FILTERING AND COMPRESSION OF STOCHASTIC SIGNALS UNDER CONSTRAINT OF VARIABLE FINITE MEMORY

Anatoli Torokhti, Stan Miklavcic

2009

Abstract

We study a new technique for optimal data compression subject to conditions of causality and different types of memory. The technique is based on the assumption that certain covariance matrices formed from observed data, reference signal and compressed signal are known or can be estimated. In particular, such an information can be obtained from the known solution of the associated problem with no constraints related to causality and memory. This allows us to consider two separate problems related to compression and de-compression subject to those constraints. Their solutions are given and the analysis of the associated errors is provided.

Paper Citation

in Harvard Style

Torokhti A. and Miklavcic S. (2009). FILTERING AND COMPRESSION OF STOCHASTIC SIGNALS UNDER CONSTRAINT OF VARIABLE FINITE MEMORY . In Proceedings of the 6th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-989-674-001-6, pages 104-109. DOI: 10.5220/0002206501040109

in Bibtex Style

@conference{icinco09,
author={Anatoli Torokhti and Stan Miklavcic},
title={FILTERING AND COMPRESSION OF STOCHASTIC SIGNALS UNDER CONSTRAINT OF VARIABLE FINITE MEMORY},
booktitle={Proceedings of the 6th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2009},
pages={104-109},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002206501040109},
isbn={978-989-674-001-6},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - FILTERING AND COMPRESSION OF STOCHASTIC SIGNALS UNDER CONSTRAINT OF VARIABLE FINITE MEMORY
SN - 978-989-674-001-6
AU - Torokhti A.
AU - Miklavcic S.
PY - 2009
SP - 104
EP - 109
DO - 10.5220/0002206501040109