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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| SIP | 2016-08-26 10:35 |
Chiba | Chiba Institute of Technology, Tsudanuma Campus | Super resolution of vein images using convolutional neural networks Koji Kashihara (Tokushima Univ.)  SIP2016-80 |
If super-resolution techniques improve the quality of near-infrared images with a low signal-to-noise ratio, they could ... [more] | SIP2016-80 pp.39-44 |
| SP, IPSJ-SLP (Joint) |
2014-07-25 10:20 |
Iwate | Hotel Hanamaki | [Invited Talk]
Voice conversion based on sparse representation and its application to articulation disorders Tetsuya Takiguchi (Kobe Univ.) SP2014-66 |
In recent years, approaches based on sparse representations have gained interest in a broad range of signal processing. ... [more] | SP2014-66 pp.19-24 |
| CS, CAS, SIP | 2014-03-07 13:00 |
Osaka | Osaka City University Media Center | Pool size control of boolean compressive sensing for adaptive group testing Yohei Kawaguchi, Tatsuhiko Osa, Shubhranshu Barnwal, Hisashi Nagano, Masahito Togami (Hitachi) CAS2013-128 SIP2013-174 CS2013-141 |
We propose a new method for adaptive group testing. A non-adaptive group testing based on boolean compressive sensing ha... [more] | CAS2013-128 SIP2013-174 CS2013-141 pp.221-225 |
| AP | 2014-01-23 11:45 |
Kagoshima | Hozan Hall (Kagoshima Prefectural Culture Center) | A Sparse Signal Processing Using Information of Incident Signal Power for DOA Estimation Yoshiki Takahashi, Nobuhiro Suzuki, Toshio Wakayama, Takeshi Amishima, Rokuzo Hara (Mitsubishi Electric) AP2013-152 |
A sparse signal processing (Compressed/Compressive Sensing) for Direction of Arrival (DOA) estimation is expected to be... [more] |
AP2013-152 pp.105-110 |
| SIP, CAS, MSS, VLD | 2013-07-12 10:50 |
Kumamoto | Kumamoto Univ. | Framewise DOA estimation for a target sound source based on DUET Nobuo Iwasaki, Katsuhiro Inoue (Kyutech), Hiromu Gotanda (Kinki Univ.) CAS2013-22 VLD2013-32 SIP2013-52 MSS2013-22 |
Based on the sparcity of sounds, this paper proposes a frame-wise DOA (direction of arrival) estimation of a target so... [more] |
CAS2013-22 VLD2013-32 SIP2013-52 MSS2013-22 pp.119-124 |
| NS | 2013-04-18 15:10 |
Ishikawa | The Wajima Chamber of Commerce and Industry | A Low-Quality Link Detection Scheme Using Compressed Sensing-Based Network Tomography Kazushi Takemoto, Takahiro Matsuda, Tetsuya Takine (Osaka Univ.) NS2013-1 |
Network tomography is an inference technique for internal network characteristics from end-to-end measurements. In this... [more] | NS2013-1 pp.1-6 |
| SANE | 2013-01-24 16:50 |
Nagasaki | Nagasaki Prefectural Art Museum | A Sparse Signal Processing for Angular Estimation of Spread Wave Sources Hiroaki Tsukagoshi, Rokuzo Hara, Kazufumi Hirata (Mitsubishi Electric Corp.) SANE2012-138 |
In this paper, we propose a width estimation method for an angular spread wave source using sparse signal processing, be... [more] | SANE2012-138 pp.67-71 |
| NS, IN (Joint) |
2012-03-09 13:30 |
Miyazaki | Miyazaki Seagia | Path Construction for Sparsity-Constrained Network Tomography Kazushi Takemoto, Takahiro Matsuda, Tetsuya Takine (Osaka Univ.) IN2011-199 |
Network tomography is an inference technique for internal network characteristics from end-to-end measurements. In this ... [more] | IN2011-199 pp.371-376 |
| SIP, CAS, CS | 2010-03-02 09:40 |
Okinawa | Hotel Breeze Bay Marina, Miyakojima | Sampling Theory for Signals with Finite Rate of Innovation and Application to Image Feature Extraction Akira Hirabayashi (Yamaguchi Univ.), Pier-Luigi Dragotti (Imperial Coll, London.) CAS2009-109 SIP2009-154 CS2009-104 |
We present a survey of sampling theory for signals with a finite rate of innovations, which is one of the current hot to... [more] | CAS2009-109 SIP2009-154 CS2009-104 pp.179-184 |
| SIS | 2007-12-11 13:50 |
Hyogo | Impact Ｎoise Ｓuppression for Ｓpeech Ｓignals by Ｕsing a Ｍorphological Ｃomponent Ａnalysis with DFT Hiroaki Hayashi, Makoto Nakashizuka, Youji Iiguni (Osaka Univ.) SIS2007-66 |
Morphological component analysis (MCA) is a signal separation method using sparse signal representations. For separation... [more] | SIS2007-66 pp.47-52 |
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