Презентация, доклад Overcoming line broadening in real-time pure shift NMR spectroscopy


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Презентации» Физика» Overcoming line broadening in real-time pure shift NMR spectroscopy
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Overcoming line broadening in real-time pure shift NMR spectroscopy Alexandra Shchukina, Krzysztof Kazimierczuk University of Warsaw, Centre of New Technologies, Poland Craig Butts, Ikenna Ndukwe Bristol University, UK

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Overcoming line broadening in real-time pure shift NMR spectroscopy ...with what? Alexandra Shchukina, Krzysztof Kazimierczuk University of Warsaw, Centre of New Technologies, Poland Craig Butts, Ikenna Ndukwe Bristol University, UK


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Overcoming line broadening in real-time pure shift NMR spectroscopy ...with CS reconstruction! Alexandra Shchukina, Krzysztof Kazimierczuk University of Warsaw, Centre of New Technologies, Poland Craig Butts, Ikenna Ndukwe Bristol University, UK

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Pure shift NMR: what for and how Line broadening in real-time pure shift NMR CS reconstruction as a remedy Details of CS: the idea and its realization Applications

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Pure shift NMR: what for and how Line broadening in real-time pure shift NMR CS reconstruction as a remedy Details of CS: the idea and its realization Applications

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Pure shift NMR as a tool for homodecoupling

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Selective pulses Spacially selective or Frequency-selective or BIRD-based pulse sequences ...

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Pseudo-2D and real-time pure shift NMR

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Line broadening with concatenation

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Line broadening with concatenation

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Compressed Sensing – basic idea A signal, which is sparse in some representation, can be undersampled (skip measurements) and then reconstructed mathematically

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Compressed Sensing – basic idea A signal, which is sparse in some representation, can be undersampled (skip measurements) and then reconstructed mathematically for NMR: spectrum (Fourier transform of FID)

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Compressed Sensing – basic idea A signal, which is sparse in some representation, can be undersampled (skip measurements) and then reconstructed mathematically for NMR: spectrum (Fourier transform of FID)

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Compressed Sensing – basic idea A signal, which is sparse in some representation, can be undersampled (skip measurements) and then reconstructed mathematically Full sampling: (full system), – inverse Fourier transform, – spectrum, – FID

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Compressed Sensing – basic idea A signal, which is sparse in some representation, can be undersampled (skip measurements) and then reconstructed mathematically Full sampling: (full system), – inverse Fourier transform, – spectrum, – FID Undersampling: (undetermined system)

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Compressed Sensing – basic idea A signal, which is sparse in some representation, can be undersampled (skip measurements) and then reconstructed mathematically Full sampling: (full system), – inverse Fourier transform, – spectrum, – FID Undersampling: (undetermined system) CS reconstruction: subject to (out of all possible FIDs choose the one which gives the sparsest spectrum)

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Compressed Sensing – basic idea A signal, which is sparse in some representation, can be undersampled (skip measurements) and then reconstructed mathematically Full sampling: (full system), – inverse Fourier transform, – spectrum, – FID Undersampling: (undetermined system) CS reconstruction: subject to (out of all possible FIDs choose the one which gives the sparsest spectrum) Taking noise into account:

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Compressed Sensing – basic idea A signal, which is sparse in some representation, can be undersampled (skip measurements) and then reconstructed mathematically Full sampling: (full system), – inverse Fourier transform, – spectrum, – FID Undersampling: (undetermined system) CS reconstruction: subject to (out of all possible FIDs choose the one which gives the sparsest spectrum) Taking noise into account:

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Compressed Sensing – basic idea A signal, which is sparse in some representation, can be undersampled (skip measurements) and then reconstructed mathematically Full sampling: (full system), – inverse Fourier transform, – spectrum, – FID Undersampling: (undetermined system) CS reconstruction: subject to (out of all possible FIDs choose the one which gives the sparsest spectrum) Taking noise into account: Iterative solution → family of algorithms

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