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Dipl.-Ing. Steffen Limmer

Lupe [1]

Research:

  • Distributed and sparse signal processing
  • Model-based design of neural networks
  • Kernel Methods and Machine Learning
  • Nonlinear estimation theory

Talks:

  • CoSIP Winter Retreat 2016: An Introduction to Function Computation in Wireless Networks [2]
  • MLSP 2016: Towards optimal nonlinearities for sparse recovery using higher-order statistics
  • Allerton 2015: A Simple Algorithm for Approximation by Nomographic Functions [3]
  • Jahreskonferenz »Next Generation ID« 2015: Enhanced Security for Wireless Communications
  • ITW 2014: On lp-norm Computation over Multiple-Access Channels

Current Projects:

  • DFG-SPP 1798 (CoSIP): Joint Design of Compressed Sensing and Network Coding for Wireless Meshed Networks

Supervised Theses:

  • Dan Mou, "Detection of Jamming based on Network Simulations with NS-3" (2015)

Acknowledgements:

  • supported by an AWS in Education Research Grant award
  • received a DAAD travel grant to attend ITW 2014

Find me also on:

  • arXiv [4]
  • GitHub
    [5]
  • LinkedIn [6]

Publications:

S. Limmer and S. Stanczak (2018). A Neural Architecture for Bayesian Compressive Sensing over the Simplex via Laplace Techniques [7]. IEEE Transactions on Signal Processing


M. Raceala-Motoc and S. Limmer and I. Bjelakovic and S. Stanczak (2018). Distributed Machine Learning in the Context of Function Computation over Wireless Networks [8]. 52nd Asilomar Conference on Signals, Systems and Computers 2018, Preprint.


S. Limmer and S. Stanczak (2016). Towards optimal nonlinearities for sparse recovery using higher-order statistics [9]. IEEE International Workshop on Machine Learning For Signal Processing (MLSP)


J. Mohammadi and S. Limmer and S. Stanczak (2016). A Decentralized Eigenvalue Computation Method for Spectrum Sensing Based on Average Consensus [10]. Frequenz


S. Limmer and J. Mohammadi and S. Stanczak (2015). A Simple Algorithm for Approximation by Nomographic Functions [11]. Proc. 53rd Annual Allerton Conference on Communication, Control, and Computing


S. Limmer and S. Stanczak (2014). On ℓp-norm computation over multiple-access channels [12]. Information Theory Workshop (ITW), 2014 IEEE


S. Limmer and S. Stanczak and M- Goldenbaum and R.L.G. Cavalcante (2013). Exploiting Interference for Efficient Distributed Learning in Cluster-based Wireless Sensor Networks [13]. Proc. IEEE Global Conference on Signal and Information Processing (GlobalSIP) - Network Theory Symposium


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Dipl.-Ing. Univ. Steffen Limmer
Research Associate
HFT
Room 412
Einsteinufer 25
10587 Berlin
+49(0)30 314-28465
steffen.limmer {at} tu-berlin.de [15]
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