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The accuracy of global navigation satellite system (GNSS) receivers is significantly compromised by interference from jamming devices. Consequently, the detection of these jammers are crucial to mitigating such interference signals. However, robust classification of interference using machine learning (ML) models is challenging due to the lack of labeled data in real-world environments. In this paper, we propose an ML approach that achieves high generalization in classifying interference through orchestrated monitoring stations deployed along highways. We present a semi-supervised approach coupled with an uncertainty-based voting mechanism by combining Monte Carlo and Deep Ensembles that effectively minimizes the requirement for labeled training samples to less than 5% of the dataset while improving adaptability across varying environments. Our method demonstrates strong performance when adapted from indoor environments to real-world scenarios.

Module to interconnect with the traffic simulator SUMO via the traffic control interface (TraCI).

Praktikumsunterlagen (Slides, Übungen, etc.) für das Praktikum "Big Data and Machine Learning"

USB-C SPI-Flasher. Repo enthält eine GUI und den Quellcode für das Board "PLL-TEST-ADB"

Finds smallest distance between any point in R3 to a point on a specific curve

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

An extension of Construct2d code to improve compatibility with IWES-CFSD toolchain.

Sample webapp for Cloud workshop at Fraunhofer ATIM 2018 in Dresden

Me playing around with C++/Qt Quick/QML.

Hosts a vulnerable demo project for IntelliSecTest development purposes