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Found 6 papers, 1 papers with code
Resolution enhancement of NMR by decoupling with low-rank Hankel model
Nuclear magnetic resonance (NMR) spectroscopy has become a formidable tool for biochemistry and medicine.
NMR spectrum reconstruction as a pattern recognition problem
A new deep neural network based on the WaveNet architecture (WNN) is presented, which is designed to grasp specific patterns in the NMR spectra.
A Sparse Model-inspired Deep Thresholding Network for Exponential Signal Reconstruction -- Application in Fast Biological Spectroscopy
The non-uniform sampling is a powerful approach to enable fast acquisition but requires sophisticated reconstruction algorithms.
Exponential Signal Reconstruction with Deep Hankel Matrix Factorization
Exponential is a basic signal form, and how to fast acquire this signal is one of the fundamental problems and frontiers in signal processing.
Review and Prospect: Deep Learning in Nuclear Magnetic Resonance Spectroscopy
In this Minireview, we summarize applications of DL in Nuclear Magnetic Resonance (NMR) spectroscopy and outline a perspective for DL as entirely new approaches that are likely to transform NMR spectroscopy into a much more efficient and powerful technique in chemistry and life science.
Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning
Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biology but often suffers from long experimental time.
