Adaptive anytime data transmission of non-stationary signals

Research output: Contribution to journalArticle

Abstract

The never unseen information explosion in data transmission and communication called for new methods in signal coding and reconstruction. To minimize the channel capacity needed for the transmission urged researchers to find techniques which are flexible and can adapt to the available space and time. Anytime techniques are good candidates for such purposes. If the signal/data to be transmitted can be characterized as sequence of stationary intervals overcomplete signal representations can be applied. These techniques can be operated in an anytime manner as well, i.e., are excellent tools for handling the capacity problems. This paper introduces the concept of anytime recursive overcomplete signal representations using different recursive signal processing algorithms. The novelty of the approach is that an on-going set of signal transformations together with appropriate (e.g., L 1 norm) minimization procedures can provide optimal and flexible anytime on-going representations, ongoing signal segmentations into stationary intervals, and on-going feature extractions for immediate utilization in data transmission, communication, diagnostics, or other applications. The proposed technique may be advantageous if the transmission channel is overloaded and in case of processing non-stationary signals when complete signal representations can be used only with serious limitations because of their relative weakness in adaptive matching of signal structures.

Original languageEnglish
Pages (from-to)240-246
Number of pages7
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Volume14
Issue number3
Publication statusPublished - Apr 1 2010

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Keywords

  • Anytime systems
  • Nonstationary signals
  • Overcomplete signal representation
  • Transformed domain signal processing

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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