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Multidimensional distribution data association algorithm based on DNAzyme

Multidimensional distribution data association algorithm based on DNAzyme The distribution function of the current multidimensional distribution data association algorithm does not have timeliness, which causes the data to generate interference signals during the distribution process, and correlation trajectory errors. Based on DNAzyme, a multidimensional distribution data association algorithm is proposed. The classification characteristics of mostly distributed data is adopted to extract the characteristic parameters of the data. At the same time, the internal storage structure of different data is strengthened to ensure the safe storage of data and connect the signal between the sensor and the data in time, and as a result, establishes good data communication relationship. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Biometrics Inderscience Publishers

Multidimensional distribution data association algorithm based on DNAzyme

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1755-8301
eISSN
1755-831X
DOI
10.1504/ijbm.2022.121811
Publisher site
See Article on Publisher Site

Abstract

The distribution function of the current multidimensional distribution data association algorithm does not have timeliness, which causes the data to generate interference signals during the distribution process, and correlation trajectory errors. Based on DNAzyme, a multidimensional distribution data association algorithm is proposed. The classification characteristics of mostly distributed data is adopted to extract the characteristic parameters of the data. At the same time, the internal storage structure of different data is strengthened to ensure the safe storage of data and connect the signal between the sensor and the data in time, and as a result, establishes good data communication relationship.

Journal

International Journal of BiometricsInderscience Publishers

Published: Jan 1, 2022

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