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Rheumatic heart disease (RHD) management can be divided into two main strategies: prevention and long-term management.Primary PreventionPrimary prevention focuses on timely diagnosis and management of group A streptococcal pharyngitis to prevent acute rheumatic fever. The most widely used antibiotic for treating this condition is intramuscular benzathine penicillin G.Acute Rheumatic Fever TreatmentThe primary treatment goal for a patient diagnosed with acute rheumatic fever is to suppress the...
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Rheumatic heart disease or RHD is a chronic condition that results from rheumatic fever, causing permanent damage to the heart valves.Etiology and Risk FactorsIt primarily arises from rheumatic fever, an inflammatory disease that can develop after untreated or inadequately treated group A streptococcal (GAS) pharyngitis. Streptococcus spreads through direct contact with oral or respiratory secretions. While the bacteria are the causative agents, factors like malnutrition, overcrowding, poor...
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Spirochetes, unique bacteria in the phylum Spirochaetes, are gram-negative, motile, tightly coiled, slender, and flexible. They inhabit aquatic sediments and animals, with some causing diseases like syphilis. Spirochetes are classified into eight genera based on habitat, pathogenicity, phylogeny, and characteristics.Their distinctive motility arises from endoflagella, located within the cell’s periplasm. These endoflagella anchor at the cell poles and extend along the cell length, encased...
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基于深度学习算法,为高空平台站合成多个V/H定向光束.

Korany R Mahmoud1,2, Ahmed M Montaser3

  • 1Electronics and Communications Department, Faculty of Engineering, Helwan University, Cairo, 11795, Egypt. kurany_hameda@h-eng.helwan.edu.eg.

Scientific reports
|March 29, 2025
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概括

这项研究通过使用深度学习和专门的天线阵列来增强高海拔平台站 (HAPS) 覆盖范围. 这种新的方法优化了光束成型,实现了动态的广域网络覆盖,克服了传统的局限性.

关键词:
这是一个天线阵列.梁成型技术 梁成型技术深度学习是一种深度学习.深度神经网络是一个神经网络.优化算法优化算法

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科学领域:

  • 无线通信是一种无线通信.
  • 天线阵列设计设计天线阵列设计
  • 机器学习应用程序 机器学习应用程序

背景情况:

  • 传统的高空平台站 (HAPS) 覆盖范围仅限于一个固定的圆形区域.
  • 需要动态光束成型来适应不同的地理地形和用户需求.
  • 优化天线元件料阶段对于灵活的覆盖模式至关重要.

研究的目的:

  • 调查HAPS与深度学习 (DL) 的整合,以提高覆盖范围.
  • 提出一种新的方法,使用60个元素的同心圆形阵列 (CCA) 进行动态光束生成.
  • 为了能够有效地覆盖多样化和具有挑战性的道路路径.

主要方法:

  • 使用一个以60元为中心的圆形阵列 (CCA),频率为2.1GHz.
  • 集成深度神经网络 (DNN) 与修改后的引力搜索算法和粒子集群优化 (MGSA-PSO).
  • 采用计算机模拟技术-微波工作室套件 (CST) 和地球探测器 (EE) 来实现现实的场景建模.

主要成果:

  • 混合DNN-MGSA-PSO方法成功优化了CCA养阶段.
  • 实现了多个垂直/水平 (V/H) 定向光束的动态生成.
  • 通过模拟,通过各种地形证明了复杂道路路径的有效覆盖.

结论:

  • 拟议的HAPS-DL集成为无线覆盖能力提供了显著的进步.
  • 优化的束形策略有效地解决了传统HAPS的局限性.
  • 这项技术有望在多样化和具有挑战性的环境中实现可靠的通信.