To improve the accuracy of polarization random noise signal recognition and the noise suppression effect, a polarization random method of noise suppression in a two-dimensional force sensor based on random forest is proposed.

Continuous quality monitoring has become increasingly important in industrial processes, such as raw-milk monitoring. This can often be achieved by detecting individual process markers in the gas phase, which requires inexpensive analytical systems. Therefore, an easy-to-implement three-step concept has been developed that aims to bring together the chemical–analytical and sensor–technical sides. This concept is designed to be applicable in a wide variety of cases and to a range of sensors.

We present a sensor platform that can detect the oral cancer signature of biomarkers in saliva. This system works by arraying metal–oxide–semiconductor field-effect transistors (MOSFETs) and electrolyte-gated transistors (EGTs), with each targeting a biorecognition element (BRE) from a DNA sequence found in the promoter region of most biomarker genes. This system is also applicable to the simultaneous diagnosis of TNF-α and IL-1β immune-modulator biomarkers.

A simple physically motivated model for measurements obtained by nondestructive testing and evaluation using eddy-current methods is created by using equivalent circuit networks that include all relevant effects. The transfer function of a network is derived and its physical meaning discussed. For use in process control, a sensor system with low-latency parameter identification is integrated into a forming process, and its abilities to monitor microstructure changes are demonstrated.