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

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