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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| SR | 2024-05-21 10:30 |
Kagoshima | Yokacenter (Kagoshima) (Primary: On-site, Secondary: Online) |
[Short Paper]
Distributed Learning with Deep Joint Source Channel Coding for Overfitting Avoidance Issa Matsumura, Katsuya Suto (UEC) |
(To be available after the conference date) [more] | |
| HCGSYMPO (2nd) |
2023-12-11 - 2023-12-13 |
Fukuoka | Asia pacific Import Mart (Kitakyushu) (Primary: On-site, Secondary: Online) |
In rehabilitation conducted by physical therapists, one of the primary methods for assessing body movements is joint ra... [more] | ||
| CS | 2023-11-09 10:55 |
Shizuoka | Plaza Verde | Deep Joint Source-Channel Coding using Overlap Image Division for Block Noise Reduction Ryunosuke Yamamoto, Yoshiaki Inoue, Daisuke Hisano (Osaka Univ.)  CS2023-65 |
Deep Joint Source-Channel Coding (Deep JSCC), which uses deep learning to perform source and channel coding simultaneous... [more] | CS2023-65 pp.16-18 |
| SR | 2023-11-10 10:55 |
Miyagi | (Primary: On-site, Secondary: Online) |
[Short Paper]
On Model Transfer with Deep Joint Source Channel Coding Katsuya Suto, Issa Matsumura, Junichiro Yamada (UEC) SR2023-58 |
Based on the source channel separation theorem, the current multimedia transfer system employs independently designed so... [more] | SR2023-58 pp.61-63 |
| SR | 2023-11-10 11:10 |
Miyagi | (Primary: On-site, Secondary: Online) |
[Short Paper]
Model Sharing and Learning for Visually Secure Deep Joint Source Channel Coding Yuyang Fu, Katsuya Suto (UEC) SR2023-59 |
Research and development of Deep Joint Source Channel Coding (DeepJSCC) technology, which is a data-driven design of inf... [more] | SR2023-59 pp.64-66 |
| MIKA (3rd) |
2023-10-11 14:30 |
Okinawa | Okinawa Jichikaikan (Primary: On-site, Secondary: Online) |
[Poster Presentation]
Performance Evaluation of MIMO Transmission in Information Source Communication Channel Batch Coded Modulation Based on Deep Learning Shion Inokuma, Yuki Sasaki (Tokyo Univ. of Science), Daisuke Hisano (Osaka Univ.), Yu Nakayama (TUAT), Kazuki Maruta (Tokyo Univ. of Science) |
We present the results of a fundamental investigation of deep learning-based Joint Source-Channel Coding and Modulation ... [more] | |
| CS | 2023-07-28 14:10 |
Tokyo | Hachijo-machi Chamber of Commerce and Industry | Impact of Learning Models on Deep Joint Source Channel Coding Adaptable to 5G Systems Ryunosuke Yamamoto, Keigo Matsumoto, Yoshiaki Inoue (Osaka Univ.), Yuko Hara-Azumi (Tokyo Tech), Kazuki Maruta (TUS), Yu Nakayama (TUAT), Daisuke Hisano (Osaka Univ.) CS2023-56 |
With the development of 5G technology and the proliferation of IoT devices, Deep Joint Source-Channel Coding (Deep JSCC)... [more] | CS2023-56 pp.151-156 |
| SeMI, SeMI (Joint) |
2023-01-19 14:10 |
Tokushima | Naruto grand hotel (Primary: On-site, Secondary: Online) |
[Short Paper]
A Study of Joint Control Method of Wireless LAN and Machine Learning Settings for Communication-efficient Split Computing Kojin Yorita, Takayuki Nishio (Tokyo Tech), Daiki Yoda, Toshihisa Nabetani (Toshiba) SeMI2022-77 |
Split computing (SC) enables machine learning (ML) inference with a deep neural network on resource-constrained devices.... [more] | SeMI2022-77 pp.28-29 |
| MIKA (3rd) |
2022-10-14 10:40 |
Niigata | Niigata Citizens Plaza (Primary: On-site, Secondary: Online) |
[Poster Presentation]
Channel and Data Estimation via Deep Unfolding-Aided BiGaBP for Correlated Massive MIMO Koichi Maki, Tetsushi Ikegami (Meiji Univ.) |
This paper applies deep unfolding (DU) to a joint channel and data estimation (JCDE) scheme via bilinear belief propagat... [more] | |
| MBE, NC (Joint) |
2022-03-04 11:00 |
Online | Online | Observational learning with an entorhinal-hippocampal spiking neural network encoding the position of self and other Katsuya Chiguchi, Katsumi Tateno, Kensuke Takada (Kyutech) NC2021-72 |
This study proposes an entorhinal-hippocampal spiking neural network (SNN) encoding the position of self and other, and ... [more] | NC2021-72 p.138 |
| MI | 2022-01-26 14:05 |
Online | Online | [Short Paper]
Joint Learning for Multi-Phase CT Image Registration and Automatic Recognition of Anatomical Structures Based on a Deep Neural Network Ryotaro Fuwa, Xiangrong Zhou, Takeshi Hara, Hiroshi Fujita (Gifu Univ.) MI2021-64 |
Computer-aided diagnosis (CAD) systems require image registration and automatic recognition of anatomical structures on ... [more] | MI2021-64 pp.82-85 |
| SeMI | 2022-01-20 15:10 |
Nagano | (Primary: On-site, Secondary: Online) |
[Short Paper]
A Study of Joint Control of Machine Learning Model and Wireless LAN Parameters in Split inference by Reinforcement Learning Kojin Yorita (Tokyo Tech.), Sohei Itahara (Kyoto Univ.), Takayuki Nishio (Tokyo Tech.), Daiki Yoda, Toshihisa Nabetani (Toshiba) SeMI2021-66 |
Distributed inference (DI) enables machine learning (ML) inference with a deep neural network on resource-constrained de... [more] | SeMI2021-66 pp.51-54 |
| HCGSYMPO (2nd) |
2021-12-15 - 2021-12-17 |
Online | Online | The Development of Online Shared Dining System
-- Designing the Online Learning Environment to Improve Collective Metacognition -- Nahoko Kusaka (DWCLA), Yuichi Nakamura (Kyoto University), Mutsuo Sano (Osaka Institute of Technology), Mutsuo Sano (KPUM), Kenji Kanbara (Kagawa University), Hideaki Hasuo (Kansai Medical University), Nobuyuki Ueda (DWCLA) |
In this research, we focus on the function of shared joint attention to improve dyadic interaction using the remote meet... [more] | |
| PRMU, IPSJ-CVIM | 2021-03-05 16:10 |
Online | Online | Cross-view Non-local Neural Networks for Joint Representation Learning between First and Third Person Videos Zhehao Zhu, Yusuke Sugano, Yoichi Sato (UTokyo) PRMU2020-99 |
This paper introduces a cross-view non-local neural network to learn joint representations for understandinghuman activi... [more] | PRMU2020-99 pp.170-175 |
| HCGSYMPO (2nd) |
2019-12-11 - 2019-12-13 |
Hiroshima | Hiroshima-ken Joho Plaza (Hiroshima) | Development of a joint attention identification system for mother and infant interactions during shared book reading Seigo Kobayashi, Ayumi Sato, Masahiko Nawate (Shimane Univ.) |
Joint attention is important for the language development of infant and often observed in interactions during shared boo... [more] | |
| ET | 2018-09-15 11:10 |
Yamaguchi | Yamaguchi University | Development of Thinking Support Tools for Remote Joint Lesson Makoto Yokoyama, Ryo Takaoka (Yamagushi Univ.) ET2018-30 |
Over the past three years, we have designed and developed tools to support remote joint classes for (extremely) small-sc... [more] | ET2018-30 pp.13-18 |
| SP | 2018-08-27 11:35 |
Kyoto | Kyoto Univ. | [Poster Presentation]
Question answering system by integration of intention understanding and keyword extraction using attention-based LSTM Yuki Matsuyoshi, Tetsuya Takiguchi, Yasuo Ariki (Kobe Univ.) SP2018-24 |
In an information society, we have many opportunities to use machines and computer software in various fields. Before us... [more] | SP2018-24 pp.9-14 |
| BioX | 2017-10-12 15:35 |
Okinawa | Nobumoto Ohama Memorial Hall | Performance Evaluation of Gait Recognition by Metric Learning using Joint Intensity Histogram Yushiro Kashimoto, Daigo Muramatsu, Yasushi Makihara, Yasushi Yagi (Osaka Univ.) BioX2017-28 |
We evaluate the performance of gait recognition algorithm using metric learning based on log-likelihood ratio of joint i... [more] | BioX2017-28 pp.17-22 |
| ET | 2017-09-09 13:50 |
Hiroshima | Hiroshima University High School, Fukuyama | A Development of a Digital Tool Box to Deepen thought of Learner in Remote Joint Lesson Makoto Yokoyama, Ryo Takaoka (Yamaguchi Univ.) ET2017-35 |
We expect that students in primary or junior high school get not only the attitude to join in remote class and the compe... [more] | ET2017-35 pp.23-28 |
| PRMU, BioX | 2017-03-20 10:00 |
Aichi | Robust Gait Recognition for Carrying-Status by SVM-based Metric Learning using Joint Intensity Histogram Atsuyuki Suzuki, Daigo Muramatsu, Yasushi Makihara, Yasushi Yagi (Osaka Univ.) BioX2016-37 PRMU2016-200 |
This paper describes a method of joint intensity metric learning to improve the robustness of gait recognition under car... [more] | BioX2016-37 PRMU2016-200 pp.23-28 |
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