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.

This paper shows a new Double Gate Junctionless (DGJL) MOSFET design based on both highly doped drain/source extensions and gate material engineering. The device electrical behavior at the nanoscale level is investigated using an accurate numerical computation based on TCAD simulation provided by ATLAS 2D simulator, where quantum confinement effects and the modified drift-diffusion transport model that takes into account SCEs are included. Circuit performance parameters are also developed incorporating the impact of dual-material gate engineering and highly doped extension regions. The effect of the proposed design amendments on the performance of the common source single stage amplifier circuit based on DGJL MOSFET is also performed, where the proposed design exhibits an outstanding capability for offering improved amplifier properties. In order to analyze the characteristics and circuit performance of the device, we have adopted mixed mode simulation under Silvaco environment for the implementation of the inverter circuit. Based on the numerical, outcomes satisfactory results are recorded in comparison with the conventional nanoscale junctionless design.

This paper presents the particle swarm optimization (PSO) algorithm in conjuction with the fuzzy logic method in order to achieve an optimized tuning of a proportional integral derivative controller (PID) in the DTC control loops of dual star induction motor (DSIM). The fuzzy controller is insensitive to parametric variations, however, with the PSO-based optimization approach we obtain a judicious choice of the gains to make the system more robust. According to Matlab simulation, the results demonstrate that the hybrid DTC of DSIM improves the speed loop response, ensures the system stability, reduces the steady state error and enhances the rising time. Moreover, with this controller, the disturbances do not affect the motor performances.

The disturbances are the significant issue for the trajectory tracking of mobile robots. Therefore, an adequate control law is presented in this paper and this one is based on Global Terminal Sliding Mode (GTSM) with fuzzy control. This control law aims to guarantee the avoidance of the kinematic disturbances which are injected in the angular and linear velocities, respectively. Moreover, the dynamic model based on exponential reaching law is presented to avoid the uncertainties. The control law provides the asymptotic stability by taking into account the fuzzy rules and Lyapunov theory. Thus, the chattering phenomenon should be avoided. The simulation works prove the robustness of the proposed control law by considering the disturbances function and the robot can follow the desired trajectories.