Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
RISING (3rd) |
2023-10-31 10:45 |
Hokkaido |
Kaderu 2・7 (Sapporo) |
[Poster Presentation]
A Study on Channel Access Using Multi-Armed Bandit Algorithm Mai Ohta, Takashi Imanaka, Makoto Taromaru (Fukuoka Univ.) |
With the development of wireless communication technology, spectrum sharing needs due to the increase in the number of s... [more] |
|
EMM, BioX, ISEC, SITE, ICSS, HWS, IPSJ-CSEC, IPSJ-SPT [detail] |
2023-07-25 09:00 |
Hokkaido |
Hokkaido Jichiro Kaikan |
CNN-Based Iris Recognition Using Multi-spectral Iris Images Ryosuke Kuroda, Tetsuya Honda, Hironobu Takano (Toyama Prefectural Univ.) ISEC2023-36 SITE2023-30 BioX2023-39 HWS2023-36 ICSS2023-33 EMM2023-36 |
Iris recognition using a near-infrared camera is generally known as a biometric authentication method with high accuracy... [more] |
ISEC2023-36 SITE2023-30 BioX2023-39 HWS2023-36 ICSS2023-33 EMM2023-36 pp.147-151 |
EA, ASJ-H, ASJ-MA, ASJ-SP |
2023-07-03 10:45 |
Hokkaido |
|
An Idea about Pretraining in EEG Domain Xianhua Su (Univ. Yamanashi/HDU), Wanzeng Kong, Xuanyu Jin (HDU), Teruki Toya, Kenji Ozawa (Univ. Yamanashi) EA2023-15 |
Given that pre-training in the EEG domain is currently performed using unsupervised training, this approach can currentl... [more] |
EA2023-15 pp.58-63 |
IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] |
2023-02-21 15:45 |
Hokkaido |
Hokkaido Univ. |
A Residual U-Net Architecture for Shuttlecock Detection Muhammad Abdul Haq (TMU), Shuhei Tarashima (NTT Com), Norio Tagawa (TMU) |
Detection of fast-moving shuttlecocks is essential for badminton video analysis. Several methods based on deep learning ... [more] |
|
SIP, BioX, IE, MI, ITE-IST, ITE-ME [detail] |
2022-05-20 17:00 |
Kumamoto |
Kumamoto University Kurokami Campus (Primary: On-site, Secondary: Online) |
Deformable registration of 3D medical images with Deep Residual UNet Taiga Nakamura, Yuki Sato, Hiroyuki Kudo, Hotaka Takizawa (Univ. of Tsukuba) SIP2022-30 BioX2022-30 IE2022-30 MI2022-30 |
(To be available after the conference date) [more] |
SIP2022-30 BioX2022-30 IE2022-30 MI2022-30 pp.156-160 |
SR |
2022-01-25 11:15 |
Online |
Online |
An evaluation of CNN using Deep Residual Learning and Long Short-term Memory for LTE and WLAN Systems Classifications Teruji Ide (NIT, Kagoshima college), Rozeha Rashid, M A Sarijari (UTM) SR2021-75 |
In this study, we investigate and present a deep residual (ResNet) learning for modulation classification. The simulatio... [more] |
SR2021-75 pp.82-89 |
IBISML |
2022-01-17 10:40 |
Online |
Online |
Automatic Makeup Transfer with GANs and Its Quantitative Evaluation Cuilin Wang, Jun'ichi Takeuchi (Kyushu Univ.) IBISML2021-20 |
Transferring makeup from a reference image with makeup to a source image without makeup has a wide range of application ... [more] |
IBISML2021-20 pp.17-22 |
IMQ |
2021-10-22 13:45 |
Osaka |
Osaka Univ. |
A Tiny Convolutional Neural Network for Image Super-Resolution Kazuya Urazoe, Nobutaka Kuroki, Yu Kato, Shinya Ohtani (Kobe Univ.), Tetsuya Hirose (Osaka Univ.), Masahiro Numa (Kobe Univ.) IMQ2021-7 |
This paper surveys three techniques for reducing computational costs of convolutional neural network (CNN) for image sup... [more] |
IMQ2021-7 pp.2-7 |
SR |
2021-05-21 10:00 |
Online |
Online |
An evaluation of CNN using Deep Residual Learning for OFDM and Single Carrier Modulation Classification Teruji Ide (NIT, Kagoshima College), Rozeha A Rashid, Leon Chin, M A Sarijari, Rubita Sudirman (UTM) SR2021-9 |
In this study, we investigate and present a deep residual learning for modulation classification. The simulation results... [more] |
SR2021-9 pp.57-64 |
SR |
2020-11-18 11:15 |
Online |
Online |
CNN using Deep Residual Learning for Modulation Classification Teruji Ide (NIT, Kagoshima College), Rozeha A. Rashid, Leon Chin, M A Sarijari, Rubita Sudirman (UTM) SR2020-25 |
In this study, we investigate and present a deep residual learning for modulation classification. The simulation results... [more] |
SR2020-25 pp.17-21 |
IE |
2018-06-29 10:20 |
Okinawa |
|
Single-image Rain Removal Using Residual Deep Learning Takuro Matsui, Masaaki Ikehara, Takanori Fujisawa (Keio Univ.) IE2018-23 |
Most outdoor vision systems can be influenced by rainy weather conditions. In this paper, we address a rain removal prob... [more] |
IE2018-23 pp.13-18 |
PRMU |
2017-10-12 13:30 |
Kumamoto |
|
Yoshihiro Yamada, Masakazu Iwamura, Koichi Kise (Osaka Pref. Univ.) PRMU2017-72 |
(To be available after the conference date) [more] |
PRMU2017-72 pp.55-60 |
SANE |
2017-08-24 13:50 |
Osaka |
OIT UMEDA Campus |
Deep Learning for Target Classification from SAR Imagery
-- Data Augmentation and Translation Invariance -- Hidetoshi Furukawa (Toshiba Infrastructure Systems & Solutions) SANE2017-30 |
This report deals with translation invariance of convolutional neural networks (CNNs) for automatic target recognition (... [more] |
SANE2017-30 pp.13-17 |
PRMU, CNR |
2017-02-18 10:55 |
Hokkaido |
|
PRMU2016-158 CNR2016-25 |
(To be available after the conference date) [more] |
PRMU2016-158 CNR2016-25 pp.35-40 |
PRMU, MVE, IPSJ-CVIM [detail] |
2011-01-21 15:45 |
Shiga |
|
High Frequency Compensated Face Hallucination Method So Sasatani, Xian-Hua Han, Motonori Ohashi, Yutaro Iwamoto, Yen-Wei Chen (Ritsumeikan Univ.) PRMU2010-189 MVE2010-114 |
Face Hallucination method is one of learning-based super-resolution techniques, which can reconstruct a high-resolution ... [more] |
PRMU2010-189 MVE2010-114 pp.323-328 |