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Distributed Computing and Artificial Intelligence pp 1–8Cite as

Feature Selection Method for Classification of New and Used Bills

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Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 79))

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.

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References

  1. 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)

    Article  Google Scholar 

  2. 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)

    Google Scholar 

  3. Widrow, B., Stearns, S.D.: Adaptive Signal Processing. Prentice-Hall, New Jersey (1985)

    MATH  Google Scholar 

  4. Mallat, S.: Wavelet Tour of Signal Processing. Acaemic Press, New York (1998)

    MATH  Google Scholar 

  5. 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)

    Article  Google Scholar 

  6. Rumelhart, D.E., McClelland, J.L.: PDP Group: Parallel Distributed Processing. In: Explorations in the Microsteucture of Cognition, vol. 1. MIT Press, Massachusetts (1987)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Engineering, Osaka Institute of Technology, 5-16-1, Omiya, Asahi-ku, 535-8585, Osaka, Japan

    Sigeru Omatu, Masao Fujimura & Toshihisa Kosaka

Authors
  1. Sigeru Omatu
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  2. Masao Fujimura
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  3. Toshihisa Kosaka
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Sao Paulo at Sao Carlos, Sao Carlos, SP, Brazil

    Andre Ponce de Leon F. de Carvalho

  2. Department of Computing, Science and Control, Faculty of Science, University of Salamanca, Plaza de la Merced S/N, 37008, Salamanca, Spain

    Sara Rodríguez-González

  3. Department of Computing, Science and Control, Faculty of Science, University of Salamanca, Plaza de la Merced S/N, 37008, Salamanca, Spain

    Juan F. De Paz Santana  & Juan M. Corchado Rodríguez  & 

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© 2010 Springer-Verlag Berlin Heidelberg

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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., Rodríguez-González, S., De Paz Santana, J.F., Rodríguez, 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

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  • 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)

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