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On the Relevance of Discrepancy Norm for Similarity-Based Clustering of Delta-Event Sequences

  • B. Moser
  • F. Eibensteiner
  • J. Kogler
  • Gernot Stübl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8111)

Abstract

In contrast to sampling a signal at equidistant points in time the on-delta-send sampling principle relies on discretizing the signal due to equidistant points in the range. On-delta-send sampling is encountered in asynchronous event-based data acquisition of wireless sensor networks in order to reduce the amount of data transfer, in event-based imaging in order to realize high-dynamic range image acquisition or, via the integrate-and-fire principle, in biology in terms of neuronal spike trains. It turns out that the set of event sequences that result from a bounded set of signals by applying on-delta-send sampling can be characterized by means of the ball with respect to the so-called discrepancy norm as metric. This metric relies on a maximal principle that evaluates intervals of maximal partial sums. It is discussed how this property can be used to construct novel matching algorithms for such sequences. Simulations based on test signals show its pontential above all regarding robustness.

Keywords

Wireless Sensor Network Event Sequence Normalize Correlation Event Function Equidistant Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • B. Moser
    • 1
  • F. Eibensteiner
    • 2
  • J. Kogler
    • 3
  • Gernot Stübl
    • 1
  1. 1.Software Competence Center Hagenberg (SCCH)Austria
  2. 2.Upper Austria University of Applied SciencesHagenbergAustria
  3. 3.Austrian Institute of Technolog (AIT)ViennaAustria

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