• Quasi Monte Carlo time-frequency analysis 

      Levie, Ron; Avron, Haim; Kutyniok, Gitta Astrid Hildegard (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-09-30)
      We study signal processing tasks in which the signal is mapped via some generalized time-frequency transform to a higher dimensional time-frequency space, processed there, and synthesized to an output signal. We show how to approximate such methods using a quasi-Monte Carlo (QMC) approach. We consider cases where the time-frequency representation is redundant, having feature axes in addition to the ...
    • Real-Time Outdoor Localization Using Radio Maps: A Deep Learning Approach 

      Yapar, Cagkan; Levie, Ron; Kutyniok, Gitta Astrid Hildegard; Caire, Giuseppe (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-05-10)
      Global Navigation Satellite Systems typically perform poorly in urban environments, where the likelihood of line-of-sight conditions between devices and satellites is low. Therefore, alternative location methods are required to achieve good accuracy. We present LocUNet: A convolutional, end-to-end trained neural network (NN) for the localization task, which is able to estimate the position of a user ...
    • Transferability of graph neural networks: An extended graphon approach 

      Maskey, Sohir; Levie, Ron; Kutyniok, Gitta Astrid Hildegard (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-11-28)
      We study spectral graph convolutional neural networks (GCNNs), where filters are defined as continuous functions of the graph shift operator (GSO) through functional calculus. A spectral GCNN is not tailored to one specific graph and can be transferred between different graphs. It is hence important to study the GCNN transferability: the capacity of the network to have approximately the same ...