Convolutional neural network-based data page classification for holographic memory

Shimobaba, Tomoyoshi; Kuwata, Naoki; Homma, Mizuha; Takahashi, Takayuki; Nagahama, Yuki; Sano, M.; Hasegawa, Satoki; Hirayama, Ryuji; Kakue, Takashi; Shiraki, Atsushi; Tsuji, Naoki; Ito, Tomoyoshi

doi:10.1364/ao.56.007327

Cited by 35 publications

(24 citation statements)

References 13 publications

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“…In hydro-climatological research, MLP is employed to establish the relationship between predictors and predictands [22]. MLP is a classical structure of Deep Neural Network (DNN) [23]characterized with several layers with numerous neurons. The first layer of MLP is known as the input layer whereas the last layer represents the output layer.…”

Section: Modelsmentioning

confidence: 99%

Rainfall Prediction Using Machine Learning Algorithms for the Various Ecological Zones of Ghana

et al. 2022

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Accurate rainfall prediction has become very complicated in recent times due to climate change and variability. The efficiency of classification algorithms in rainfall prediction has flourished. The study contributes to using various classification algorithms for rainfall prediction in the different ecological zones of Ghana. The classification algorithms include Decision Tree (DT), Random Forest (RF), Multilayer Perceptron (MLP), Extreme Gradient Boosting (XGB) and K-Nearest Neighbour (KNN). The dataset, consisting of various climatic attributes, was sourced from the Ghana Meteorological Agency spanning 1980 -2019. The performance of the classification algorithms was examined based on precision, recall, f1-score, accuracy, and execution time with various training and testing data ratios. On all three training and testing ratios: 70:30, 80:20 and 90:10, RF, XGB and MLP performed well, whereas KNN performed least across all zones. In terms of the execution time of the models, Decision Tree is consistently portrayed as the fastest, whereas MLP used the most run time.

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Section: Modelsmentioning

confidence: 99%

Rainfall Prediction Using Machine Learning Algorithms for the Various Ecological Zones of Ghana

et al. 2022

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“…In the past few years, deep learning methods receive much attention and exert considerable impact in many fields. Very recently, deep learning has been introduced to holographic research area and gained success in different applications [37][38][39][40][41][42].…”

Section: Jpeg Image Compression and Proposed Artifact Reduction Schem...mentioning

confidence: 99%

Compression of Phase-Only Holograms with JPEG Standard and Deep Learning

et al. 2018

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It is a critical issue to reduce the enormous amount of data in the processing, storage and transmission of a hologram in digital format. In photograph compression, the JPEG standard is commonly supported by almost every system and device. It will be favorable if JPEG standard is applicable to hologram compression, with advantages of universal compatibility. However, the reconstructed image from a JPEG compressed hologram suffers from severe quality degradation since some high frequency features in the hologram will be lost during the compression process. In this work, we employ a deep convolutional neural network to reduce the artifacts in a JPEG compressed hologram. Simulation and experimental results reveal that our proposed "JPEG + deep learning" hologram compression scheme can achieve satisfactory reconstruction results for a computer-generated phase-only hologram after compression.

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confidence: 99%

“…In one work, robust data read-out from holographic memory was realized using convolutional ANNs. 31 The potential of ANNs has recently been demonstrated also in classification and inverse design of input: spectrum (polarized) 4bit sequence encoding: "iv,iii,ii,i" "1001" output: bit sequence 0: "0000" 1: "0001" 2: "0010" 3: "0011" SiO 2 Si…”

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confidence: 99%

“…In one work, robust data read-out from holographic memory was realized using convolutional ANNs. 31 The potential of ANNs has recently been demonstrated also in classification and inverse design of nanoparticles. [32][33][34] We train the digital-information retrieval neural network, on the measured scattering spectra of several hundred fabricated copies of each nanostructure corresponding to a binary sequence.…”

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Pushing the limits of optical information storage using deep learning

et al. 2019

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Diffraction drastically limits the bit density in optical data storage. To increase the storage density, alternative strategies involving supplementary recording dimensions and robust read-out schemes must be explored. Here, we propose to encode multiple bits of information in the geometry of subwavelength dielectric nanostructures. A crucial problem in high-density information storage concepts is the robustness of the information readout with respect to fabrication errors and experimental noise. Using a machine-learning based approach in which the scattering spectra are analyzed by an artificial neural network, we achieve quasi error free read-out of sequences of up to 9 bit, encoded in top-down fabricated silicon nanostructures. We demonstrate that probing few wavelengths instead of the entire spectrum is sufficient for robust information retrieval and that the readout can be further simplified, exploiting the RGB values from microscopy images. Our work paves the way towards high-density optical information storage using planar silicon nanostructures, compatible with mass-production ready CMOS technology.Optical information storage promises perennial longevity, high information densities and low energy consumption compared to magnetic storage media. 1,2The compact disc (CD) and its successors, the DVD and the blue-ray disc, broadly established optical storage in our society. 3,4 Those media are based on storing a single binary digit per diffraction limited area ("zero" or "one"). Several concepts have been proposed to increase the information density in optical storage. Examples are schemes exploiting polarization-sensitive digits, 5 near-field optical recording, 6 the use of fluorescent dyes 7 or three-dimensional approaches like two-photon point-excitation 8 . Yet, all these alternatives suffer from major drawbacks. Either they are hardly superior to commercial planar solutions (polarization-sensitive patterns) or they require very complex storage media (fluorescence) or sophisticated read-out schemes (nearfield recording, two-photon point-excitation). The most promising alternative seemed to be holographic memory, which was proposed in the early 1960ies and makes use of the volume of the storage medium. To date, however, there is still no commercial product available, despite several announcements in the past 20 years. 9,10 In the last decades, photonic nanostructures emerged as powerful instruments to control light at the nanometer scale. 11,12 Localized surface plasmons (LSP) in metal nanoparticles 13 or Mie-type resonances in high-index dielectric structures 14 cover the entire visible spectrum and can be tuned by designing appropriate geometric features. 15,16 Furthermore, the high scattering efficiencies of photonic nanostructures render single-particle spectroscopy relatively easy. In consequence, the idea has been raised to encode information in the rich scattering spectra of plasmonic nanostructures, denser than a single data bit. 17-21 The information density might be even further increased by addre...

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Convolutional neural network-based data page classification for holographic memory

Cited by 35 publications

References 13 publications

Rainfall Prediction Using Machine Learning Algorithms for the Various Ecological Zones of Ghana

Rainfall Prediction Using Machine Learning Algorithms for the Various Ecological Zones of Ghana

Compression of Phase-Only Holograms with JPEG Standard and Deep Learning

Pushing the limits of optical information storage using deep learning

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