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Kernel-based Learning for Fault Detection and identification in fuel cell Systems.

, , , , , and . Handbook of Pattern Recognition and Computer Vision, World Scientific, (2016)

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Machine Learning for Wireless Communications: Hybrid Data-Driven and Model-Based Approaches.. Royal Institute of Technology, Stockholm, Sweden, (2022)base-search.net (ftkthstockholm:oai:DiVA.org:kth-321435).Deep unfolding of the weighted MMSE beamforming algorithm., , and . CoRR, (2020)Clock synchronization over networks using sawtooth models., , , , and . CoRR, (2019)Deep Weighted MMSE Downlink Beamforming., , and . ICASSP, page 4915-4919. IEEE, (2021)Kernel-based Learning for Fault Detection and identification in fuel cell Systems., , , , , and . Handbook of Pattern Recognition and Computer Vision, World Scientific, (2016)Clock Synchronization Over Networks Using Sawtooth Models., , , , and . ICASSP, page 5945-5949. IEEE, (2020)Matrix-Inverse-Free Deep Unfolding of the Weighted MMSE Beamforming Algorithm., , and . IEEE Open J. Commun. Soc., (2022)Wireless link adaptation with outdated CSI - a hybrid data-driven and model-based approach., , , and . SPAWC, page 1-5. IEEE, (2020)A Matrix-Inverse-Free Implementation of the MU-MIMO WMMSE Beamforming Algorithm., and . IEEE Trans. Signal Process., (2022)Clock synchronization over networks - Identifiability of the sawtooth model., , , , and . CoRR, (2019)