Contrast enhancement and background suppression of chemosensor array patterns with the KIII model

Study on Interference Suppression Algorithms for Electronic Noses: A Review

Electronic noses (e-nose) are composed of an appropriate pattern recognition system and a gas sensor array with a certain degree of specificity and broad spectrum characteristics. The gas sensors have their own shortcomings of being highly sensitive to interferences which has an impact on the detection of target gases. When there are interferences, the performance of the e-nose will deteriorate. Therefore, it is urgent to study interference suppression techniques for e-noses. This paper summarizes the sources of interferences and reviews the advances made in recent years in interference suppression for e-noses. According to the factors which cause interference, interferences can be classified into two types: interference caused by changes of operating conditions and interference caused by hardware failures. The existing suppression methods were summarized and analyzed from these two aspects. Since the interferences of e-noses are uncertain and unstable, it can be found that some nonlinear methods have good effects for interference suppression, such as methods based on transfer learning, adaptive methods, etc.

Adaptive microsensor systems

We provide a broad review of approaches for developing chemosensor systems whose operating parameters can adapt in response to environmental changes or application needs. Adaptation may take place at the instrumentation level (e.g., tunable sensors) and at the data-analysis level (e.g., adaptive classifiers). We discuss several strategies that provide tunability at the device level: modulation of internal sensing parameters, such as frequencies and operation voltages; variation of external parameters, such as exposure times and catalysts; and development of compact microanalysis systems with multiple tuning options. At the data-analysis level, we consider adaptive filters for change, interference, and drift rejection; pattern classifiers that can adapt to changes in the statistical properties of training data; and active-sensing techniques that can tune sensing parameters in real time. We conclude with a discussion of future opportunities for adaptive sensing in wireless distributed sensor systems.