Convolutional neural network-based data page classification for holographic memory

被引：34

作者：

Shimobaba, Tomoyoshi ^{[1
]}

Kuwata, Naoki ^{[1
]}

Homma, Mizuha ^{[2
]}

Takahashi, Takayuki ^{[1
]}

Nagahama, Yuki ^{[1
]}

Sano, Marie ^{[1
]}

Hasegawa, Satoki ^{[1
]}

Hirayama, Ryuji ^{[1
]}

Kakue, Takashi ^{[1
]}

Shiraki, Atsushi ^{[3
]}

Takada, Naoki ^{[4
]}

Ito, Tomoyoshi ^{[1
]}

机构：

[1] Chiba Univ, Grad Sch Engn, Inage Ku, 1-33 Yayoi Cho, Chiba 2638522, Japan

[2] Chiba Univ, Dept Elect & Elect Engn, Inage Ku, 1-33 Yayoi Cho, Chiba 2638522, Japan

[3] Chiba Univ, Inst Management & Informat Technol, Inage Ku, 1-33 Yayoi Cho, Chiba 2638522, Japan

[4] Kochi Univ, Res & Educ Fac, Sci Dept, Nat Sci Cluster, Kochi 7808520, Japan

来源：

APPLIED OPTICS | 2017年 / 56卷 / 26期

基金：

日本学术振兴会;

关键词：

STORAGE;

D O I：

10.1364/AO.56.007327

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

We propose a deep-learning- based classification of data pages used in holographic memory. We numerically investigated the classification performance of a conventional multilayer perceptron (MLP) and a deep neural network, under the condition that reconstructed page data are contaminated by some noise and are randomly laterally shifted. When data pages are randomly laterally shifted, the MLP was found to have a classification accuracy of 93.02%, whereas the deep neural network was able to classify data pages at an accuracy of 99.98%. The accuracy of the deep neural network is 2 orders of magnitude better than the MLP. (C) 2017 Optical Society of America

引用

页码：7327 / 7330

页数：4

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