Souhil KOUDA, Zohir D, Fayçal M, Samir B, Abdelghani D.

Optimization of TiO_{2} concentration effect for a chemical humidity sensing mechanism. Sensor Review [Internet]. 2011;31 (1) :18-25.

Publisher's VersionAbstract
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Purpose

The purpose of this paper is to propose a new approach to optimize the TiO_{2} concentration on a resistive‐type humidity sensing mechanism (RHSM) based on artificial neural network.

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Design/methodology/approach

First is the modeling of the sensing mechanism. Using neuronal networks and Matlab environment to accurately express the output of the sensing mechanism, this model thus takes into

account the parameter, non‐linearity, hysteresis, temperature and frequency; furthermore, the TiO_{2} concentration effect on the humidity sensing properties in the model is investigated. In a second step, the Matlab environment is used to create a database for an ideal model for the sensing mechanism, where the response of this ideal model is linear for any above parameters value.

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Findings

An analytical model for the sensing mechanism “SM” and the ideal model “IM” has been created. The bias matrix and the weights matrix were used to establish the SM model and the IM on performance simulation program with integrated circuit emphasis simulator, where the output of the first is identical to the RHSM output and the output of the last is the ideal response.

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Originality/value

The paper proposes an electrical circuit used to optimize the TiO_{2} concentration of a resistive humidity sensing mechanism.

Souhil KOUDA, Zohir D, Samir B, Abdelghani D, Fayçal M.

ANN Modeling of a Smart MEMS-based Capacitive Humidity Sensor. International Journal of Control, Automation, and Systems [Internet]. 2011;9 (1) :197-202.

Publisher's VersionAbstractThis paper presents a design of a smart humidity sensor. First we begin by the modeling of a Capacitive MEMS-based humidity sensor. Using neuronal networks and Matlab environment to accurately express the non-linearity, the hysteresis effect and the cross sensitivity of the output humidity sensor used. We have done the training to create an analytical model CHS “Capacitive Humidity Sensor”. Because our sensor is a capacitive type, the obtained model on PSPICE reflects the humidity variation by a capacity variation, which is a passive magnitude; it requires a conversion to an active magnitude, why we realize a conversion capacity/voltage using a switched capacitor circuit SCC. In a second step a linearization, by Matlab program, is applied to CHS response whose goal is to create a database for an element of correction “CORRECTOR”. After that we use the bias matrix and the weights matrix obtained by training to establish the CHS model and the CORRECTOR model on PSPICE simulator, where the output of the first is identical to the output of the CHS and the last correct its nonlinear response, and eliminate its hysteresis effect and cross sensitivity. The three blocks; CHS model, CORRECTOR model and the capacity/voltage converter, represent the smart sensor.