Modeling and design of rectangular microstrip patch printed on isotropic or anisotropic substrate are accomplished in this paper. The use of spectral domain method in conjunction with artificial neural networks (ANNs) to compute the resonant characteristics of rectangular microstrip patch printed on isotropic or anisotropic substrates. The moment method implemented in the spectral domain offers good accurateness, but its computational cost is high owing to the evaluation of the slowly decaying integrals and the iterative nature of the solution process. The paper introduces the electromagnetic knowledge combined with ANN in the analysis of rectangular microstrip antenna on uniaxially anisotropic substrate to reduce the complexity of the spectral domain method and to minimize the CPU time necessary to obtain the numerical results. The numerical comparison between neurospectral and conventional moment methods shows significant improvements in time convergence and computational cost. Hence, the use of neurospectral approach presented here as a promising fast technique in the design of microstrip antennas.    

Modeling and design of rectangular microstrip patch printed on isotropic or anisotropic substrate are accomplished in this paper. The use of spectral domain method in conjunction with artificial neural networks (ANNs) to compute the resonant characteristics of rectangular microstrip patch printed on isotropic or anisotropic substrates. The moment method implemented in the spectral domain offers good accurateness, but its computational cost is high owing to the evaluation of the slowly decaying integrals and the iterative nature of the solution process. The paper introduces the electromagnetic knowledge combined with ANN in the analysis of rectangular microstrip antenna on uniaxially anisotropic substrate to reduce the complexity of the spectral domain method and to minimize the CPU time necessary to obtain the numerical results. The numerical comparison between neurospectral and conventional moment methods shows significant improvements in time convergence and computational cost. Hence, the use of neurospectral approach presented here as a promising fast technique in the design of microstrip antennas.    

Modeling and design of rectangular microstrip patch printed on isotropic or anisotropic substrate are accomplished in this paper. The use of spectral domain method in conjunction with artificial neural networks (ANNs) to compute the resonant characteristics of rectangular microstrip patch printed on isotropic or anisotropic substrates. The moment method implemented in the spectral domain offers good accurateness, but its computational cost is high owing to the evaluation of the slowly decaying integrals and the iterative nature of the solution process. The paper introduces the electromagnetic knowledge combined with ANN in the analysis of rectangular microstrip antenna on uniaxially anisotropic substrate to reduce the complexity of the spectral domain method and to minimize the CPU time necessary to obtain the numerical results. The numerical comparison between neurospectral and conventional moment methods shows significant improvements in time convergence and computational cost. Hence, the use of neurospectral approach presented here as a promising fast technique in the design of microstrip antennas.    

Modeling and design of rectangular microstrip patch printed on isotropic or anisotropic substrate are accomplished in this paper. The use of spectral domain method in conjunction with artificial neural networks (ANNs) to compute the resonant characteristics of rectangular microstrip patch printed on isotropic or anisotropic substrates. The moment method implemented in the spectral domain offers good accurateness, but its computational cost is high owing to the evaluation of the slowly decaying integrals and the iterative nature of the solution process. The paper introduces the electromagnetic knowledge combined with ANN in the analysis of rectangular microstrip antenna on uniaxially anisotropic substrate to reduce the complexity of the spectral domain method and to minimize the CPU time necessary to obtain the numerical results. The numerical comparison between neurospectral and conventional moment methods shows significant improvements in time convergence and computational cost. Hence, the use of neurospectral approach presented here as a promising fast technique in the design of microstrip antennas.    

During the last years, in industrial process control, requirements for Continuous Emission Monitoring (CEM) have increased significantly. Most plants are investing in CEM systems in order to burn waste, obtain ISO 14000 and 18001 certification to protect operator’s life. In this work, our approach describes development of a novel internet and fuzzy-based CEM system(IFCEMS). This system contains operator’s stations for the bag filter’s automation system and monitored using Internet. The object of the present installation consists of a suction ventilator, a bag filter and a system for collecting and evacuating the dust. To optimize the running of the bag filter workshop and ensure continuous control, the process based on one of the most powerful Artificial Intelligence techniques which is fuzzy logic involves the removal or filtration of smoke from the kiln and/or cement mill with control of temperature and pressure of the fumes

During the last years, in industrial process control, requirements for Continuous Emission Monitoring (CEM) have increased significantly. Most plants are investing in CEM systems in order to burn waste, obtain ISO 14000 and 18001 certification to protect operator’s life. In this work, our approach describes development of a novel internet and fuzzy-based CEM system(IFCEMS). This system contains operator’s stations for the bag filter’s automation system and monitored using Internet. The object of the present installation consists of a suction ventilator, a bag filter and a system for collecting and evacuating the dust. To optimize the running of the bag filter workshop and ensure continuous control, the process based on one of the most powerful Artificial Intelligence techniques which is fuzzy logic involves the removal or filtration of smoke from the kiln and/or cement mill with control of temperature and pressure of the fumes