Two presentation in September 2016
30th Eurosensors Conference, EUROSENSORS 2016 ### Wireless for Space and Extreme Environments, WISEE 2016
Nico Hartgenbusch, Mykhailo Borysov, Reiner Jedermann, Walter Lang:
Pulsed excitation of thermal flow sensors for reduced power consumption and expanded measurement range
30th Eurosensors Conference, EUROSENSORS 2016, 4-7 September 2016, Budapest, Hungary, eurosensors.akcongress.com
Abstract: This paper presents a new method of low power excitation for thermal flow sensors by driving them with voltage pulses. The impulses are generated by a capacitor circuit and can have duration of some ms or less, depending on the voltage and the required resolution. Although the sensor, due to the short duration of the pulse, does not reach thermal equilibrium, the dynamic heating curve provides enough information to determine the flow rate.
This pulsed excitation method provides a substantial reduction of the power consumption (by 78%, in the case of pulses of 1 ms and 5 V). Moreover the additional information contained in the shape of the impulse response allows to expand the measurement range by a factor of 8 or more.
Reiner Jedermann, Henning Paul and Walter Lang:
Compressed Radio Transmission of Spatial Field Measurements by Virtual Sensors
Wireless for Space and Extreme Environments, WISEE 2016
ICT cubes, Aachen, Germany, September 26 - 29, 2016
https://www.ti.rwth-aachen.de/WiSEE2016/
Abstract: The remote exploration or monitoring of an environment often includes sensor measurements at multiple probe points and reconstruction of the spatial distribution of the observed physical quantity by a regression model. Especially for long distances between the observer and the environment, required data volume for transmitting a parametric description of the spatial distribution becomes critical. Simple physical models or assumption of parametric base functions do not provide sufficient prediction accuracy. Statistically based methods for field reconstruction such as Kriging or Gaussian Process Regression provide good accuracy, even if the measurements are overlaid with noise, provided all sensor data is transmitted. The new method presented in this paper calculates a small set of quasi optimal virtual sensor positions located in-between the real sensors. By transmitting only the predicted values of these virtual sensors, the spatial field can be reconstructed with less transmitted data and without significantly increasing the prediction error. The new approach was verified in a simulation scenario for a temperature field caused by diffusion and advection phenomena, which yielded a data compression by a factor of up to four. For large variations of the number of sensors and of the magnitude of measurement noise, the prediction error was always lower compared with the parametric base function models.