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Data compression and information retrieval via symbolization.

X. Z. Tang1, E. R. Tracy

  • 1Department of Applied Physics, Columbia University, New York, New York 10027.

Chaos (Woodbury, N.Y.)
|June 5, 2003
PubMed
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Converting continuous signals into symbolic data streams offers effective data compression. This method preserves dynamical information and enables efficient retrieval of signal characteristics like correlation time scales and periodicity, even in noisy or chaotic data.

Area of Science:

  • Signal Processing
  • Data Compression
  • Dynamical Systems Analysis

Background:

  • Continuous signals contain rich dynamical information.
  • Data compression is crucial for efficient data handling.
  • Extracting specific information from complex signals can be challenging.

Purpose of the Study:

  • To present a method for converting continuous signals into multisymbol streams for data compression.
  • To demonstrate the preservation of dynamical information.
  • To show the utility of this method for information retrieval.

Main Methods:

  • Symbolization of continuous signals into a multisymbol stream.
  • Utilizing binary operations for information retrieval from symbolic data.
  • Varying time delay for symbolization to recover correlation time scales.

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Main Results:

  • The conversion method effectively compresses data while preserving dynamical information.
  • Symbolic data allows for optimal information retrieval using binary operations on digital computers.
  • Correlation time scales are recoverable even at high noise levels.
  • Periodicity in signals can be reliably detected despite chaotic or stochastic backgrounds.

Conclusions:

  • Signal symbolization is an efficient data compression technique.
  • This method facilitates optimal retrieval of dynamical information.
  • The approach is robust for detecting signal features in complex environments.