| IEICE Technical Committee Submission System Conference Schedule |
Online Proceedings [Sign in] Tech. Rep. Archives |
All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| RCS | 2024-06-19 11:10 |
Okinawa | (Primary: On-site, Secondary: Online) |
Evaluation of Information Set Optimization for Neural Network Decoder for Polar Codes and Convolutional Polar Codes Yuto Nakamura (Kagawa Univ.), Satoshi Suyama, Satoshi Nagata (DOCOMO), Nobuhiko Miki (Kagawa Univ.) |
(To be available after the conference date) [more] | |
| NLP, CCS | 2024-06-06 16:45 |
Fukuoka | West Japan General Exhibition Center AIM | Hierarchical lossless depth image compression based on depth map colorization by cellular neural networks Tasuku Kuroda, Seiya Kushi, Shungo Saizuka (Chukyo Univ), Tsuyoshi Otake (Tamagawa Univ), Hisashi Aomori (Chukyo Univ)  NLP2024-27 CCS2024-14 |
The widespread of compact and inexpensive RGB-D sensors has recently led to the increased utilization of RGB-D images in... [more] | NLP2024-27 CCS2024-14 pp.57-60 |
| ITE-ME, ITE-IST, BioX, SIP, MI, IE [detail] | 2024-06-07 10:30 |
Niigata | Nigata University (Ekinan-Campus "TOKIMATE") | Evaluating qualities of NeRF and NeRF-W by changing Positional Encoding Yosei Noguchi, Jiro Katto (Waseda Univ.) SIP2024-11 BioX2024-11 IE2024-11 MI2024-11 |
We evaluate qualities of NeRF using images captured under controlled conditions and NeRF-W using images under uncontroll... [more] | SIP2024-11 BioX2024-11 IE2024-11 MI2024-11 pp.54-59 |
| NLP | 2024-05-10 11:20 |
Kagawa | Kagawa Prefecture Social Welfare Center | Lossless Color Image Compression Based on Colorization by Cellular Neural Networks Shungo Saizuka, Seiya Kushi, Tasuku Kuroda (Chukyo Univ.), Tsuyoshi Otake (Tamagawa Univ.), Hisashi Aomori (Chukyo Univ.) NLP2024-13 |
Colorization is the process that restores colors on a grayscale image. Recently, various colorization-based encoding me... [more] |
NLP2024-13 pp.63-67 |
| NC, MBE (Joint) |
2024-03-12 11:40 |
Tokyo | The Univ. of Tokyo (Primary: On-site, Secondary: Online) |
Generalization Model of Monkey V4 Neurons based on FCN encoder Tsubasa Saito, Taisei Hara, Ko Sakai (Univ. of Tsukuba) NC2023-54 |
The V4 field in the ventral visual pathway is situated as an intermediary region processing visual information crucial f... [more] | NC2023-54 pp.65-68 |
| NC, MBE, NLP, MICT (Joint) [detail] |
2024-01-24 10:00 |
Tokushima | Naruto University of Education | Hierarchical lossless compression of high dynamic range images using predictors based on cellular neural networks Seiya Kushi, Kazuki Nakashima, Hideharu Toda (Chukyo Univ.), Tsuyoshi Otake (Tamagawa Univ.), Hisashi Aomori (Chukyo Univ.) NLP2023-85 MICT2023-40 MBE2023-31 |
We have been developing a scalable lossless coding method using cellular neural networks (CNN) as predictors. This metho... [more] | NLP2023-85 MICT2023-40 MBE2023-31 pp.12-15 |
| QIT (2nd) |
2023-12-17 17:30 |
Okinawa | OIST (Primary: On-site, Secondary: Online) |
[Poster Presentation]
Analyze convergence of Quantum-Neural -Networks in the over-parametrized regime Kaito Tanaka (Keio Univ.), Naoki Yamamoto (KQCC) |
Quantum neural networks (QNN) are one of the quantum-classical hybrid algorithms, which can be realized with the current... [more] | |
| MRIS, ITE-MMS | 2023-12-08 09:30 |
Ehime | Ehime Univ. (CITE) (Primary: On-site, Secondary: Online) |
Performance evaluation of SP decoding considering the influence of recording pattern using neural network Madoka Nishikawa, Yasuaki Nakamura (Ehime Univ.), Yasushi Kanai (Niigata Ins. of Tec.), Yoshihiro Okamoto (Ehime Univ.) MRIS2023-27 |
We study an LDPC (low-density parity-check) encoding and iterative decoding method which combines LDPC codes and SP (sum... [more] | MRIS2023-27 pp.30-35 |
| TL | 2023-09-30 12:40 |
Tokyo | University of Tokyo | Shared Neural Representations of Semantic Categories for Images and Words
-- A Study Using Decoding Analysis of MEG Data -- Kai Nakajima, Jion Tominaga, Dmitry Patashov (Waseda Univ.), Keita Tanaka, Akihiko Tsukahara (TDU), Hiroki Miyanaga, Shoji Tsunematsu (SHI), Rieko Osu, Hiromu Sakai (Waseda Univ.) TL2023-16 |
Even when objects are presented as words or images, humans can identify their semantic categories. The extent to which t... [more] | TL2023-16 pp.3-8 |
| IMQ, IE, MVE, CQ (Joint) [detail] |
2023-03-16 15:15 |
Okinawa | Okinawaken Seinenkaikan (Naha-shi) (Primary: On-site, Secondary: Online) |
A Method for Fast Compression of Sign Bits of DCT Coefficients in Image Coding Fuma Ito, Chihiro Tsutake, Keita Takahashi, Toshiaki Fujii (Nagoya Univ.) IMQ2022-56 IE2022-133 MVE2022-86 |
Compressing the signs of DCT coefficients is an intractable problem in image coding because of their equiprobable charac... [more] | IMQ2022-56 IE2022-133 MVE2022-86 pp.178-181 |
| RCC, ISEC, IT, WBS | 2023-03-14 15:45 |
Yamaguchi | (Primary: On-site, Secondary: Online) |
Improvement of the Performance for Quantum Neural Network Classifiers based on Optimal Quantum Measurement Decoding Yusaku Yamada, Jun Suzuki (UEC) IT2022-106 ISEC2022-85 WBS2022-103 RCC2022-103 |
In this work, we study the problem of supervised label classification using quantum neural network (QNN). We propose a m... [more] | IT2022-106 ISEC2022-85 WBS2022-103 RCC2022-103 pp.242-247 |
| SIS | 2023-03-02 14:40 |
Chiba | Chiba Institute of Technology (Primary: On-site, Secondary: Online) |
QR code image dnoising netwroks based on decodability assessment Kazumitsu Takahashi, Makoto Nakashizuka (CIT) SIS2022-46 |
In this paper, an image denoising method for QR code images is proposed. The image recovery from the degraded QR code im... [more] | SIS2022-46 pp.33-36 |
| RCS, SR, SRW (Joint) |
2023-03-01 16:05 |
Tokyo | Tokyo Institute of Technology, and Online (Primary: On-site, Secondary: Online) |
Neural Network Based Wideband Digtial Post-distortion for Virtualized Terminal Taishi Watanabe, Takeo Ohseki, Yoshiaki Amano (KDDI Research, Inc.) RCS2022-262 |
A virtualised terminal consisting of a normal user terminal and multiple RF relay devices that perform frequency conver... [more] |
RCS2022-262 pp.88-93 |
| SP, IPSJ-SLP, EA, SIP [detail] | 2023-03-01 09:30 |
Okinawa | (Primary: On-site, Secondary: Online) |
A Study on Scheduled Sampling for Neural Transducer-based ASR Takafumi Moriya, Takanori Ashihara, Hiroshi Sato, Kohei Matsuura, Tomohiro Tanaka, Ryo Masumura (NTT) EA2022-100 SIP2022-144 SP2022-64 |
In this paper, we propose scheduled sampling approaches suited for the recurrent neural network-transducer (RNNT) that i... [more] | EA2022-100 SIP2022-144 SP2022-64 pp.147-152 |
| IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] | 2023-02-21 15:30 |
Hokkaido | Hokkaido Univ. | A Study on Adaptation Methods for Universal Deep Image Compression Koki Tsubota, Kiyoharu Aizawa (UTokyo) ITS2022-56 IE2022-73 |
In this study, we tackle universal deep image compression, which aims to compress images in arbitrary domains such as li... [more] | ITS2022-56 IE2022-73 pp.77-82 |
| IE | 2023-02-02 15:30 |
Tokyo | NII (Primary: On-site, Secondary: Online) |
[Invited Talk]
How Can We Compress Signs of DCT Coefficients in Image Coding?
-- A Method Inspired by Phase Retrieval -- Chihiro Tsutake (Nagoya Univ.) IE2022-55 |
Compressing the signs of DCT coefficients is an intractable problem in image coding because of their equiprobable charac... [more] | IE2022-55 p.19 |
| IT, RCS, SIP | 2023-01-25 10:25 |
Gunma | Maebashi Terrsa (Primary: On-site, Secondary: Online) |
A Fundamental Study on Decoding Short Length Polar Codes by Deep Learning Reona Kumaki, Hiroshi Tsutsui, Takeo Ohgane (Hokkaido Univ.) IT2022-52 SIP2022-103 RCS2022-231 |
LDPC codes, Turbo codes, and polar codes are currently known as the best channel codes achieving near Shannon limit.... [more] |
IT2022-52 SIP2022-103 RCS2022-231 pp.132-135 |
| VLD, DC, RECONF, ICD, IPSJ-SLDM [detail] | 2022-11-30 16:15 |
Kumamoto | (Primary: On-site, Secondary: Online) |
FPGA Implementation of Learned Image Compression Heming Sun (Waseda U), Qingyang Yi (UTokyo), Jiro Katto (Waseda U), Masahiro Fujita (UTokyo) VLD2022-53 ICD2022-70 DC2022-69 RECONF2022-76 |
Learned image compression (LIC) has reached a superior coding gain than traditional hand-crafted standards such as JPEG ... [more] | VLD2022-53 ICD2022-70 DC2022-69 RECONF2022-76 pp.194-199 |
| CAS, NLP | 2022-10-20 14:55 |
Niigata | (Primary: On-site, Secondary: Online) |
Hierarchical Lossless Coding with Arithmetic Coders for Each CNN Predictor Kazuki Nakashima, Ryo Nakazawa, Hideharu Toda, Hisashi Aomori (Chukyo Univ.), Tsuyoshi Otake (Tamagawa Univ.), Ichiro Matsuda, Susumu Itoh (TUS) CAS2022-23 NLP2022-43 |
We have been developing a scalable lossless coding method using the cellular neural networks (CNN) as predictors. This ... [more] |
CAS2022-23 NLP2022-43 pp.20-24 |
| 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 |
[Return to Top Page]