A5082336442- NMR spectroscopy and applications
- Metabolomics and Mass Spectrometry Studies
- Advanced MRI Techniques and Applications
- Advanced NMR Techniques and Applications
- Spectroscopy and Chemometric Analyses
- Image and Signal Denoising Methods
- Analytical Chemistry and Chromatography
- Mineral Processing and Grinding
- Fermentation and Sensory Analysis
- Sparse and Compressive Sensing Techniques
- Mass Spectrometry Techniques and Applications
- Insect and Pesticide Research
- Bee Products Chemical Analysis
- Advanced Neuroimaging Techniques and Applications
- Medical Image Segmentation Techniques
- Healthcare and Venom Research
- Microfluidic and Capillary Electrophoresis Applications
- Digital Image Processing Techniques
- Seismic Imaging and Inversion Techniques
- Medical Imaging Techniques and Applications
- Image Retrieval and Classification Techniques
- Topological and Geometric Data Analysis
- Advanced Image Processing Techniques
- Minerals Flotation and Separation Techniques
University of Canterbury
2017-2023
Siemens Healthcare (United States)
2016
University of Colorado Boulder
2013-2014
Benchtop NMR analysis combined with model-based fitting protocols can detect commercial honey adulteration down to 5 wt%.
Abstract Nuclear magnetic resonance (NMR) spectroscopy is widely used for applications in the field of reaction and process monitoring. When complex mixtures are studied, NMR spectra often suffer from low resolution overlapping peaks, which places high demands on method to acquire or analyse spectra. This work presents two methods that help overcome these challenges: 2D non‐uniform sampling (NUS) a recently proposed model‐based fitting approach analysis 1D We use glycerol with acetic acid as...
Denoising is an indispensable step in processing low-dose X-ray fluoroscopic images that requires development of specialized high-quality algorithms able to operate near real-time. We address this problem with efficient deep learning approach based on the process-centric view traditional iterative thresholding methods. develop a novel trainable patch-based multiscale framework for sparse image representation. In computationally way, it allows us accurately reconstruct important features...
Low spectral resolution and extensive peak overlap are the common challenges that preclude quantitative analysis of nuclear magnetic resonance (NMR) data with established integration method. While numerous model-based approaches overcome these obstacles enable quantification, they intrinsically rely on rigid assumptions about functional forms for peaks, which often insufficient to account all unforeseen imperfections in experimental data. Indeed, even spectra well-separated peaks whose is...
Abstract The measurement of self‐diffusion coefficients using pulsed‐field gradient (PFG) nuclear magnetic resonance (NMR) spectroscopy is a well‐established method. Recently, benchtop NMR spectrometers with coils have also been used, which greatly simplify these measurements. However, disadvantage the lower resolution acquired signals compared to high‐field spectrometers, requires sophisticated analysis methods. In this work, we use recently developed quantum mechanical (QM) model‐based...
Abstract. We proposed an effective and computationally simple mechanism to improve the accuracy of model-based quantification in NMR data analysis. The adjustment procedure aims account for all useful signal left residual after usual least squares fit, which can signify a case model misspecification – problem notoriously difficult avoid most qNMR methods. Our alternative optimization criterion explicitly relies on denoising smoothing remaining baseline is particularly correcting errors...
Local patch-based models have been shown to be effective in numerous image processing applications and become the core of state-of-the-art denoising, inpainting structural editing algorithms. Most such modeling approaches mainly rely on searching for similar patches set available patches. However, apparent similarity between sufficiently small (e.g., 5×5 pixels) regions motivates them with a low-dimensional manifold instead suggests existence simple parametrization it. Although there exist...
Solving inverse problems in signal processing often involves making prior assumptions about the being reconstructed. Here appropriateness of chosen model greatly determines quality final result. Recently it has been proposed to images by representing them as sets smaller patches arising from an underlying manifold. This shown be surprisingly effective tasks such denoising, inpainting, and superresolution. However, a representation is fraught with difficulty finding intersection many...
Nuclear magnetic resonance (NMR) spectroscopy is widely used for applications in the field of reaction and process monitoring. When complex mixtures are studied, NMR spectra often suffer from low resolution overlapping peaks, which places high demands on method to acquire or analyse spectra. This work presents two methods that help overcome these challenges: 2D non-uniform sampling (NUS) a recently proposed model-based fitting approach analysis 1D spectra.We use glycerol with acetic acid as...