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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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 |
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Engineering Supachai Phaiboon Pisit Phokharatkul Piti Kittithamavongs Mobile path loss prediction with image segmentation and classification |
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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. |
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Mahidol University |
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Mahidol University Supachai Phaiboon Pisit Phokharatkul Piti Kittithamavongs |
format |
Conference or Workshop Item |
author |
Supachai Phaiboon Pisit Phokharatkul Piti Kittithamavongs |
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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 |
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2018 |
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https://repository.li.mahidol.ac.th/handle/123456789/24448 |
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1763498140941418496 |