| 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 # | |
| SIP, SP, EA, IPSJ-SLP [detail] | 2024-02-29 16:20 |
Okinawa | (Primary: On-site, Secondary: Online) |
 EA2023-77 SIP2023-124 SP2023-59 |
In this paper, we consider a dynamic sensor placement problem where sensors can move within a network over time. Sensor ... [more] | EA2023-77 SIP2023-124 SP2023-59 pp.97-102 |
| EMM | 2024-01-16 14:25 |
Miyagi | Tohoku Univ. (Primary: On-site, Secondary: Online) |
Comparison of multi-factor authentication and improved authentication methods for increased security Kazuki Takeda, Youichi Takashima (Kaishi Professional Univ) EMM2023-81 |
Password authentication is used as an authentication method for various services, and while ID+password authentication i... [more] | EMM2023-81 pp.7-12 |
| ICSS, IPSJ-SPT | 2023-03-13 17:30 |
Okinawa | Okinawaken Seinenkaikan (Primary: On-site, Secondary: Online) |
Lessons Learned from Construction of the Security Technical Term Dictionary Misato Matsuda, Akira Fujita, Yu Tsuda, piyokango, Daisuke Inoue (NICT) ICSS2022-65 |
In order to do OSINT, natural language processing technology for processing documents is an important elemental technolo... [more] | ICSS2022-65 pp.103-108 |
| SIS | 2023-03-02 11:00 |
Chiba | Chiba Institute of Technology (Primary: On-site, Secondary: Online) |
Blink detection from one-dimensional face signal by using convolutional sparse dictionary learning Souichiro Maruyama, Makoto Nakashizuka (CIT) SIS2022-40 |
In this report, a blink detection method from average intensities of whole facial videos using convolutional dictionary... [more] | SIS2022-40 pp.1-4 |
| SIP | 2022-08-25 14:15 |
Okinawa | Nobumoto Ohama Memorial Hall (Ishigaki Island) (Primary: On-site, Secondary: Online) |
Multiresolution Convolutional-Sparse-Coded Dynamic Mode Decomposition and Its Application to Riverbed State Estimation Eisuke Kobayashi, Shogo Muramatsu, Hiroyasu Yasuda, Kiyoshi Hayasaka (Niigata Univ.) SIP2022-54 |
In this report, we propose a method that incorporates multi-resolution representation into Convolutional-Sparse-Coded Dy... [more] | SIP2022-54 pp.25-30 |
| SIP | 2022-08-26 15:51 |
Okinawa | Nobumoto Ohama Memorial Hall (Ishigaki Island) (Primary: On-site, Secondary: Online) |
Design of Structured Convolutional Dictionary by Manifold Optimization for Image Restoration Soushi Takahashi, Shogo Muramatsu (Niigata Univ.) SIP2022-76 |
This work reports on the effectiveness of a structured convolutional dictionary learning that introduces manifold optimi... [more] | SIP2022-76 pp.134-139 |
| IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] | 2022-02-21 12:45 |
Online | Online | Quality Assessment for 3D CG Image Colorization Using Visible Digital Watermarking after Noise Removal Based on Sparse Dictionary Learning Coding Norifumi Kawabata (Hokkaido Univ.) |
Thus far, we discussed to represent image data whether it is possible or not to represent meaning image how requirement ... [more] | |
| IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] | 2022-02-22 11:40 |
Online | Online | English Situation-dependent Sentiment Polarity Dictionary Toshiki Matsumura, Atsushi Takada (UTokyo), Yoshinobu Kano (Shizuoka Univ.), Toshihiko Yamasaki (UTokyo) ITS2021-49 IE2021-58 |
An sentiment polarity dictionary is indispensable for reputation and sentiment analysis using Social Network Services (S... [more] | ITS2021-49 IE2021-58 pp.145-150 |
| IA, ICSS | 2021-06-22 11:15 |
Online | Online | A Solution for Recovering Missing Links in Network Topology using Sparse Modeling Ryotaro Matsuo, Hiroyuki Ohsaki (Kwansei Gakuin Univ.) IA2021-14 ICSS2021-14 |
In recent years, sparse modeling, which is a statistical approach, has been applied to many practical problems mostly in... [more] | IA2021-14 ICSS2021-14 pp.74-79 |
| EMM, IT | 2021-05-20 16:10 |
Online | Online | [Invited Talk]
Secure Computation of Sparse Modeling
-- Edge AI with Lightweight and Small Amounts of Data -- Takayuki Nakachi (Univ. of the Ryukyus) IT2021-6 EMM2021-6 |
With the advent of the big data, IoT, AI era, all digital contents continue to increase. Sparse modeling is drawing atte... [more] | IT2021-6 EMM2021-6 pp.31-36 |
| WIT, IPSJ-AAC | 2021-03-06 15:05 |
Online | Online | Development of Sign Language Dictionary System with Sign Language Recognition Function Kimura Tsutomu, Kosuke Suzuki, Ishihama Haru, Yoshida Asuka (NIT, Toyota College), Kanda Kazuyuki (National Museum of Ethnology) WIT2020-39 |
In this paper, we describe a sign language dictionary system with a sign language recognition function. Kimura Laborator... [more] | WIT2020-39 pp.53-58 |
| IA | 2020-10-01 13:15 |
Online | Online | A Study on Recovering Network Topology with Missing Links using Sparse Modeling Ryotaro Matsuo, Hiroyuki Ohsaki (Kwansei Gakuin Univ.) IA2020-3 |
In recent years, sparse modeling, which is a statistical approach, has been applied to many practical problems mostly in... [more] | IA2020-3 pp.10-13 |
| MI, IE, SIP, BioX, ITE-IST, ITE-ME [detail] | 2020-05-28 10:50 |
Online | Online | [Special Talk]
High-dimensional Signal Restoration by Convolutional Networks Driving Fusion Across Multiple Disciplines
-- Sparse Modeling and Convolutional Dictionary Learning -- Shogo Muramatsu (Niigata Univ.) |
This talk outlines a restoration process of high-dimensional signals such as image and volumetric data. With the develop... [more] | |
| NLP, NC (Joint) |
2020-01-24 11:10 |
Okinawa | Miyakojima Marine Terminal | Proposal of Compression Method for Planetary Surface Image using Sparse Coding Yoshifumi Uesaka, Hayaru Shouno (UEC) NC2019-65 |
In recent years, the demand for space development has been increasing. We treat an efficient image transmitting system f... [more] | NC2019-65 pp.33-38 |
| OFT | 2020-01-17 09:00 |
Aichi | NINS Okazaki Conference Center | Denoising of Brillouin gain spectrum via dictionary learning considering strain variation Shogo Shimizu, Shota Kitamura, Shusuke Narieda, Hiroshi Naruse (Mie Univ.) OFT2019-52 |
In the optical fiber strain measurement using the Brillouin scattering phenomenon, the strain is obtained through the an... [more] | OFT2019-52 pp.29-34 |
| IMQ | 2019-10-04 14:00 |
Osaka | Osaka University | 3D CG Image Quality Assessment Including Noise Removal Based on Sparse Dictionary Learning Coding Norifumi Kawabata (Tokyo Univ. of Science) IMQ2019-6 |
By appearing of high-definition and high-quality images, it comes to increase many chance to process image big data. If ... [more] | IMQ2019-6 pp.1-10 |
| SIS, IPSJ-AVM, ITE-3DMT [detail] | 2019-06-13 14:25 |
Nagasaki | Fukue Culture Center | Single Image Super-Resolution for being flexible to downsampling kernel by using self-examplers Shogo Seta, Takuro Yamaguchi, Masaaki Ikehara (Keio Univ) SIS2019-6 |
We propose a fast single super resolution which is comparable image quality with conventional methods without being bas... [more] | SIS2019-6 pp.29-34 |
| EA, SIP, SP | 2019-03-14 13:30 |
Nagasaki | i+Land nagasaki (Nagasaki-shi) | [Poster Presentation]
MR Image Reconstruction Using Two Types of Dictionaries and the Diagonalization of a BCCB Matrix Kazuma Nakamoto, Kosuke Fujii, Daichi Kitahara, Akira Hirabayashi (Ritsumeikan Univ.) EA2018-112 SIP2018-118 SP2018-74 |
We propose a high-quality MR image reconstruction method using both of an adaptive orthogonal dictionary and a pre-train... [more] | EA2018-112 SIP2018-118 SP2018-74 pp.75-80 |
| EMM | 2019-03-14 11:30 |
Okinawa | TBD | Image Patch Modeling using Secure Computation of Sparse Dictionary Learning Takayuki Nakachi (NTT), Hitoshi Kiya (Tokyo Metro. Univ.) EMM2018-115 |
With the advent of the big data era, digital content continues to increase. Sparse coding is attracting attention as an... [more] |
EMM2018-115 pp.129-134 |
| SIS | 2019-03-06 15:10 |
Tokyo | Tokyo Univ. Science, Katsushika Campus | Secure Computation of Sparse Dictionary Learning Takayuki Nakachi, Yukihiro Bandoh (NTT), Hitoshi Kiya (Tokyo Metro. Univ.) SIS2018-43 |
With the advent of the big data era, all digital contents continue to increase. Sparse modeling is drawing attention as ... [more] | SIS2018-43 pp.35-40 |
[Return to Top Page]