Semi-parametric linear mixed effects model for vehicles identification

Yow-Jen Jou*, Chai-Tzu Yang, Chien-Chia Huang, Jennifer Yuh-Jen Wu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

In order to make the detecting of the lanes and the types of the vehicles traveling on various roadways affordable, radio-frequency (RF) system-on-chip is designed and will be mounted on the roadside to collect vehicle information. We use the Fast Fourier Transform (FFT) to transform the signal of the reflecting wave radar into the numerical data, and utilize it by the statistical approach to discriminate the size of cars and the lanes. In order to classify the types of the vehicles, two models are proposed to model the data. One is multivariate analysis of variance model to account for the main effect and the interaction effect between type and lane, the other is the semi-parametric linear mixed effect model to emphasize the functional characteristic of the data. Both models work well when the number of groups is small but deteriorate when the number of groups increases.
Original languageEnglish
Title of host publicationComputation In Modern Science And Engineering Vol 2, Pts A And B
EditorsG Maroulis, TE Simos
Pages984-+
Volume2
DOIs
StatePublished - 2007
EventInternational Conference on Computational Methods in Science and Engineering 2007, ICCMSE 2007 - Corfu, Greece
Duration: 25 Sep 200730 Sep 2007

Publication series

NameAIP 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

  • MANOVA; semi-parametric linear mixed effect model; fast Fourier transform (FFT); smoothing spline analysis of variance decompositions; radar

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