Exploiting plume structure to decode gas source distance using metal-oxide gas sensors

Supplementary code

This repository contains code to reproduce all figures from the manuscript:

Michael Schmuker, Viktor Bahr, Ramón Huerta (2016): Exploiting plume structure to decode gas source distance using metal-oxide gas sensors. Sensors and Actuators B: Chemical 235:636-646 (2016). doi:10.1016/j.snb.2016.05.098

The accepted manuscript is available for free on ArXiv under arxiv:1602.01815.

Getting started

1. Download the data that A. Vergara and colleagues have collected and published in 2013 from the UCI Machine learning Repository).

2. Extract the archive into the same top-level directory that you cloned this repository in. For example, if you clone this repo into /home/user/plume_distance/, you should extract the data into the same directory. That directory should afterwards contain at least the two entries exploiting_plume_structure (i.e., this repository), and WTD_upload (the dataset).

3. Go to the ipnotebooks directory, fire up an ipython/jupyter notebook session, and you should be good to go.

Make sure you have all dependencies installed:

• jupyter (to open the notebooks in the first place)
• numpy
• scipy
• matplot
• tables
• sklearn
• mdp
• pandas

We recommend the Anaconda Python distribution because it made scientific python a breeze on every computer and platform we were working on so far.

If you run into issues please use the issue tracker - we'll do our best to respond as quickly as possible.