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The WireX repo contains the main codebase for the Cable-driven Parallel Robots project. Its core components are WireLib and WireCenter. A number of related libraries, tools, and resources are kept in the same repo.

Software-seitige Implementierung Expertensystem

The accuracy and reliability of vehicle localization on roads are crucial for applications such as self-driving cars, toll systems, and digital tachographs. To achieve accurate positioning, vehicles typically use global navigation satellite system (GNSS) receivers to validate their absolute positions. However, GNSS-based positioning can be compromised by interference signals, necessitating the identification, classification, determination of purpose, and localization of such interference to mitigate or eliminate it. Recent approaches based on machine learning (ML) have shown superior performance in monitoring interference. However, their feasibility in real-world applications and environments has yet to be assessed. Effective implementation of ML techniques requires training datasets that incorporate realistic interference signals, including real-world noise and potential multipath effects that may occur between transmitter, receiver, and satellite in the operational area. Additionally, these datasets require reference labels. Creating such datasets is often challenging due to legal restrictions, as causing interference to GNSS sources is strictly prohibited. Consequently, the performance of ML-based methods in practical applications remains unclear. To address this gap, we describe a series of large-scale measurement campaigns conducted in real-world settings at two highway locations in Germany and the Seetal Alps in Austria, and in large-scale controlled indoor environments. We evaluate the latest supervised ML-based methods to report on their performance in real-world settings and present the applicability of pseudo-labeling for unsupervised learning. We demonstrate the challenges of combining datasets due to data discrepancies and evaluate outlier detection, domain adaptation, and data augmentation techniques to present the models' capabilities to adapt to changes in the datasets.

This repository contains the components necessary to integrate a JupyterHub installation with openBIS.

This project provides data to complement the master thesis 'Development of a Redox-Flow-Battery Stack in Cascade Configuration'

An extension of the baseline XFOIL from Mark Drela to include the effects of Vortex Generators. Further references available here: https://doi.org/10.1177/0309524X18780390

[CVPR 2021] Instance Localization for Self-supervised Detection Pretraining

Vocabulary Provider (open source)

Interference signals cause position errors and outages to global navigation satellite system (GNSS) receivers. However, to solve these problems, the interference needs to be detected, classified, its purpose determined, and localized, such that it can be eliminated. Several interference monitoring solutions exist, but these are expensive, resulting in fewer nodes that may miss spatially sparse interference signals. This paper introduces a low-cost commercial-off-the-shelf (COTS) GNSS interference monitoring, detection, and classification receiver. It employs machine learning (ML) on tailored signal pre-possessing of the raw signal samples and GNSS measurements to facilitate a generalized, high-performance architecture that does not require human in the loop (HIL) calibration. Therefore, the low-cost receivers with high performance can justify significantly more receivers to be deployed, resulting in a significantly higher probability of intercept (POI). The initial results of controlled interference scenarios demonstrate detection and classification capabilities exceeding conventional approaches.

Eine Laserpuls-Visualisierung die Kreisformen erzeugt die auch genaue Einstellungen des Lasers entsprechen. Es ist möglich Bursts, eine Linie mit Bursts und eine Kavität von Bursts zu visualisieren.