Signal recognition by nonparametric discrimination of functional data for vehicle transportation system

Yow-Jen Jou*, Yung-Tsai Lu, Chien Lun Lan, Hsia Lee

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Traffic congestion is a serious and general problem in our daily life. Real time vehicle information is essential to the advanced traffic management. The recognition and statistics of traffic flow among different types of vehicles would be contributive to improve traffic block. This paper considers the dataset recorded by the radar microwave detector with the intensity of reflecting waves and the types of vehicles. The data is treated as functional data and then classification would be proposed to be performed by nonparametfic discrimination of functional data with three forms of Proximity. The proximity with lower misclassification rate would be adopted for the data. The results show that the misclassification rate is pretty satisfactory if the number of groups is two and as the number of group increases the misclassification rate increases as expected.
Original languageEnglish
Title of host publicationComputation In Modern Science And Engineering Vol 2, Pts A And B
Pages980-+
Volume2
StatePublished - 2007
EventInternational Conference on Computational Methods in Science and Engineering 2007, ICCMSE 2007 - Corfu, Greece
Duration: 25 Sep 200730 Sep 2007

Publication series

Name AIP Conference Proceedings
Volume2
ISSN (Print)0094-243X

Conference

ConferenceInternational Conference on Computational Methods in Science and Engineering 2007, ICCMSE 2007
CountryGreece
CityCorfu
Period25/09/0730/09/07

Keywords

  • partial least square regression; kernel; proximity; K nearest neighbors; Nonparametric classification; radar cross section; fast Fourier transform (FFT)

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  • Cite this

    Jou, Y-J., Lu, Y-T., Lan, C. L., & Lee, H. (2007). Signal recognition by nonparametric discrimination of functional data for vehicle transportation system. In Computation In Modern Science And Engineering Vol 2, Pts A And B (Vol. 2, pp. 980-+). ( AIP Conference Proceedings; Vol. 2).