We present DAV³E, a MATLAB toolbox for feature extraction from, and evaluation of, cyclic sensor data. These kind of data arise from many real-world applications like gas sensors in temperature cycled operation or condition monitoring of hydraulic machines. DAV³E enables interactive shape-describing feature extraction from such datasets, which is lacking in current machine learning tools, with subsequent methods to build validated statistical models for the prediction of unknown data. It also provides more sophisticated methods like model hierarchies, exhaustive parameter search, and automatic data fusion, which can all be accessed in the same graphical user interface for a streamlined and efficient workflow, or via command line for more advanced users. New features and visualization methods can be added with minimal MATLAB knowledge through the plug-in system. We describe ideas and concepts implemented in the software, as well as the currently existing modules, and demonstrate its capabilities for one synthetic and two real datasets. An executable version of DAV³E can be found at http://www.lmt.uni-saarland.de/dave (last access: 14 September 2018). The source code is available on request.

Gas sensitive metal/metal-oxide field effect transistors based on silicon carbide were used to study the sensor response to benzene (C₆H₆) at the low parts per billion (ppb) concentration range. A combination of iridium and tungsten trioxide was used to develop the sensing layer. High sensitivity to 10 ppb C₆H₆ was demonstrated during several repeated measurements at a constant temperature from 180 to 300 °C. The sensor performance were studied also as a function of the electrical operating point of the device, i.e., linear, onset of saturation, and saturation mode. Measurements performed in saturation mode gave a sensor response up to 52 % higher than those performed in linear mode.