We propose a data refinement and channel selection method for vapor classification in a portable e-nose system. For the robust e-nose system in a real environment, we propose to reduce the noise in the data measured by sensor arrays and distinguish the important part in the data by the use of feature feedback. Experimental results on different volatile organic compounds data show that the proposed data refinement method gives good clustering for different classes and improves the classification performance. Also, we design a new sensor array that consists only of the useful channels. For this purpose, each channel is evaluated by measuring its discriminative power based on the feature mask used in the data refinement. Through the experimental results, we show that the new sensor array improves both the classification rates and the efficiency in computation and data storage.

This paper presents a new JPEG quantization table design method for mobile phone images. Although the screen size of mobile phones is very small, the full information of the image should nevertheless be represented. Moreover, the high frequency components of the mobile phone images may contain important information. In order to enhance the performance of mobile JPEG images, these high frequency components should be compensated using an appropriate quantization table. Considering these characteristics, we propose a modeling method of the quantization table for compensating the high frequency components of the mobile images while sacrificing their low frequency components. We select the optimized pre-emphasis factor and bias factor using various sets of 240× 320 images and show that the proposed method improves the performance in terms of size and PSNR.