Sparse fast Fourier transform for exactly sparse signals and signals with additive Gaussian noise

Ermeydan, Esra Şengün; Çankaya, İlyas

doi:10.1007/s11760-017-1177-5

Cited by 4 publications

(1 citation statement)

References 22 publications

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“…Established compressed sensing strategies include random sampling [16][17][18] , uniformly spaced sampling 17,[19][20][21] , sampling based on a model of a sample 22,23 , partials scans with fixed paths 14 , dynamic sampling to minimize entropy [24][25][26][27] and dynamic sampling based on supervised learning 28 . Complete signals can be extrapolated from partial scans by an infilling algorithm, estimating their fast Fourier transforms 29 or inferred by an artificial neural network 14,21 (ANN). The best sampling strategy varies, for example, uniformly spaced sampling is often better than spiral paths for oversampled STEM images 14 .…”

Section: Introductionmentioning

confidence: 99%

Adaptive Partial Scanning Transmission Electron Microscopy with Reinforcement Learning

Ede¹

2020

Preprint

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Compressed sensing is applied to scanning transmission electron microscopy to decrease electron dose and scan time. However, established methods use static sampling strategies that do not adapt to samples. We have extended recurrent deterministic policy gradients to train deep LSTMs and differentiable neural computers to adaptively sample scan path segments. Recurrent agents cooperate with a convolutional generator to complete partial scans. We show that our approach outperforms established algorithms based on spiral scans, and we expect our results to be generalizable to other scan systems. Source code, pretrained models and training data is available at https://github.com/Jeffrey-Ede/Adaptive-Partial-STEM.

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Section: Introductionmentioning

confidence: 99%

Adaptive Partial Scanning Transmission Electron Microscopy with Reinforcement Learning

Ede¹

2020

Preprint

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Adaptive partial scanning transmission electron microscopy with reinforcement learning

Ede

2021

Mach. Learn.: Sci. Technol.

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Compressed sensing can decrease scanning transmission electron microscopy electron dose and scan time with minimal information loss. Traditionally, sparse scans used in compressed sensing sample a static set of probing locations. However, dynamic scans that adapt to specimens are expected to be able to match or surpass the performance of static scans as static scans are a subset of possible dynamic scans. Thus, we present a prototype for a contiguous sparse scan system that piecewise adapts scan paths to specimens as they are scanned. Sampling directions for scan segments are chosen by a recurrent neural network (RNN) based on previously observed scan segments. The RNN is trained by reinforcement learning to cooperate with a feedforward convolutional neural network that completes the sparse scans. This paper presents our learning policy, experiments, and example partial scans, and discusses future research directions. Source code, pretrained models, and training data is openly accessible at https://github.com/Jeffrey-Ede/adaptive-scans.

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Rolling Element Bearing Fault Diagnosis Based on EEMD and Index of Envelope Spectrum Sparse Ratio

Zhang

et al. 2021

2021 International Conference on Intelligent Transportation, Big Data &Amp; Smart City (ICITBS)

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Sparse fast Fourier transform for exactly sparse signals and signals with additive Gaussian noise

Cited by 4 publications

References 22 publications

Adaptive Partial Scanning Transmission Electron Microscopy with Reinforcement Learning

Adaptive Partial Scanning Transmission Electron Microscopy with Reinforcement Learning

Adaptive partial scanning transmission electron microscopy with reinforcement learning

Rolling Element Bearing Fault Diagnosis Based on EEMD and Index of Envelope Spectrum Sparse Ratio

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