A wireless sensor network is made up of a large number of small sensor nodes with limited energy resources, which is a real problem for this network. In this article, we will study the ingestion of node energy in these networks at the routing level. In addition, we are modifying one of the most popular routing algorithms for data communication in the WSN: LEACH (Adaptive Hierarchy with Low Power Consumption). The modified version of the LEACH base version "MG_LEACH" uses an intermediate cluster header to transmit data, extend the network lifetime and send more data than the original protocol. Our proposed algorithm is simulated using MATLAB to verify the effectiveness of improving the lifetime of this network. The results of the simulation confirmed that the system was working better than the LEACH basic system and that the network life had been improved. <p> </p>
<span lang="EN-US">In recent years, there has been a strong demand for self-driving cars. For safe navigation, self-driving cars need both precise localization and robust mapping. While global navigation satellite system (GNSS) can be used to locate vehicles, it has some limitations, such as satellite signal absence (tunnels and caves), which restrict its use in urban scenarios. Simultaneous localization and mapping (SLAM) are an excellent solution for identifying a vehicle’s position while at the same time constructing a representation of the environment. SLAM-based visual and light detection and ranging (LIDAR) refer to using cameras and LIDAR as source of external information. This paper presents an implementation of SLAM algorithm for building a map of environment and obtaining car’s trajectory using LIDAR scans. A detailed overview of current visual and LIDAR SLAM approaches has also been provided and discussed. Simulation results referred to LIDAR scans indicate that SLAM is convenient and helpful in localization and mapping.</span>
In order to reduce the inconvenience resulting from the use of the traditional energy sources (oil, gas and coal), the integration of renewable energy sources is among the better solutions. With the integration of green energy sources, there are several strategies that can be adopted, including the combination of clean energy sources (solar, wind, and biomass) with each other, or the combination of renewable sources with conventional sources. In this article, we focus on a photovoltaic system allowing the storage of energy in a battery with a coupling to the electrical grid. In order to overcome the problems related to the random operation that accompanies the use of photovoltaic systems, we have developed a control technique based on the use of artificial neural network technology. The complete system was designed and simulated on MATLAB Simulink.
<span lang="EN-US">Among the most widespread renewable energy sources is solar energy; Solar panels offer a green, clean, and environmentally friendly source of energy. In the presence of several advantages of the use of photovoltaic systems, the random operation of the photovoltaic generator presents a great challenge, in the presence of a critical load. Among the most used solutions to overcome this problem is the combination of solar panels with generators or with the public grid or both. In this paper, an energy management strategy is proposed with a safety aspect by using artificial neural networks (ANNs), in order to ensure a continuous supply of electricity to consumers with a maximum solicitation of renewable energy.</span>
Due to the variability of weather conditions and equipment properties the maximum power point tracking (MPPT) performance is influenced. MPPT controllers are widely used to improve photovoltaic (PV) efficiency because MPPT can produce maximum power under various weather conditions. Among the most used techniques and representing a satisfactory efficiency are those based on artificial intelligence. Since the use of neural networks requires resources at the implementation level, the optimization of these systems is an important phase. This work represents an optimized system for tracking the maximum power point, the latter based on a multi-layer neural network. The optimized multi layer perceptron (MLP) will ensure a fast convergence to the maximum power point with a low oscillation compared to the classical method.
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