Abstract
The increasingly ubiquitous use of embedded devices calls for autonomous optimizations of sensor performance with meager computing resources. Due to the heavy computing needs, such optimization is rarely performed, and almost never carried out on-the-fly, resulting in a vast underutilization of deployed assets. Aiming at improving the measurement efficiency, we show an OED (Optimal Experimental Design) routine where quantities of interest of probable samples are partitioned into distinctive classes, with the corresponding sensor signals learned by supervised learning models. The trained models, digesting the compressed live data, are subsequently executed at the constrained device for continuous classification and optimization of measurements. We demonstrate the closed-loop method with multidimensional NMR (Nuclear Magnetic Resonance) relaxometry, an analytical technique seeing a substantial growth of field applications in recent years, on a wide range of complex fluids. The realtime portion of the procedure demands minimal computing load, and is ideally suited for instruments that are widely used in remote sensing and IoT networks.
Highlights
NMR, considered as one of the most potent analytical methods, traditionally requires dedicated personnel and delicate equipment thanks to the use of superconducting magnets, sizable electronics, and intricate probe and antenna placements[1]
The needs for optimizing NMR spectroscopy become more pressing when considering that the quantities of interest, such as relaxation times (T1 and T2), diffusion coefficient, J-coupling, and chemical shift oftentimes span a large numerical range up to several orders of magnitude[13]
Another challenge for autonomous optimization at the embedded sensing devices stems from the limited computing infrastructure, where microprocessors of merely tens of MHz CPU clock-rate and fast memories of tens of KB to a few MB are available[17]
Summary
NMR, considered as one of the most potent analytical methods, traditionally requires dedicated personnel and delicate equipment thanks to the use of superconducting magnets, sizable electronics, and intricate probe and antenna placements[1]. We wondered whether it would be possible to optimize multidimensional NMR relaxometry that measures NMR relaxation times[20] of complex fluids, in realtime, on a mobile sensor generally regarded too “dumb” to perform such tasks. As sensors generally couldn’t foresee temporal progression of sample properties, any combinations of fluid class and pulse sequence are practically probable.
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