Explore projects

Deploying OpenRoberta with a Docker container

We developed a hardware setup that captures short, wideband snapshots in both E1 and E6 GNSS bands. This setup is mounted to a bridge over a motorway. The setup records 20 ms raw IQ snapshots triggered from the energy with a sample rate of 62.5 MHz, an analog bandwidth of 50MHz and an 8 bit bit-width. At certain frequencies the GPS/Galileo or GLONASS signals can easily be seen as a slight increase in the spectrum. Note that experts manually analyzed the datastreams by thresholding CN/0 and AGC values. Manual labeling of these snapshots has resulted in 11 classes: classes 0 to 2 represent samples with no interferences, distinguished by variations in background intensity, while classes 3 to 10 contain different interferences. The challenge lies in adapting to positive class labels with only a limited number of samples available. We partition the dataset into a 64% training set, 16% validation set, and a 20% test set split (balanced over the classes).

Scripts for download and conversion of OSTIA data to wrf intermediate

Middle voltage switchgear PLC for HIL-GridCoP

A powerful example of variational quantum algorithms is the so-called quantum circuit learning algorithm (QCL), which approximates functions and can solve non-linear differential equations by using the parameter shift rule. This demonstrator aims to explain the basics of QCL and uses examples to show how different functions can be approximated and differential equations can be solved.

An alternative problem encoding for QAOA is used to accelerate cost function computation

The demonstrator shows the time evolution of a small network with failures.

This is a documentation and code for the light detector used for the spatter detection project by Philipp Kohlwes.

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%.

Apache CloudStack Terraform VM abstraction

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.