Summary
A new pre-processing algorithm for improved discrimination of odor samples is proposed. The pre-processed odor sample outputs from two sensors are input using a learning-vector quantization (LVQ) classifier as a means of odor recognition to be employed within electronic nose applications. The proposed algorithm brings out highly scattered classes while minimizing the within-class scatter of the samples given an odor class. LVQ is observed to operate robustly and reliably in terms of variation of parameters of interest, mainly a learning parameter. Due to the increased performance along with computational simplicity and robustness, the scheme is suitable to sample-by-sample identification of olfactory sensory data and can be easily adapted to hierarchical processing with other sensory data in real-time.
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Extract
An Improved Odor Recognition System Using Learning Vector Quantization with a New Discriminant Analysis
1. Introduction
Despite considerable progress toward other sensory systems such as visual and audio, the olfactory system has not been understood very clearly because of diverse excitation, and intricate signal processing involved, [1]. Recently, considerable research in the form of neurobiological information processing has been directed at artificial olfaction to identify and classify odors, [2, 3]. The objective of an artificial olfactory system i...See the full content of this document
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