| IEICE Technical Committee Submission System Conference Schedule |
Online Proceedings [Sign in] Tech. Rep. Archives |
All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| SP, EA, SIP | 2020-03-02 13:00 |
Okinawa | Okinawa Industry Support Center (Cancelled but technical report was issued) |
[Poster Presentation]
Restoration of clipped signal using oversampling based on differentiable and convex loss function Natsuki Ueno, Shoichi Koyama, Hiroshi Saruwatari (Univ. Tokyo)  EA2019-126 SIP2019-128 SP2019-75 |
A signal reconstruction method of clipped time-continuous signal using oversampling is proposed. The signal reconstructi... [more] | EA2019-126 SIP2019-128 SP2019-75 pp.147-152 |
| SP, EA, SIP | 2020-03-03 16:15 |
Okinawa | Okinawa Industry Support Center (Cancelled but technical report was issued) |
A Portscan Detection Based on Low-rankness of Destination Port Matrices Hiroki Nousou, Masao Yamagishi, Isao Yamada (Tokyo Tech) EA2019-167 SIP2019-169 SP2019-116 |
The detection of port scans as possible preliminaries to more serious attacks is important for system administrators and... [more] | EA2019-167 SIP2019-169 SP2019-116 pp.385-390 |
| WBS, MICT | 2019-07-16 12:30 |
Ibaraki | Ibaraki University(Mito Campus) | [Poster Presentation]
Performance Improvement of PAPR Reduction Method Using Dither Signal in OFDM-IM Shinya Watanabe, Teruyuki Miyajima, Yoshiki Sugitani (Ibaraki Univ.) WBS2019-10 MICT2019-11 |
In this paper, we consider a performance improvement of PAPR reduction method using a dither sig- nal in OFDM-IM systems... [more] | WBS2019-10 MICT2019-11 pp.1-6 |
| SeMI, RCS, NS, SR, RCC (Joint) |
2019-07-10 15:45 |
Osaka | I-Site Nanba（Osaka） | Robust Transmit Beamforming Design via Fractional Programming for Downlink Power-Domain NOMA Systems Hiroki Iimori, Koji Ishibashi (UEC), Giuseppe Abreu (JUB) RCC2019-16 NS2019-49 RCS2019-106 SR2019-25 SeMI2019-25 |
Taking into account imperfect channel state information, we study a robust transmit beamforming design for downlink powe... [more] | RCC2019-16 NS2019-49 RCS2019-106 SR2019-25 SeMI2019-25 pp.27-32(RCC), pp.37-42(NS), pp.31-36(RCS), pp.37-42(SR), pp.41-46(SeMI) |
| SIS, IPSJ-AVM, ITE-3DMT [detail] | 2019-06-13 16:25 |
Nagasaki | Fukue Culture Center | [Invited Talk]
Image Processing Based on Sparse and Low-rank Modeling Seisuke Kyochi (The Univ. of Kitakyushu) SIS2019-10 |
This paper presents fundamental tools for image recovery by convex optimization and introduces some case study from the ... [more] | SIS2019-10 pp.55-60 |
| RCS, SR, SRW (Joint) |
2018-03-02 15:45 |
Kanagawa | YRP | Rate Maximization via Probabilistic Constellation Shaping in AWGN Channels with Non-linear Distortion Hiroki Iimori, Giuseppe Thadeu Freitas de Abreu (Ritsumeikan Uni.) RCS2017-401 |
It is well known that distortion in wireless transmit signals occurs due to the non-linearity of power amplifiers. The t... [more] | RCS2017-401 pp.459-464 |
| US, EA (Joint) |
2018-01-23 13:00 |
Osaka | [Poster Presentation]
Performance improvement of gridless sound field decomposition using signal separation based on convex optimization Yuhta Takida, Shoichi Koyama, Natsuki Ueno, Hiroshi Saruwatari (The Univ. of Tokyo) EA2017-87 |
A sound field reconstruction method inside a region including sound sources is proposed. Previously, a method based on a... [more] | EA2017-87 pp.19-24 |
|
| IBISML | 2017-11-09 13:00 |
Tokyo | Univ. of Tokyo | Online Optimization Method for Generalized $ell_1$ Regularized Problems Yoshihiro Nakazato, Kazuto Fukuchi (Tsukuba Univ.), Jun Sakuma (Tsukuba Univ./Riken/JST) IBISML2017-47 |
Structured sparse regularization is vital to enhance the precision and the interpretability of the model by introducing ... [more] | IBISML2017-47 pp.93-100 |
| IE | 2017-06-29 14:15 |
Okinawa | IE2017-27 |
(To be available after the conference date) [more] | IE2017-27 pp.13-18 |
|
| NC, IPSJ-BIO, IBISML, IPSJ-MPS [detail] | 2017-06-25 10:20 |
Okinawa | Okinawa Institute of Science and Technology | Learning with linearly transformed l0 sparsity Naoki Marumo, Tomoharu Iwata (NTT) IBISML2017-8 |
We consider a class of non-convex optimization problems with linearly transformed sparsity constraints, which includes... [more] |
IBISML2017-8 pp.193-199 |
| IBISML | 2017-03-07 10:30 |
Tokyo | Tokyo Institute of Technology | Doubly Accelerated Stochastic Variance Reduced Gradient Method for Regularized Empirical Risk Minimization Tomoya Murata, Taiji Suzuki (Tokyo Tech) IBISML2016-106 |
We develop a new stochastic gradient method for solving convex regularized empirical risk minimization problem in mini-b... [more] | IBISML2016-106 pp.49-56 |
| PRMU, CNR | 2017-02-19 11:20 |
Hokkaido | [Poster Presentation]
Compressed Sensing for 4D-MRI
-- Fast Algorithm of Image Reconstruction -- Kohei Mochizuki, Tomoya Sakai (Nagasaki Univ.), Yukinojo Kitakami, Hideaki Haneishi (Chiba Univ.) PRMU2016-181 CNR2016-48 |
This work aims to reduce measurement time and improve the computational efficiency of four-dimensional magnetic resonanc... [more] | PRMU2016-181 CNR2016-48 pp.159-160 |
|
| IBISML | 2016-11-16 15:00 |
Kyoto | Kyoto Univ. | Proximal Average Accelerated Proximal Gradient Algorithm with Adaptive Restart Yoshihiro Nakazato, Kazuto Fukuchi, Jun Sakuma (Univ. Tsukuba) IBISML2016-55 |
When using multiple regularizers, their proximal mapping is not easily available in closed form. The method to calculat... [more] |
IBISML2016-55 pp.65-71 |
| PRMU, IPSJ-CVIM, IBISML [detail] | 2016-09-05 15:45 |
Toyama | Sparse learning for pattern mining problem by using Safe Pattern Pruning method Kazuya Nakagawa, Shinya Suzumura, Masayuki Karasuyama (NIT), Koji Tsuda (Univ. of Tokyo), Ichiro Takeuchi (NIT) PRMU2016-70 IBISML2016-25 |
In this paper we study predictive pattern mining problems where the goal is to construct a predictive model based on a s... [more] | PRMU2016-70 IBISML2016-25 pp.127-134 |
|
| PRMU, IE, MI, SIP | 2016-05-19 15:10 |
Aichi | High accuracy reconstruction algorithm for CS-MRI using SDMM Motoi Shibata, Norihito Inamuro, Takashi Ijiri, Akira Hirabayashi (Ritsumeikan Univ.) SIP2016-12 IE2016-12 PRMU2016-12 MI2016-12 |
We propose a high accuracy magnetic resonance imaging (MRI) reconstruction algorithm from compressively sampled measurem... [more] | SIP2016-12 IE2016-12 PRMU2016-12 MI2016-12 pp.59-64 |
|
| RCS, IT, SIP | 2016-01-19 11:25 |
Osaka | Kwansei Gakuin Univ. Osaka Umeda Campus | Robust beamforming for physical-layer secrecy Jingbo Zou, Shuichi Ohno (Hiroshima Univ.) IT2015-92 SIP2015-106 RCS2015-324 |
(To be available after the conference date) [more] | IT2015-92 SIP2015-106 RCS2015-324 pp.243-246 |
| IBISML | 2015-11-27 14:00 |
Ibaraki | Epochal Tsukuba | [Poster Presentation]
Secure Approximation Guarantee for Private Empirical Risk Minimization with Homomorphic Encryption Toshiyuki Takada, Hiroyuki Hanada (NIT), Jun Sakuma (Univ.Tsukuba), Ichiro takeuchi (NIT) IBISML2015-86 |
Privacy concern has been increasingly important in many machine learning problems. In this paper, we study empirical ris... [more] | IBISML2015-86 pp.249-256 |
| IBISML | 2015-03-06 15:45 |
Kyoto | Kyoto University | Model selection with approximate validation error guarantee for (L^2_2) regularized convex loss minimization problems Atsushi Shibagaki, Yoshiki Suzuki, Ichiro Takeuchi (NIT) IBISML2014-96 |
In this paper we propose a new algorithm that can select an approximately optimal regularization parameter in a class of... [more] | IBISML2014-96 pp.79-86 |
| IBISML | 2014-11-17 17:00 |
Aichi | Nagoya Univ. | [Poster Presentation]
Efficient leave-one-out cross-validation for L2-regularized classifier Shota Okumura, Yoshiki Suzuki, Kohei Ogawa, Yuki Shinmura, Ichiro Takeuchi (NIT) IBISML2014-44 |
Leave-one-out cross-validation (LOOCV) is a useful tool for estimating generalization performances of various machine ... [more] |
IBISML2014-44 pp.73-80 |
| RCC, ASN, NS, RCS, SR (Joint) |
2014-07-30 17:00 |
Kyoto | Kyoto Terrsa | On numerical computation of sparse optimal control Takuya Ikeda, Masaaki Nagahara (Kyoto Univ.) RCC2014-25 |
In this article, we consider sparse optimal control with L2 regularization for the states. Under the normality assumptio... [more] | RCC2014-25 pp.19-22 |
[Return to Top Page]