by Keyword: Metal oxide semiconductor
Robbiani, Stefano, Benegiamo, Alessandro, Capelli, Laura, Marco, Santiago, Dellaca, Raffaele, (2024). Dielectric excitation of Metal Oxide Semiconductor sensors: an exploratory performances analysis 2024 Ieee International Symposium On Olfaction And Electronic Nose (Isoen)
Metal Oxide Semiconductor (MOX) sensors are among the most widespread devices in chemical sensing, but their use is hindered due to several limitations, including crosssensitivity to temperature and humidity. Few studies suggested that the dielectric excitation readout of MOX sensors can increase the linearity and reduce cross-sensitivity. A bench test on two commercially available MOX sensors was designed and used to evaluate the dielectric excitation readout performances at different concentrations of acetone and ethanol when temperature and humidity were changed. Results show that not only both the real and imaginary parts of the sensors' electrical impedance are strongly frequency dependent, but also the dynamics of the sensors' response. Furthermore, the calculation of cross-sensitivity shows that there are regions of the spectra that allow for a reduction of cross-sensitivity to environmental interferences ranging from 2 to 10 times between 50 and 100 KHz.
JTD Keywords: Confounding factor, Dielectric excitation, Metal oxide semiconductor sensors
Bouras, A, Gutierrez-Galvez, A, Burgués, J, Bouzid, Y, Pardo, A, Guiatni, M, Marco, S, (2023). Concentration map reconstruction for gas source location using nano quadcopters: Metal oxide semiconductor sensor implementation and indoor experiments validation Measurement 213, 112638
Burgués, Javier, Marco, Santiago, (2020). Feature extraction for transient chemical sensor signals in response to turbulent plumes: Application to chemical source distance prediction Sensors and Actuators B: Chemical 320, 128235
This paper describes the design of a linear phase low-pass differentiator filter with a finite impulse response (FIR) for extracting transient features of gas sensor signals (the so-called “bouts”). The detection of these bouts is relevant for estimating the distance of a gas source in a turbulent plume. Our current proposal addresses the shortcomings of previous ‘bout’ estimation methods, namely: (i) they were based in non-causal digital filters precluding real time operation, (ii) they used non-linear phase filters leading to waveform distortions and (iii) the smoothing action was achieved by two filters in cascade, precluding an easy tuning of filter performance. The presented method is based on a low-pass FIR differentiator, plus proper post-processing, allowing easy algorithmic implementation for real-time robotic exploration. Linear phase filters preserve signal waveform in the bandpass region for maximum reliability concerning both ‘bout’ detection and amplitude estimation. As a case study, we apply the proposed filter to predict the source distance from recordings obtained with metal oxide (MOX) gas sensors in a wind tunnel. We first perform a joint optimization of the cut-off frequency of the filter and the bout amplitude threshold, for different wind speeds, uncovering interesting relationships between these two parameters. We demonstrate that certain combinations of parameters can reduce the prediction error to 8 cm (in a distance range of 1.45 m) improving previously reported performances in the same dataset by a factor of 2.5. These results are benchmarked against traditional source distance estimators such as the mean, variance and maximum of the response. We also study how the length of the measurement window affects the performance of different signal features, and how to select the filter parameters to make the predictive models more robust to changes in wind speed. Finally, we provide a MATLAB implementation of the bout detection algorithm and all analysis code used in this study.
JTD Keywords: Gas sensors, Differentiator, Low pass filter, Metal oxide semiconductor, MOX sensors, Signal processing, Feature extraction, Gas source localization, Robotics
