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Global Positioning System (GPS) technology has revolutionized navigation and positioning, but its accuracy is often compromised by various errors. These errors, stemming from environmental, satellite, and receiver-related factors, require careful mitigation to ensure reliable performance across applications.Atmospheric ErrorsGPS signals travel through the Earth’s ionosphere and troposphere, introducing delays which affect accuracy. The ionosphere is strongly influenced by charged particles,...
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Double resonance techniques in Nuclear Magnetic Resonance (NMR) spectroscopy involve the simultaneous application of two different frequencies or radiofrequency pulses to manipulate and observe two distinct nuclear spins. One important application of double resonance is spin decoupling, which selectively suppresses coupling with one type of nucleus while observing the NMR signal from another nucleus, simplifying the spectrum and enhancing resolution.
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Insensitive Nuclei Enhanced by Polarization Transfer (INEPT) is an advanced Nuclear Magnetic Resonance (NMR) technique specifically designed to detect and enhance the signals of low-abundance nuclei, such as carbon-13 and nitrogen-15, in small molecules. The fundamental principle behind INEPT is the transfer of polarization from a more abundant and highly polarizable nucleus, typically hydrogen-1, to the low-abundance nucleus of interest. This process effectively boosts the NMR signal of the...
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In atomic emission spectroscopy (AES), high-temperature atomizers excite a broad range of elements and molecules that generate complex emissions from sources such as oxides, hydroxides, and flame combustion products in the flame or plasma. Several strategies can be employed to minimize spectral interferences caused by overlapping emission lines or bands. These include increasing instrument resolution, choosing alternative emission lines, optimally placing the detector in low-background regions,...
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基于SAR目标数据的属性特征扰乱增强.

Rubo Jin1, Jianda Cheng2, Wei Wang1

  • 1National Key Laboratory of Science and Technology on Automatic Target Recognition, College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China.

Sensors (Basel, Switzerland)
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PubMed
概括

本研究引入了一种新的功能级数据增强方法,用于合成光圈雷达 (SAR) 图像,增强深度学习模型. 该技术提高了图像质量,并减少了训练数据要求,以更好地检测目标.

关键词:
注意力机制注意力机制囊神经网络 (CapsNet) 是一个神经网络.数据增强数据增强脱的脱方式功能级别的功能级别的功能级别.合成光圈雷达 (SAR) 图像

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科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 遥感 遥感 遥感 遥感

背景情况:

  • 针对目标检测的深度学习需要大量,多样化的数据集.
  • 现有的合成光圈雷达 (SAR) 图像增强方法存在不稳定性,质量差以及缺乏物理解释性.

研究的目的:

  • 提出SAR图像的特征级数据增强方法,以解决当前技术的局限性.
  • 改进基于深度学习的目标检测和识别算法的通用化.

主要方法:

  • 利用一个增强的囊神经网络 (CapsNet) 进行特征提取和属性解.
  • 采用基于注意力机制的框架来实现有效的特征表示.
  • 扰乱脱的特征 (幅度,角度,形状) 并使用像素和感知损失重建图像.

主要成果:

  • 达到了 21.6845.5 的峰值信号噪声比 (PSNR).
  • 获得的放射分辨率 (RL) 为 3.7114 和动态范围 (DR) 为 24.0654.
  • 与随机噪声方法相比,在增强SAR目标图像方面表现出卓越的性能.

结论:

  • 拟议的特征级增强方法提高了SAR图像质量和多样性.
  • 这种方法有效地减少了对广泛培训数据的需求.
  • 该方法显示了基于深度学习的SAR目标检测和识别的显著改进.