In this paper, we propose the modeling of an industrial gas sensor “MQ-9”, where our modeling is based on ANNs “artificial neural networks The gas sensor model, obtained, operated under a dynamic environment and expresses accurately the MQ-9 gas sensor behavior. Accordingly, it takes into account the nonlinearity and the cross sensitivity in gas selectivity, temperature and humidity. This model is implemented into PSPICE “performance simulation program with integrated circuit emphasis” simulator as an electrical circuit in order to prove the similarity of the analytical model output with that of the MQ-9 gas sensor.
The selectivity is one of the main challenges to develop a gas sensor, the good chemical species detection in a gaseous mixture decreasing the missed detections. The present paper proposes a new solution for gas sensor selectivity based on artificial neural networks (ANNs) and fuzzy logic (FL) algorithm. We first use ANNs to develop a gas sensor model in order to accurately express its behavior. In a second step, the FL and Matlab environment are used to create a database for a selective model, where the response of this one only depends on one chemical species. Analytical models for the gas sensor and its selective model are implemented into a Performance Simulation Program with Integrated Circuit Emphasis (PSPICE) simulator as an electrical circuit in order to prove the similarity of the analytical model output with that of the MQ-9 gas sensor where the output of the selective model only depends on one gas. Our results indicate the capability of the ANN-FL hybrid modeling for an accurate sensing analysis.
In this work, a new approach is proposed to design the neutron angle and fluence radiation sensor. It is based on an Artificial Neural Network (ANN) model which has the advantage of efficient nonlinear mapping in addition to noise tolerance. This model allows us to obtain the angle and the fluence radiation from the drain source current, the drain source voltage, the gate source voltage and the temperature parameters. The obtained results are nearly closed to the experimental results in the literature.
The increasing architecture complexity of data converters makes it necessary to use behavioral models to simulate their electrical performance and to determine their relevant data features. For this purpose, a specific data converter simulation environment has been developed which allows designers to perform time-domain behavioral simulations of pipelined analog to digital converters (ADCs). All the necessary blocks of this specific simulation environment have been implemented using the popular Matlab simulink environment. The purpose of this paper is to present the behavioral models of these blocks taking into account most of the pipelined ADC non-idealities, such as sampling jitter, noise, and operational amplifier parameters (white noise, finite DC gain, finite bandwidth, slew rate, and saturation voltages). Simulations, using a 10-bit pipelined ADC as a design example, show that in addition to the limits analysis and the electrical features extraction, designers can determine the specifications of the basic blocks in order to meet the given data converter requirements.
The purpose of this paper is to propose a new approach to optimize the TiO2 concentration on a resistive‐type humidity sensing mechanism (RHSM) based on artificial neural network.
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 TiO2 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.
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.
The paper proposes an electrical circuit used to optimize the TiO2 concentration of a resistive humidity sensing mechanism.
This 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.