Mobile path loss prediction with image segmentation and classification

This paper presents an intelligent radio wave propagation prediction model by using the 2-dimension aerial image which is taken from the actual area. An suburban area is used as examples. The prediction procedure is done in three steps. First, the image segmentation is employed to divide the area im...

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Main Authors: Supachai Phaiboon, Pisit Phokharatkul, Piti Kittithamavongs
Other Authors: Mahidol University
Format: Conference or Workshop Item
Published: 2018
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Online Access:https://repository.li.mahidol.ac.th/handle/123456789/24448
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spelling th-mahidol.244482018-08-24T08:49:19Z Mobile path loss prediction with image segmentation and classification Supachai Phaiboon Pisit Phokharatkul Piti Kittithamavongs Mahidol University King Mongkut's Institute of Technology Ladkrabang Engineering This paper presents an intelligent radio wave propagation prediction model by using the 2-dimension aerial image which is taken from the actual area. An suburban area is used as examples. The prediction procedure is done in three steps. First, the image segmentation is employed to divide the area image into subgroups by using Maximum Likelihood algorithm. The second step uses the subgroup images from step 1 to determine the parameters for the fuzzy model that we use to classify the propagation areas. The final step is to plot the path loss contour on the image so the cellular cell site can be chosen. The research results show that the proposed segmentation provides an accuracy of 80-90% compared with the actual area. Therefore, cell site selection can be designed on the 2-dimension aerial map with the error less than 8 dB. 2018-08-24T01:49:19Z 2018-08-24T01:49:19Z 2007-10-01 Conference Paper 2007 International Conference on Microwave and Millimeter Wave Technology, ICMMT '07. (2007) 10.1109/ICMMT.2007.381345 2-s2.0-34748843408 https://repository.li.mahidol.ac.th/handle/123456789/24448 Mahidol University SCOPUS https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=34748843408&origin=inward
institution Mahidol University
building Mahidol University Library
continent Asia
country Thailand
Thailand
content_provider Mahidol University Library
collection Mahidol University Institutional Repository
topic Engineering
spellingShingle Engineering
Supachai Phaiboon
Pisit Phokharatkul
Piti Kittithamavongs
Mobile path loss prediction with image segmentation and classification
description This paper presents an intelligent radio wave propagation prediction model by using the 2-dimension aerial image which is taken from the actual area. An suburban area is used as examples. The prediction procedure is done in three steps. First, the image segmentation is employed to divide the area image into subgroups by using Maximum Likelihood algorithm. The second step uses the subgroup images from step 1 to determine the parameters for the fuzzy model that we use to classify the propagation areas. The final step is to plot the path loss contour on the image so the cellular cell site can be chosen. The research results show that the proposed segmentation provides an accuracy of 80-90% compared with the actual area. Therefore, cell site selection can be designed on the 2-dimension aerial map with the error less than 8 dB.
author2 Mahidol University
author_facet Mahidol University
Supachai Phaiboon
Pisit Phokharatkul
Piti Kittithamavongs
format Conference or Workshop Item
author Supachai Phaiboon
Pisit Phokharatkul
Piti Kittithamavongs
author_sort Supachai Phaiboon
title Mobile path loss prediction with image segmentation and classification
title_short Mobile path loss prediction with image segmentation and classification
title_full Mobile path loss prediction with image segmentation and classification
title_fullStr Mobile path loss prediction with image segmentation and classification
title_full_unstemmed Mobile path loss prediction with image segmentation and classification
title_sort mobile path loss prediction with image segmentation and classification
publishDate 2018
url https://repository.li.mahidol.ac.th/handle/123456789/24448
_version_ 1763498140941418496