Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Tulczyjew, Lukasz"'
The integration of advanced technologies into telecommunication networks complicates troubleshooting, posing challenges for manual error identification in Packet Capture (PCAP) data. This manual approach, requiring substantial resources, becomes impr
Externí odkaz:
http://arxiv.org/abs/2407.06085
Detecting failures via analysis of Packet Capture (PCAP) files is crucial for maintaining network reliability and performance, especially in large-scale telecommunications networks. Traditional methods, relying on manual inspection and rule-based sys
Externí odkaz:
http://arxiv.org/abs/2407.11021
Autor:
Zaigrajew, Vladimir, Baniecki, Hubert, Tulczyjew, Lukasz, Wijata, Agata M., Nalepa, Jakub, Longépé, Nicolas, Biecek, Przemyslaw
Remote sensing (RS) applications in the space domain demand machine learning (ML) models that are reliable, robust, and quality-assured, making red teaming a vital approach for identifying and exposing potential flaws and biases. Since both fields ad
Externí odkaz:
http://arxiv.org/abs/2403.08017
Publikováno v:
IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022, Art no. 6011105
Hyperspectral unmixing remains one of the most challenging tasks in the analysis of such data. Deep learning has been blooming in the field and proved to outperform other classic unmixing techniques, and can be effectively deployed onboard Earth obse
Externí odkaz:
http://arxiv.org/abs/2208.02361
Publikováno v:
IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022, Art no. 5513105
Maintaining farm sustainability through optimizing the agricultural management practices helps build more planet-friendly environment. The emerging satellite missions can acquire multi- and hyperspectral imagery which captures more detailed spectral
Externí odkaz:
http://arxiv.org/abs/2208.02349
Hyperspectral imaging provides detailed information about the scanned objects, as it captures their spectral characteristics within a large number of wavelength bands. Classification of such data has become an active research topic due to its wide ap
Externí odkaz:
http://arxiv.org/abs/1907.11935
This paper introduces new attention-based convolutional neural networks for selecting bands from hyperspectral images. The proposed approach re-uses convolutional activations at different depths, identifying the most informative regions of the spectr
Externí odkaz:
http://arxiv.org/abs/1811.02667
Akademický článek
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Akademický článek
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Publikováno v:
In The Journal of Space Safety Engineering December 2021 8(4):339-344