Computational parallels between the biological olfactory pathway and its analogue `The Electronic Nose': Part II. Sensor-based machine olfaction

Pearce, Timothy Charles

doi:10.1016/s0303-2647(96)01660-7

Cited by 102 publications

(69 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, Table II provides a few representative publications of these pattern classification methods in e-nose applications. Additional references can be found in [66], [67].…”

Section: E Comparison Between Quadratic Knn Mlp and Rbf Classifiersmentioning

confidence: 99%

Pattern analysis for machine olfaction: a review

2002

View full text Add to dashboard Cite

Abstract-Pattern analysis constitutes a critical building block in the development of gas sensor array instruments capable of detecting, identifying, and measuring volatile compounds, a technology that has been proposed as an artificial substitute of the human olfactory system. The successful design of a pattern analysis system for machine olfaction requires a careful consideration of the various issues involved in processing multivariate data: signal-preprocessing, feature extraction, feature selection, classification, regression, clustering, and validation. A considerable number of methods from statistical pattern recognition, neural networks, chemometrics, machine learning, and biological cybernetics has been used to process electronic nose data. The objective of this review paper is to provide a summary and guidelines for using the most widely used pattern analysis techniques, as well as to identify research directions that are at the frontier of sensor-based machine olfaction.

show abstract

“…Finally, Table II provides a few representative publications of these pattern classification methods in e-nose applications. Additional references can be found in [66], [67].…”

Section: E Comparison Between Quadratic Knn Mlp and Rbf Classifiersmentioning

confidence: 99%

Pattern analysis for machine olfaction: a review

2002

View full text Add to dashboard Cite

show abstract

“…In this sensory domain, the sensors detect electrical changes due to the odorant molecules [SG92]. It is possible to integrate the sensors on to the actual CMOS chip [Pea97]. Although the idea is relatively straightforward (altering the electrical properties of an insulating (polymer) layer as a result of the arrival of air-borne molecules), there are difficulties both in delivery, and, because of the chemical nature of the sensing, due to issues of drift and poisoning.…”

Section: Other Sensory Neuromorphic Systemsmentioning

confidence: 99%

Neuromorphic Systems: Past, Present and Future

Smith

2009

Advances in Experimental Medicine and Biology

View full text Add to dashboard Cite

Neuromorphic systems are implementations in silicon of elements of neural systems. The idea of electronic implementation is not new, but modern microelectronics has provided opportunities for producing systems for both sensing and neural modelling that can be mass produced straightforwardly. We review the the history of neuromorphic systems, and discuss the range of neuromorphic systems that have ben developed. We discuss recent ideas for overcoming some of the problems, particularly providing effective adaptive synapses in large numbers.

show abstract

“…Normalization has been previously employed in gas discrimination applications where the identification must be based on signature pattern, and not on the concentration dependent amplitudes [8]- [10]. On one hand, normalization is useful to set the range of values for sensors' output to range in order to avoid the data pattern with larger signal magnitude to dominate in the data space.…”

Section: B Experimental Characterizationmentioning

confidence: 99%

Fast and robust gas identification system using an integrated gas sensor technology and Gaussian mixture models

et al. 2005

View full text Add to dashboard Cite

Abstract-Among the most serious limitations facing the success of future consumer gas identification systems are the drift problem and the real-time detection due to the slow response of most of today's gas sensors. This paper shows that the combination of an integrated sensor array and a Gaussian mixture model permits success in gas identification problems. An integrated sensor array has been designed with the aim of combustion gases identification. Our identification system is able to quickly recognize gases with more than 96% accuracy. Robust detection is introduced through a drift counteraction approach based on extending the training data set using a simulated drift.

show abstract

Computational parallels between the biological olfactory pathway and its analogue `The Electronic Nose': Part II. Sensor-based machine olfaction

Cited by 102 publications

References 68 publications

Pattern analysis for machine olfaction: a review

Pattern analysis for machine olfaction: a review

Neuromorphic Systems: Past, Present and Future

Fast and robust gas identification system using an integrated gas sensor technology and Gaussian mixture models

Contact Info

Product

Resources

About