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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IT, ISEC, RCC, WBS | 2022-03-10 14:40 |
Online | Online | Optimum Clustering Method for Data Driven Consensus Problem considering Network Centrality Shoya Ogawa, Koji Ishii (Kagawa Univ)  IT2021-109 ISEC2021-74 WBS2021-77 RCC2021-84 |
In consensus problems in complex networks, the convergence performance deeply depends on the weighting factors. IKishida... [more] | IT2021-109 ISEC2021-74 WBS2021-77 RCC2021-84 pp.155-160 |
| RCS, SR, SRW (Joint) |
2022-03-04 09:55 |
Online | Online | Low-overhead Beam and Power Allocation Using Deep Learning for mmWave Networks Yuwen Cao, Tomoaki Ohtsuki (Keio Univ.) RCS2021-284 |
In this report, we develop a novel deep learning (DL)-based hybrid beam and power allocation approach for multiuser mill... [more] | RCS2021-284 pp.159-163 |
| EA, SIP, SP, IPSJ-SLP [detail] | 2022-03-01 14:20 |
Okinawa | (Primary: On-site, Secondary: Online) |
Fast Distortion Pedal Modeling with Fine-Tuning Haruki Shoji, Kento Yoshimoto, Daiki Saka, Hiroki Kuroda, Daichi Kitahara, Kenichiro Tanaka, Akira Hirabayashi (Ritsumeikan Univ.) EA2021-75 SIP2021-102 SP2021-60 |
We propose a fast modeling method for distortion pedals based on deep learning. For modeling many times with different p... [more] | EA2021-75 SIP2021-102 SP2021-60 pp.70-75 |
| LOIS, ICM | 2022-01-27 13:00 |
Online | Online | Proposal and Evaluation of a Methodology to Estimate Cause of Failure Based on Multiple Monitoring Data on Microservice Systems Shun Matsumoto, Masaru Sakai, Kensuke Takahashi, Satoshi Kondoh (NTT) ICM2021-33 LOIS2021-31 |
Microservice architecture, which divides service components into small components and provides services by having each c... [more] | ICM2021-33 LOIS2021-31 pp.1-6 |
| MI | 2022-01-26 13:52 |
Online | Online | Analysis of Object Detection Model Towards Precise Colorectal-Polyp Detection Hayato Itoh (Nagoya Univ.), Masashi Misawa (Showa Univ.), Yuichi Mori (Oslo Univ.), Shin-Ei Kudo (Showa Univ.), Masahiro Oda, Kensaku Mori (Nagoya Univ.) MI2021-63 |
We propose new visual explanation methods for a trained object detector toward precise automated polyp localisation. Sev... [more] | MI2021-63 pp.76-81 |
| IE | 2022-01-24 13:05 |
Tokyo | National Institute of Informatics (Primary: On-site, Secondary: Online) |
Reduction of Truncation Artifacts by Massive-Training Artificial Neural Network (MTANN) in Fast-Acquisition MRI of the Knee Maodong Xiang, Ze Jin, Kenji Suzuki (Tokyo Tech) IE2021-31 |
MRI has a relatively long acquisition time, leading to patient comfort problems and artifacts from patient motion. Accel... [more] | IE2021-31 pp.21-26 |
| AP, RCS (Joint) |
2021-11-11 10:55 |
Nagasaki | NBC-Bekkan (Nagasaki) (Primary: On-site, Secondary: Online) |
A Study on Receive Beamforming for Multi-User Detection with Trainable Gaussian Belief Propagation Takanobu Doi, Jun Shikida, Kazushi Muraoka, Naoto Ishii (NEC), Daichi Shirase, Takumi Takahashi (Osaka Univ.), Shinsuke Ibi (Doshisha Univ.) RCS2021-159 |
We propose two digital receive beamforming (BF) methods for low-complexity and high-accuracy uplink signal detection via... [more] | RCS2021-159 pp.86-91 |
| ICTSSL, IEE-SMF, IN | 2021-10-22 10:50 |
Online | Online | Differences in Classification Accuracy of Landslide Hazard using Fixed-point Observation Images due to Network and Image Processing in Deep Learning Keisuke Tokumoto, Makoto Kobayashi, Koichi Shin, Masahiro Nishi (Hiroshima City Univ.) ICTSSL2021-26 |
In recent years，several landslides included by heavy rains have caused a lot of human damage in Hiroshima. Early evacuat... [more] | ICTSSL2021-26 pp.48-53 |
| PRMU | 2021-10-09 09:30 |
Online | Online | Explaining Adversarial Examples by the Embedding Structure of Data Manifold Hajime Tasaki, Yuji Kaneko, Jinhui Chao (Chuo Univ.) PRMU2021-19 |
It is widely known that adversarial examples cause misclassification in classifiers using deep learning. Inspite of nume... [more] | PRMU2021-19 pp.17-21 |
| PRMU | 2021-08-26 10:00 |
Online | Online | Unsupervised non-rigid alignment for multiple noisy images Takanori Asanomi, Kazuya Nishimura, Heon Song, Junya Hayashida (Kyushu Univ.), Hiroyuki Sekiguchi (Kyoto Univ.), Takayuki Yagi (Luxonus), Imari Sato (NII), Ryoma Bise (Kyushu Univ.) PRMU2021-7 |
We propose a deep non-rigid alignment network that can simultaneously perform non-rigid alignment and noise decompositio... [more] | PRMU2021-7 pp.1-6 |
| RCS | 2021-06-25 10:20 |
Online | Online | A Study on Deep Unfolding-Aided Quantized AMP for High-Dimensional Signal Detection Atsunori Shimamura, Takumi Takahashi (Osaka Univ), Shinsuke Ibi (Doshisha Univ), Seiichi Sampei (Osaka Univ) RCS2021-68 |
We consider a look-up table (LUT)-based multi-user detection (MUD) via quantized approximate message passing (AMP) in la... [more] | RCS2021-68 pp.226-231 |
| MI | 2021-03-15 15:15 |
Online | Online | Deep State-Space Modeling of FMRI Images with Disentangle Attributes Koki Kusano (Kobe Univ.), Takashi Matsubara (Osaka Univ.), Kuniaki Uehara (Osaka Gakuin Univ.) MI2020-59 |
As well as the disorder and other targets, nuisance attributes such as age, gender, and scanner specifications underlie ... [more] | MI2020-59 pp.56-61 |
| PRMU, IPSJ-CVIM | 2021-03-04 16:35 |
Online | Online | Quantifying detection quality in the presence of adversarial inputs in dermatological images Mishra Sourav (UTokyo), Hideaki Imaizumi (exMedio), Toshihiko Yamasaki (UTokyo) PRMU2020-82 |
We have tested deep learning based detection on dermatological conditions commonly encountered in clinical settings. Des... [more] | PRMU2020-82 pp.77-82 |
| IBISML | 2021-03-03 11:15 |
Online | Online | IBISML2020-46 |
Developing a profitable trading strategy is a central problem in the financial industry. In this presentation, we develo... [more] | IBISML2020-46 p.38 |
| PRMU | 2020-12-18 14:55 |
Online | Online | Regularization Using Knowledge Distillation in Learning Small Datasets Ryota Higashi, Toshikazu Wada (Wakayama Univ.) PRMU2020-61 |
Knowledge distillation is a method mainly used for compressing deep learning models, but it has recently gained attentio... [more] | PRMU2020-61 pp.133-138 |
| BioX | 2020-11-25 11:10 |
Online | Online | GAN based feature-level supportive method for improved adversarial attacks on face recognition Zhengwei Yin (USTC/Hosei Univ.), Kaoru Uchida (Hosei Univ.) BioX2020-35 |
With the rapid development of deep neural networks (DNN), DNN-based face recognition technologies are also achieving gre... [more] | BioX2020-35 pp.1-6 |
| MBE, NC, NLP, CAS (Joint) [detail] |
2020-10-30 17:35 |
Online | Online | Hyperbolic Space Embedding for Open Set Recognition Shota Tatarai (Senshu Univ.), Yuta Ashihara (Nihon Univ/Glia Computing Co.,Ltd.), Kenji Aoki (Glia Computing Co.,Ltd.), Masahiko Osaawa (Nihon Univ./Senshu Univ.) NC2020-27 |
Many of deep learning algorithms perform well when the training and testing data are sampled from the same class space.... [more] |
NC2020-27 pp.100-105 |
| IBISML | 2020-10-21 14:40 |
Online | Online | IBISML2020-23 |
Continuous deep learning architectures have recently re-emerged as variants of Neural Ordinary Differential Equations (N... [more] | IBISML2020-23 p.41 |
| PRMU | 2020-10-09 13:45 |
Online | Online | A Tennis Racket Tip Detection Method Using A Convolutional Neural Network Generating Confidence Maps Taichi Hosoi, Hirohisa Hioki (Kyoto Univ.) PRMU2020-26 |
As image processing technologies develop recently, many studies on sports video analysis are performed for various purpo... [more] | PRMU2020-26 pp.44-49 |
| SCE | 2020-09-02 13:10 |
Online | Online | Development of Superconducting Filter for Deep Space Exploration New Antenna Takuma Hayashi, Naoto Sekiya (Univ. of Yamanashi), Takeshi Ohno (Nitsuki,Japan Communication Equipment) SCE2020-1 |
A high-temperature superconducting (HTS) compact bandpass filter (BPF) for deep space exploration receiving system is de... [more] | SCE2020-1 pp.1-4 |
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