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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| PRMU, IE, MI, SIP | 2016-05-20 14:40 |
Aichi | Sequential Pool-Design for Adaptive Boolean Compressive Sensing Yohei Kawaguchi, Masahito Togami (Hitachi)  SIP2016-27 IE2016-27 PRMU2016-27 MI2016-27 |
A new method for solving adaptive Boolean compressive sensing is proposed. A conventional method determining a pool for... [more] |
SIP2016-27 IE2016-27 PRMU2016-27 MI2016-27 pp.141-145 |
|
| SANE | 2016-04-22 11:35 |
Tokyo | Kikai-Shinko-Kaikan Bldg. | Lagrange multiplier setting method for lp Compressive Sensing based DOA estimation Takeshi Amishima, Nobuhiro Suzuki (Mitsubishi Electric) SANE2016-5 |
This paper considers the problem on setting the appropriate value of Lagrange multiplier for lp Compressive Sensing base... [more] | SANE2016-5 pp.23-28 |
| MI | 2015-03-02 09:17 |
Okinawa | Hotel Miyahira | 4D-MRI Reconstruction using the low-rank plus sparse matrix decomposition Yukinojo Kitakami, Takashi Ohnishi, Yoshitada Masuda (Chiba Univ. Engineering), Koji Matsumoto (Chiba University Hospital), Hideaki Haneishi (Chiba Univ. Engineering) MI2014-54 |
4D-MRI can visualize and quantify the three-dimensional dynamics of the thoracoabdominal respiratory movement and allows... [more] | MI2014-54 pp.7-11 |
| EMM | 2015-01-29 13:15 |
Miyagi | Tohoku University | Embedding Method by using Wet Paper Code with FIST Method Takahiro Yamamoto, Masaki Kawamura (Yamaguchi Univ.) EMM2014-74 |
We propose an embedding method by using wet paper code (WPC) with FIST method. The watermark is embedded into DWT coef... [more] |
EMM2014-74 pp.45-50 |
| MBE, NC (Joint) |
2014-11-21 11:50 |
Miyagi | Tohoku University | Hyper-parameter estimation for compressive sensing with a Bernoulli-Gauss prior distribution Toshiyuki Watanabe, Jun-ichi Inoue (Hokkaido Univ.) NC2014-28 |
Compressive sensing is a theory that estimates sparse information signals which has few non-zero elements from less ... [more] |
NC2014-28 pp.15-20 |
| MICT, RCC | 2014-05-30 09:45 |
Tokyo | Kikai-Shinko-Kaikan Bldg | DOA estimation with compressive sensing for Khatri-Rao product array Hirotaka Mukumoto, Kazunori Hayashi, Megumi Kaneko (Kyoto Univ.) RCC2014-11 MICT2014-11 |
In this report, we propose DOA (Direction-of-Arrival) estimation scheme with compressive sensing for Khatri-Rao (KR) pro... [more] | RCC2014-11 MICT2014-11 pp.51-56 |
| ASN | 2014-05-30 15:35 |
Tokyo | Convention Hall, RCAST, The University of Tokyo | [Encouragement Talk]
Pattern-based Matrix-size Optimization Algorithm for Compressive Sensing in Real-world Body Sensor Networks Akito Ito, Naoya Namatame, Jin Nakazawa, Hideyuki Tokuda (Keio Univ.) ASN2014-38 |
Compressive Sensing (CS) is a novel approach for data representation, which can represent signals at a rate below the Ny... [more] | ASN2014-38 pp.149-154 |
| 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 |
| MI | 2014-01-27 09:25 |
Okinawa | Bunka Tenbusu Kan | Preliminary study on fast 4D-MRI acquisition by using sparse and low-rank structures Yukinojo Kitakami, Takashi Ohnishi (Chiba Univ), Yoshitada Masuda, Koji Matsumoto (Chiba University Hospital), Hideaki Haneishi (Chiba Univ) MI2013-91 |
4D-MRI can visualize and quantify the three-dimensional dynamics of the thoracoabdominal respiratory movement and allows... [more] | MI2013-91 pp.193-198 |
| ASN, MoNA (Joint) |
2014-01-24 16:15 |
Ehime | Hotel Okudogo (Matsuyama) | F-CODE: A data abstraction approach for Compressive Sensing in Mobile Sensing Application Akito Ito, Jin Nakazawa, Kazunori Takashio, Hideyuki Tokuda (Keio Univ.) ASN2013-160 |
Mobile sensing is attractive area for researchers and developers in recent years. Especially, the emergence of Smartphon... [more] | ASN2013-160 pp.237-241 |
| IT | 2013-07-25 14:55 |
Tokyo | Nishi-Waseda campus, Waseda University | Interpolation of Reconstruction Condition for Compressed Sensing
-- Extended Implications of Restricted Isometry Property -- Shinsuke Nakajima, Tsutomu Kawabata (UEC) IT2013-15 |
It is fundamental in the compressed sensing, to consider a sufficient reconstruction condition in $l_{1}$ norm of a spa... [more] |
IT2013-15 pp.23-26 |
| ASN, RCS, NS, SR (Joint) |
2013-07-18 10:20 |
Shizuoka | Hamamatsu Act City | [Poster Presentation]
Pattern-based Matrix-size Optimization Algorithm for Compressive Sensing in Real-world Body Sensor Networks Akito Ito, Naoya Namatame, Jin Nakazawa, Hideyuki Tokuda (Keio Univ.) NS2013-41 RCS2013-92 SR2013-29 ASN2013-59 |
Compressive Sensing (CS) is a novel approach for data representation, which can represent signals at a rate below the Ny... [more] | NS2013-41 RCS2013-92 SR2013-29 ASN2013-59 pp.43-48(NS), pp.85-90(RCS), pp.51-56(SR), pp.71-76(ASN) |
| EMM | 2013-05-24 14:00 |
Kochi | Kochijyo Hall | Consideration of introduction of IST method to wet paper code and its problem Takahiro Yamamoto, Masaki Kawamura (Yamaguchi Univ.) EMM2013-4 |
We introduce the IST method to wet paper code (WPC). Smaller degradation for a stego image is preferred for digital wate... [more] | EMM2013-4 pp.19-24 |
| MoNA, IPSJ-DPS, IPSJ-MBL | 2013-05-23 09:30 |
Okinawa | Ishigaki City Hall | A Study on Reconstruction Control in Compressive Sensing for Change Detection of Multi-dimensional Data Hiroki Furusawa, Hiroyuki Kasai (Univ. of Electro- Comm.) MoNA2013-1 |
One way to detect the change of multi-dimensional data is tensor decomposition. Sensing vectors obtained using the core ... [more] | MoNA2013-1 pp.1-6 |
| RCS, SR, SRW (Joint) |
2013-03-01 11:30 |
Tokyo | Waseda Univ. | Least Mean Square Algorithm with Application to Improved Adaptive Sparse Channel Estimation Guan Gui, Wei Peng, Fumiyuki Adachi (Tohoku Univ.) RCS2012-359 |
Least mean square (LMS) based adaptive algorithms have been attracted much attention since their low computational compl... [more] | RCS2012-359 pp.447-452 |
| SR, AN, USN, RCS (Joint) |
2012-10-18 16:50 |
Fukuoka | Fukuoka univ. | [Invited Talk]
Applications of Compressed Sensing in Wireless Communication Systems Doohwan Lee (Univ. of Tokyo) RCS2012-145 SR2012-65 AN2012-39 USN2012-42 |
Compressed sensing has been drawn explosive attention during last several years. It is a new framework that uses signal ... [more] | RCS2012-145 SR2012-65 AN2012-39 USN2012-42 p.121(RCS), p.179(SR), p.91(AN), p.85(USN) |
| MoNA, IPSJ-MBL, IPSJ-DPS | 2011-06-02 11:35 |
Okayama | Osaka Univ. 50th Anniversary Hall | [Encouragement Talk]
Image Coding by Compressive Sensing by using Relationship between Texture and Structure Component Chihiro Suzuki, Takamichi Miyata, Yoshinori Sakai (Tokyo Tech) MoMuC2011-2 |
Compressing texture features of natural images by conventional JPEG method requires many bits to preserve their feature ... [more] | MoMuC2011-2 pp.21-26 |
| RCS, AN, MoNA, SR (Joint) |
2010-03-05 15:30 |
Kanagawa | YRP | A Heterogeneous Network System with Flexible Access Points and Protocol-free Signal Processing Part
-- Part2: Highly Efficient Radio Wave Data Compression Method Employing Compressed Sensing Technology -- Doohwan Lee, Takayuki Yamada, Hiroyuki Shiba, Yo Yamaguchi, Kazuhiro Uehara (NTT Corp.) RCS2009-322 MoMuC2009-95 SR2009-119 AN2009-88 |
Rapid developments and changes of wireless radio environments require a unified platform which can flexibly deal with va... [more] | RCS2009-322 MoMuC2009-95 SR2009-119 AN2009-88 pp.379-384(RCS), pp.129-134(MoMuC), pp.189-194(SR), pp.117-122(AN) |
| SR | 2010-01-21 11:30 |
Tokyo | Univ. of Electro-Communications | Combined Nyquist and compressed sampling method for the wireless multiband receiver Doohwan Lee, Takayuki Yamada, Hiroyuki Shiba, Yo Yamaguchi, Kazuhiro Uehara (NTT) SR2009-77 |
The recently developed compressed sensing theory enables signal recovery from incomplete information with high probabili... [more] | SR2009-77 pp.21-26 |
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