Electronic Nose, as an artificial olfaction system, has potential applications in environmental monitoring because of its proven ability to recognize and discriminate between a variety of different gases and odors. In this paper, we used a chemical sensor array to develop an electronic nose to detect and identify seven different gases (H₂, C₂H₂, CH₄, CH₃OCH₃, CO, NO₂, and NH₃). These gas sensors are chosen because of its hierarchical/doped nanostructure characteristics, which give them a very high sensitivity and low response time; we improve the linearity response and temperature dependence using models based on artificial neural networks. We used in Electronic nose a pattern recognition based on artificial neural network, which discriminates qualitatively and quantitatively seven gases and has a fast response.

The estimation of the junction temperature (Tj) is very important factor for improving the reliability and efficiency of the power electronic converters. A new electro-thermal (ET) model of low voltage power MOSFET is described in this paper. The electro-thermal model allows fast estimation of the junction temperature, based on the transient thermal impedances (Zth) using the (RC) Foster thermal network model and total power losses. The parameters of the (RC) Foster thermal network model are extracted from the data provided by the manufacturer’s datasheet using particle swarm optimization (PSO) method. Moreover, a dc/dc Buck converter is also analyzed by simulation to evaluate the electro-thermal model. The simulation results indicate a good agreement between the proposed model and manufacturer’s data. Finally, the electro-thermal (ET) model simulation using this (RC) Foster thermal network model shows a reasonable accuracy for estimating the junction temperature in a Dc/Dc buck converter.

This paper presents a hybrid strategy combining compact analytical models of short channel Gate-All-Around Junctionless (GAAJ) MOSFET and metaheuristic-based approach for parameters optimization. The proposed GAAJ MOSFET design includes highly extension regions doping. The aim is to investigate the impact of this design on the RF and analog performances systematically and to show the immunity behavior against the short channel effects (SCEs) degradation. In this context, an analytical model via the meticulous solution of 2D Poisson equation, incorporating source/drain (S/D) extensions effect, has been developed and verified by comparing it with TCAD simulation results. A comparative evaluation between the proposed GAAJ MOSFET structure and the classical device in terms of RF/Analog performances is also investigated. The proposed design provides RF/Analog performances improvement. Furthermore, based on the presented analytical models, Genetic Algorithms (GA) optimization approach is used to optimize the design of S/D parameters. The optimized structure exhibits better performances, i.e., cut-off frequency and drive current are improved. Besides, it shows superior immunity behavior against the RF/Analog degradation due to the unwanted SCEs. The insights offered by the proposed paradigm will help to enlighten designer in future challenges facing the GAAJ MOSFET technology for high RF/analog applications.

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 proposes a new method based on a genetic algorithm (GA) approach to optimize the electrical parameters such as height barrier, ideality factor, fill factor, open-circuit voltage and power conversion efficiency, in order to improve the electrical performance of Schottky solar cells in an over wide range of temperature. Thus the parameters research process called objective function is used to find the optimal electrical parameters providing greater conversion efficiency. The proposed model results are also compared to experimental and analytical I-V data, where a good agreement has been found between them. Therefore, this approach may provide a theoretical basis and physical insights for Schottky solar cells.

In this paper, a new particle swarm optimization‐based approach is proposed for the geometrical optimization of the nanowires solar cells to achieve improved optical performance. The proposed hybrid approach combines the 3‐D numerical analysis using accurate solutions of Maxwell's equations and metaheuristic investigation to boost the solar cell total absorbance efficiency. Our purpose resides on modulating the electric field and increasing the light trapping capability by optimizing the radial solar cell geometrical parameters. Moreover, a comprehensive study of vertical core‐shell nanowire arrays optical parameters such as the integral absorption, reflection, and total absorbance efficiency is carried out, in order to reveal the optimized radial solar cells optical performance for low‐cost photovoltaic applications. We find that the proposed hybrid approach plays a crucial role in improving the nanowires solar cells optical performance, where the optimized design exhibits superior total absorbance efficiency and lower total reflection in comparison with those provided by the conventional planar design. The obtained results make the proposed global optimization approach valuable for providing high‐efficiency nanowires solar cells.