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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 33  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
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
PRMU 2019-10-18
14:50
Tokyo   From Cats to Cars: A Data Set Generation Strategy for U-net based Autonomous Driving
Hiroki Hiraoka, Atsushi Imiya (Chiba Univ.) PRMU2019-38
The deep convolutional neural networks (DCNNs)
require data to supervise the network structures.
A semi-automatic da... [more]
PRMU2019-38
pp.35-40
NC, MBE
(Joint)
2019-03-04
14:40
Tokyo University of Electro Communications A study of the Multi Resolution CONP using variable time constants for time series signals
Haruka Kato, Susumu Kuroyanagi (NIT) NC2018-57
Hearing is an important sense for understanding the surrounding environment because it can obtain information from the b... [more] NC2018-57
pp.79-84
EA, ASJ-H 2016-08-10
10:00
Miyagi Tohoku Gakuin Univ., Tagajo Campus The Methods to Denoize the Fetal Heart Doppler Sound Obtained by Ultrasound Cardiograph Using Wavelet Analysis
Chikahiro Araki (Univ. of Fukui), Mikio Mori, Tatsuya Asai, Chiyo Tamamura, Nozomu Takahashi, Kouji Nishijima, Yoshio Yoshida (niv. of Fukui)
The technique of analyzing Fetal Heart Rate (FHR) variability has been known to be effective to diagnose the fetal healt... [more] EA2016-25
pp.31-36
AP, SAT, SANE
(Joint)
2016-07-21
11:20
Hokkaido Otaru port marina Acquisition Method of Median Level Variation Based on Variation Mechanism in Mobile Communications with Low Base Station Antenna Height
Masaki Murasugi, Hiroaki Nakabayashi (Chiba Inst. of Tech.) AP2016-45
This report shows an acquisition method of a median variation in consideration of a variation mechanism. The method was ... [more] AP2016-45
pp.49-53
MBE, NC
(Joint)
2016-03-22
11:50
Tokyo Tamagawa University Calcium imaging data analysis by multi resolution non-negative matrix factorization
Tsubasa Ito (Tokyo Tech), Keisuke Ota (RIKEN/JSPS), Masanori Murayama (RIKEN), Toru Aonishi (Tokyo Tech) NC2015-92
Ca2+ imaging is the imaging method of neuronal activity in the brain. Recently, the technological advance in Ca2+ imagin... [more] NC2015-92
pp.131-136
HCS, HIP, HI-SIGCOASTER [detail] 2015-05-20
12:30
Okinawa Okinawa Industry Support Center Discrimination of Forearm's Motions using Evaluation Values of Muscular Activity based on Wavelet Coefficients of Surface EMG
Kyosuke Yoshimura, Hidetoshi Nagai, Teigo Nakamura (Kyutech) HCS2015-28 HIP2015-28
We develop a myoelectric forearm prosthesis.
In our previous work, we used wavelet coefficients,
which are extracted a... [more]
HCS2015-28 HIP2015-28
pp.181-186
IE, ITE-AIT, ITE-ME [detail] 2014-11-07
09:00
Kagoshima   An image fusion method for color image dehazing using a near-infrared image
Kohei Yamaguchi, Akira Kubota (Chuo Univ.) IE2014-55
In this paper, we propose a method to dehaze images using both color and near-infrared image.
In our method, first, we ... [more]
IE2014-55
pp.33-37
IPSJ-SLDM, CPSY, RECONF, VLD [detail] 2014-01-28
16:55
Kanagawa Hiyoshi Campus, Keio University An Image Recognition System with Multi-Resolutional Feature Learning on the 3D FPGA-Array "Vocalise"
Baku Ogasawara, Satoru Yokota, Jiang Li, Yusuke Atsumari, Hiromasa Kubo, Masatoshi Sekine (TUAT) VLD2013-116 CPSY2013-87 RECONF2013-70
We propose and develop "an image recognition system" with multi-resolutional feature learning function. The feature lear... [more] VLD2013-116 CPSY2013-87 RECONF2013-70
pp.85-90
EMCJ, EMD 2013-07-12
15:25
Tokyo Kikai-Shinko-Kaikan Bldg. Analysis of high-frequency components in electrostatic discharge events -- Assessing the effect of discharge electrode material and relative humidity --
Masao Masugi (Ritsumeikan Univ.), Yuichiro Okugawa, Yoshiharu Akiyama (NTT), Kazuo Murakawa (NTT East) EMCJ2013-47 EMD2013-32
This report describes evaluations of electrostatic discharge (ESD) events from electromagnetic interference (EMI) aspect... [more] EMCJ2013-47 EMD2013-32
pp.43-46
CAS 2013-01-29
16:45
Oita Beppu International Convention Center Phoneme preprocessing circuit of Sound recognition system
Keita Okamoto, Yuichi Ogishima, Hakaru Tamukoh, Masatoshi Sekine (TUAT) CAS2012-92
The human audition can quickly estimate direction of sound and realize speech recognition under the noisy environment. I... [more] CAS2012-92
pp.143-148
HCGSYMPO
(2nd)
2012-12-10
- 2012-12-12
Kumamoto Kumamoto-Shintoshin-plaza A Method to Detect Changes of Muscle Activity Using Difference Transition Based on Center-of-Balance Transition Method for Inaudible Speech Recognition
Hidetoshi Nagai, Saki Kanaida, Kazushi Miyatake, Teigo Nakamura (Kyushu Inst. of Tech.)
We research on inaudible speech recognition with surface EMGs at around a speaker's lip and neck. When a person speaks n... [more]
VLD, DC, IPSJ-SLDM, CPSY, RECONF, ICD, CPM
(Joint) [detail]
2012-11-28
14:55
Fukuoka Centennial Hall Kyushu University School of Medicine Implementation of an Image Recognition System with Hierarchical Feature Learning Function
Baku Ogasawara, Satoru Yokota, Hakaru Tamukoh, Masatoshi Sekine (TUAT) RECONF2012-60
We propose and develop “an image recognition system” having a hierarchical feature learning function.The feature learnin... [more] RECONF2012-60
pp.77-82
PRMU, IBISML, IPSJ-CVIM
(Joint) [detail]
2012-09-02
14:45
Tokyo   Detection of Text Area Inserted by Image Editing
Hiromi Hirano (RIT), Makoto Okabe (UEC/JST) PRMU2012-37 IBISML2012-20
Traditionally, Optical Character Recognition (OCR) technologies have been applied to recognize texts embedded in an imag... [more] PRMU2012-37 IBISML2012-20
pp.49-56
VLD, CPSY, RECONF, IPSJ-SLDM [detail] 2012-01-25
10:50
Kanagawa Hiyoshi Campus, Keio University Sound preprocessing circuit by consonant and vowel recognition system
Keita Okamoto, Hakaru Tamukoh, Masatoshi Sekine (TUAT) VLD2011-93 CPSY2011-56 RECONF2011-52
The human audition can quickly estimate direction of sound and realize speech recognition under the noisy environment. I... [more] VLD2011-93 CPSY2011-56 RECONF2011-52
pp.13-18
VLD, CPSY, RECONF, IPSJ-SLDM [detail] 2012-01-25
11:40
Kanagawa Hiyoshi Campus, Keio University An Image Recognition System with Hierarchical Feature Learning Function
Masahiro Ariizumi, Baku Ogasawara, Hakaru Tamukoh, Masatoshi Sekine (TUAT) VLD2011-95 CPSY2011-58 RECONF2011-54
In this paper we describe an image recognition system having a hierarchical feature learning function. The feature learn... [more] VLD2011-95 CPSY2011-58 RECONF2011-54
pp.25-30
AP, SAT
(Joint)
2011-07-14
10:05
Nagano Fac. Eng., Shinshu Univ. Wavelet Multi-resolution Analysis of Reception Level Variations in Urban Mobile Communication Environment
Tetsuro Imai, Koshiro Kitao (NTT DOCOMO) AP2011-34
In mobile communication environment, reception level variations are generally modeled using three kind of variations, su... [more] AP2011-34
pp.41-46
NLP 2011-05-27
14:05
Kagawa Olive park olive memorial hall Evaluation and Improvement of Circuit Analysis Method using Haar Wavelet
Seiichiro Moro, Tatsuyuki Shoji (Univ. of Fukui) NLP2011-19
Recently, much attention have been paid to the methods for circuit analysis using wavelet transform. In particular, we h... [more] NLP2011-19
pp.89-93
NLC, SP
(Joint) [detail]
2010-12-21
11:30
Tokyo National Olympics Memorial Youth Center Voice Recognition Method Based on Features of Consonant Using Pipelined Matching Circuit
Jun Nonaka, Keita Okamoto, Hakaru Tamukoh, Masatoshi Sekine (Tokyo Univ. of Agr and Tech.) NLC2010-22 SP2010-95
The human audition extracts voice characteristics from the sounds flowing into two ears. The recognition circuit of the ... [more] NLC2010-22 SP2010-95
pp.83-88
PRMU, FM 2010-12-10
11:40
Yamaguchi   An Image Recognition System by Tree Structured SOM with Feature Learning Function
Masahiro Ariizumi, Shuta Saito, Hakaru Tamukoh, Masatoshi Sekine (TUAT) PRMU2010-143
In this paper, we describe an image recognition system combining a tree structured Self Organizing Map(SOM) for feature ... [more] PRMU2010-143
pp.97-102
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