odor space navigation using multisensory e-nose
电子鼻技术用于空间环境气味扫描识别追踪
v. v. krylov
alekseev nizhny novgorod state technical university, nizhny novgorod, russia
: vkrylov
received may 11, 2016
abstract—a spatial odor distribution in an environment can be used for navigation, goal search, localization and mapping, like by video, ultrasonic, temperature and other sensors. modern e-noses(cyranose 320) can perform the selective detection of different gases with an extremely low concentration but the source localization algorithms of a selected gas against the background of other odors are still underinvestigated. this paper studies an odor field representation in terms of an e-nose based on an array of low-selective sensors. using a simulation model, we show how the vector measurements of a field of several odor sources can be processed to navigate for reaching a selected odor source. in addition, we demonstrate that the source having a high level of odor intensity can interfere with the search of another odor source of a low intensity. the well-known class of matching receivers does not solve this problem. however, a solution can be obtained by distributed measurements. as shown below, the spatial structure of an odor field allows to implement vector selection. using deep learning machines, we may reach a high resolution of odor sources in the space. our future research will be focused on augmented odor reality and autonomous e-nose (e-dog) design.
环境中的空间气味分布可用于导航、目标搜索、定位和绘图,如视频、超声波、温度和其他传感器。电子鼻(cyranose 320)可以对浓度极低的不同气体进行选择性检测,但在其他气味的背景下,对所选气体的源定位算法仍缺乏研究。本文研究了一种基于低选择性传感器阵列的电子鼻气味场表示方法。利用一个模拟模型,我们展示了如何处理多个气味源场的矢量测量,从而导航到选定的气味源。此外,我们还证明,气味强度高的来源会干扰寻找另一种低强度的气味来源。*的匹配接收机并不能解决这个问题。然而,可以通过分布式测量获得解决方案。如下图所示,气味场的空间结构允许实现向量选择。使用深度学习机器,我们可以在空间中获得高分辨率的气味源。我们未来的研究将集中在增强气味现实和自主移动电子鼻(e-dog)设计上。
keywords:odor, e-nose, sensors, odor space, space based selection, navigation, computer simulation,nonnegative matrix factorization
doi: 10.1134/s0005117918010149
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