Arwin Gansekoele

Full Name

A.W. Gansekoele

Email

Arwin.Gansekoele@cwi.nl

Phone

+31 20 592 4013

Room

M325

Department(s)

Stochastics

Function(s)

PhD student

Arwin Gansekoele

Gansekoele, A. (2025, September 12). Deep learning for next-generation communication technologies. Retrieved from http://hdl.handle.net/1871.1/c9cf88f3-f113-4700-9412-4cffc19dcbd1
Gansekoele, A., Balalsoukas-Slimming, A., Brusse, T., Hoogendoorn, M., Bhulai, S., & van der Mei, R. (2025). Joint demapping of QAM and APSK constellations using machine learning. IEEE Open Journal of the Communications Society, 6, 1695–1709. doi:10.1109/OJCOMS.2025.3547261
Gansekoele, A., Balalsoukas-Slimming, A., Brusse, T., Hoogendoorn, M., Bhulai, S., & van der Mei, R. (2024). A machine learning approach for simultaneous demapping of QAM and APSK constellations. In Proceedings of IEEE International Conference on Machine Learning for Communication and Networking (pp. 13–18). doi:10.1109/ICMLCN59089.2024.10624805
Gansekoele, A., Bot, T., van der Mei, R., Bhulai, S., & Hoogendoorn, M. (2023). Unveiling the potential: Harnessing deep metric learning to circumvent video streaming encryption. In IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (pp. 163–170). doi:10.1109/WI-IAT59888.2023.00028
van Leeuwen, B., Gansekoele, A., Pries, J., van de Bijl, E., & Klein, J. (2022). Explainable kinship: The importance of facial features in kinship recognition. In IARIA Congress 2022: The 2022 IARIA Annual Congress on Frontiers in Science, Technology, Services, and Applications (pp. 54–60).

Bestrijding cybercrime met behulp van Kunstmatige Intelligentie (PPS Cybercrime AI)