Uncertainty-Complexity Trade-Offs for Sensor Compensation Design

In this work we focus on the design of reduced-complexity sensor compensation modules based on learning-from-examples techniques. A multi-objective optimization design framework is proposed, where system complexity and compensation uncertainty are considered as two conflicting costs to be jointly minimized. In addition, suitable statistical techniques are applied to cope with the variability in the uncertainty estimation arising from the limited availability of data at design time. Experimental results on a synthetic benchmark are provided to show the validity of the proposed methodology.