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ROS2 Security / ROS2 TPM / sros2-tpm
Apache License 2.0Updated -
IPA Quantum / QKMTuner
MIT LicenseUpdated -
Florian Schiffel / ICV-mmdetection_baseCode
Apache License 2.0Updated -
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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.
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Read data from ros bags or topics, and format into feature vectors for ML
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Dependency management system, with support for ROS1, ROS2, JAX, and IPOPT
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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%.
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SIT / ckanext-aiembeddings
GNU Affero General Public License v3.0Updated -
GF7_public / IWM-GDTool
GNU General Public License v3.0 or laterConversion of MDBW data from GraphDesigner-Excel to RDF
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UPM / SanDy PALM
GNU Affero General Public License v3.0Updated -
IPA Quantum / ALB QUBO
MIT LicenseA python package to formulate the Assembly Line Balancing Problem as Quadratic Unconstrained Binary Optimization Problem.
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