Naim Dahnoun

Naim Dahnoun serves as Professor of Teaching and Learning in Signal Processing at the University of Bristol's School of Electrical, Electronic and Mechanical Engineering, where he contributes to the Visual Information Laboratory and Photonics and Quantum research theme. His career uniquely integrates advanced technical research with pedagogical innovation in engineering education.

He holds an Ingenieurd'Etat degree from Oran and a Ph.D. from the University of Leicester, establishing his foundation in both theoretical and applied engineering. His academic journey reflects continuous engagement with cutting-edge signal processing technologies and educational methodologies.

Research interests center on Signal Processing with specialized expertise in Digital Signal Processing implementation, mmWave Radar systems for human monitoring, Biomedical Signal Processing (particularly foetal EEG and vital sign detection), and Engineering Education pedagogy. His work develops real-time radar-based solutions for posture estimation, fall detection, and non-contact health monitoring while pioneering FPGA-based DSP teaching approaches.

Analysis of his 91 publications reveals a distinct dual trajectory: since 2020, approximately 60% of his work focuses on mmWave radar applications in healthcare (including neonatal monitoring and elderly care), while the remainder advances DSP education through project-based FPGA implementations and e-learning tools. This synergy between technical innovation and teaching methodology defines his scholarly contribution.

His recognition includes:

• Students’ Award for Outstanding Teaching (Engineering), 2018

As a research supervisor, he has guided 7 students through advanced projects while securing funding as co-investigator on interdisciplinary grants including a national database study on neonatal outcomes (2018-2019) and Bristol Harbour water quality monitoring (2017). His grant portfolio demonstrates consistent translation of signal processing techniques to healthcare and environmental applications.

Within the Engineering Education Research Group, he leads initiatives in DSP pedagogy and digital learning tools, while his technical work in the Visual Information Laboratory drives radar-based sensing innovations. Current projects emphasize low-cost, real-time implementations for both medical monitoring systems and educational platforms.