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A multimedia dataset for object-centric business process mining in IT asset management

Visualize Tensorflow serving metrics on Grafana using Prometheus.

We propose a quantum version of the deterministic annealing algorithm to verify the input-output relations of a neural network. We apply the algorithm to traffic sign recognition, an important task for self-driving vehicles.

Link zur Doku: http://fre10753.pages.fraunhofer.de/mkdocs-example

Public mirror of internal repository

JavaScript library to wrap in the browser with the factory pattern a client for the STOMP protocol over web sockets.

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

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This tool allows cloud customers to manage both their Access Lists and Firewall entries. Supports custom rules and Intranet-Rules which are automatically updated using the Intranet list provided by CC-Daten

Token Manager for (Flex-based) Abaqus Tokens. Configurable through policy file.

Folder containing public accessible tutorials for wind turbine rotor blades aerodynamic applications in OpenFOAM

Interference signals degrade and disrupt Global Navigation Satellite System (GNSS) receivers, impacting their localization accuracy. Therefore, they need to be detected, classified, and located to ensure GNSS operation. State-of-the-art techniques employ supervised deep learning to detect and classify potential interference signals. Here, literature proposes ResNet18 and TS-Transformer as they provide the most accurate classification rates on quasi-realistic GNSS signals. However, employing these methods individually, they only focus on either spatial or temporal information and discard information during optimization, thereby degrading classification accuracy. This paper proposes a deep learning framework that considers both the spatial and temporal relationships between samples when fusing ResNet18 and TS-Transformers with a joint loss function to compensate for the weaknesses of both methods considered individually. Our real-world experiments show that our novel fusion pipeline with an adapted late fusion technique and uncertainty measure significantly outperforms the state-of-the-art classifiers by 6.7% on average, even in complicated realistic scenarios with multipath propagation and environmental dynamics. This works even well (F-β=2 score about 80.1%), when we fuse both modalities only from a single bandwidth-limited low-cost sensor, instead of a fine-grained high-resolution sensor and coarse-grained low-resolution low-cost sensor. By using late fusion the classification accuracy of the classes FreqHopper, Modulated, and Noise increases while lowering the uncertainty of Multitone, Noise, and Pulsed. The improved classification capabilities allow for more reliable results even in challenging scenarios.

Sensor Software Setup Solution for Microcontrollers by Eric Kondratenko