Speaker
Description
Instruments known as biological field effect transistors (BioFETs) have potential to offer affordable and highly sensitive medical diagnostics that can be administered point of care. Signal is separated from noise in these instruments with stochastic regression, a technique that involves modeling the signal with a linear deterministic drift term and a white noise term that captures stochastic behavior. Both of these terms have coefficients that are estimated from data or simulation with local weighted regression and maximum likelihood estimation. Crucially, these coefficient estimation techniques depend on an averaging function--known as a kernel function--and an averaging window, the size of which is governed by variable known as the bandwidth parameter. In this talk, we determine optimal bandwidth parameters associated with an experimental BioFET measurement and simulation data. Cross validation is performed with respect to different instrument aspect ratios. Results show optimal badwidth parameters are surprisingly consistent across aspect ratios, and suggest a choice of kernel function.
