Abstract
According to the progress of office automation, it becomes important to classify new and old bills automatically. In this paper, we adopt a new type of sub-band adaptive digital filters to extract the feature for classification of new and fatigued bills. First, we use wavelet transform to resolve the measurement signal into various frequency bands. For the data in each band, we construct an adaptive digital filter to cancel the noise included in the frequency band. Then we summarize the output of the filter output in each frequency band. The experimental results show the effectiveness of the proposed method to remove the noise.
This work was supported by Research Project-in-Aid for Scientific Research (2010) No. 20360178 in JSPS, Japan.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Teranishi, M., Omatu, S., Kosaka, T.: Continuous Fatigue Level Estimation for the Classification of Fatigued Bills Based on an Acoustic Signal Feature by a Supervised SOM. Artificial Life and Robotics 13(2), 547–550 (2009)
Kang, D., Omatu, S., Yoshioka, M.: New and Used Bills Classification Using Neural Networks. IEICE Trans. on Fundamentals of Electronics, Communications, and Computer Sciences E82A(8), 1511–1516 (1999)
Widrow, B., Stearns, S.D.: Adaptive Signal Processing. Prentice-Hall, New Jersey (1985)
Mallat, S.: Wavelet Tour of Signal Processing. Acaemic Press, New York (1998)
Wang, B., Omatu, S., Abe, T.: Identification of the Defective Transmission Devices Using the Wavelet Transfom. IEEE Trans. on Pattern Analysis and Machine Intelligence 27(3), 919–928 (2005)
Rumelhart, D.E., McClelland, J.L.: PDP Group: Parallel Distributed Processing. In: Explorations in the Microsteucture of Cognition, vol. 1. MIT Press, Massachusetts (1987)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Omatu, S., Fujimura, M., Kosaka, T. (2010). Feature Selection Method for Classification of New and Used Bills. In: de Leon F. de Carvalho, A.P., RodrÃguez-González, S., De Paz Santana, J.F., RodrÃguez, J.M.C. (eds) Distributed Computing and Artificial Intelligence. Advances in Intelligent and Soft Computing, vol 79. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14883-5_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-14883-5_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14882-8
Online ISBN: 978-3-642-14883-5
eBook Packages: EngineeringEngineering (R0)