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相关概念视频

RACE - Rapid Amplification of cDNA Ends02:35

RACE - Rapid Amplification of cDNA Ends

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Rapid Amplification of cDNA Ends, or RACE, is one of the most effective methods to obtain a full-length cDNA from an mRNA sequence between a known internal region to the unknown sequence at the 5’ or 3’ end. The unknown region is cloned in the cDNA by a gene-specific primer that binds the known end, and a hybrid primer that attaches a predefined anchor sequence to the unknown end of the cDNA. The sequence in between is amplified by PCR with an anchor primer and a gene-specific...
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通过使用PRI的声音和深度学习技术来增强PRIM的认可.

Seyed Majid Hasani Azhdari1, Azar Mahmoodzadeh1, Mohammad Khishe2

  • 1Department of Electrical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran.

PloS one
|May 1, 2024
PubMed
概括

这项研究引入了一种新的三阶段方法,使用脉冲重复间隔 (PRI) 声音来增强雷达识别. 该方法有效地解决了电子支持措施 (ESM) 和电子情报 (ELINT) 系统中的扭曲,提高了准确性.

科学领域:

  • 电气工程 电气工程
  • 信号处理 信号处理
  • 人工智能的人工智能

背景情况:

  • 脉冲重复间隔调制 (PRIM) 对于电子支持措施 (ESM) 和电子情报 (ELINT) 系统中的雷达识别至关重要.
  • 像缺失脉冲,虚假脉冲,动和噪声等扭曲在现实场景中挑战了准确的PRIM识别.

研究的目的:

  • 开发和评估一种新的三阶段方法,以使用PRI声音进行强大的PRIM认可.
  • 为了提高雷达识别系统的准确性和可靠性,面临信号扭曲.

主要方法:

  • 这是一个三阶段的方法,它结合了转移学习辅助的深度卷积神经网络 (DCNN) 来进行特征提取,极端学习机器 (ELM) 来进行分类,以及灰狼优化器 (GWO) 来进行改进.
  • 利用了PRI声音模式的实验数据集,并评估了八个预训练的DCNN架构.

主要成果:

  • VGG16和ResNet50V2 DCNN实现了高识别精度 (分别为97.53%和96.92%).
  • 与ELM和GWO的集成进一步优化了特定配置的精度,达到98.80%和97.58%.
  • 提出的方法在各种扭曲的情况下,在承认PRIM方面表现出显著的有效性.

结论:

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  • 新的三阶段方法有效地提高了PRIM识别的准确性,特别是在具有挑战性的ESM/ELINT环境中.
  • 利用PRI声音为改善雷达识别系统以应对现实世界的信号损害提供了一个有希望的途径.
  • DCNN,ELM和GWO的组合为先进的雷达信号处理提供了一个强大的策略.