scholarly journals Deep Nearest Class Mean Model for Incremental Odor Classification

2019 ◽  
Vol 68 (4) ◽  
pp. 952-962 ◽  
Author(s):  
Yu Cheng ◽  
Kin-Yeung Wong ◽  
Kevin Hung ◽  
Weitong Li ◽  
Zhizhong Li ◽  
...  
Keyword(s):  
Odor Classification

Olfaction and the Brain. Olfaction, Taste, and Cognition. C. Rouby, B. Schaal, D. Doubois, R. Gervais, and A. Holley (Eds.). 2002. Cambridge, UK: Cambridge University Press. 462 pp., $95.00.

2003 ◽  
Vol 9 (7) ◽  
pp. 1101-1103
Author(s):  
Richard L. Doty
Keyword(s):  
Cambridge University ◽  
Odor Memory ◽  
Odor Classification ◽  
The Brain ◽  
Odor Conditioning

This 27-chapter volume arose from a symposium held in Lyon, France, in June of 1999, that sought to integrate information from academic scientific studies of olfaction, largely psychological in nature, with knowledge derived from the perfume industry. It is dedicated to the French perfumer Edmond Roudnitska, known for creating such classic fragrances as “Femme” (1944, Rochas), “Diorama” (1948, Dior), L'Eau” (1951, Hermès), “Diorissimo” (1956, Dior), “Eau Savage” (1966, Dior), and “Diorella” (1972, Dior), and addresses such topics as odor classification, odor memory, odor conditioning, and the plasticity of chemosensation.

Insect-Like Odor Classification and Localization on an Autonomous Robot

2012 ◽  
pp. 371-372 ◽  
Author(s):  
Lucas L. López-Serrano ◽  
Vasiliki Vouloutsi ◽  
Alex Escudero Chimeno ◽  
Zenon Mathews ◽  
Paul F. M. J. Verschure
Keyword(s):  
Autonomous Robot ◽  
Odor Classification

Metal Oxides Semiconductor Sensors for Odor Classification

2017 ◽  
Vol 6 (3) ◽  
pp. 133
Author(s):  
Nyayu Latifah Husni ◽  
Ade Silvia ◽  
Siti Nurmaini ◽  
Irsyadi Yani
Keyword(s):  
Metal Oxides ◽  
Gas Sensors ◽  
Experimental Test ◽  
Sampling Method ◽  
Performance Test ◽  
Dynamic Experiment ◽  
Semiconductor Sensor ◽  
Surrounding Environment ◽  
Expected Outcome ◽  
Odor Classification

<span>The performance of gas sensor will differ and vary due to the surrounding environment changing, the way of implementation, and the position of the sensors to the source. To reach a good result on gas sensors implementation, a performance test on sensors is needed. The results of the tests are useful for characterizing the properties of the particular material or device. This paper discusses the performances of metal oxides semiconductor (MOS) sensors. The sensors are tested to determine the sensors' time response, sensors' peak duration, sensors' sensitivity, and sensors' stability of the sensor when applied to the various sources at different range. Three sources were used in experimental test, namely: ethanol, methanol, and acetone. The gas sensors characteristics are analyzed in open sampling method in order to see the sensors' sensitivity to the uncertainty disturbances, such as wind. The result shows that metal oxides semiconductor sensor was responsive to the 3 sources not only in static but also dynamic conditions. The expected outcome of this study is to predict the MOS sensors' performance when they are applied in robotic implementation. This performance was considered as the training datasets of the sensor for odor classification in this research. From the experiments, It was got, in dynamic experiment, the senrors has average of precision of 93.8-97%, the accuracy 93.3-96.7%, and the recall 93.3-96.7%. This values indicates that the sensors were selective to the odor they sensed.</span>

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