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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| 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 |
|
| IN, NS (Joint) |
2023-03-02 14:00 |
Okinawa | Okinawa Convention Centre + Online (Primary: On-site, Secondary: Online) |
A Routing Protocol Using Probabilistic Route Selection Based on Residual Capacity Ratio of Batteries for Wireless Sensor Networks Yuki Ofuchi, Shigetomo Kimura (Univ. of Tsukuba) IN2022-80 |
Corona-based wireless sensor networks form concentric clusters based on hop counts from the sink node. For the networks,... [more] | IN2022-80 pp.85-90 |
| 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] | |
| ICTSSL, CAS | 2023-01-27 09:25 |
Tokyo | TBD (Primary: On-site, Secondary: Online) |
On approximating chaotic behavior of a Colpitts circuit using residual nets and LSTM Kazuya Ozawa, Hideaki Okazaki (Shonan Inst. Tech) CAS2022-76 ICTSSL2022-40 |
LSTM (Long-Short Term Memory) is a neural network suitable for processing time series data. In this report, we apply LST... [more] | CAS2022-76 ICTSSL2022-40 pp.77-82 |
| 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 |
| NC, MBE (Joint) |
2021-03-03 13:25 |
Online | Online | Visualization of CNNs using Preferred Stimulus in Receptive Fields Genta Kobayashi, Hayaru Shouno (UEC) NC2020-47 |
Convolutional neural networks have shown high performance at image processing task, and they are interpreted by variou... [more] |
NC2020-47 pp.25-30 |
| ICSS, IPSJ-SPT | 2021-03-01 11:55 |
Online | Online | Implementation of Assessment System for Residual Risks of Information Leakage in Incident Countermeasures Tomohiro Noda, Hirokazu Hasegawa, Hajime Shimada, Yukiko Yamaguchi (Nagoya Univ.), Hiroki Takakura (NII) ICSS2020-32 |
Recent sophisticated targeted attacks make it difficult for us to protect our corporate resources perfectly. Therefore, ... [more] | ICSS2020-32 pp.37-42 |
| ICSS | 2020-11-26 15:25 |
Online | Online | Initial Study of Assessment System for Residual Risks of Information Leakage in Incident Countermeasures Tomohiro Noda, Hirokazu Hasegawa (Nagoya Univ.), Hiroki Takakura (NII) ICSS2020-23 |
Recently, cyber attacks become more sophisticated and cause serious damage. Especially in targeted attacks, it is diffi... [more] |
ICSS2020-23 pp.21-25 |
| MSS, CAS, IPSJ-AL [detail] | 2020-11-26 13:50 |
Online | Online | On an approximating polynomials by a Pseudo Residual Neural Network with a Power Activation Function Kazuya Ozawa, Kaito Isogai, Hideaki Okazaki (Shonan Inst. Tech) CAS2020-31 MSS2020-23 |
Since a series of successes of Deep neural networks (DNNs) with rectified linear units (ReLUs), many approximations by N... [more] | CAS2020-31 MSS2020-23 pp.68-72 |
| 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 |
| IN | 2019-01-21 15:35 |
Aichi | WINC AICHI | An Energy-harvesting-aware Routing Algorithm Considering Residual Capacity of Batteries for WSN Zihao Zhang, Shigetomo Kimura (Univ. of Tsukuba) IN2018-77 |
In a wireless sensor network, a large number of sensor nodes equipped with calculation, sensing, and wirelesscommunicati... [more] | IN2018-77 pp.31-36 |
| 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 |
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