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Classification of Data Series at Vehicle Detection
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2009 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This paper purposes a new, simple and lightweight approach of previously studied algorithms that can be used for extracting of feature vectors that in turn enables one to classify a vehicle based on its magnetic signature shape.This algorithm is called ASWA that stands for Adaptive Spectral and Wavelet Analysis and it is a combination of features of a signal extracted by both of the spectral and wavelet analysis algorithms. The performance of classifiers using this feature vectors is compared to another feature vectors consisting of features extracted by Fourier transform and pattern information of the signal extracted by Hill-Pattern algorithm (CFTHP). By using ASWA-based feature vectors, there have been improvements in all of classification algorithms results such as K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Probabilistic Neural Networks (PNN). However, the best improvement rate achieved using an ASWA-Based feature vectors in K-NN algorithm. The correct rate of the classifier using CFTHP-based feature vectors was 39.82 %, which have improved to 69.93 % by using ASWA. This is corresponding an overall improvement by 76 % in correct classification rates.

Place, publisher, year, edition, pages
2009.
Series
UPTEC IT, ISSN 1401-5749 ; 09 007
Identifiers
URN: urn:nbn:se:uu:diva-111163OAI: oai:DiVA.org:uu-111163DiVA, id: diva2:279559
Presentation
(English)
Uppsok
Technology
Supervisors
Examiners
Available from: 2009-12-04 Created: 2009-12-04 Last updated: 2009-12-04Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NB
  • sv-SE
  • Other locale
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Output format
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