RIOT: Recursive Inertial Odometry Transformer for Localisation from Low-Cost IMU Measurements.

Inertial localisation is an important technique as it enables ego-motion estimation in conditions where external observers are unavailable. However, low-cost inertial sensors are inherently corrupted by bias and noise, which lead to unbound errors, making straight integration for position intractable. Traditional mathematical approaches are reliant on prior system knowledge, geometric theories and are constrained by predefined dynamics.

Leveraging Self-Attention Mechanism for Attitude Estimation in Smartphones.

Inertial attitude estimation is a crucial component of many modern systems and applications. Attitude estimation from commercial-grade inertial sensors has been the subject of an abundance of research in recent years due to the proliferation of Inertial Measurement Units (IMUs) in mobile devices, such as the smartphone. Traditional methodologies involve probabilistic, iterative-state estimation; however, these approaches do not generalise well over changing motion dynamics and environmental conditions, as they require context-specific parameter tuning.

Robust time-frequency analysis of newborn EEG seizure corrupted by impulsive artefacts.

The newborn EEG seizure is a nonstationary signal. The time-varying nature of the newborn EEG seizure can be characterized by time-frequency representations (TFRs) such as quadratic time-frequency distributions. The underlying time-frequency signatures of newborn EEG seizure, however, can be severely masked by short-time and high amplitude (STHA), or impulsive, artefacts.