This paper proposes an odor source localization method based on bionic olfaction. The special nasal cavity and turnable head make mammalian have excellent odor source localization ability. According to this principle, a turnable bionic odor sensing device is proposed by this paper for odor source localization system. This sensing device can rotate freely within the range level 360°, and the detection directions of its sensing channels are different. This also proposes a pattern recognition algorithm based on K-L transform to analyze the data collected by odor sensing device, and the features of odor source are extracted correctly. Experimental results for odor source localization demonstrate the feasibility of the proposed approach.
This paper summarizes some current typical odor source localization strategies and algorithms. And their advantages and disadvantages are pointed out. Some typical olfactory robots and achievements are listed. It is pointed out that the current questions of odor source localization robot based on bionic olfaction are how to build an accurate gas diffusion model and combining multi-information technology.
General review of Change-Points detection methods applied in Interrupted Time Series Analysis for recent years. Articles from domains like meteorology, hydrology, stock analysis, sequences mining et al. are compared together. The literatures range from the 1980s to 2013. The methods are generally classified in Parametric, Semi-Parametric, and Nonparametric. Some non-statistical methods are also mentioned in this review. Characters of each method are briefly summarized. As all methods mentioned in this review share a common purpose that to detect change-points, most of them can be used in other domains after some proper adjustment.
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