Abstract
Cross-perception is a perceptual phenomenon that demonstrates an interaction between two or more different sensory perception where the stimulus of one sensory system guides the instinctive response of another sense. In this work, a cross-perception model imitating multiple human’s perception by multi-sensor data fusion was proposed for the instrumental study. “Electronic nose” and “electronic tongue” were employed for detection of aroma and taste respectively of black tea samples. The data collected from two different sensory systems were pre-processed with suitable pre-processing technique and merged prior to further use. Two cross-perception variables i.e. cross correlation factor of aroma on taste and vice versa were assigned using principal component analysis and multiple linear regression. KNN classifier was comparatively used for classification of the conventional fusion model as well as cross-perception multi sensor fusion model. Results indicated that the cross-perception multi-sensors data fusion demonstrated noticeable superiority to the conventional fusion methodologies.
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Banerjee, M.B., Roy, R.B., Tudu, B., Bandyopadhyay, R., Bhattacharyya, N. (2017). Cross-Perception Fusion Model of Electronic Nose and Electronic Tongue for Black Tea Classification. In: Mandal, J., Dutta, P., Mukhopadhyay, S. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2017. Communications in Computer and Information Science, vol 775. Springer, Singapore. https://doi.org/10.1007/978-981-10-6427-2_33
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DOI: https://doi.org/10.1007/978-981-10-6427-2_33
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