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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| SR | 2018-10-31 10:25 |
Overseas | Mandarin Hotel, Bangkok, Thailand | [Poster Presentation]
Spatial Spectrum Sharing among Micro Operators considering Distribution on Measurement-based Spectrum Database Hirofumi Nakajo, Yuya Aoki, Keita Katagiri, Takeo Fujii (UEC)  SR2018-78 |
This paper proposes probabilistic protection of micro cells using measurement-based spectrum database. In the 5th genera... [more] | SR2018-78 pp.45-46 |
| CQ | 2018-08-24 11:05 |
Shiga | Ritsumeikan Univ. Biwako-Kusatsu Campus (BKC) | System Time Characteristics and Optimal Aggregation Number of Non-statistical Data Aggregation Scheme Hideaki Yoshino, Kenko Ota, Takefumi Hiraguri (NIT) CQ2018-55 |
In IoT systems utilizing a large amount of small-sized sensor data, a data aggregation function, which summarizes spatia... [more] | CQ2018-55 pp.53-58 |
| ASN, NS, RCS, SR, RCC (Joint) |
2018-07-12 10:55 |
Hokkaido | Hakodate Arena | [Poster Presentation]
Proposal of Real-Spatial based data management architecture for heterogeneous service cooperation Kentaro Nagao (kyutech), Yuzo Taenaka (NAIST), Akira Nagata (iD), Hitomi Tamura (FIT), Kazuya Tsukamoto (kyutech) RCC2018-39 NS2018-52 RCS2018-97 SR2018-36 ASN2018-33 |
Recently, IoT services that mutually cooperate with data of thing, services and personal data, within people's living ar... [more] | RCC2018-39 NS2018-52 RCS2018-97 SR2018-36 ASN2018-33 pp.79-84(RCC), pp.85-90(NS), pp.97-102(RCS), pp.89-94(SR), pp.95-100(ASN) |
| NS | 2018-05-17 11:15 |
Kanagawa | Yokohama City Education Center | Dynamic power consumption prediction of data center by using deep learning and computational fluid dynamics Hayato Kuwahara, Ying-Feng Hsu (Osaka Univ.), Kazuhiro Matsuda (NTT-AT), Morito Matsuoka (Osaka Univ.) NS2018-18 |
In this paper, simply by using computational fluid dynamics (CFD) and a power consumption model incorporating each piece... [more] | NS2018-18 pp.19-24 |
| PRMU, MI, IE, SIP | 2018-05-17 15:15 |
Gifu | On OCT Volumetric Data Restoration via Hierarchical Sparsity and Hard Constraint Shogo Muramatsu, Satoshi Nagayama, Samuel Choi (Niigata Univ.), Shunsuke Ono (Tokyo Institute of Tech.), Takeru Ota, Fumiaki Nin, Hiroshi Hibino (Niigata Univ.) SIP2018-3 IE2018-3 PRMU2018-3 MI2018-3 |
This work proposes a novel restoration method for optical coherence tomography (OCT) data. OCT is a measurement techniqu... [more] | SIP2018-3 IE2018-3 PRMU2018-3 MI2018-3 pp.7-12 |
|
| VLD, DC, CPSY, RECONF, CPM, ICD, IE, IPSJ-SLDM, IPSJ-EMB, IPSJ-ARC (Joint) [detail] |
2017-11-06 14:55 |
Kumamoto | Kumamoto-Kenminkouryukan Parea | An Approach to Selection of Classifiers and their Thresholds for Machine Learning Based Fail Chip Prediction Daichi Yuruki, Satoshi Ohtake (Oita Univ), Yoshiyuki Nakamura (Renesas Electronics) VLD2017-36 DC2017-42 |
Today, semiconductor technologies have developed and advance the integration density of LSI circuits. A technique which... [more] |
VLD2017-36 DC2017-42 pp.55-60 |
| IA | 2017-08-28 13:45 |
Tokyo | IIJ Seminar Room | A Consideration of Scalable Multicast Implementation in a Datacenter Network exploiting SDN Satoshi Tanita, Toyokazu Akiyama (Kyoto Sangyo Univ) IA2017-13 |
Multicast is required for group communications in datacenter networks. However, traditional IP multicast has several is... [more] |
IA2017-13 pp.7-12 |
| MRIS, ITE-MMS | 2017-07-07 15:45 |
Tokyo | Tokyo Tech | Analysis of Holographic Scattering in Holographic Data Storage Recording Media Takeru Utsugi (HLDS) MR2017-15 |
We are developing a holographic data storage as a next generation optical disk capable of high density recording of 1 TB... [more] | MR2017-15 pp.31-36 |
| IBISML | 2016-11-16 15:00 |
Kyoto | Kyoto Univ. | Robust supervised learning under uncertainty in dataset shift Weihua Hu, Issei Sato (UTokyo), Masashi Sugiyama (RIKEN/UTokyo) IBISML2016-50 |
When machine learning is deployed in the real world, its performance can be significantly undermined because test data m... [more] | IBISML2016-50 pp.37-44 |
| ET | 2016-10-22 14:40 |
Nagasaki | Nagasaki Univ. (Bunkyo Campus) | Implementation of Conditional Branch Function in Support System for Web Survey Using LAPP Toru Nakamizo, Sachiko Morita, Hisao Hukumoto, Tatuya Hurukawa (Saga Univ) ET2016-50 |
Recently，the Web survey forms are widely utilized on various fields. The Web survey has an advantages to reduce costs i... [more] |
ET2016-50 pp.51-56 |
| RCS, CCS, SR, SRW (Joint) |
2016-03-03 10:15 |
Tokyo | Tokyo Institute of Technology | Accuracy Improvement for Spectrum Database considering Primary Signal in Time Domain Under Fading Environment Hao Wang, Koya Sato, Takeo Fujii (UEC) SR2015-99 |
Radio Environment Database (RED), as a practical technology to radio propagation estimation, is a promising solution to ... [more] | SR2015-99 pp.65-70 |
| CS, CAS | 2016-02-26 10:55 |
Wakayama | Laforet Nanki-Shirahama Hotel | The Evaluation of Colleration between Two Variables using Mean and Standard Deviation of Edge Length in Minimum Spanning Tree Okuya Fuminori, Kawahara Yoshihiro, Asami Tohru (UTokyo) CAS2015-88 CS2015-93 |
Pearson product-moment correlation coefficient is famous for evaluating association of two individual variables. Howeve... [more] |
CAS2015-88 CS2015-93 pp.45-50 |
| ITS, IE, ITE-AIT, ITE-HI, ITE-ME, ITE-MMS, ITE-CE [detail] | 2016-02-22 11:00 |
Hokkaido | Hokkaido Univ. | Multi-Volume Super Resolution for Mouse MR Images Yutaro Iwamoto, Xian-Hua Han (Ritsumei Univ.), Akihiko Shiino (Shiga Univ. of Medical Science), Yen-Wei Chen (Ritsumei Univ.) ITS2015-62 IE2015-104 |
We propose the multi-volume super-resolution method to reconstruct isotropic voxels by merging several low resolution (L... [more] | ITS2015-62 IE2015-104 pp.35-39 |
| PRMU, CNR | 2016-02-22 13:30 |
Fukuoka | [Invited Talk]
Big Data-Based Disaster Reduction, and the Role of Humans and Machines Asanobu Kitamoto (NII) PRMU2015-159 CNR2015-60 |
The potential of big data-based disaster reduction was clearly recognized after Great East Japan Earthquake in March 201... [more] | PRMU2015-159 CNR2015-60 p.131 |
|
| NS, RCS (Joint) |
2015-12-18 13:55 |
Ehime | Matsuyama Community Center | A Consideration on Content Naming Scheme in ICN Wataru Kameyama, Yong-jin Park (Waseda Univ.) NS2015-145 |
ICN is getting much attention by many researchers as one of the promising next generation networks. The content names us... [more] | NS2015-145 pp.107-112 |
| SR, SRW (Joint) |
2015-10-27 09:30 |
Tokyo | KKE | [Poster Presentation]
Active Period Detection Method of Primary Signal for Radio Environment Database Hao Wang, Takeo Fujii (UEC) SR2015-50 SRW2015-31 |
As a solution to the spectrum shortage problem, a Radio Environment Database (RED), which is an external support to prov... [more] | SR2015-50 SRW2015-31 pp.21-22 |
| NS, IN (Joint) |
2015-03-02 09:50 |
Okinawa | Okinawa Convention Center | A network model for prediction of temperature distribution in data center Shinya Tashiro, Yuya Tarutani, Go Hasegawa, Yutaka Nakamura (Osaka Univ.), Kazuhiro Matsuda (NTT - AT), Morito Matsuoka (Osaka Univ.) NS2014-190 |
In this report, we propose a network model for real-time prediction of temperature distribution in data center required ... [more] | NS2014-190 pp.81-86 |
| NS, IN (Joint) |
2015-03-02 10:10 |
Okinawa | Okinawa Convention Center | Temperature prediction for energy optimization in data centers by machine learning approaches Kazuyuki Hashimoto, Yuya Tarutani, Go Hasegawa (Osaka Univ.), Kazuhiro Matsuda, Takumi Tamura (NTT-AT), Yutaka Nakamura, Morito Matsuoka (Osaka Univ.) NS2014-191 |
In this report, we propose a temperature prediction method for energy optimization in data centers by machine learning a... [more] | NS2014-191 pp.87-92 |
| NS, IN (Joint) |
2015-03-02 11:00 |
Okinawa | Okinawa Convention Center | Prediction of temperature distribution by gaussian process dynamical model for green data center Koji Suganuma (NAIST), Yuya Tarutani, Go Hasegawa, Yutaka Nakamura (Osaka Univ.), Norimichi Ukita (NAIST), Kazuhiro Matsuda (NTT - AT), Morito Matsuoka (Osaka Univ.) NS2014-204 |
The prediction of temperature distribution is required for reducing the power consumption of the data center. In this pa... [more] | NS2014-204 pp.155-160 |
| RECONF, CPSY, VLD, IPSJ-SLDM [detail] | 2015-01-29 15:25 |
Kanagawa | Hiyoshi Campus, Keio University | Small Bandwidth Compression Hardware Exploited Distribution of Length of Prediction Residual Tomohiro Ueno, Ryo Ito, Kentaro Sano, Satoru Yamamoto (Tohoku Univ.) VLD2014-123 CPSY2014-132 RECONF2014-56 |
This paper shows a compact bandwidth compressor to increase the performance of numerical computation on FPGA. We must re... [more] | VLD2014-123 CPSY2014-132 RECONF2014-56 pp.73-78 |
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