Demodulation of Multi-Level Data Using Convolutional Neural Network in Holographic Data Storage

被引：0

作者：

Katano, Yutaro ^{[1
]}

Muroi, Tetsuhiko ^{[1
]}

Kinoshita, Nobuhiro ^{[1
]}

Ishii, Norihiko ^{[1
]}

机构：

[1] NHK Sci & Technol Res Labs, Adv Funct Devices Res Div, Tokyo, Japan

来源：

2018 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING: TECHNIQUES AND APPLICATIONS (DICTA) | 2018年

关键词：

holographic data storage; convolutional neural network; pattern recognition; RETRIEVAL; SYSTEM;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

We evaluated a deep learning-based data demodulation method for multi-level recording data in holographic data storage. This method demodulates reproduced data as pattern recognition using a convolutional neural network. The network learns the rule of demodulation in consideration of optical noise that deteriorates the quality of reproduced data. Unlike with a conventional hard decision method, the learnt network demodulated the noise-added data accurately and decreased demodulation errors.

引用

页码：728 / 732

页数：5

共 50 条

[31] Neural Network Equalizer Matched to Recording Code in Holographic Data Storage
Osawa, Hisashi
Natsui, Shogo
Okamoto, Yoshihiro
Nakamura, Yasuaki
JAPANESE JOURNAL OF APPLIED PHYSICS, 2011, 50 (09)
[32] Convolutional neural network-based data page classification for holographic memory
Shimobaba, Tomoyoshi
Kuwata, Naoki
Homma, Mizuha
Takahashi, Takayuki
Nagahama, Yuki
Sano, Marie
Hasegawa, Satoki
Hirayama, Ryuji
Kakue, Takashi
Shiraki, Atsushi
Takada, Naoki
Ito, Tomoyoshi
APPLIED OPTICS, 2017, 56 (26) : 7327 - 7330
[33] A multi-level probabilistic neural network
Zong, Ning
Hong, Xia
ADVANCES IN NEURAL NETWORKS - ISNN 2007, PT 2, PROCEEDINGS, 2007, 4492 : 516 - +
[34] Multi-level data storage system using phase-change optical discs
O'Neill, MP
Wong, TL
2000 OPTICAL DATA STORAGE, CONFERENCE DIGEST, 2000, : 170 - 172
[35] Coupling importance sampling neural network for imbalanced data classification with multi-level learning bias
Huang, Zhan ao
Xiao, Wei
Yang, Zhipeng
Li, Xiaojie
Wu, Xi
NEUROCOMPUTING, 2025, 623
[36] Multi-level spatial attention network for image data segmentation
Guo, Jun
Jiang, Zhixiong
Jiang, Dingchao
INTERNATIONAL JOURNAL OF EMBEDDED SYSTEMS, 2021, 14 (03) : 289 - 299
[37] A recursive inference method based on invertible neural network for multi-level model updating using video monitoring data
Zeng, Jice
Todd, Michael D.
Hu, Zhen
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2023, 203
[38] Hierarchical multi-level dynamic hyperparameter deformable image registration with convolutional neural network
Zhu, Zhenyu
Li, Qianqian
Wei, Ying
Song, Rui
PHYSICS IN MEDICINE AND BIOLOGY, 2024, 69 (17):
[39] Automatic Retinal Blood Vessel Segmentation Based on Multi-level Convolutional Neural Network
Guo, Jinnan
Ren, Shiwei
Shi, Yueting
Wang, Haoyu
2018 11TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, BIOMEDICAL ENGINEERING AND INFORMATICS (CISP-BMEI 2018), 2018,
[40] An Efficient Multi-Level Convolutional Neural Network Approach for White Blood Cells Classification
Cheuque, Cesar
Querales, Marvin
Leon, Roberto
Salas, Rodrigo
Torres, Romina
DIAGNOSTICS, 2022, 12 (02)

← 1 2 3 4 5 →