Agenda

PhD Thesis Defence

Tuesday, 20 October 2026
10:00-11:30
Aula Senaatszaal

High-Linearity Load Current Sensing for Switching Power Stages

Heng Ma

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PhD Thesis Defence

Tuesday, 20 October 2026
17:30
Aula Senaatszaal

Cold Atmospheric Plasma Devices for Agricultural & Medical Applications

Luutzen Wymenga

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PhD Thesis Defence

Wednesday, 21 October 2026
17:30-18:30
Aula Senaatszaal

Towards Robust Radar Perception in Autonomous Vehicles: Deep Learning Methods for Object Detection, Velocity Estimation, and Data Association

Mujtaba Hassan

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PhD Thesis Defence

Tuesday, 27 October 2026
17:30-18:30
Aula Senaatszaal

Channel-aware OTFS modulation for underwater acoustic communications

Ids van der Werf

Underwater wireless communication is a cornerstone of modern marine exploration, infrastructure maintenance, and naval operations. While terrestrial systems rely on electromagnetic radio-frequency (RF) waves, these are impractical underwater due to rapid attenuation. Consequently, underwater communication typically utilizes acoustic pressure waves, which can propagate over several kilometers. However, underwater acoustic (UWA) communication is characterized by extremely limited bandwidth, high latency due to slow propagation, and severe distortions from multipath-induced delay and motioninduced Doppler spreads. This dissertation investigates the design of an UWA system that is both analytically tractable and sufficiently flexible to adapt to these volatile conditions in real time.

The research begins by examining state-of-the-art communication schemes in both UWA and terrestrial domains. A key theoretical contribution is the demonstration that two prominent schemes, developed independently in their respective fields, are mathematically equivalent. Further analysis of the time-frequency signal structure reveals that the benefit of these schemes stems from the fact that information is spread across both time and frequency.

A central challenge addressed is the trade-off between training and data transmission. By proposing a novel training design, this dissertation demonstrates a 50% reduction in training overhead compared to conventional designs without sacrificing estimation accuracy. Finally, to bridge the gap between theory and application, a channel-aware adaptation protocol is introduced. This allows the system to optimize parameters insitu, ensuring a robust link. The approach was validated through synthetic simulations, channel replay, and real-world experiments.

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