Driven by deep learning techniques, perception technology in autonomous driving has
developed rapidly in recent years, enabling vehicles to accurately detect and interpret …

With the rapid advancements of sensor technology and deep learning, autonomous driving
systems are providing safe and efficient access to intelligent vehicles as well as intelligent …

Physical reservoir computing leverages the dynamical properties of complex physical
systems to process information efficiently, significantly reducing training efforts and energy …

Machine Learning (ML) methods have become state of the art in radar signal processing,
particularly for classification tasks (eg, of different human activities). Radar classification can …

Spiking neural networks offer a promising approach for low power edge applications,
especially when run on neuromorphic hardware. However, there are no well established …

Sensing technology plays a crucial role in bridging the physical and digital worlds. By
transforming a multitude of physical phenomena into digital data, it significantly enhances …

In this paper, we introduce the Label-Aware Ranked loss, a novel metric loss function.
Compared to the state-of-the-art Deep Metric Learning losses, this function takes advantage …

Spiking neural networks (SNN) are a promising approach for low-power edge AI (artificial
intelligence), especially when run on dedicated neuromorphic hardware. In this work we set …

Due to the irregular distribution of 3-D point clouds and the random movement of the
passengers, it is difficult to accurately and rapidly determine the passenger occupancy …

The radar signal processing algorithm is one of the core components in through-wall radar
human detection technology. Traditional algorithms (eg, DFT and matched filtering) struggle …