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Found 8 papers, 1 papers with code
Velocity-Based Channel Charting with Spatial Distribution Map Matching
However, current channel-charting approaches lag behind fingerprinting in their positioning accuracy and still require reference samples for localization, regular data recording and labeling to keep the models up to date.
Indoor Localization with Robust Global Channel Charting: A Time-Distance-Based Approach
While CC has shown promising results in modelling the geometry of the radio environment, a deeper insight into CC for localization using multi-anchor large-bandwidth measurements is still pending.
How to Learn from Risk: Explicit Risk-Utility Reinforcement Learning for Efficient and Safe Driving Strategies
This paper proposes SafeDQN, which allows to make the behavior of autonomous vehicles safe and interpretable while still being efficient.
An Introduction to Multi-Agent Reinforcement Learning and Review of its Application to Autonomous Mobility
Multi-Agent Reinforcement Learning (MARL) is a research field that aims to find optimal solutions for multiple agents that interact with each other.
Rigid and non-rigid motion compensation in weight-bearing cone-beam CT of the knee using (noisy) inertial measurements
We perform a simulation study using real motion recorded with an optical tracking system.
Inertial Measurements for Motion Compensation in Weight-bearing Cone-beam CT of the Knee
In our proposed multi-stage algorithm, these signals are transformed to the global coordinate system of the CT scan and applied for motion compensation during reconstruction.
Stride Length Estimation with Deep Learning
The achieved precision outperforms state-of-the-art methods evaluated on this benchmark dataset by 3. 0 cm (36%).
Sensor-based Gait Parameter Extraction with Deep Convolutional Neural Networks
Regarding mobile gait analysis, this enables integration-free and data-driven extraction of a set of 8 spatio-temporal stride parameters.
