العربية
Prof. Ahmed Al-Shamma’a
Biography
Professor Ahmed Al-Shamma’a is a Professor of Engineering and an internationally experienced academic and institutional leader with over 30 years of experience in higher education, research, innovation, and industry engagement across the UK, the UAE, and internationally. He has held senior academic leadership positions, including Chancellor of the University of Khorfakkan, UAE; Dean of the College of Engineering at the University of Sharjah, UAE; Pro Vice-Chancellor and Executive Dean at Liverpool John Moores University; and other senior positions at the University of Liverpool, UK.
Throughout his career, Professor Al-Shamma’a has combined academic leadership with research and innovation. His experience includes curriculum development, teaching and learning, accreditation, academic quality assurance, research leadership, postgraduate supervision, international partnerships, and industry collaboration. He has taught across Foundation, undergraduate, postgraduate, and professional programmes. He has contributed to Electrical and Electronic Engineering, Computer Programming, Electromagnetic Waves, Digital Signal Processing, Automation and Control, Sensors, Digital Circuits, Applied Mathematics, Engineering Systems, Industrial Engineering, Human-Computer Interaction, Risk Management, and Engineering Projects.
His research focuses on Applied Electromagnetism, Sensing and Communications, with multidisciplinary applications across engineering, physics, chemistry, biology, and computer science. His work encompasses microwave sensing and plasma, environmental and sustainable technologies, energy, water, food, healthcare, automotive applications, Free Electron Lasers, terahertz imaging, and underwater and wireless communications.
Professor Al-Shamma’a has authored more than 300 refereed journal and conference papers, over 80 technical papers and reports, 18 book chapters, and one jointly authored book. He has coordinated more than 40 international research projects and supervised 41 PhD students, as well as MSc students, research assistants, and postdoctoral researchers.
Qualification
- PhD, Electrical & Electronics Engineering, University of Liverpool, UK, 1993/94.
- MSc, Electrical & Electronics Engineering, University of Liverpool, UK, 1989/90.
- BSc, Electrical Engineering and Electronics, University of Baghdad, Iraq, 1987/88.
Teaching Interest
- Electrical and Electronic Engineering
- Electromagnetic Waves and Applied Electromagnetics
- Microwave Engineering and Applications
- Sensors and Instrumentation
- Digital Circuits
- Engineering Systems
- Industrial Engineering
- Engineering Projects
- Engineering Research Methodology
- Project & Risk Management
Research Interest
- Applied Electromagnetism
- Microwave Sensing and Spectroscopy
- Electromagnetic and Wireless Sensors
- Microwave Plasma and Industrial Applications
- Environmental and Sustainable Technologies
- Renewable Energy and Energy from Waste
- Water Quality and Water Purification
- Food Quality and Safety Monitoring
- Biomedical and Healthcare Sensing
- Underwater Communications
- IoT, Smart Sensing and Data-Driven Technologies
Publications
- Mason et al. (2018) – Real-Time Microwave, Dielectric, and Optical Sensing of Lincomycin and Tylosin Antibiotics in Water: Sensor Fusion for Environmental Safety, Journal of Sensors. DOI: 10.1155/2018/7976105.
- Teng et al. (2019) – Embedded Smart Antenna for Non-Destructive Testing and Evaluation (NDT&E) of Moisture Content and Deterioration in Concrete, Sensors, 19(3), 547.
- Hashim et al. (2021) – Water purification from metal ions in the presence of organic matter using electromagnetic radiation-assisted treatment, Journal of Cleaner Production, 280, 124427.
- Farooq et al. (2022) – Machine learning and the Internet of Things security: Solutions and open challenges, Journal of Parallel and Distributed Computing, 162, 89–104.
- Kleanthous et al. (2022) – A survey of machine learning approaches in animal behaviour, Neurocomputing. DOI: 10.1016/j.neucom.2021.10.126.
- Jia et al. (2022) – An Incentive Mechanism in Expert-decision-based Crowd Sensing Networks, Applied Soft Computing, 122, 108834. DOI: 10.1016/j.asoc.2022.108834.
- Nasir et al. (2022) – Water Quality Classification using Machine Learning, Journal of Water Process Engineering, 48, 102920.
- Al-Shamma’a et al. – Propagation of Electromagnetic Waves at MHz Frequencies Through Seawater, IEEE Transactions on Antennas and Propagation, 52(11), 2843–2849.