A new feature extraction method for odour classification

Sensors and Actuators, B: Chemical

Volumn 158, Issue 1, 2011, Pages 75-88

  Ehret, Bernd ^a   Safenreiter, Konstantin ^a   Lorenz, Frank ^b   Biermann, Joachim ^a  

^a FRAUNHOFER FKIE   (Germany)

^b FRAUNHOFER INSTITUTE FOR HIGH FREQUENCY PHYSICS AND RADAR TECHNIQUES FHR   (Germany)

Author keywords

Odour classification; Pattern recognition; Sensor signal features extraction; Support vector machines; Volatile substance traces detection

Indexed keywords

CHARACTERISTIC SIGNAL; CHEMICAL SUBSTANCE; CLASSIFICATION METHODS; CLASSIFICATION RESULTS; CONVENTIONAL METHODS; CORRELATIVE RELATIONSHIP; ELECTROCHEMICAL SIGNALS; FEATURE EXTRACTION METHODS; FEATURE REDUCTION; FEATURE TYPES; FREQUENCY DOMAINS; GAUSSIAN PLUME MODELS; LEARNING PROCESS; LOW-FREQUENCY SIGNALS; NITRO-AROMATICS; NON-LINEAR; PRESENT STAGE; REAL MEASUREMENTS; REAL TIME; RECOGNITION RATES; SEMICONDUCTOR GAS SENSORS; SEMICONDUCTOR SENSORS; SENSOR MEASUREMENTS; SENSOR SIGNAL FEATURES EXTRACTION; SENSOR SIGNALS; STATISTICAL LEARNING THEORY; SUPPORT VECTOR; SYNTHETIC SIGNALS; TRAINING DATA SETS; VOLATILE CHEMICALS; VOLATILE SUBSTANCES;

CHEMICAL DETECTION; CHEMICAL SENSORS; CLASSIFICATION (OF INFORMATION); MAXIMUM LIKELIHOOD ESTIMATION; SIGNAL DETECTION; SUPPORT VECTOR MACHINES;

FEATURE EXTRACTION;

EID: 79960464622     PISSN: 09254005     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.snb.2011.05.042     Document Type: Article

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