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 2010

Fingerprint

Data communication systems
Communication
Channel capacity
Explosions
Feature extraction
Signal processing
Processing

Keywords

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

ASJC Scopus subject areas

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

Cite this

@article{4c69bb787e19467d86d1c6f108634783,
title = "Adaptive anytime data transmission of non-stationary signals",
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.",
keywords = "Anytime systems, Nonstationary signals, Overcomplete signal representation, Transformed domain signal processing",
author = "A. V{\'a}rkonyi-K{\'o}czy",
year = "2010",
month = "4",
language = "English",
volume = "14",
pages = "240--246",
journal = "Journal of Advanced Computational Intelligence and Intelligent Informatics",
issn = "1343-0130",
publisher = "Fuji Technology Press",
number = "3",

}

TY - JOUR

T1 - Adaptive anytime data transmission of non-stationary signals

AU - Várkonyi-Kóczy, A.

PY - 2010/4

Y1 - 2010/4

N2 - 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.

AB - 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.

KW - Anytime systems

KW - Nonstationary signals

KW - Overcomplete signal representation

KW - Transformed domain signal processing

UR - http://www.scopus.com/inward/record.url?scp=77951225873&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77951225873&partnerID=8YFLogxK

M3 - Article

VL - 14

SP - 240

EP - 246

JO - Journal of Advanced Computational Intelligence and Intelligent Informatics

JF - Journal of Advanced Computational Intelligence and Intelligent Informatics

SN - 1343-0130

IS - 3

ER -