摘要：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.
