ehab Khalaf, Professor of Electronics and Communications Engineering and Vice Dean of the Faculty of Engineering at Aswan University for Community Service and Environmental Development. This meeting took place on the sidelines of Professor Khalaf's participation in the examination of the Master's thesis submitted by Engineer Ahmed Mohamed Omar Mohamed, an engineer at the Egyptian Natural Gas Company (GASCO).

Also present at the meeting were Professor Shehata El-Dabaa Abdel-Rahim, Vice Dean for Graduate Studies and Research; Professor Mohamed Safwat Abu-Rayeh, Vice Dean for Education and Student Affairs; Professor Mohamed Abbas Abdel-Radi, Head of the Electrical Engineering Department and thesis supervisor; and Professor Hammad Abu-Zeid, Supervisor of the Scientific Equipment Maintenance Center.

The thesis is titled "Efficient Physical Design for Hand Gesture Classification in Enhanced Prosthetics Using Deep Learning."

The thesis was supervised by Professor Mohamed Abbas Abdel-Radi, Professor Khalil Ismail Khalil, and Associate Professor Qassem Khalil, all professors in the Electrical Engineering Department at Assiut University. The thesis examination committee consisted of Professor Hany Selim, Professor in the Electrical Engineering Department, Assiut University, and Professor Ehab Khalaf, Professor in the Electrical Engineering Department and Vice Dean of the Faculty of Engineering for Environmental Affairs and Community Service, Aswan University, in addition to the supervisory committee.

Professor Mohamed Abbas Abdel-Radi explained that the thesis is a first step towards acquiring the knowledge necessary to design effective and affordable prosthetic limbs. The thesis focuses on designing and developing low-cost, highly efficient integrated systems for smart prosthetic limbs, aiming to help amputees integrate into society and perform their daily tasks. This is achieved by utilizing surface muscle signaling (sEMG) and deep learning algorithms to accurately and quickly classify hand movements, supporting real-time operation.