Bouslimani S, Drid S, Chrifi-Alaoui L.
Sensorless control and diagnosis of synchronous generator used in wind energy conversion system under inter turn short-circuit fault. International Journal of Power and Energy ConversionInternational Journal of Power and Energy Conversion. 2018.
Magisano D, Charkaluk E, de Saxcé G, KANIT T.
Shakedown within polycrystals: a direct numerical assessment. In: Advances in direct methods for materials and structures. Springer ; 2018. pp. 29-50.
Magisano D, Charkaluk E, de Saxcé G, KANIT T.
Shakedown within polycrystals: a direct numerical assessment. In: Advances in direct methods for materials and structures. Springer ; 2018. pp. 29-50.
Magisano D, Charkaluk E, de Saxcé G, KANIT T.
Shakedown within polycrystals: a direct numerical assessment. In: Advances in direct methods for materials and structures. Springer ; 2018. pp. 29-50.
Magisano D, Charkaluk E, de Saxcé G, KANIT T.
Shakedown within polycrystals: a direct numerical assessment. In: Advances in direct methods for materials and structures. Springer ; 2018. pp. 29-50.
Bouguerra F, Saidi L.
Simplified ANN for 256 QAM Symbol Equalization Over OFDM Rayleigh Channel. 2018 International Conference on Smart Communications in Network Technologies (SaCoNeT). 2018 :19-24.
Bouguerra F, Saidi L.
Simplified ANN for 256 QAM Symbol Equalization Over OFDM Rayleigh Channel. International Conference on Smart Communications in Network Technologies (SaCoNeT) [Internet]. 2018.
Publisher's VersionAbstract
Increasing the specter efficiency has been an object for many studies. In this paper, we investigate the higher modulation 256 QAM using Artificial Neural Networks (ANN) as an equalization model. Multilayer perceptron (MLP) and Radial Basis Function (RBF) are considered as non-linear equalizer based on back-propagation and Euclidian norm respectively. They are designed in a simplified architecture and employing some performing strategies for a better learning and an increased processing speed. ANNs are presented and applied with Orthogonal Frequency Division Multiplexing (OFDM) over Rayleigh fading channel in order to optimize the modulation scheme's processing and performances despite its sensitivity to noise. The models will be compared to the theoretical BER simulation in terms of BER, and also in terms of MSE to show performance and efficiency; by that, this work will show the supremacy of MLP in decision making with 256 QAM.
Bouguerra F, Saidi L.
Simplified ANN for 256 QAM Symbol Equalization Over OFDM Rayleigh Channel. International Conference on Smart Communications in Network Technologies (SaCoNeT) [Internet]. 2018.
Publisher's VersionAbstract
Increasing the specter efficiency has been an object for many studies. In this paper, we investigate the higher modulation 256 QAM using Artificial Neural Networks (ANN) as an equalization model. Multilayer perceptron (MLP) and Radial Basis Function (RBF) are considered as non-linear equalizer based on back-propagation and Euclidian norm respectively. They are designed in a simplified architecture and employing some performing strategies for a better learning and an increased processing speed. ANNs are presented and applied with Orthogonal Frequency Division Multiplexing (OFDM) over Rayleigh fading channel in order to optimize the modulation scheme's processing and performances despite its sensitivity to noise. The models will be compared to the theoretical BER simulation in terms of BER, and also in terms of MSE to show performance and efficiency; by that, this work will show the supremacy of MLP in decision making with 256 QAM.
Bouguerra F, Saidi L.
Simplified ANN for 256 QAM Symbol Equalization Over OFDM Rayleigh Channel. 2018 International Conference on Smart Communications in Network Technologies (SaCoNeT). 2018 :19-24.
Kadri A, Menacer F, DJEFFAL F.
Simulation and analysis of Graphene-based nanoelectronic circuits using ANN method. Materials Today: ProceedingsMaterials Today: Proceedings. 2018;5 :15959-15967.
Abdelmalek K, Farid M, Fayçal DJEFFAL.
Simulation and analysis of Graphene-based nanoelectronic circuits using ANN method. Part of special issue: 14th International Conference on Nanosciences & Nanotechnologies (NN17), 4-7 July, 2017 [Internet]. 2018.
Publisher's VersionAbstract
The Graphene–based Double Gate Field-Effect Transistors (G-DG FETs) have received great attention in recent years due to their high electrical performance provided for analog and Radio-frequency (RF) nanoelectronic applications. To calculate accurately the drain current and the Figures-of-Merit (FoMs) of the nanoscale G-DG FETs requires the solution of Schrödinger/Poisson equations, assuming the quantum effects are to be fully accounted. However, for nanoelectronic circuit simulation, the 2D numerical solution through the fully self-consistent coupled Schrödinger/Poisson equations is an overkill approach in terms of both complexity and computational time cost. Hence, new approach and simulation tools which can be applied to design and simulate Graphene-based nanoelectronic circuits are required to overcome the limitations imposed by the accuracy and computational time cost. In this paper, we investigate the efficiency of a new approach based on the ANN-based computation (Artificial neural network) to analyze and simulate Graphene-based nanoelectronic circuits. In this context, this work presents the applicability of ANN for the simulation of the voltage amplifier by investigating the impact of the G-DG FET design parameters on the analog and RF performances. The ANN-based model can be easily implemented into commercial circuit simulators like: SPICE, Cadence and Silvaco.
Abdelmalek K, Farid M, Fayçal DJEFFAL.
Simulation and analysis of Graphene-based nanoelectronic circuits using ANN method. Part of special issue: 14th International Conference on Nanosciences & Nanotechnologies (NN17), 4-7 July, 2017 [Internet]. 2018.
Publisher's VersionAbstract
The Graphene–based Double Gate Field-Effect Transistors (G-DG FETs) have received great attention in recent years due to their high electrical performance provided for analog and Radio-frequency (RF) nanoelectronic applications. To calculate accurately the drain current and the Figures-of-Merit (FoMs) of the nanoscale G-DG FETs requires the solution of Schrödinger/Poisson equations, assuming the quantum effects are to be fully accounted. However, for nanoelectronic circuit simulation, the 2D numerical solution through the fully self-consistent coupled Schrödinger/Poisson equations is an overkill approach in terms of both complexity and computational time cost. Hence, new approach and simulation tools which can be applied to design and simulate Graphene-based nanoelectronic circuits are required to overcome the limitations imposed by the accuracy and computational time cost. In this paper, we investigate the efficiency of a new approach based on the ANN-based computation (Artificial neural network) to analyze and simulate Graphene-based nanoelectronic circuits. In this context, this work presents the applicability of ANN for the simulation of the voltage amplifier by investigating the impact of the G-DG FET design parameters on the analog and RF performances. The ANN-based model can be easily implemented into commercial circuit simulators like: SPICE, Cadence and Silvaco.
Abdelmalek K, Farid M, Fayçal DJEFFAL.
Simulation and analysis of Graphene-based nanoelectronic circuits using ANN method. Part of special issue: 14th International Conference on Nanosciences & Nanotechnologies (NN17), 4-7 July, 2017 [Internet]. 2018.
Publisher's VersionAbstract
The Graphene–based Double Gate Field-Effect Transistors (G-DG FETs) have received great attention in recent years due to their high electrical performance provided for analog and Radio-frequency (RF) nanoelectronic applications. To calculate accurately the drain current and the Figures-of-Merit (FoMs) of the nanoscale G-DG FETs requires the solution of Schrödinger/Poisson equations, assuming the quantum effects are to be fully accounted. However, for nanoelectronic circuit simulation, the 2D numerical solution through the fully self-consistent coupled Schrödinger/Poisson equations is an overkill approach in terms of both complexity and computational time cost. Hence, new approach and simulation tools which can be applied to design and simulate Graphene-based nanoelectronic circuits are required to overcome the limitations imposed by the accuracy and computational time cost. In this paper, we investigate the efficiency of a new approach based on the ANN-based computation (Artificial neural network) to analyze and simulate Graphene-based nanoelectronic circuits. In this context, this work presents the applicability of ANN for the simulation of the voltage amplifier by investigating the impact of the G-DG FET design parameters on the analog and RF performances. The ANN-based model can be easily implemented into commercial circuit simulators like: SPICE, Cadence and Silvaco.
Kadri A, Menacer F, DJEFFAL F.
Simulation and analysis of Graphene-based nanoelectronic circuits using ANN method. Materials Today: ProceedingsMaterials Today: Proceedings. 2018;5 :15959-15967.
Kadri A, Menacer F, DJEFFAL F.
Simulation and analysis of Graphene-based nanoelectronic circuits using ANN method. Materials Today: ProceedingsMaterials Today: Proceedings. 2018;5 :15959-15967.
OUZANI R, SI-AMEUR M.
Simulation Numérique Des Grandes Echelles Du Mélange Turbulent Dans Les Ecoulements Compressibles. 2ème Conférence Nationale sur les CFD et la Technologie (CFD & Tech 2018) .12-14 Novembre, CRN Draria. 2018.
OUZANI R, SI-AMEUR M.
Simulation Numérique Des Grandes Echelles Du Mélange Turbulent Dans Les Ecoulements Compressibles. 2ème Conférence Nationale sur les CFD et la Technologie (CFD & Tech 2018) .12-14 Novembre, CRN Draria. 2018.
Chibani A, Bougriou C, Merouani S.
Simulation of hydrogen absorption/desorption on metal hydride LaNi5-H2: mass and heat transfer. Applied Thermal EngineeringApplied Thermal Engineering. 2018;142 :110-117.
Chibani A, Bougriou C, Merouani S.
Simulation of hydrogen absorption/desorption on metal hydride LaNi5-H2: mass and heat transfer. Applied Thermal EngineeringApplied Thermal Engineering. 2018;142 :110-117.
Chibani A, Bougriou C, Merouani S.
Simulation of hydrogen absorption/desorption on metal hydride LaNi5-H2: mass and heat transfer. Applied Thermal EngineeringApplied Thermal Engineering. 2018;142 :110-117.