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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| RCS, SIP, IT | 2022-01-20 15:10 |
Online | Online | [Invited Talk]
Wireless link quality prediction using physical space information in Society 5.0 Riichi Kudo, Kahoko Takahashi, Hisashi Nagata, Tomoki Murakami, Tomoaki Ogawa (NTT)  IT2021-44 SIP2021-52 RCS2021-212 |
Thanks to the great advances in wireless communication systems, many types of the wireless terminals are available. It i... [more] | IT2021-44 SIP2021-52 RCS2021-212 pp.93-94 |
| RCS, SIP, IT | 2022-01-20 17:20 |
Online | Online | Hyper Parameter Optimization with Genetic Algorithm in Reservoir Computing Based MIMO-OFDM Detection Yuanyou Chen, Hiroshi Tsutsui, Takeo Ohgane (Hokkaido Univ.) IT2021-46 SIP2021-54 RCS2021-214 |
In this paper, we investigate a MIMO-OFDM symbol detection based on reservoir computing (RC), which is a special type of... [more] | IT2021-46 SIP2021-54 RCS2021-214 pp.96-100 |
| VLD, DC, RECONF, ICD, IPSJ-SLDM (Joint) [detail] |
2021-12-02 10:10 |
Online | Online | Design Method of ECG Measurement System Using Compression Sensing Yuki Matsumura, Daisuke Kanemoto, Osamu Maida, Tetsuya Hirose (Osaka Univ) VLD2021-34 ICD2021-44 DC2021-40 RECONF2021-42 |
In recent years, there has been an increasing demand for real-time health information, e.g., electrocardiograms, to be p... [more] | VLD2021-34 ICD2021-44 DC2021-40 RECONF2021-42 pp.99-104 |
| RISING (3rd) |
2021-11-17 09:00 |
Tokyo | (Primary: On-site, Secondary: Online) |
Proposal of Incentive Mechanism for Cross-Silo Federated Learning with Differential Privacy Shota Miyagoshi, Takuji Tachibana (Univ. Fukui) |
In cross-silo federated learning, where multiple companies/organizations participate, the prediction accuracy of the glo... [more] | |
| SIS, ITE-BCT | 2021-10-07 11:40 |
Online | Online | Wireless Channel Prediction with Gaussian Process Yitu Wang (NTT), Takayuki Nakachi (former NTT), Takeru Inoue, Toru Mano, Kudo Riichi (NTT) SIS2021-12 |
With accurate knowledge of future Channel State Information (CSI), it becomes possible to better manipulate the wireless... [more] | SIS2021-12 pp.11-16 |
| AP, SANE, SAT (Joint) |
2021-07-28 12:40 |
Online | Online | Microwave Blood Pressure Estimation Using Machine Learning Kota Sasaki, Naoki Honma, Kentaro Murata, Morio Iwai, Koichiro Kobayashi (Iwate Univ.), Atsushi Sato (EQUOS RESEARCH) AP2021-27 SANE2021-17 |
This report proposes and experimentally assesses a non-contact blood pressure estimation method using a microwave and KN... [more] | AP2021-27 SANE2021-17 pp.19-24(AP), pp.12-17(SANE) |
| RCS, SR, NS, SeMI, RCC (Joint) |
2021-07-14 10:30 |
Online | Online | Deep-Unfolding Aided Optimization of Edge Weights and Step Sizes for Diffusion LMS Algorithm Yuto Nishihata, Koji Ishii (Kagawa Univ.) RCC2021-22 |
This study proposes a deep-unfolding aided parameter setting for a diffusion LMS algorithm. Distributed signal processin... [more] | RCC2021-22 pp.1-6 |
| RCS, SR, NS, SeMI, RCC (Joint) |
2021-07-14 10:55 |
Online | Online | Relaxation of Network Restriction for Deep Learning Based Consensus Problem with Eigenvector Centrality Shoya Ogawa, Koji Ishii (Kagawa Univ.) RCC2021-23 |
he convergence performance of consensus problems depends on the applied weighting factors into individual edges. Unfortu... [more] | RCC2021-23 pp.7-12 |
| PRMU, IPSJ-CVIM, IPSJ-NL | 2021-05-21 10:00 |
Online | Online | Scene Recognition of Omni-Directional Images by Patch-Based CNN Takumi Hatogai, Takao Yamanaka (Sophia Univ.) PRMU2021-3 |
In this paper, a scene recognition method for omni-directional images is proposed using patch-based convolutional neural... [more] | PRMU2021-3 pp.13-18 |
| RCS, SR, SRW (Joint) |
2021-03-05 16:30 |
Online | Online | [Invited Lecture]
A Hazardous Spot Detection Framework by Mobile Sensing and V2V Opportunistic Networks Yoshito Watanabe, Yozo Shoji (NICT) SR2020-88 |
This study proposes a framework to detect hazardous spots on roads by combining mobile sensing on commercial-use vehicle... [more] | SR2020-88 pp.91-98 |
| ITS, WBS, RCC | 2020-12-14 10:45 |
Online | Online | A Study on Relaxation of Network Restriction in Deep-Unfolding aided Consensus Shoya Ogawa, Ishii Koji (Kagawa Univ.) WBS2020-11 ITS2020-7 RCC2020-14 |
In the consensus problem with a complex network, the convergence performance deeply depends on the given parameters, i.e... [more] | WBS2020-11 ITS2020-7 RCC2020-14 pp.19-24 |
| SR, NS, SeMI, RCC, RCS (Joint) |
2020-07-09 15:00 |
Online | Online | [Invited Lecture]
Blind Adaptive Array Interference Suppression Performance with Deep Learning based SIR Estimation Kazuki Maruta (Tokyo Tech), Shun Kojima (Chiba Univ.), Daisuke Hisano (Osaka Univ.), Yu Nakayama (TUAT) RCC2020-8 NS2020-37 RCS2020-71 SR2020-16 SeMI2020-8 |
This paper proposes a blind interference estimation via deep learning approach exploiting the visualized wireless signal... [more] | RCC2020-8 NS2020-37 RCS2020-71 SR2020-16 SeMI2020-8 pp.37-41(RCC), pp.37-41(NS), pp.79-83(RCS), pp.43-47(SR), pp.31-35(SeMI) |
| NLP | 2020-05-15 13:25 |
Online | Online | Design of a Distributed Algorithm for Principal Component Analysis based on Power Method and Average Consensus Mutsuki Oura, Tsuyoshi Migita, Norikazu Takahashi (Okayama Univ.) NLP2020-4 |
Principal component analysis is one of the most important methods of multivariate analysis, and has been applied in a wi... [more] | NLP2020-4 pp.17-22 |
| ET | 2020-03-07 11:25 |
Kagawa | National Institute of Technology, Kagawa Collage (Cancelled but technical report was issued) |
Reversi's strategy learning program for junior high school students Yuya Tanaka, Akiyoshi Miyatake (NIT, Kagawa College) ET2019-99 |
Programming education will be compulsory subject at elementary school from 2020. Programming is so important in modern s... [more] | ET2019-99 pp.131-134 |
| NC, MBE (Joint) |
2020-03-05 10:20 |
Tokyo | University of Electro Communications (Cancelled but technical report was issued) |
An extension of the H_infinity learning to deep neural networks Yasuhiro Sugawara, Kiyoshi Nishiyama (Iwate University) NC2019-92 |
In recent years, deep neural networks have achieved remarkable research results. In this study, we propose a method to e... [more] | NC2019-92 pp.95-100 |
| COMP | 2020-03-01 16:50 |
Tokyo | The University of Electro-Communications (Cancelled but technical report was issued) |
Online Learning for A Repeated Markovian Game with 2 States Shangtong Wang, Shuji Kijima (Kyushu Univ.) COMP2019-55 |
We consider a new problem of learning in repeated games. In our model, the players play on one of the several game matri... [more] | COMP2019-55 pp.65-68 |
| ITE-HI, IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] | 2020-02-27 13:30 |
Hokkaido | Hokkaido Univ. (Cancelled but technical report was issued) |
A Note on Estimation of rock compressive strength index from drilling data based on online learning Kentaro Yamamoto, Ren Togo, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
It is necessary to evaluate the ground condition of the rock mass for safe and efficient tunnel construction. Currently,... [more] | |
| NLP, NC (Joint) |
2020-01-24 13:50 |
Okinawa | Miyakojima Marine Terminal | Ternarized Backpropagation for Edge AI and its FPGA Implementation Tatsuya Kaneko, Yoshiharu Yamagishi, Hiroshi Momose, Tetsuya Asai (Hokkaido Univ.) NLP2019-95 |
In recent years there has been growing more interest in machine/deep learning. As following this movement, many types ... [more] |
NLP2019-95 pp.53-58 |
| IPSJ-SLDM, RECONF, VLD, CPSY, IPSJ-ARC [detail] | 2020-01-23 14:45 |
Kanagawa | Raiosha, Hiyoshi Campus, Keio University | Study of a Simplified Digital Spiking Neuron and Its FPGA Implementation Tomohiro Yoneda (NII) VLD2019-75 CPSY2019-73 RECONF2019-65 |
A simplified digital spiking neural network implementable on FPGAs is proposed in order to reduce necessary resources an... [more] | VLD2019-75 CPSY2019-73 RECONF2019-65 pp.135-140 |
| QIT (2nd) |
2019-11-18 13:50 |
Tokyo | Gakushuin University | [Poster Presentation]
Predicting Time-Series Data with Quantum Recurrent Neural Network Yuto Takaki, Kosuke Mitarai, Makoto Negoro, Keisuke Fujii, Masahiro Kitagawa (Osaka Univ.) |
We propose quantum algorithm for time-series prediction by using a quantum circuit that has similar structure as the rec... [more] | |
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