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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 152  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
SCE 2023-10-30
15:20
Miyagi RIEC, Tohoku Univ.
(Primary: On-site, Secondary: Online)
Jitter Analysis and Theoretical Calculation for Ti/Au Transition-Edge Sensor
Akihiro Kato (AIST/SOKENDAI), Kaori Hattori (AIST/KEK QUP/OPERANDO-OIL), Sachiko Takasu (AIST), Daiji Fukuda (AIST/OPERANDO-OIL) SCE2023-14
The Transition-Edge Sensor (TES) is a high-resolution detector capable of distinguishing the number of photons in visibl... [more] SCE2023-14
pp.11-15
SCE 2023-10-31
13:25
Miyagi RIEC, Tohoku Univ.
(Primary: On-site, Secondary: Online)
Fabrication of Superconducting Tunnel Junction X-ray Detector in Qufab
Tsuyoshi Noguchi (Saitama univ./AIST), Go Fujii (AIST), Tohru Taino (Saitama univ.) SCE2023-20
Superconducting tunnel junction (STJ) detectors can theoretically achieve several ten times higher energy resolution tha... [more] SCE2023-20
pp.40-43
EMM, ITE-ME, IE, LOIS, IEE-CMN, IPSJ-AVM [detail] 2023-09-07
16:30
Osaka Osaka Metropolitan University - Nakamozu Campus- (Hybrid)
(Primary: On-site, Secondary: Online)
Image Restoration using Super-Resolution and Deblur
Akari Dakeno, Terumasa Aoki (TUT) LOIS2023-9 IE2023-19 EMM2023-56
With the development of deep learning, many studies on Super-Resolution (SR) using CNN have widely been done in the worl... [more] LOIS2023-9 IE2023-19 EMM2023-56
pp.12-17
CQ, MIKA
(Joint)
(2nd)
2023-08-31
10:50
Fukushima Tenjin-Misaki Sports Park [Poster Presentation] An Exploration of a Graph Super-Resolution Algorithm using Spectra and Degree Distributionof a Graph Super-Resolution Algorithm using Spectra and Degree Distribution
Hiroyoshi Sawano (Kwansei Gakuin Univ), Ryotaro Matsuo (Fukuoka Univ), Hiroyuki Ohsaki (Kwansei Gakuin Univ)
Generally, for large-scale and complex networks, obtaining information on the entire topology is not straightforward due... [more]
MI 2023-03-06
09:57
Okinawa OKINAWA SEINENKAIKAN
(Primary: On-site, Secondary: Online)
Multi-label multi-class estimation of pathology in high-resolution chest CT images using SRGAN
Tetsuya Asakawa, Riku Tsuneda, Yuki Sugimoto (TUT), Kazuki Shimizu, Takuyuki Komoda (THC), Masaki Aono (TUT) MI2022-76
The purpose of this research, three pathologies (thickening, calcification, and cavitation) were accurately estimated as... [more] MI2022-76
pp.14-19
IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] 2023-02-22
11:30
Hokkaido Hokkaido Univ. Implementation of Hierarchical Object Detection Method for Super High-Definition Image Sensing
Makoto Sugaya, Yusei Horikawa, Renpei Yoshida, Tetsuya Matsumura (Nihon Univ.) ITS2022-65 IE2022-82
In this paper, we propose a hierarchical object detection method for a 4K super-high definition. This method is a three-... [more] ITS2022-65 IE2022-82
pp.130-135
SCE 2023-01-20
15:10
Tokyo Kikai-Shinko-Kaikan Bldg.
(Primary: On-site, Secondary: Online)
Evaluation of current-voltage characteristics of STJ detectors using membrane
Tsuyoshi Noguchi, Taiga Shibasaki (Saitama Univ.), Go Fujii, Shigetomo Shiki, Takahiro Kikuchi (AIST), Tohru Taino (Saitama Univ.) SCE2022-17
Superconducting tunnel junction (STJ) detectors can theoretically achieve several ten times higher energy resolution tha... [more] SCE2022-17
pp.23-26
SDM 2022-11-11
09:30
Online Online [Invited Talk] Toward Super Temporal Resolution by Suppression of Mixing Effects of Electrons
Takeharu Goji Etoh (Osaka Univ.), Kazuhiro Shimonomura, Taeko Ando, Yoshiyuki Matsunaga, Yutaka Hirose (Ritsumeikan Univ.), Takayoshi Shimura, Heiji Watanabe (Osaka Univ.), Yoshinari Kamakura (OIT), Hideki Mutoh (Link Research) SDM2022-71
The theoretical temporal resolution limit of silicon (Si) image sensors is 11.1 ps. The super temporal resolution (STR) ... [more] SDM2022-71
pp.32-39
PRMU, IPSJ-CVIM 2022-05-13
11:00
Aichi Toyota Technological Institute Relationship between Perceptual and Image Qualities of Training and Reconstruction Images in Video Super-Resolution
Hiroshi Mori, Norimichi Ukita (TTI) PRMU2022-5
Super resolution is a technique for converting low-resolution images into high-resolution images. Recent research has sh... [more] PRMU2022-5
pp.24-29
MW 2022-03-04
11:10
Online Online Deep-Learning Based Anomaly Detection Method for Microwave Non-destructive Road Monitoring
Takahide Morooka, Shouhei Kidera (Univ. of Electro-Communications) MW2021-134
Microwave radar is promising as large-scale and speedy non-destructive monitoring tool for aging road or tunnel because ... [more] MW2021-134
pp.128-133
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
MI 2022-01-26
13:00
Online Online Relationship between Image Quality and Learning Effect in Color Laparoscopic Images Generation by Generative Adversarial Networks
Norifumi Kawabata (Hokkaido Univ.), Toshiya Nakaguchi (Chiba Univ.) MI2021-59
Improving of personal computer performance, it is possible for healthcare workers and related researchers to support for... [more] MI2021-59
pp.59-64
NLP 2021-12-18
15:15
Oita J:COM Horuto Hall OITA On Weight Filter Generation Using an Attention Module in a Super-Resolution Method
Keitaro Otani, Hidehiro Nakano (Tokyo City Univ.) NLP2021-66
In recent years, the development of computer technology has led to an increase in the number of systems that require lar... [more] NLP2021-66
pp.104-109
OFT, OCS, IEE-CMN, ITE-BCT [detail] 2021-11-19
10:50
Online Online Super-simplified optical correlation-domain reflectometry
Takaki Kiyozumi, Tomoya Miyamae (YNU), Kohei Noda (Tokyo Tech/YNU), Heeyoung Lee (SIT), Kentaro Nakamura (Tokyo Tech), Yosuke Mizuno (YNU) OFT2021-53
Optical fiber reflectometry has been widely studied as a method for diagnosing the soundness of fiber networks. Of numer... [more] OFT2021-53
pp.13-16
IMQ 2021-10-22
13:45
Osaka Osaka Univ. A Tiny Convolutional Neural Network for Image Super-Resolution
Kazuya Urazoe, Nobutaka Kuroki, Yu Kato, Shinya Ohtani (Kobe Univ.), Tetsuya Hirose (Osaka Univ.), Masahiro Numa (Kobe Univ.) IMQ2021-7
This paper surveys three techniques for reducing computational costs of convolutional neural network (CNN) for image sup... [more] IMQ2021-7
pp.2-7
PRMU 2021-08-26
16:00
Online Online A Study of Low-Resolution Iris Biometrics using Single Image Super-Resolution
Tsubasa Bora, Daisuke Uenoyama (UEC), Takahiro Toizumi, Yuka Ogino, Masato Tsukada (NEC), Masatsugu Ichino (UEC) PRMU2021-14
It requires a high-quality iris image in general, which means that subject must look into the camera, which is highly in... [more] PRMU2021-14
pp.42-47
MI 2021-05-17
11:00
Online Online [Short Paper] Watershed-based Alveoli Segmentation from Micro-focus X-ray CT Volumes of Dissected Human Lungs
Takeru Shiina, Hirohisa Oda, Tong Zheng, Shota Nakamura, Masahiro Oda, Kensaku Mori (Nagoya Univ.) MI2021-3
We propose a segmentation method of the alveoli from μCT volumes. The peripheral lung mainly consists of tiny spherical ... [more] MI2021-3
pp.9-10
MI 2021-05-17
14:40
Online Online MR super-resolution based on signal-image domain learning using phase scrambling Fourier transform imaging
Kazuki Yamato, Hiromichi Wakatsuki, Satoshi Ito (Utsunomiya Univ.) MI2021-6
In the phase-scrambling Fourier transform (PSFT) imaging, the signals not sampled during imaging can be extrapolated and... [more] MI2021-6
pp.14-19
MI 2021-03-17
14:15
Online Online Super-resolution of thoracic CT volumes using high-frequency learning
Ryosuke Kawai, Atsushi Saito (TUAT), Shoji Kido (Osaka Univ), Kunihiro Inai, Hirohiko Kimura (Fukui Univ), Akinobu Shimizu (TUAT) MI2020-97
We report the results of super-resolution using a new network model. Specifically, the reconstructed image is represente... [more] MI2020-97
pp.218-219
NC, NLP
(Joint)
2021-01-21
12:05
Online Online Examination of precipitation estimation using atmospheric variables
Takanori Ito, Motoki Amagasaki, Kei Ishida, Masato Kiyama, Masahiro Iida (GSST Kumamoto University) NC2020-34
In this paper, we developed a model for SR using ConvLSTM to improve the resolution of precipitation data.
In the relat... [more]
NC2020-34
pp.13-17
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