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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
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Committee Date Time Place Paper Title / Authors Abstract Paper #
RCC, ISEC, IT, WBS 2024-03-13
Osaka Osaka Univ. (Suita Campus) Sparse Superposition Codes Using Second Order Reed-Muller: Orthogonal Encoding
Tsukasa Osaka (Doshisha Univ.), Guanghui Song (Xidian Univ.), Tomotaka Kimura, Jun Cheng (Doshisha Univ.)
(To be available after the conference date) [more]
EMM 2024-03-02
Overseas Day1:JEJU TECHNOPARK, Day2:JEJU Business Agency [Poster Presentation] Classification of AI generated images by sparse coding
Daishi Tanaka, Michiharu Niimi (KIT) EMM2023-89
(To be available after the conference date) [more] EMM2023-89
SIP, SP, EA, IPSJ-SLP [detail] 2024-02-29
(Primary: On-site, Secondary: Online)
EA2023-77 SIP2023-124 SP2023-59 In this paper, we consider a dynamic sensor placement problem where sensors can move within a network over time. Sensor ... [more] EA2023-77 SIP2023-124 SP2023-59
SIS 2023-03-02
Chiba Chiba Institute of Technology
(Primary: On-site, Secondary: Online)
Blink detection from one-dimensional face signal by using convolutional sparse dictionary learning
Souichiro Maruyama, Makoto Nakashizuka (CIT) SIS2022-40
In this report, a blink detection method from average intensities of whole facial videos using convolutional dictionary... [more] SIS2022-40
EMM 2023-01-26
Miyagi Tohoku Univ.
(Primary: On-site, Secondary: Online)
Audio zero-watermarking method based on auditory spectral representation
Atsuki Ichikawa, Masashi Unoki (JAIST) EMM2022-65
Audio zero-watermark technique creates a detection key from watermark and binary pattern generated from features of the ... [more] EMM2022-65
IE, ITS, ITE-AIT, ITE-ME, ITE-MMS [detail] 2022-02-21
Online Online Quality Assessment for 3D CG Image Colorization Using Visible Digital Watermarking after Noise Removal Based on Sparse Dictionary Learning Coding
Norifumi Kawabata (Hokkaido Univ.)
Thus far, we discussed to represent image data whether it is possible or not to represent meaning image how requirement ... [more]
EMM 2022-01-27
Online Online Auditory Representation of Speech Signals Using a Matching Pursuit Algorithm and Sparse Coding
Dung Kim Tran, Masashi Unoki (JAIST) EMM2021-87
Speech signals are the natural carrier of information such as linguistic, speaker individuality, and emotions, etc. Ther... [more] EMM2021-87
Online Online Evaluation of effect of source noise on magnetoencephalography source estimation using a structured sparse model
Kai Miyazaki, Shun Nirasawa, Kazuaki Akamatsu, Yoichi Miyawaki (UEC) NC2020-56
Magnetoencephalography (MEG) is a method to acquire human brain activity at a high temporal resolution, but its spatial ... [more] NC2020-56
IT 2020-12-03
Online Online Downlink Non-Orthogonal Multiple Access (NOMA) Using sparse superposition codes
Hayato Tachiwana, Yutaka Jitsumatsu (Kyushu Univ.) IT2020-54
Non-orthogonal multiple access (NOMA) is considered to be a most promising next generation mobile communication systems.... [more] IT2020-54
SIS, ITE-BCT 2020-10-01
Online Online Fast Beamforming using Sparse Coding for mmWave Communications
Yitu Wang, Takayuki Nakachi (NTT Lab) SIS2020-19
The sensitivity of mmWave to blockages together with its directionality bring new technical challenges in the vehicular ... [more] SIS2020-19
RCS 2020-06-26
Online Online A Study on Gaussian Belief Propagation for Sparse Superposition Code in Massive NOMA Detection
Ryuichi Kume, Shinsuke Ibi (Doshisha Univ.), Takumi Takahashi (Osaka Univ.), Hisato Iwai (Doshisha Univ.) RCS2020-45
In this paper, we propose an iterative detection strategy of Gaussian belief propagation (GaBP) for non-orthogonal multi... [more] RCS2020-45
SP, EA, SIP 2020-03-02
Okinawa Okinawa Industry Support Center
(Cancelled but technical report was issued)
A Pattern Recognition Method Using Secure Sparse Representations in L0 Norm Minimization
Takayuki Nakachi, Yitu Wang (NTT), Hitoshi Kiya (Tokyo Metro. Univ.) EA2019-130 SIP2019-132 SP2019-79
In this paper, we propose a privacy-preserving pattern recognition method using encrypted sparse representations in L0 n... [more] EA2019-130 SIP2019-132 SP2019-79
CS, CAS 2020-02-27
Kumamoto   An Estimation of Network Traffic Validation based on Sparse Coding
Takayuki Nakachi, Yitu Wang (NTT) CAS2019-107 CS2019-107
With accurate network traffic prediction, future communication networks can realize self-management and enjoy intelligen... [more] CAS2019-107 CS2019-107
Okinawa Miyakojima Marine Terminal Proposal of Compression Method for Planetary Surface Image using Sparse Coding
Yoshifumi Uesaka, Hayaru Shouno (UEC) NC2019-65
In recent years, the demand for space development has been increasing. We treat an efficient image transmitting system f... [more] NC2019-65
IE, CS, IPSJ-AVM, ITE-BCT [detail] 2019-12-05
Iwate Aiina Center [Special Talk] Representation of moving-image's sparsity and its applications to adaptive moving-image restoration
Takahiro Saito (Kanagawa Univ.) CS2019-75 IE2019-55
This talk states that statistical sparsity of a moving-image sequence can be properly represented in the domain of the 3... [more] CS2019-75 IE2019-55
ITE-BCT, SIS 2019-10-25
Fukui Fukui International Activities Plaza Image Compression in Encryption-then-Compression System Using Secure Sparse Representations
Takayuki Nakachi (NTT), Hitoshi Kiya (Tokyo Metro. Univ.) SIS2019-21
n this paper, we propose a image compression method using secure sparse representations in Encryption-then-Compression (... [more] SIS2019-21
OCS, LQE, OPE 2019-10-18
Kagoshima   Intelligent Monitoring of Optical Fiber Transmission Using Sparse Coding
Takayuki Nakachi, Yitu Wang, Tetsuro Inui, Takafumi Tanaka, Takahiro Yamaguchi, Katsuhiro Shimano (NTT) OCS2019-42 OPE2019-80 LQE2019-58
This paper proposes a sparse coding-based intelligent constellation diagram analyzer for optical fiber communications. I... [more] OCS2019-42 OPE2019-80 LQE2019-58
IMQ 2019-10-04
Osaka Osaka University 3D CG Image Quality Assessment Including Noise Removal Based on Sparse Dictionary Learning Coding
Norifumi Kawabata (Tokyo Univ. of Science) IMQ2019-6
By appearing of high-definition and high-quality images, it comes to increase many chance to process image big data. If ... [more] IMQ2019-6
IE, EMM, LOIS, IEE-CMN, ITE-ME, IPSJ-AVM [detail] 2019-09-19
Niigata Tokimeito, Niigata University Secure sparse representations in L0 norm minimization
Takayuki Nakachi (NTT), Hitoshi Kiya (Tokyo Metro. Univ.) LOIS2019-11 IE2019-24 EMM2019-68
In this paper, we propose a method to estimate secure sparse representations in L0 norm minimization, and evaluate the e... [more] LOIS2019-11 IE2019-24 EMM2019-68
IT 2019-09-06
Oita Yufuin Kenshujo, Nippon Bunri University The Recovery of (0,1)-Vector Based on Deep Neural Network
Lantian Wei, Shan Lu, Hiroshi Kamabe (Gifu Univ.) IT2019-28
In this paper, we consider the recovery of sparse (0,1)-vectors from sparse signature matrix based on deep neural networ... [more] IT2019-28
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