Deep Learning

A Machine Learning Perspective on Automotive Radar Direction of Arrival Estimation

Millimeter-wave sensing using automotive radar imposes high requirements on the applied signal processing in order to obtain the necessary resolution for current imaging radar. High-resolution direction of arrival estimation is needed to achieve the …

Data-Driven Radar Processing Using a Parametric Convolutional Neural Network for Human Activity Classification

The paper proposes a data-driven pre-processing optimization for radar data using a parametric convolutional neural network. The proposed method is applied on human activity classification as a use case. Present radar-based activity recognition …

Explainable machine learning determines effects on the sound absorption coefficient measured in the impedance tube

Measurements of acoustic properties of sound absorbing materials in impedance tubes show poor reproducibility, which was demonstrated in round robin tests. The impedance tube measurements are standardized but lack precise definitions of the actual …

Deep learning for brake squeal: Brake noise detection, characterization and prediction

Despite significant advances in modeling of friction-induced vibrations and brake squeal, the majority of industrial research and design is still conducted experimentally, since many aspects of squeal and its mechanisms involved remain unknown. In …