7 papers
The Confusion is Real: GRAPHIC -- A Network Science Approach to Confusion Matrices in Deep Learning
Johanna S. Fröhlich, Bastian Heinlein, Jan U. Claar +3
Explainable artificial intelligence has emerged as a promising field of research to address reliability concerns in artificial intelligence. Despite significant progress in explain…
Source Distance Estimation in Turbulent Airflow: Exploiting Molecule Degradation Diversity
Bastian Heinlein, Timo Jakumeit, Robert Schober +2
In nature, estimating the location of a molecule source in turbulent airflow is a central, and yet highly challenging problem for mate search and foraging. Recently, it has also re…
Autoencoder-based Optimization of Multi-user Molecule Mixture Communication Systems
Bastian Heinlein, Nuria Zurita Jiménez, Kaikai Zhu +4
In this paper, we introduce an autoencoder (AE)-based scheme for end-to-end optimization of a multi-user molecule mixture communication system. In the proposed scheme, each transmi…
Matched Filter-Based Molecule Source Localization in Advection-Diffusion-Driven Pipe Networks with Known Topology
Timo Jakumeit, Bastian Heinlein, VukaÅ¡in SpasojeviÄ +3
Synthetic molecular communication (MC) has emerged as a powerful framework for modeling, analyzing, and designing communication systems where information is encoded into properties…
Molecule Mixture Detection and Design for MC Systems with Non-linear, Cross-reactive Receiver Arrays
Bastian Heinlein, Kaikai Zhu, Sümeyye Carkit-Yilmaz +6
Air-based molecular communication (MC) has the potential to be one of the first MC systems to be deployed in real-world applications, enabled by commercially available sensors. How…
Mixture of Inverse Gaussians for Hemodynamic Transport (MIGHT) in Multiple-Input Multiple-Output Vascular Networks
Timo Jakumeit, Bastian Heinlein, Nunzio Tuccitto +3
Synthetic molecular communication (MC) in the cardiovascular system is a key enabler for many envisioned medical applications inside the human body, such as targeted drug delivery,…