Resolution enhancement of NMR by decoupling with low-rank Hankel model

no code implementations • 2 Dec 2022 • Tianyu Qiu, Amir Jahangiri, Xiao Han, Dmitry Lesovoy, Tatiana Agback, Peter Agback, Adnane Achour, Xiaobo Qu, Vladislav Orekhov

Nuclear magnetic resonance (NMR) spectroscopy has become a formidable tool for biochemistry and medicine.

NMR spectrum reconstruction as a pattern recognition problem

no code implementations • 4 Aug 2022 • Amir Jahangiri, Xiao Han, Dmitry Lesovoy, Tatiana Agback, Peter Agback, Adnane Achour, Vladislav Orekhov

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

1 code implementation • 29 Dec 2020 • Zi Wang, Di Guo, Zhangren Tu, Yihui Huang, Yirong Zhou, Jian Wang, Liubin Feng, Donghai Lin, Yongfu You, Tatiana Agback, Vladislav Orekhov, Xiaobo Qu

The non-uniform sampling is a powerful approach to enable fast acquisition but requires sophisticated reconstruction algorithms.

Cloud Computing

Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning

no code implementations • 9 Apr 2019 • Xiaobo Qu, Yihui Huang, Hengfa Lu, Tianyu Qiu, Di Guo, Tatiana Agback, Vladislav Orekhov, Zhong Chen

Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biology but often suffers from long experimental time.