Gas Recognition under Sensor Drift by Using Deep Learning

Drift Compensation for Electronic Nose by Semi-Supervised Domain Adaption

(2013) Qihe Liu et al.   IEEE SENSORS JOURNAL

Semi-Supervised Learning Techniques in Artificial Olfaction: A Novel Approach to Classification Problems and Drift Counteraction

(2012) S. De Vito et al.   IEEE SENSORS JOURNAL

Chemical gas sensor drift compensation using classifier ensembles

(2012) Alexander Vergara et al.   SENSORS AND ACTUATORS B-CHEMICAL

Modeling electroencephalography waveforms with semi-supervised deep belief nets: fast classification and anomaly measurement

(2011) D F Wulsin et al.   Journal of Neural Engineering

Direct Identification of Bacteria in Blood Culture Samples Using an Electronic Nose

(2010) M Trincavelli et al.   IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING

Device-independent, real-time identification of bacterial pathogens with a metal oxide-based olfactory sensor

(2009) M. Bruins et al.   EUROPEAN JOURNAL OF CLINICAL MICROBIOLOGY & INFECTIOUS DISEASES

Long term stability of metal oxide-based gas sensors for e-nose environmental applications: An overview

(2009) A.C. Romain et al.   SENSORS AND ACTUATORS B-CHEMICAL

Acceleration of chemo-sensory information processing using transient features☆

(2008) Mehmet K. Muezzinoglu et al.   SENSORS AND ACTUATORS B-CHEMICAL