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Search Results: Conference Papers
 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 817  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
IA, CQ, MIKA
(Joint)
2022-09-15
14:55
Hokkaido Hokkaido Citizens Actives Center
(Primary: On-site, Secondary: Online)
[Invited Talk] Artificial Intelligence Approaches for Curling
Masahito Yamamoto (Hokkaido Univ.)
(To be available after the conference date) [more]
PRMU 2022-09-14
10:00
Kanagawa
(Primary: On-site, Secondary: Online)
Sampling-based path planning using trajectories obtained by reinforcement learning
Keigo Kamiyama, Kei Ota, Ryosuke Takanami, Asako Kanezaki (Tokyo Tech)
(To be available after the conference date) [more]
PN 2022-08-29
10:20
Hokkaido
(Primary: On-site, Secondary: Online)
Compensation Performance of DNN-based Nonlinear Equalizer for Optical Communication Systems
Jinya Nakamura, Kai Ikuta, Daisuke Motai, Moriya Nakamura (Meiji Univ.)
(To be available after the conference date) [more]
SIP 2022-08-26
09:54
Okinawa Nobumoto Ohama Memorial Hall (Ishigaki Island)
(Primary: On-site, Secondary: Online)
A study on rate control using Deep Q-network in IEEE802.11
Tomoki Nakashima (Kyutech), Leonardo Lanante (Ofinno), Masayuki Kurosaki, Hiroshi Ochi (Kyutech) SIP2022-63
(To be available after the conference date) [more] SIP2022-63
pp.70-74
IN, CCS
(Joint)
2022-08-04
10:00
Hokkaido Hokkaido University(Centennial Hall)
(Primary: On-site, Secondary: Online)
Investigation on Applying Data Augmentation to CycleGAN
Syuhei Kanzaki, Hidehiro Nakano (Tokyo City Univ.) CCS2022-26
In machine learning and deep learning, a huge amount of data is required for training. The image generation model GAN ex... [more] CCS2022-26
pp.1-5
EA, ASJ-H 2022-08-04
15:15
Miyagi
(Primary: On-site, Secondary: Online)
[Invited Talk] Audio Source Separation Combining Wavelet Transform and Deep Neural Network
Tomohiko Nakamura (Univ. Tokyo) EA2022-32
Audio source separation is a technique of separating an observed audio signal into individual source signals. The use of... [more] EA2022-32
p.25
SS, IPSJ-SE, KBSE [detail] 2022-07-28
16:30
Hokkaido Hokkaido-Jichiro-Kaikan (Sapporo)
(Primary: On-site, Secondary: Online)
Deep Learning Power Analysis Against Protected PRINCE
Shu Takemoto, Yoshiya Ikezaki, Yusuke Nozaki, Masaya Yoshikawa (Meijo Univ.) SS2022-4 KBSE2022-14
In recent years, with the development of deep learning, AI has been incorporated in the field of cyber security. On the ... [more] SS2022-4 KBSE2022-14
pp.19-24
SS, IPSJ-SE, KBSE [detail] 2022-07-29
13:25
Hokkaido Hokkaido-Jichiro-Kaikan (Sapporo)
(Primary: On-site, Secondary: Online)
Fault Localization for RNNs Based on Probabilistic Automata and n-grams
Yuta Ishimoto, Masanari Kondo, Naoyasu Ubayashi, Yasutaka Kamei (Kyushu Univ.) SS2022-10 KBSE2022-20
If deep learning models misbehave, serious accidents may occur.Previous studies have proposed approaches to overcome suc... [more] SS2022-10 KBSE2022-20
pp.55-60
AP, SANE, SAT
(Joint)
2022-07-27
09:25
Hokkaido Asahikawa Taisetsu Crystal Hall
(Primary: On-site, Secondary: Online)
[Invited Lecture] A Study of Rain Attenuation Prediction Method by Deep Learning
Yuji Komatsuya, Tetsuro Imai (TDU), Miyuki Hirose (Kyutech) AP2022-34
Recently, the frequency used in wireless systems has got higher significantly, such as B5G, HAPS, etc., and the importan... [more] AP2022-34
pp.1-5
AP, SANE, SAT
(Joint)
2022-07-27
10:15
Hokkaido Asahikawa Taisetsu Crystal Hall
(Primary: On-site, Secondary: Online)
[Invited Lecture] RNN Based Proactive Prediction of Received Power Using Environmental Information
Motoharu Sasaki, Naoki Shibuya, Kenichi Kawamura, Nobuaki Kuno, Minoru Inomata, Wataru Yamada, Takatsune Moriyama (NTT) AP2022-36
We report a method for predicting received power using a GRU (Gated Recurrent Unit), which is one of the RNNs (Recurrent... [more] AP2022-36
pp.12-16
CPSY, DC, IPSJ-ARC [detail] 2022-07-28
15:45
Yamaguchi Kaikyo Messe Shimonoseki
(Primary: On-site, Secondary: Online)
Acceleration of HE-Transformer with bit reduced SEAL and HEXL
Xinyi Li, Masaki Nishi, Teppei Shishido, Keiji Kimura (Waseda Univ.) CPSY2022-12 DC2022-12
While cloud applications are covering people's privacy data, their related risks, such as theft risk, leak risk, and oth... [more] CPSY2022-12 DC2022-12
pp.65-70
EMM, BioX, ISEC, SITE, ICSS, HWS, IPSJ-CSEC, IPSJ-SPT [detail] 2022-07-19
13:00
Online Online Side-Channel Attacks on Post-Quantum KEMs Using Multi-class Classification Neural Network
Yutaro Tanaka, Rei Ueno (Tohoku Univ.), Keita Xagawa, Akitra Ito, Junko Takahashi (NTT), Naofumi Homma (Tohoku Univ.) ISEC2022-7 SITE2022-11 BioX2022-32 HWS2022-7 ICSS2022-15 EMM2022-15
(To be available after the conference date) [more] ISEC2022-7 SITE2022-11 BioX2022-32 HWS2022-7 ICSS2022-15 EMM2022-15
pp.1-6
EMM, BioX, ISEC, SITE, ICSS, HWS, IPSJ-CSEC, IPSJ-SPT [detail] 2022-07-19
13:25
Online Online Revisit of Non Profiled Side Channel Analysis via Deep Learning
Kentaro Imafuku, Shinichi Kawamura, Hanae Nozaki, Junichi Sakamoto, Saki Osuka (AIST) ISEC2022-8 SITE2022-12 BioX2022-33 HWS2022-8 ICSS2022-16 EMM2022-16
After a brief review of non-profile side channel analysis using deep learning, we propose some improved versions of the ... [more] ISEC2022-8 SITE2022-12 BioX2022-33 HWS2022-8 ICSS2022-16 EMM2022-16
pp.7-12
EMD, WPT, EMCJ, PEM
(Joint)
2022-07-15
13:50
Tokyo
(Primary: On-site, Secondary: Online)
Prediction of E-field Distribution in Indoor Environments Using Deep Learning Technique
Liu Sen, Onishi Teruo, Taki Masao, Watanabe Soichi (NICT) EMCJ2022-34
As one of the important aspects of monitoring electromagnetic field (EMF) exposure levels, comprehensively grasping the ... [more] EMCJ2022-34
pp.1-5
CS 2022-07-14
12:00
Kagoshima Yakushima Environmental and Cultural Village Center
(Primary: On-site, Secondary: Online)
Studys on pollination route estimation method for flowers of pear using field images
Keita Endo (NIT), Tomotaka Kimura (Doshisha Univ.), Hiroyuki Shimizu (NIT), Yoshihiro Takemura (Tottori Univ.), Takefumi Hiraguri (NIT) CS2022-19
Fruit cultivation requires hand and long working compared with vegetable cultivation because careful cares greatly affec... [more] CS2022-19
pp.34-35
CS 2022-07-15
09:55
Kagoshima Yakushima Environmental and Cultural Village Center
(Primary: On-site, Secondary: Online)
Research on OFDM Receivers Using Deep Learning
You Yong, Chenggao Han (UEC) CS2022-30
Orthogonal Frequency-Division Multiplexing (OFDM) is widely used in wideband wireless communication systems due to its f... [more] CS2022-30
pp.74-77
NS, SR, RCS, SeMI, RCC
(Joint)
2022-07-14
13:25
Ishikawa The Kanazawa Theatre + Online
(Primary: On-site, Secondary: Online)
Deep Reinforcement Learning-based IRS-aided Wireless Communication without Channel State Information
Hashida Hiroaki, Kawamoto Yuichi, Kato Nei (Tohoku Univ.), Iwabuchi Masashi, Murakami Tomoki (NTT) RCS2022-85
Intelligent reflecting surfaces (IRSs) have attracted attention as devices that enable radio propagation, which has been... [more] RCS2022-85
pp.84-89
NS, SR, RCS, SeMI, RCC
(Joint)
2022-07-15
13:30
Ishikawa The Kanazawa Theatre + Online
(Primary: On-site, Secondary: Online)
[Short Paper] Evaluations on RNN-based interference suppression method for CS radar
Ryoto Koizumi, Xiaoyan Wang (Ibaraki Univ.), Masahiro Umehira (Nanzan Univ.), Shigeki Takeda (Ibaraki Univ.) SR2022-39
In recent years, high-resolution 77-GHz-band onboard radar has been extensively investigated since its inevitable role i... [more] SR2022-39
pp.84-86
MI 2022-07-09
10:30
Hokkaido
(Primary: On-site, Secondary: Online)
Non-rigid registration method for longitudinal chest CT images in Covid-19
Yuma Iwao (QST), Naoko Kawata, Yuki Segiguchi, Hideaki Haneishi (Chiba Univ) MI2022-41
In time series analysis of the lungs, a non-rigid registration method that compensates for differences in respiratory st... [more] MI2022-41
pp.34-38
MI 2022-07-09
10:50
Hokkaido
(Primary: On-site, Secondary: Online)
Development of a prognosis prediction for Covid-19 using deep learning
Yuma Iwao (QST), Naoko Kawata, Yuki Sekiguchi, Hideaki Haneishi (Chiba Univ) MI2022-42
Many studies have been reported on the prediction of COVID-19 severity using deep learning. However, most of them predic... [more] MI2022-42
pp.39-41
 Results 1 - 20 of 817  /  [Next]  
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