초록 ▼

A substance detection system having an electronic nose and classifier. The system may extract feature from the electronic signals representing the smells from the electronic nose sensing an unknown substance. The features may be formatted as smellprints that are synthesized as data. The features ma

A substance detection system having an electronic nose and classifier. The system may extract feature from the electronic signals representing the smells from the electronic nose sensing an unknown substance. The features may be formatted as smellprints that are synthesized as data. The features may be classified, and in a binary tournament fashion, as an illustrative example, may be mapped to be compared or correlated with features of known substances. The known substance or substances having the highest scores for good mapping, comparison or correlation with the sensed substance, may be reviewed in view of decision criteria to determine an identification of the sensed substance.

대표청구항 ▼

What is claimed is: 1. A substance detection system comprising: an electronic nose; a feature extraction module connected to the electronic nose; a data synthesis module connected to the feature extraction module; a classification module connected to the data synthesis module; and a decision criter

What is claimed is: 1. A substance detection system comprising: an electronic nose; a feature extraction module connected to the electronic nose; a data synthesis module connected to the feature extraction module; a classification module connected to the data synthesis module; and a decision criteria module connected to the classification module; wherein the classification module comprises a support vector machine the feature extraction module provides a smellprint to the data synthesis module; the smellprint comprises a final activation and initial response data; the output classification module has an output for classification results; the decision criteria module is for filtering the classification results; and wherein feature extraction module comprises: a baseline flag; a baseline resistance processor having a first input from the electronic nose and a second input from the baseline flag; a fractional normalizer having a first input from the electronic nose and a second input from the baseline resistance; a filter having an input connected to the fractional normalizer; a gradient indicator having an input connected to the filter; a steady-state period indicator at the baseline purge having an input connected to the baseline flag; a sample draw flag; a transient period indicator at sample draw having an input connected to the sample draw flag; an indicator of average or absolute variation at baseline having a first input connected to the steady-state period indicator at baseline purge and a second input connected to the gradient indicator; an indicator of maximum absolute variation at initial response having a first input connected to the transient period indicator at sample draw and having a second input connected to the gradient indicator; a relative variation processor having a first input connected to the indicator of average of absolute variation at baseline and a second input connected to the indicator of maximum absolute variation at initial response; a vector normalizer having an input connected to the relative variation processor; and an initial response feature vector indicator having an input connected to the vector normalizer and an output connected to the data synthesis module. 2. Means for detecting a substance comprising: means for sensing a substance via a smell; means for converting the smell into at least one signal; means for extracting features from the at least one signal; means for mapping features to features of each of a plurality of known substances; means for indicating a score of mapping for each of the known substances; means for selecting the known substance having the highest score; and means for determining whether the sensed substance is identified as the same as the known substance having the highest score; wherein the means for determining comprises a criterion selected from a group of the threshold levels and winner-take-all criteria; and the mapping of features is a binary tournament classification. 3. The means of claim 2, wherein the binary tournament classification comprises a support vector machine.