One of the key challenges is reliable communication for Unmanned Underwater Vehicles (UUVs) since underwater environments are dynamic and challenging for signal transmission. The traditional acoustic communication systems allow long-distance communication but are limited by the low bandwidth, high latency, and doppler effect. Optical wireless, on the other hand, provides high data rates with water absorption and scattering occurring within the transmission distance, but has a short range of communication. To overcome these drawbacks, a smart hybrid optical-acoustic communication system is proposed that is a combination of Wavelength Division Multiplexing and On-Off Keying (WDM-OOK) to transmit a high-speed signal over optical and acoustic channels to achieve high reliability of long-range communication. The design of an intelligent hybrid optical–acoustic communications network to improve the continuity, reliability, and throughput of the communication between a UUV system is the focus of this research. It is a combination of a simple model of Multilayer Perceptron (MLP), a real-time communication mode switch, an optical subsystem (Wavelength Division Multiplexing On–Off Keying (WDM-OOK)), and an acoustic communication subsystem. The system has been modelled and simulated in MATLAB/Simulink and synthesized in HDL Coder as it will be implemented on an FPGA (Kintex-7 325T). The results of the simulations validate the superiority of the proposed hybrid method (hybrid with AI) over the purely optical method, the purely acoustic method, and the hybrid method without AI. The minimum BER recorded on this platform was (10-7), the average throughput was 152 Mbps, and the end-to-end latency time was 12.7ms with good water clarity. The FPGA synthesis results also show that the model was appropriate for real-time implementation with only 18.1% of the LUTs, 10.5% of the flip-flops, 23.6% of the BRAMs, and 17.4% of the DSPs utilized for the Kintex-7 325T FPGA. The results verified the feasibility of the proposed hybrid optical–acoustic communication system, where the high-speed feature of using the optical communication WDM-OOK system and the robustness of the acoustic communication system are provided by the intelligent adaptive switching. The proposed framework is feasible and is scalable for underwater reliable real-time communication between unmanned optical vehicles (UOVs) in underwater unmanned vehicle networks (UUVnets) in the near future.
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... Show MoreIn light of the intellectual and technological progress within the current developments of time, as well as the emergence of digital tools and means of display and communication, which had a major role in the shifts of the time of globalization in various commercial and economic fields, as well as areas of transferring the design image and its stages of development to customers and the convergence of views between the customer and the interior designer, which are the most important pillars of the design process As a whole, and accordingly, there is an urgent need for a process of intellectual balance between them through digital tools from the technical side and through social media from the intellectual side. Customer comments via socia
... Show MoreSoftware-Defined Networking (SDN) has evolved network management by detaching the control plane from the data forwarding plane, resulting in unparalleled flexibility and efficiency in network administration. However, the heterogeneity of traffic in SDN presents issues in achieving Quality of Service (QoS) demands and efficiently managing network resources. SDN traffic flows are often divided into elephant flows (EFs) and mice flows (MFs). EFs, which are distinguished by their huge packet sizes and long durations, account for a small amount of total traffic but require disproportionate network resources, thus causing congestion and delays for smaller MFs. MFs, on the other hand, have a short lifetime and are latency-sensitive, but they accou
... Show MoreThis paper proposes improving the structure of the neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Two learning algorithms are used to adjust the parameters weight of the hybrid neural structure with its serial-parallel configuration; the first one is supervised learning algorithm based Back Propagation Algorithm (BPA) and the second one is an intelligent algorithm n
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