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project for monitoring purposes

matplotlib: plotting with Python

This is a personal project, to learn and play with power electronics models created in Matlab / Simulink

Plug-and-Produce... safely! End-to-End Model-Based Safety Assurance for Reconfigurable Industry 4.0

Allows reading finite element meshes into pandas dataframes. LS-Dyna and Abaqus mesh files are supported.

Zabbix module for checking the backup state of network components in the backup software Oxidized

Provides clean hello world templates for msbuild.exe including wdm/ntifs based header usage.

Apache CloudStack Terraform VM abstraction

Minimal Cam Demo Application Plugin

Jamming devices pose a significant threat by disrupting signals from the global navigation satellite system (GNSS), compromising the robustness of accurate positioning. Detecting anomalies in frequency snapshots is crucial to counteract these interferences effectively. The ability to adapt to diverse, unseen interference characteristics is essential for ensuring the reliability of GNSS in real-world applications. In this paper, we propose a few-shot learning (FSL) approach to adapt to new interference classes. Our method employs quadruplet selection for the model to learn representations using various positive and negative interference classes. Furthermore, our quadruplet variant selects pairs based on the aleatoric and epistemic uncertainty to differentiate between similar classes. We recorded a dataset at a motorway with eight interference classes on which our FSL method with quadruplet loss outperforms other FSL techniques in jammer classification accuracy with 97.66%.