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All Technical Committee Conferences (All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| RECONF | 2022-06-08 15:25 |
Ibaraki | CCS, Univ. of Tsukuba (Primary: On-site, Secondary: Online) |
A Compact High-Speed CNN Implementation based on Redundant Computational Analysis and FPGA Acceleration Li Qi, Li Hengyi, Meng Lin (Ritsumeikan Univ.)  RECONF2022-21 |
Convolutional Neural Networks (CNNs) have achieved high performance and are widely used in various applications. However... [more] | RECONF2022-21 pp.89-94 |
| CQ, IMQ, MVE, IE (Joint) [detail] |
2022-03-09 10:10 |
Online | Online (Zoom) | A study on player and ball tracking in tennis videos. Kosuke Matsumoto (Kobe univ.), Junki Tamae (iret), Nobutaka Kuroki (Kobe univ.), Kensuke Hirano (iret), Masahiro Numa (Kobe univ.) IMQ2021-16 IE2021-78 MVE2021-45 |
This paper proposes a player and ball tracking method in tennis videos with image processing techniques. The proposed me... [more] | IMQ2021-16 IE2021-78 MVE2021-45 pp.33-38 |
| IBISML | 2022-03-09 09:05 |
Online | Online | [Invited Talk]
--- Koji Fukagata (Keio Univ.) IBISML2021-39 |
In recent years, the application of machine learning to various problems of fluid mechanics has been actively studied. I... [more] | IBISML2021-39 p.32 |
| CNR, BioX | 2022-03-03 14:00 |
Online | Online | A Study on Ear Personal Authentication System Using Spectrogram Sora Masuda (Kansai Univ.), Shunsuke Kita (ORIST), Yoshinobu Kajikawa (Kansai Univ.) BioX2021-48 CNR2021-29 |
In recent years, biometric authentication, such as fingerprint and face recognition, has become widespread in smartphone... [more] | BioX2021-48 CNR2021-29 pp.13-16 |
| NLP, MICT, MBE, NC (Joint) [detail] |
2022-01-23 11:45 |
Online | Online | Adversarial Training with Knowledge Distillation considering Intermediate Feature Representation in CNNs Hikaru Higuchi (The Univ. of Electro-Communications), Satoshi Suzuki (former NTT), Hayaru Shouno (The Univ. of Electro-Communications) NC2021-44 |
Adversarial examples are one of the vulnerability attacks to the convolution neural network (CNN). The adversarialexampl... [more] | NC2021-44 pp.59-64 |
| EA, US (Joint) |
2021-12-22 15:50 |
Kumamoto | Sojo University | [Poster Presentation]
Basic study about CNN classification of liver fibrosis stages of ultrasonic B-mode images including their amplitude-envelope statistics Akiho Isshiki, Yuki Ujihara (Chiba Univ.), Dar-In Tai, Po-Hsiang Tsui (Chang Gung Univ.), Kenji Yoshida, Tadashi Yamaguchi, Shinnosuke Hirata (Chiba Univ.) US2021-52 |
The progression and therapeutic effect of diffuse liver disease can be quantitatively evaluated by the estimation of liv... [more] | US2021-52 pp.26-30 |
| RCS, NS (Joint) |
2021-12-17 14:05 |
Nara | Nara-ken Bunka Kaikan and Online (Primary: On-site, Secondary: Online) |
[Invited Lecture]
Performance evaluation on QoS Prediction between Terminals and Access Points Using Convolutional Neural Network Hiroya Ono, Yuki Sakaue, Satoshi Narikawa (NTT) NS2021-108 RCS2021-191 |
Recent mobile terminals have been able to choose from multiple connection options, and optimally accommodating them to e... [more] | NS2021-108 RCS2021-191 pp.59-64(NS), pp.82-87(RCS) |
| VLD, DC, RECONF, ICD, IPSJ-SLDM (Joint) [detail] |
2021-12-01 10:35 |
Online | Online | Basic evaluation of ReNA, a DNN accelerator using numerical representation posit Yasuhiro Nakahara, Yuta Masuda, Masato Kiyama, Motoki Amagasaki, Masahiro Iida (Kumamoto Univ.) VLD2021-24 ICD2021-34 DC2021-30 RECONF2021-32 |
In Convolutional Neural Network (CNN) accelerators for edge, numerical precision of data should be reduced as much as po... [more] | VLD2021-24 ICD2021-34 DC2021-30 RECONF2021-32 pp.43-48 |
| MI, MICT [detail] | 2021-11-05 10:00 |
Online | Online | [Short Paper]
Prediction of therapeutic response in Sjogren's syndrome using ultrasound images of parotid glands Kohei Fujiwara, Takeda Keita, Yukinori Takagi, Miho Sasaki, Sato Eida, Ikuo Katayama, Misa Sumi, Tomoya Sakai (Nagasaki Univ.) MICT2021-30 MI2021-28 |
The purpose of this study was to predict the response to treatment of SS from ultrasound (US) images of salivary glands ... [more] | MICT2021-30 MI2021-28 pp.15-16 |
| MI, MICT [detail] | 2021-11-05 10:25 |
Online | Online | Performance Improvement of Alzheimer's Disease Identification Using Cognitive Function Test Scores Daiki Endo, Koichi Ito, Takafumi Aoki (Tohoku Univ.) MICT2021-31 MI2021-29 |
As the population ages, the prevalence of Alzheimer’s disease (AD) is expected to increase. AD causes progressive brain ... [more] | MICT2021-31 MI2021-29 pp.17-21 |
| EMT, IEE-EMT | 2021-11-05 11:15 |
Online | Online | Quaternion convolutional neural networks for PolSAR land classification Yuya Matsumoto, Ryo Natsuaki, Akira Hirose (UTokyo) EMT2021-43 |
We propose a quaternion convolutional neural network (QCNN) for Polarimetric synthetic aperture radar (PolSAR) land cla... [more] |
EMT2021-43 pp.76-81 |
| 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 |
| RECONF | 2021-09-10 10:20 |
Online | Online | Convolutional neural network implementations using Vitis AI Akihiko Ushiroyama, Nobuya Watanabe, Akira Nagoya, Minoru Watanabe (Okayama Univ.) RECONF2021-19 |
Recently, Xilinx provides an FPGA-based Vitis AI development environment which is one of deep learning frameworks to acc... [more] | RECONF2021-19 pp.13-18 |
| CS | 2021-07-16 09:40 |
Online | Online | Joint Transmit Power and Beamforming Control based on Unsupervised Machine Learning for MIMO Wireless Communication Networks Naoto Tamada, Yuyuan Chang, Kazuhiko Fukawa (Tokyo Tech) CS2021-29 |
In mobile communications, densely deployed cell systems are expected to improve the system capacity drastically. However... [more] | CS2021-29 pp.63-68 |
| EMCJ | 2021-06-11 13:35 |
Online | Online | Defective Judgment Method for Automotive Wire Harness Using Convolutional Neural Network Hiromi Itaya, Tadatoshi Sekine, Shin Usuki, Kenjiro T. Miura (Shizuoka Univ.) EMCJ2021-11 |
This report proposes a defective judgment method, where a common mode current is used as an performance index, by a deep... [more] | EMCJ2021-11 pp.1-6 |
| CCS | 2021-03-29 15:40 |
Online | Online | A 3DCNN with Reduced Parameters Using Depthwise Separable Convolution Koki Ito, Hidehiro Nakano, Arata Miyauchi (Tokyo City Univ.) CCS2020-27 |
Convolutional Neural Networks (CNNs) have been used in various fields such as image and speech. In recent years, CNNs ha... [more] | CCS2020-27 pp.37-41 |
| NC, MBE (Joint) |
2021-03-03 13:00 |
Online | Online | Hybrid Sparsity in Convolutional Neural Networks Shoma Noguchi, Yukari Yamauchi (Nihon Univ.) NC2020-46 |
Convolutional neural networks (CNNs) have achieved high accuracy in areas such as image classification and object detect... [more] | NC2020-46 pp.21-24 |
| NC, MBE (Joint) |
2021-03-04 14:10 |
Online | Online | What characteristics are acquired in coding self-motion from visual motion?
-- Reconstruction of statistical relationship by neural network and its internal representation -- Daiki Nakamura, Hiroaki Gomi (NTT) NC2020-57 |
Efficient coding is a prevailing computational models of sensory coding in the brain. If the sensory information is tran... [more] | NC2020-57 pp.83-88 |
| NC, MBE (Joint) |
2021-03-04 16:25 |
Online | Online | Hierarchical Feature Extraction for Dynamic Q-Network Taishi Komatsu, Yukari Yamauchi (Nihon Univ.) NC2020-62 |
Recently, Convolutional Neural Networks (CNN), which have been successful in the field of image recognition, use a hiera... [more] | NC2020-62 pp.112-116 |
| NC, MBE (Joint) |
2021-03-05 13:25 |
Online | Online | Applying Ensemble Learning in Relay BP Keisuke Toyama, Yukari Yamauchi (Nihon Univ.) NC2020-70 |
Convolutional Neural Network (CNN) is one of the network models that can produce highly accurate output even though it u... [more] | NC2020-70 pp.157-162 |
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