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- W2895809375 abstract "As the bridge between the analog world and digital computers, analog-to-digital converters are generally used in modern information systems such as radar, surveillance, and communications. For the configuration of analog-to-digital converters in future high-frequency broadband systems, we introduce a revolutionary architecture that adopts deep learning technology to overcome tradeoffs between bandwidth, sampling rate, and accuracy. A photonic front-end provides broadband capability for direct sampling and speed multiplication. Trained deep neural networks learn the patterns of system defects, maintaining high accuracy of quantized data in a succinct and adaptive manner. Based on numerical and experimental demonstrations, we show that the proposed architecture outperforms state-of-the-art analog-to-digital converters, confirming the potential of our approach in future analog-to-digital converter design and performance enhancement of future information systems." @default.
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- W2895809375 date "2018-10-21" @default.
- W2895809375 modified "2023-10-16" @default.
- W2895809375 title "Analog-to-digital conversion revolutionized by deep learning" @default.
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- W2895809375 doi "https://doi.org/10.48550/arxiv.1810.08906" @default.
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