Signal quality improvement of holographic data storage using adaptive two-dimensional filter

Takahata, Yosuke; Kondo, Yo; Yoshida, Shuhei; Yamamoto, Masaaki

doi:10.1007/s10043-010-0036-1

Cited by 3 publications

(1 citation statement)

References 3 publications

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“…The average error rates are listed in Table III. The error rate of CNN demodulation was the lowest without requiring any process to improve the SNR, even when a finite impulse response (FIR) filter 31) was applied for the hard decision method to compensate for interpixel interference. The average time to demodulate the 164 modulation blocks using CNN was 2.7 ms; that is, the demodulation processing time per modulation block is roughly estimated to be 0.16 ms.…”

Section: Demodulation Characteristicsmentioning

confidence: 99%

Data demodulation using convolutional neural networks for holographic data storage

Katano

Muroi

Kinoshita

et al. 2018

Jpn. J. Appl. Phys.

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We propose a data demodulation method based on a deep-learning algorithm. A convolutional neural network (CNN), which can accurately classify images, was used in the demodulation of data reproduced from holographic data storage (HDS). We designed CNNs and taught them the rules for demodulation based on the optical characteristics of the HDS using 700 reproduced data pages. The CNNs that learned could demodulate the data and decrease the number of demodulation errors by about 75% compared with hard decision image classification methods. This result showed an improvement in optical noise tolerance, which enhances the HDS with larger capacity and higher data-transfer rate.

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Section: Demodulation Characteristicsmentioning

confidence: 99%