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.

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.

There are several forms of electricity generation, first, by burning fuels, such as coal, natural gas or oil, which have an effect on the atmosphere, especially increasing greenhouse gases, or, second, from renewable sources, such as wind, hydro and solar, which are clean and renewable sources of energy. Our work focuses on solar sources, especially photovoltaics; we have treated the steering part of photovoltaic generators using artificial intelligence methods, specifically, case-based reasoning. The system we have built generates actions to be applied to the generator based on its current state and reasoning from previous cases recorded in the case ba

The article describes a study on the influence of RF magnetron cathode sputter thickness, (09) Ni/NiO thin multilayer (each multilayer varies from 35Å to 3000Å). Measurements exploited by different MEB and XRD measurement techniques were obtained to give structural results and the average variation of the multilayer strength and the hysteresis cycles follow the two directions parallel and perpendicular to the substrate by VSM at room temperature. magnetic results.

Because we usually deal with real - world systems with real - world constraints (cost, computer resources, size, weight, power, heat dissipation, etc.), it is understood that the simplest method to accomplish a task is the one that should be used. Experts usually rely on common sense when they solve problems. They also use vague and ambiguous terms. Other experts have no difficulties with understanding and interpreting this statement because they have the background to hearing problems described like this. However, a knowledge engineer would have difficulties providing a computer with the same level of understanding. In a complex industrial process, how can we represent expert knowledge that uses vague and fuzzy terms in a computer to control it? In this work, the application is developed to control the pretreatment and pasteurization station of milk localized in Batna (Algeria) by adopting a control approach based on expert knowledge and fuzzy logic.

Une étude stratigraphique et sédimentologique a été réalisée sur une terrasse alluviale de la région d’Oued El Hay, au sud de Batna (NE Algérie). Les structures sédimentaires observées sont principalement des zones d’érosion, des zones inondables, des figures en biseau et des variations d’épaisseur associées à des joints de stratification. Du point de vue sédimentologique, les sédiments sont dominés par des fractions argilo-limono-sableuses, suivies par des fractions de gravier et de cailloux. Les teneurs modérées en CaCO3 résultent de la dissolution des reliefs calcaires à proximité (calcaire crétacé) associée à l’augmentation de la température et l’humidité du milieu. L’observation morphoscopique de grains de quartz avec une loupe binoculaire permet d’offrir une évolution plus ou moins importante, car ils sont souvent émoussés ou sous-arrondis. L’étude sédimentologique a été réalisée sur 16 échantillons prélevés systématiquement du haut vers le bas de la coupe stratigraphique. L’examen de la taille brute de la fraction inférieure à 2 mm a permis d’individualiser 03 niveaux dans des couches sédimentaires, certaines fractions grossières riches (gravier, cailloux et blocs) alternant avec des couches riches en sable et en limon. Le pourcentage de fraction d’argile est dominant et reste pratiquement constant presque tout au long de la section stratigraphique.