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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 8 of 8  /   
Committee Date Time Place Paper Title / Authors Abstract Paper #
VLD, DC, RECONF, ICD, IPSJ-SLDM
(Joint) [detail]
2021-12-01
09:45
Online Online Low quiescent current LDO with FVF based PSRR enhanced circuit for wearable EEG measurement devices
Kenji Mii, Daisuke Kanemoto, Osamu Maida, Tetsuya Hirose (Osaka Univ.) VLD2021-18 ICD2021-28 DC2021-24 RECONF2021-26
This paper proposes a low quiescent current low-dropout regulator (LDO) with a flipped voltage follower (FVF)-based powe... [more] VLD2021-18 ICD2021-28 DC2021-24 RECONF2021-26
pp.7-12
NLP, MSS
(Joint)
2019-03-14
15:35
Fukui Bunkyo Camp., Univ. of Fukui Effect of Dropout Layers of Neural Networks Applied for Iterative Error-Detection Clustering Method
Taishi Watanabe, Masayuki Yamauchi (Hiroshima Institute of Tech.), Mamoru Tanaka (Sophia Univ.) NLP2018-130
IoT has been continued to develop greatly. It is important that cluttered big data need to be used effectively on intern... [more] NLP2018-130
pp.31-36
EMCJ, IEE-EMC, IEE-MAG 2018-11-22
15:10
Overseas KAIST Ground Integrity Analysis in an On-chip Low-Dropout Regulator to PCB Hierarchical Power Distribution Network
Shinyoung Park, Subin Kim, Joungho Kim (KAIST) EMCJ2018-77
Ground noise generated from external switching circuits can be coupled to the SoC of the interest via off-chip PDNs and ... [more] EMCJ2018-77
p.57
IBISML 2017-11-10
13:00
Tokyo Univ. of Tokyo Analysis of Dropout in online learning
Kazuyuki Hara (Nihon Univ.) IBISML2017-61
Deep learning is the state-of-the-art in fields such as visual object recognition and speech recognition.
This learning... [more]
IBISML2017-61
pp.201-206
NLP 2017-05-11
14:05
Okayama Okayama University of Science Learning of Neural Network with Coupled Chaotic Map
Shu Sato, Chihiro Ikuta, Yuichi Nakamura (NIT, Anan College), Yoko Uwate, Yoshifumi Nishio (Tokushima Univ.) NLP2017-9
In recent years, research on neural networks has been actively conducted. Neural network refers to the human brain netwo... [more] NLP2017-9
pp.43-46
NC, NLP
(Joint)
2016-01-29
16:15
Fukuoka Kyushu Institute of Technology Proposal of novel dropout method and its analysis of dynamic property
Daisuke Saitoh, Tasuku Kondo, Kazuyuki Hara (Nihon Univ.) NC2015-67
Deep learning that use a large network and includes many units tends to occur the overfitting. Therefore, to avoid the o... [more] NC2015-67
pp.55-60
IBISML 2014-11-18
15:00
Aichi Nagoya Univ. [Poster Presentation] Combination of LSTM and CNN on recognizing mathematical symbols
Hai Nguyen Dai, Anh Le Duc, Masaki Nakagawa (TUAT) IBISML2014-73
Combining classifiers is an approach that has been shown to be useful on numerous occasions when striving for further im... [more] IBISML2014-73
pp.287-292
ICD, SDM 2012-08-03
13:10
Hokkaido Sapporo Center for Gender Equality, Sapporo, Hokkaido A Fast-Transient-Response Digital Low-Dropout Regulator Comprising Thin-Oxide MOS Transistors in 40-nm CMOS process
Masafumi Onouchi, Kazuo Otsuga, Yasuto Igarashi, Toyohito Ikeya, Sadayuki Morita (Renesas Electronics), Koichiro Ishibashi (Univ. of Electro-Comm.), Kazumasa Yanagisawa (Renesas Electronics) SDM2012-82 ICD2012-50
A digital low-dropout (LDO) regulator comprising only thin-oxide MOS transistors was developed. The input voltage to the... [more] SDM2012-82 ICD2012-50
pp.105-110
 Results 1 - 8 of 8  /   
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