Artificial Neural Networks

Time- and Frequency-Domain Dynamic Spectrum Access: Learning Cyclic Medium Access Patterns in Partially Observable Environments

Upcoming communication systems increasingly often tackle the spectrum scarcity problem through the coexistence with legacy systems in the same frequency band. Cognitive Radio presents popular methods for Dynamic Spectrum Access (DSA) that enable …

Weak adhesion detection – Enhancing the analysis of vibroacoustic modulation by machine learning

Adhesive bonding is a well-established technique for composite materials. Despite advanced surface treatments and preparations, surface contamination and application errors still occur, resulting in localised areas with a reduced adhesion. The …

Hybrid Modelling by Machine Learning Corrections of Analytical Model Predictions towards High-Fidelity Simulation Solutions

Within the fields of materials mechanics, the consideration of physical laws in machine learning predictions besides the use of data can enable low prediction errors and robustness as opposed to predictions only based on data. On the one hand, …

Artificial Neural Networks for Sensor Data Classification on Small Embedded Systems

In this paper we investigate the usage of machine learning for interpreting measured sensor values in sensor modules. In particular we analyze the potential of artificial neural networks (ANNs) on low-cost microcontrollers with a few kilobytes of …