Array Optimization Based on Weighted and Hilbert-Schmidt Schemes of Multisensor Detection System

This article presents a novel sensor array optimization scheme for multisensor electronic nose detection system. A system architecture with multisensor is first proposed to implement the medical detection, including the bacterial culture medium detection and animal wound infection detection. The system efficiency is evaluated by comparing with the field asymmetric ion mobility spectrometry (FAIMS) system. To further improve the detection effect and reduce the number of sensors of the electronic nose system, we then derive two sensor array optimization procedures based on factor analysis and Hilbert-Schmidt independence criterion, respectively. Specifically, the weighted factor analysis method and nonweighted factor analysis method are proposed via factor analysis. Besides, the Hilbert-Schmidt independence criterion optimization design of linear kernel function and Gaussian kernel function are also exploited. The experimental results highlight that compared with the existing approaches, the proposed weighted factor analysis optimization method and Hilbert-Schmidt independence criterion optimization method (Gaussian kernel function) can achieve a significant system performance.

Automated summary

This article presents a novel sensor array optimization scheme for multisensor electronic nose detection system. A system architecture with multisensor is proposed for medical detection. Two sensor array optimization procedures based on factor analysis and Hilbert-Schmidt independence criterion are derived to improve detection effect and reduce the number of sensors. Experimental results show that the proposed methods can achieve significant system performance compared with existing approaches.