Radio propagation prediction using deep neural network and building occupancy estimation-Reference-Cited by-同舟云学术

Radio propagation prediction using deep neural network and building occupancy estimation

Published:2020-10-01 Issue:10 Volume:9 Page:506-511
ISSN:2187-0136
Container-title:IEICE Communications Express
language:en
Short-container-title:IEICE ComEX

Author:

Inoue Kazuya¹,Ichige Koichi¹,Nagao Tatsuya²,Hayashi Takahiro²

Affiliation:

1. Department of Electrical and Computer Engineering, Yokohama National University

2. KDDI Research Inc.

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

General Medicine

Link

https://www.jstage.jst.go.jp/article/comex/9/10/9_2020XBL0100/_pdf

Reference5 articles.

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2. [2] T. Hayashi, T. Nagao, and S. Ito, “A study on the variety and size of input data for radio propagation prediction using a deep neural network,” European Conference on Antennas and Propagation, 2020. DOI: 10.23919/EuCAP48036. 2020.9135876

3. [3] T. Nagao and T. Hayashi, “Study on radio propagation prediction by machine learning using urban structure maps,” European Conference on Antennas and Propagation, 2020. DOI: 10.23919/EuCAP48036.2020.9135353

4. [4] O. Ronneberger, P. Fischer, and T. Brox, “U-Net: convolutional networks for biomedical image segmentation,” Proc. Int. Conf. Medical Image Computing and Computer-Assisted Intervention, vol.9351, pp. 234-241, Oct. 2015. DOI: 10.1007/978-3-319-24574-4_28

5. [5] A. Krizhevsky, I. Sutskever, and G.E. Hinton, “ImageNet classification with deep convolutional neural networks,” Proc. Advances in Neural Information Processing Systems, pp. 1097-1105, 2012.

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