Improvement of Waveform Analysis Method Based on Polynomial Fitting for Liquid Drop Fingerprint

Qing Song; Dan Qing Du; Lu Yang; Gao Jie Meng; Xue Fei Mao

doi:10.4028/www.scientific.net/AMR.945-949.2093

Paper Titles

The Proof of Equivalence between "Moment of Inertia Based Method" and "PCA Based Method" in LOS Detection
p.2071

Research on Ranging Method Based on Binocular Stereo Vision
p.2075

Compare with the Identifying Tape Image of Longitudinal Tear in Average Filtering and Median Filtering
p.2082

Calibration Methods of Proximate Analyzer
p.2089

Improvement of Waveform Analysis Method Based on Polynomial Fitting for Liquid Drop Fingerprint
p.2093

Mathematical Model and Simulation for Testing Aspheric Surface Using the Digital Moiré Patterns by Circular Sub-Region Stitching Method
p.2097

Design and Implementation of a Kind of Ship Underwater Electric Field Signal Collection Equipment Hardware
p.2102

Study on Resolution Capability of Beam Space MUSIC Estimation Algorithm
p.2106

Design of Intelligent Dynamic Weighing Instrument Based on AT89S52
p.2111

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 945-949Improvement of Waveform Analysis Method Based on...

Improvement of Waveform Analysis Method Based on Polynomial Fitting for Liquid Drop Fingerprint

Abstract:

Waveform analysis method is widely used for feature extraction of liquid drop fingerprint, but it is easily affected by noise. To solve this problem, waveform fitting and analysis method based on polynomial fitting is proposed, though which the waveform of liquid drop fingerprint is fitted to be a smooth curve. Experimental results show that waveform fitting and analysis method is able to reduce the standard deviation and maximum difference of eigenvectors from the same kinds of liquid, and thus increase the recognition accuracy rate of 10 kinds of water based on BP neural network from 86.5% to 100%.

You might also be interested in these eBooks

Advances in Manufacturing Science and Engineering V

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 945-949)

Pages:

2093-2096

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.945-949.2093

Citation:

Cite this paper

Online since:

June 2014

Authors:

Qing Song, Dan Qing Du, Lu Yang, Gao Jie Meng, Xue Fei Mao

Keywords:

Feature Extraction, Liquid Drop Fingerprint, Polynomial Fitting, Waveform Analysis Method

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Song Qing. Research of the identification method based on Drop Analysis Technology and Droplet Fingerprint. Tianjin University (2005).

Google Scholar

[2] Song Qing, Liu Jing, Huang Jiayong. Improvement of Waveform Analysis AlgorithmBased onLiquid Drop Fingerprint, Computer Measurement and Control. Vol. 19(2011), pp.670-672.

Google Scholar

[3] KöküerMünevver, MurtaghFionn. Waveletand entropy-based feature extraction: Application to distinguishing mixtures of beverages. Proceedings of SPIE. Vol. 4877 (2002), pp.175-182.

Google Scholar

[4] Freund Rudolf J, Wilson William J, Sa Ping. Regression Analysis : statistical modeling of a response variable. Chongqing University press (2012).

Google Scholar

[5] He Xiaoqun, Liu Wenqing. Applied regression analysis. China Renmin University Press(2011).

Google Scholar

[6] Yang Shuying. Matlab technology to realize pattern recognition and intelligent computing, version(2). Electronic Industry Press (2011).

Google Scholar