Abstract: Volatile organic compounds classification by receiving test data associated with detecting volatile organic compounds (VOCs), analyzing the test data according to a set of data features associated with known VOCs, determining a match between each feature of the test data and a corresponding feature of the set of data features, yielding a set of matches, defining a first degree of anomaly for the test data according to the set of matches, and classifying the test data according to the first degree of anomaly.

Abstract: Provided is a system and method for tuning an array of sensors to enable selection of the most suitable sensors for a target application. After extracting features from sensor raw data, the extracted features are ranked with gradient boosting decision trees to assign an importance value to each extracted feature. A threshold value for the entire set of extracted features is calculated and an importance score is calculated for the individual sensors of the array. Individual sensors with an importance score on or above the threshold value are selected for the target application.

Abstract: Provided is a gas sensor system with a gas sensor, and a microprocessor programmed to control the gas sensor with at least two operational modes. The first operational mode controls the gas sensor from a baseline level through analyte detection. Upon initiation of the recovery phase after analyte withdrawal, the gas sensor system switches to the second operational mode, which changes conditions of the gas sensor to (i) accelerate removal of the analyte from the gas sensor and (ii) accelerate recovery of the gas sensor output towards the baseline level. When no further analyte is detected, the gas sensor switches back to the first operational mode or to an additional operational mode to complete recovery.

Abstract: Provided is a method and system for extracting features from raw data for machine learning processing. Using an array of gas sensors, raw data for at least one compound of interest are extracted based upon the type of output signals. Where the output signals are amplitude-variant, mean features are extracted by chunking the raw data into slices and calculating the mean area under the curve. Where the output signals are amplitude-and-time-variant, mean-plus-slope features are extracted by taking logarithmic values of the raw data and calculating the mean area under the curve.

Abstract: Volatile organic compounds classification by receiving test data associated with detecting volatile organic compounds (VOCs), analyzing the test data according to a set of data features associated with known VOCs, determining a match between each feature of the test data and a corresponding feature of the set of data features, yielding a set of matches, defining a first degree of anomaly for the test data according to the set of matches, and classifying the test data according to the first degree of anomaly.

Abstract: Provided is a system and method for tuning an array of sensors to enable selection of the most suitable sensors for a target application. After extracting features from sensor raw data, the extracted features are ranked with gradient boosting decision trees to assign an importance value to each extracted feature. A threshold value for the entire set of extracted features is calculated and an importance score is calculated for the individual sensors of the array. Individual sensors with an importance score on or above the threshold value are selected for the target application.

Abstract: Provided is a method and system for extracting features from raw data for machine learning processing. Using an array of gas sensors, raw data for at least one compound of interest are extracted based upon the type of output signals. Where the output signals are amplitude-variant, mean features are extracted by chunking the raw data into slices and calculating the mean area under the curve. Where the output signals are amplitude-and-time-variant, mean-plus-slope features are extracted by taking logarithmic values of the raw data and calculating the mean area under the curve.

Abstract: Provided is a gas sensor system with a gas sensor, and a microprocessor programmed to control the gas sensor with at least two operational modes. The first operational mode controls the gas sensor from a baseline level through analyte detection. Upon initiation of the recovery phase after analyte withdrawal, the gas sensor system switches to the second operational mode, which changes conditions of the gas sensor to (i) accelerate removal of the analyte from the gas sensor and (ii) accelerate recovery of the gas sensor output towards the baseline level. When no further analyte is detected, the gas sensor switches back to the first operational mode or to an additional operational mode to complete recovery.