Odor Classification Using Support Vector Machine

This paper discusses about the process of classifying odor using Support Vector Machine. The training data was taken using a robot that ran in indoor room. The odor was sensed by 3 gas sensors, namely: TGS 2600, TGS 2602, and TGS 2620. The experimental environment was controlled and conditioned. The temperature was kept between 27.5 0C to 30.5 0C and humidity was in the range of 65% -75 %. After simulation testing in Matlab, the classification was then done in real experiment using one versus others technique. The result shows that the classification can be achieved using simulation and real experiment

Optimal Kernel Classifier in Mobile Robots for Determining Gases Type

The use of TGS sensor and Arduino could create a robot to be capable of detecting and classifying some gases. In this research, 3 kinds of SVM Kernel Classifiers are investigated. Robots equipped with 3 TGS sensors are used to classify methanol and acetone. Xbee modul is used as a communication medium between robots and server. The robots are run in the experimental environment. When they detect the gas, they will get closer to the source and classify the gas type. The classified gas data is then sent to the server. From this research, it can concluded that polynomial and RBF have better performance in classifying methanol and acetone