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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| SeMI | 2022-01-20 15:00 |
Nagano | (Primary: On-site, Secondary: Online) |
[Short Paper]
Evaluation of Few Round Training with Distillation-Based Semi-Supervised Federated Learning Yuki Yoshida (Tokyo Tech), Sohei Itahara (Kyoto Univ.), Takayuki Nishio (Tokyo Tech)  SeMI2021-65 |
This paper studies how to reduce the number of rounds in model training using Distillation-based Semi-supervised federat... [more] | SeMI2021-65 pp.48-50 |
| SeMI | 2022-01-20 15:10 |
Nagano | (Primary: On-site, Secondary: Online) |
[Short Paper]
A Study of Joint Control of Machine Learning Model and Wireless LAN Parameters in Split inference by Reinforcement Learning Kojin Yorita (Tokyo Tech.), Sohei Itahara (Kyoto Univ.), Takayuki Nishio (Tokyo Tech.), Daiki Yoda, Toshihisa Nabetani (Toshiba) SeMI2021-66 |
Distributed inference (DI) enables machine learning (ML) inference with a deep neural network on resource-constrained de... [more] | SeMI2021-66 pp.51-54 |
| SeMI | 2022-01-20 15:20 |
Nagano | (Primary: On-site, Secondary: Online) |
[Short Paper]
A Study of Beamforming Feedback-based Model-driven Angle of Departure Estimation Sohei Itahara (Kyoto Univ.), Takayuki Nishio (Kyoto Univ./Tokyo Tech.), Koji Yamamoto (Kyoto Univ.) SeMI2021-68 |
This paper introduces the angle of departure (AoD) estimation method [1] using the multiple signal classification (MUSIC... [more] | SeMI2021-68 pp.59-61 |
| SeMI | 2022-01-20 15:30 |
Nagano | (Primary: On-site, Secondary: Online) |
[Short Paper]
A Method for Improving Accuracy of Wireless Sensing with Bi-directional Beamforming Feedback Matrices Sota Kondo, Souhei Itahara, Kota Yamashita, Koji Yamamoto (Kyoto Univ.), Yusuke Koda (Univ. Oulu), Takayuki Nishio (Kyoto University/Tokyo Tech.), Akihito Taya (Kyoto Univ./Aoyama Gakuin Univ.) SeMI2021-69 |
[more] | SeMI2021-69 pp.62-64 |
| SeMI | 2022-01-21 09:40 |
Nagano | (Primary: On-site, Secondary: Online) |
[Short Paper]
Study of ACK-Less Rate Adaptation for IEEE 802.11bc Using Deep Reinforcement Learning Takamochi Kanda (Kyoto Univ.), Yusuke Koda (Univ. of Oulu), Yuto Kihira, Koji Yamamoto (Kyoto Univ.), Takayuki Nishio (Kyoto Univ./Tokyo Tech.) SeMI2021-74 |
This paper introduces an ACK-less rate adaptation to locational variation of recipient stations (STAs) for broadcast wir... [more] | SeMI2021-74 pp.86-88 |
| SeMI | 2022-01-21 09:50 |
Nagano | (Primary: On-site, Secondary: Online) |
[Short Paper]
A Study of Computer Vision-aided Single-antenna and Single-anchor RSSI Localization Considering Movable Obstructions Tomoya Sunami, Sohei Itahara (Kyoto Univ.), Yusuke Koda (Oulu Univ.), Takayuki Nishio (Kyoto Univ./Tokyo Tech.), Koji Yamamoto (Kyoto Univ.) SeMI2021-75 |
This paper shows the feasibility of single-antenna and single-RF (radio frequency)-anchor received power strength indica... [more] | SeMI2021-75 pp.89-91 |
| OCS, CS (Joint) |
2022-01-13 14:30 |
Yamaguchi | Conference room 204A・B at KDDI Ishin-hall (Primary: On-site, Secondary: Online) |
Image Compression and Progressive Retransmission Scheme on Edge Computing System for Image Data Reduction Mutsuki Nakahara, Daisuke Hisano (Osaka Univ.), Mai Nishimura (OSX), Takayuki Nishio (Tokyo Tech.), Yoshitaka Ushiku (OSX), Kazuki Maruta (Tokyo Tech.), Yu Nakayama (Tokyo Univ. of Agriculture and Tech.) CS2021-69 |
Edge computing has been getting attention due to reducing the data traffic in the backbone network. On the other hand, t... [more] | CS2021-69 pp.7-12 |
| SRW, SeMI, CNR (Joint) |
2021-11-26 15:00 |
Tokyo | Kikai-Shinko-Kaikan Bldg. (Primary: On-site, Secondary: Online) |
[Poster Presentation]
Impact of the Layout of Devices to Estimation Accuracy in Wireless Sensing Using Beamforming Feedbacks Sota Kondo, Koji Yamamoto (Kyoto Univ.), Yusuke Koda (Univ. Oulu), Kota Yamashita (Kyoto Univ.), Takayuki Nishio (Kyoto Univ./Tokyo Tech.), Akihito Taya (Kyoto Univ./Aoyama Gakuin Univ.) SRW2021-47 SeMI2021-46 CNR2021-21 |
(To be available after the conference date) [more] | SRW2021-47 SeMI2021-46 CNR2021-21 pp.68-70(SRW), pp.55-57(SeMI), pp.45-47(CNR) |
| SRW, SeMI, CNR (Joint) |
2021-11-26 15:00 |
Tokyo | Kikai-Shinko-Kaikan Bldg. (Primary: On-site, Secondary: Online) |
[Poster Presentation]
Frame-Capture-Based CSI Recomposition Pertaining to Firmware-Agnostic WiFi Sensing Ryosuke Hanahara, Sohei Itahara, Kota Yamashita (Kyoto Univ.), Yusuke Koda (Univ. Oulu), Akihito Taya (Kyoto Univ./Aoyama Gakuin Univ.), Takayuki Nishio (Kyoto Univ./Tokyo Tech.), Koji Yamamoto (Kyoto Univ.) SRW2021-48 SeMI2021-47 CNR2021-22 |
This paper presents an estimation method of channel state information (CSI) matrices using corresponding beamforming fee... [more] | SRW2021-48 SeMI2021-47 CNR2021-22 pp.71-73(SRW), pp.58-60(SeMI), pp.48-50(CNR) |
| RCS | 2021-10-22 15:00 |
Online | Online | [Poster Presentation]
Interference Source Determination Based on History of Transmissions in WLANs Koji Yamamoto, Mayu Mieda, Sota Kondo (Kyoto Univ.), Takayuki Nishio (Kyoto Univ./Tokyo Tech.), Akihito Taya (Kyoto Univ./Aoyama Gakuin Univ.), Kazuto Yano (ATR) RCS2021-141 |
In wireless communications, in general, stations can observe the loss of transmitted frames, but it is difficult to accu... [more] | RCS2021-141 pp.122-125 |
| CQ, MIKA (Joint) |
2021-09-10 12:40 |
Online | Online | [Special Invited Talk]
Toward Communication Efficient Federated Learning Takayuki Nishio (TokyoTech) CQ2021-56 |
Federated Learning (FL) is a machine learning framework that trains models using distributed data. Since FL can utilize ... [more] | CQ2021-56 pp.94-96 |
| IN, NS (Joint) |
2021-03-05 11:00 |
Online | Online | Effect of Beamforming and Vehicle Material on Communication Quality in In-Vehicle Wireless Communication with IEEE802.11ad Takumi Shiohara (Nagoya Univ.), Takayuki Nishio (Tokyo Tech.), Tutomu Murase (Nagoya Univ.) IN2020-79 |
The effect of interference on in-vehicle wireless LAN communication with IEEE 802.11ad (hereinafter 802.11ad) for in-veh... [more] | IN2020-79 pp.150-155 |
| SeMI | 2021-01-20 13:20 |
Online | Online | A Study of Online Training Method for Image-based Wireless Link Quality Prediction Sohei Itahara (Kyoto Univ), Takayuki Nishio (Tokyo Tech), Masahiro Morikura, Koji Yamamoto (Kyoto Univ) SeMI2020-44 |
Machine-learning-based prediction of future wireless link quality is an emerging technique that can potentially improve ... [more] | SeMI2020-44 pp.7-9 |
| CQ, CBE (Joint) |
2021-01-20 10:05 |
Online | Online | IEEE 802.11ad Communication Quality Measurement in In-vehicle Wireless Communication with Real Machines Ryoko Nino (Nagoya Univ.), Takayuki Nishio (Titech), Tutomu Murase (Nagoya Univ.) CQ2020-61 |
This paper demonstrates the feasibility of IEEE 802.11ad-based in-vehicle communication for a wireless harness. IEEE 802... [more] | CQ2020-61 pp.5-10 |
| CQ, CBE (Joint) |
2021-01-21 16:00 |
Online | Online | [Poster Presentation]
QoS Measurement Using Real Machine with Interference in IEEE 802.11ad In-Vehicle Wireless Communication Ryoko Nino (Nagoya Univ.), Takayuki Nishio (Titech), Tutomu Murase (Nagoya Univ.) CQ2020-74 |
Communication quality of IEEE 802.11ad in in-vehicle communication for a wireless harness is investigated on the effect ... [more] | CQ2020-74 pp.62-63 |
| SRW, SeMI, CNR (Joint) |
2020-11-26 16:00 |
Online | Online | [Poster Presentation]
Feature Extraction Method for Wireless LANs Channel Selection Based on Contextual Bandit Learning Kota Yamashita (Kyoto Univ.), Shotaro Kamiya (Sony Corp.), Koji Yamamoto (Kyoto Univ.), Takayuki Nishio (Tokyo Tech.), Masahiro Morikura (Kyoto Univ.) SeMI2020-24 |
[more] | SeMI2020-24 pp.39-42 |
| SRW, SeMI, CNR (Joint) |
2020-11-26 16:00 |
Online | Online | [Poster Presentation]
Adversarial Reinforcement Learning-based Robust Access Point Coordination Against Uncoordinated Interference Yuto Kihira, Yusuke Koda, Koji Yamamoto (Kyoto Univ.), Takayuki Nishio (Tokyo Tech.), Masahiro Morikura (Kyoto Univ.) SeMI2020-25 |
[more] | SeMI2020-25 pp.43-46 |
| SeMI | 2020-01-31 10:00 |
Kagawa | [Poster Presentation]
Communication-Efficient Federated Learning Using Non-Labeled Data Souhei Itahara, Takayuki Nishio, Masahiro Morikura, Koji Yamamoto (Kyoto Univ) SeMI2019-109 |
Federated learning (FL) is a machine learning setting where many mobile devices collaboratively train a machine learning... [more] | SeMI2019-109 pp.47-48 |
|
| SeMI | 2020-01-31 10:00 |
Kagawa | [Poster Presentation]
Experimental Evaluation of Federated Learning in Real Networks Naoya Yoshida, Takayuki Nishio, Masahiro Morikura, Koji Yamamoto (Kyoto Univ.) SeMI2019-110 |
Federated Learning (FL) is a decentralized learning mechanism, which enables to train machine learning (ML) models using... [more] | SeMI2019-110 pp.49-50 |
|
| SeMI | 2020-01-31 10:00 |
Kagawa | [Poster Presentation]
A Study of MCS Index Prediction Using Depth Images for Bit-Rate Control in mmWave Communications Tomoya Mikuma, Takayuki Nishio, Masahiro Morikura (Kyoto Univ.), Yusuke Asai, Ryo Miyatake (NTT) SeMI2019-111 |
In millimeter-wave (mmWave) communications, link quality is decreased seriously when a line-of-sight (LOS) path is block... [more] | SeMI2019-111 pp.51-52 |
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