Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
AP |
2024-03-15 10:25 |
Fukui |
UNIVERSITY OF FUKUI (Primary: On-site, Secondary: Online) |
A Study on Path loss characteristics estimation methods considering geographical conditions for designing narrowband DR-IoT communication system Takato Ikegame, Naoki Ikeda, Motonari Imai, Tetsushi Ikegami (Meiji Univ.), Mineo Takai (Osaka Univ.), Susumu Ishihara (Shizuoka Univ.), Arata Kato, Shugo Kajita (STE) AP2023-212 |
A versatile variable-range IoT communication system using the VHF-High band, Diversified-Range IoT (DR-IoT) is being con... [more] |
AP2023-212 pp.63-67 |
RCS, SR, SRW (Joint) |
2024-03-13 16:40 |
Tokyo |
The University of Tokyo (Hongo Campus), and online (Primary: On-site, Secondary: Online) |
A Plug-and-Play Module for Enhancing Fault-Tolerant Distributed Inference Based on Gaussian Dropout Hou Zhangcheng, Ohtsuki Tomoaki (KU) RCS2023-267 |
Distributed inference (DI) in the Internet of Things (IoT) is becoming increasingly important as the demand for AI appli... [more] |
RCS2023-267 pp.77-82 |
ITS, IEE-ITS |
2024-03-11 14:00 |
Shiga |
BKC, Ritsumeikan Univ. (Primary: On-site, Secondary: Online) |
Doppler radar-based recognition of bicycle motions Ryoya Hayashi, Masao Masugi, Kenshi Saho (Ritsumeikan Univ.) ITS2023-80 |
In this report, we present the results of an experiment in which a Doppler radar-based method was used to detect four be... [more] |
ITS2023-80 pp.7-10 |
MI |
2024-03-03 10:17 |
Okinawa |
OKINAWAKEN SEINENKAIKAN (Primary: On-site, Secondary: Online) |
[Short Paper]
Electric field regression from head MR image by transformers for TMS Toyohiro Maki, Tatsuya Yokota, Akimasa Hirata, Hidekata Hontani (NIT) MI2023-36 |
Transcranial Magnetic Stimulation (TMS) is a non-invasive stimulation method by electric field induced by a coil placed ... [more] |
MI2023-36 pp.21-24 |
PRMU, MVE, VRSJ-SIG-MR, IPSJ-CVIM |
2024-01-26 15:46 |
Kanagawa |
Keio Univ. (Hiyoshi Campus) |
PRMU2023-48 |
In the realm of autonomous driving, end-to-end models (E2EDMs) have gained prominence due to their high predictive accur... [more] |
PRMU2023-48 pp.46-49 |
NC, MBE, NLP, MICT (Joint) [detail] |
2024-01-25 09:00 |
Tokushima |
Naruto University of Education |
The Relationship Between Metrics in the Latent Variable Space and Image Classification Performance Haruki Wakasa, Kenya Jin'no (Tokyo City Univ.) NLP2023-99 MICT2023-54 MBE2023-45 |
In recent years, models based on convolutional neural networks (CNNs) have exhibited high performance in image classific... [more] |
NLP2023-99 MICT2023-54 MBE2023-45 pp.78-81 |
AP, WPT (Joint) |
2024-01-17 16:00 |
Niigata |
Tokimate, Niigata University (Primary: On-site, Secondary: Online) |
[Invited Lecture]
A Proposal of Indoor Radio Propagation Estimation Method Using DCNN
-- On Input Maps for Improving Estimation Accuracy of Out-of-sight Areas -- Masamitsu Irikuchi, Tetsuro Imai (Tokyo Denki Univ.), Koshiro Kitao, Satoshi Suyama (NTTDOCOMO) AP2023-165 |
In the next generation communication system (6G), where further improvement of frequency utilization efficiency and powe... [more] |
AP2023-165 pp.30-34 |
SIS |
2023-12-08 14:10 |
Aichi |
Sakurayama Campus, Nagoya City University (Primary: On-site, Secondary: Online) |
Improvement of Multi-task Training for Detection of Calcification Regions in Dental Panoramic Radiographs Kazuki Iwasaki, Mitsuji Muneyasu, Taito Murano, Soh Yoshida, Akira Asano (Kansai Univ.), Nanae Dewake, Nobuo Yoshinari (Matsumoto Dental Univ.), Keiichi Uchida (Matsumoto Dental Univ. Hospital) SIS2023-43 |
Carotid arteries on dental panoramic radiographs may show areas of calcification, a sign of vascular disease. Detection ... [more] |
SIS2023-43 pp.105-110 |
EMCJ |
2023-11-24 13:25 |
Tokyo |
Kikai-Shinko-Kaikan (Primary: On-site, Secondary: Online) |
A Study on Explainability of Convolutional Neural Network Predicting Electric Characteristics of Automotive Wire Harness Based on Score Regression Activation Mapping (Score-RAM) Syumpei Ebina, Tadatoshi Sekine, Shin Usuki, Kenjiro T. Miura (Shizuoka Univ.) EMCJ2023-74 |
In this report, we propose score regression activation mapping (Score-RAM) based on explainable artificial intelligence.... [more] |
EMCJ2023-74 pp.13-18 |
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 |
NC, MBE (Joint) |
2023-10-27 13:55 |
Miyagi |
Tohoku Univ. (Primary: On-site, Secondary: Online) |
Effect on tuning properties to 1st- and 2nd-order stimuli by inactivation of internal units in Deep Convolutional Neural Network (DCNN) Anqi Wang, Maryu Horyozaki, Takahisa M. Sanada (IPU) NC2023-26 |
Object recognition relies not only on luminance, which is considered 1st-order visual feature, but also on more complex ... [more] |
NC2023-26 pp.6-11 |
AP |
2023-10-19 13:00 |
Iwate |
Iwate University (Primary: On-site, Secondary: Online) |
[Poster Presentation]
A Proposal of Indoor Radio Propagation Prediction Method by using DCNN Masamitsu Irikuchi, Teturo Imai (TDU), Koshiro Kitao, Satoshi Suyama (DOCOMO) AP2023-119 |
In the next generation communication system (6G), where further improvement of frequency utilization efficiency and powe... [more] |
AP2023-119 pp.115-116 |
MI |
2023-07-03 13:00 |
Miyagi |
Tohoku Univ. Sakura Hall |
[Special Talk]
Transition of Medical Imaging Koichi Ito (Tohoku Univ.) MI2023-10 |
Over the past decade, research in medical image processing has dramatically changed. In particular, feature extraction u... [more] |
MI2023-10 p.11 |
MI |
2023-03-06 13:15 |
Okinawa |
OKINAWA SEINENKAIKAN (Primary: On-site, Secondary: Online) |
[Short Paper]
Improvement of Small Organ Accuracy in Multi-Organ Segmentation of Abdominal CT Images Using 2.5D Deformable Convolutional CNN Yuya Okumura, Hiroyuki Kudo, Hotaka Takizawa (Univ of Tsukuba) MI2022-80 |
In multi-organ segmentation of abdominal CT images using deep learning, small organs such as the pancreas are difficult ... [more] |
MI2022-80 pp.38-39 |
SIS |
2023-03-02 13:30 |
Chiba |
Chiba Institute of Technology (Primary: On-site, Secondary: Online) |
An image watermarking method using adversarial perturbations Sei Takano, Mitsuji Muneyasu, Soh Yoshida (Kansai Univ.) SIS2022-43 |
The performance of convolutional neural networks (CNNs) has been dramatically improved in recent years, and they have at... [more] |
SIS2022-43 pp.15-20 |
DC |
2023-02-28 14:25 |
Tokyo |
Kikai-Shinko-Kaikan Bldg (Primary: On-site, Secondary: Online) |
Test Point Selection Method Using Graph Neural Networks and Deep Reinforcement Learning Shaoqi Wei, Kohei Shiotani, Senling Wang, Hiroshi Kai, Yoshinobu Higami, Hiroshi Takahashi (Ehime Univ.) DC2022-87 |
It is well known that selecting the optimal test point to maximize the fault coverage is NP-hard. Conventional heuristic... [more] |
DC2022-87 pp.27-32 |
IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] |
2023-02-22 10:45 |
Hokkaido |
Hokkaido Univ. |
A Study of Estimating 3D Skeleton of a Human Full-Body Using Images Acquired by Omnidirectional Cameras Attached to the Human Body and Deep Learning Yuta Arai, Jun Ohya (Waseda Univ.), Hiroyuki Ogata (Seikei Univ.), Seo Chanjin (Waseda Univ.) ITS2022-62 IE2022-79 |
In recent years, motion capture has been used for training in various sporting events. However, in sporting events invol... [more] |
ITS2022-62 IE2022-79 pp.112-117 |
IE |
2023-02-02 13:30 |
Tokyo |
NII (Primary: On-site, Secondary: Online) |
Recognition Based on Imperfect Information Using Deep Learning Model
-- Estimating the Number of Blocks Included in Three-Dimensional Structure -- Takuto Nakazawa, Shigeru Kubota (Yamagata Univ.) IE2022-51 |
Even though the visual information obtained is imperfect, we can estimate a correct answer by using a knowledge that we ... [more] |
IE2022-51 pp.1-5 |
EMM |
2023-01-26 09:55 |
Miyagi |
Tohoku Univ. (Primary: On-site, Secondary: Online) |
On the Transferability of Adversarial Examples between Isotropic Network and CNN models Miki Tanaka (Tokyo Metropolitan Univ.), Isao Echizen (NII), Hitoshi Kiya (Tokyo Metropolitan Univ.) EMM2022-62 |
Deep neural networks are well known to be vulnerable to adversarial examples (AEs). In addition, AEs generated for a sou... [more] |
EMM2022-62 pp.7-12 |
EA, US (Joint) |
2022-12-22 13:30 |
Hiroshima |
Satellite Campus Hiroshima |
[Poster Presentation]
Examination of Improvement of Sound Quality of Optical Bone Conduction Speech Using Convolutional Neural Network Daiki Kawamoto, Masashi Nakayama (Hiroshima City Univ) EA2022-62 |
Optical-bone-conduction speech can be obtained by using a contact-type optical microphone. As with general bone conducti... [more] |
EA2022-62 pp.7-12 |