• Type of project: Research Project/Hauptseminar/Thesis (literature review + coding)
  • Contact: han.wang@izfp.fraunhofer.de

Background

Compressed sensing is a signal processing technique that allows the reconstruction of a sparse or compressible signal from a small number of acquired measurements, enabling efficient data acquisition directly during the measurement process. Compression matrices tailored to a specific task can offer better performance than random ones. Recently, softmax neural networks have been shown to provide a feasible approach for the design of compression matrices that perform subsampling, an otherwise difficult combinatorial problem.

Task

  • Literature research in frequency subsampling, deep unfoldings, and softmax neural networks
  • Design of a task-based softmax neural network architecture that learns subsampling matrices
  • Training and testing of the network when applied to frequency bin subsampling of synthetic ultrasound NDT data

Reference

[1] Kirchhof, J. et al. Frequency Subsampling of Ultrasound Nondestructive Measurements: Acquisition, Reconstruction, and Performance. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 68.10 (2021): 3174-3191
[2] Diamantaras, K. et al. Sparse Antenna Array Design for MIMO Radar Using Softmax Selection. arXiv preprint arXiv:2102.05092 (2021).

Supervisor

  • Dr. Ing. Florian Römer, M. Sc. Han Wang
  • Email: han.wang@izfp.fraunhofer.de