Photonic analog-to-digital converter powered by a generalized and robust convolutional recurrent autoencoder
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TLDR
A convolutional recurrent autoencoder to compensate for time mismatches in a photonic analog-to-digital converter (PADC) that is generalized to untrained mismatches and untrained category of signals while remaining robust to system states.Abstract:
We propose a convolutional recurrent autoencoder (CRAE) to compensate for time mismatches in a photonic analog-to-digital converter (PADC). In contrast of other neural networks, the proposed CRAE is generalized to untrained mismatches and untrained category of signals while remaining robust to system states. We train the CRAE using mismatched linear frequency modulated (LFM) signals with mismatches of 35 ps and 57 ps under one system state. It can effectively compensate for mismatches of both LFM and Costas frequency modulated signals with mismatches ranging from 35 ps to 137 ps under another system state. When the spur-free dynamic range (SFDR) of the unpowered PADC decreases from 10.2 dBc to -3.0 dBc, the SFDR of the CRAE-powered PADC is over 31.6 dBc.read more
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Journal ArticleDOI
Multi-band low-noise microwave-signal-receiving system with a photonic frequency down-conversion and transfer-learning network.
Sicheng Yi,Shaofu Xu,Weiwen Zou +2 more
TL;DR: In this paper, a multi-band signal-receiving system, powered by photonic frequency down-conversion and transfer learning, was proposed and demonstrated, which can improve the signal-to-noise (SNR) ratio of signals of different types, SNR, and duty cycles.
Journal ArticleDOI
Visualizing and simplifying convolutional recurrent autoencoder for mismatch compensation of channel-interleaved photonic analog-to-digital converter.
Xiuting Zou,Shaofu Xu,Weiwen Zou +2 more
TL;DR: In this paper, a convolutional recurrent autoencoder (CRAE) was used to compensate for time mismatches for a two-channel photonic analog-to-digital converter (PADC).
Journal ArticleDOI
Adaptive Linearization for the Sub-Nyquist Photonic Receiver Based on Deep Learning
Liyuan Zhao,Jianghua Zhang,Lei Huang,Yuanxi Peng,Ke Yin,Xin Zeng,Zhuohang Zhang,Meili Shen,Denghui Song,Hongxiao Niu +9 more
TL;DR: The experiments demonstrated that the proposed Resnet could improve the spur-free dynamic range (SFDR) and the signal-to-noise ratio (SNR) significantly by testing with single-tone signals, dual- tone signals, wireless communication signals, and modulated radar signals.
High-resolution and reliable automatic target recognition based on photonic ISAR imaging system with explainable deep learning
TL;DR: Zhang et al. as mentioned in this paper exploited the inner relationship between a photonic ISAR imaging system and behaviors of a convolutional neural network (CNN) to deeply comprehend the intelligent recognition.
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