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相关概念视频

Discrete Fourier Transform01:15

Discrete Fourier Transform

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The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
180

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Updated: May 7, 2025

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基于深度学习的生物指标身份验证系统,使用高时间/频率分辨率变换.

Sajjad Maleki Lonbar1, Akram Beigi2, Nasour Bagheri1,3

  • 1CPS2 Lab, Department of Communication, Faculty of Electrical Engineering, Shahid Rajaee Teacher Training University, Tehran, Islamic Republic of Iran.

Frontiers in digital health
|January 1, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一种使用心电图 (ECG) 信号的新型身份验证系统. 该框架实现了高精度,展示了ECG.

关键词:
这是一个ECG信号.这是一个GoogleNet架构.维格纳 - 维尔分销公司基于深度学习的分类生物指标认证认证.卷积神经网络 (CNN) 是一种神经网络.身份验证身份验证身份验证信号预处理 信号预处理

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科学领域:

  • 生物识别信息 生物识别信息
  • 信号处理 信号处理
  • 机器学习 机器学习

背景情况:

  • 身份验证在现代社会中至关重要,推动了对强大的身份验证系统的需求.
  • 生物识别方式提供高准确性和抗伪造性,获得了大量的关注.
  • 心电图 (ECG) 信号具有独特的,个性化的特征,适合生物识别应用.

研究的目的:

  • 提出和评估一种使用心电图 (ECG) 信号的新型身份验证框架.
  • 通过基准数据集 (NSRDB和MITDB) 和深度学习技术来评估框架的性能.

主要方法:

  • 一个两步框架,涉及信号清理和频域转换,使用维格纳-维尔分布.
  • 将心电图信号转换为图像数据以提取特征,捕获独特的心脏信号信息.
  • 深度学习的应用,特别是GoogleNet架构 (一种卷积神经网络 - CNN),用于识别.

主要成果:

  • 在NSRDB数据集上实现了99.3%的准确性和0.8%的等错率 (EER).
  • 在MITDB数据集上证明了99.004%的准确性和0.8%的EER.
  • 在准确性和稳定性方面,其性能优于其他生物识别身份验证方法.

结论:

  • 电脑心电图信号是有效的身份验证,提供高精度和耐噪声.
  • 拟议的框架结合了维格纳-维尔分布和谷歌网,显示了生物识别领域深度学习的潜力.
  • 该方法显示出高可靠性和低错误率,具有扩展和与其他安全层集成的潜力.