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Active nonuniform sampling
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AI
DOI：10.1016/j.jmr.2026.108046.png)
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Abstract
En 中文
Nonuniform sampling (NUS) is an effective strategy for reducing experiment times in multidimensional NMR, enabling practitioners to focus instrument resources on indirect dimension times that carry maximal information about the measured signal. Given a suitably chosen sampling schedule, NUS experiments can yield higher spectral quality in less time, and while many suitable choices of schedule exist for any given experiment, their selection is still largely based on rules of thumb. This work examines the desiderata of active nonuniform sampling (ANS), which places a reconstruction algorithm within the acquisition loop to select new samples on the fly, and develops a candidate ANS algorithm in order to assess its performance and practicality in two-dimensional experiments.
Keywords:
Active learning
Compressed sensing
Nonuniform sampling
Statistical inference
Variational inference
Journal
IF:
1.9
Papers:
89
Citations:
9.9K
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