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相关概念视频

¹³C NMR: ¹H–¹³C Decoupling01:04

¹³C NMR: ¹H–¹³C Decoupling

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The probability of having two carbon-13 atoms next to each other is negligible because of the low natural abundance of carbon-13. Consequently, peak splitting due to carbon-carbon spin-spin coupling is not observed in spectra. However, protons up to three sigma bonds away split the carbon signal according to the n+1 rule, resulting in complicated spectra.
A broadband decoupling technique is used to simplify these complex, sometimes overlapping, signals. Broadband decoupling relies on a...
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Deconvolution01:20

Deconvolution

188
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
188
¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

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When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
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Sample Preparation for Analysis: Advanced Techniques01:08

Sample Preparation for Analysis: Advanced Techniques

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Accurate analysis of complex samples often requires advanced preparation techniques to achieve reliable and reproducible results. Samples containing inorganic or organic materials can be challenging to dissolve or decompose effectively. Standard sample preparation methods include acid digestion, fusion, dry ashing, and wet digestion.
Acid digestion with strong acids is commonly used to dissolve inorganic materials that are insoluble (do not dissolve) in water. This method can be useful for...
391
Downsampling01:20

Downsampling

184
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
184
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
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相关实验视频

Updated: Jul 18, 2025

Proton Transfer and Protein Conformation Dynamics in Photosensitive Proteins by Time-resolved Step-scan Fourier-transform Infrared Spectroscopy
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混合门拒绝框架使用单值值分解用于侧通道分析预处理.

Yuanzhen Wang1, Hongxin Zhang2,3, Xing Fang2

  • 1School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Entropy (Basel, Switzerland)
|August 26, 2023
PubMed
概括
此摘要是机器生成的。

一个新的混合值否定框架通过提高信号质量来增强侧通道分析. 这种方法提高了密码安全中的关键猜测准确性和攻击效率.

关键词:
施顿规范的规范低级矩阵的近似方法噪音过器 噪音过器侧通道分析信号预处理 信号预处理单一价值分解分解的方法

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Proton Transfer and Protein Conformation Dynamics in Photosensitive Proteins by Time-resolved Step-scan Fourier-transform Infrared Spectroscopy
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Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
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科学领域:

  • 密码学 密码学 密码学 密码学
  • 信号处理 信号处理
  • 计算机安全 计算机安全

背景情况:

  • 侧通道分析依赖于信号质量,以成功恢复加密密钥.
  • 获取的信号经常被内部和外部噪声降低,从而降低了特征提取效率.
  • 在侧通道分析中,预处理对于增强低信号噪声比 (SNR) 痕迹至关重要.

研究的目的:

  • 提出一个混合值,为侧通道分析预处理提供框架.
  • 改进从杂的侧通道痕迹中提取特征信息.
  • 提供一个一般的预处理方法,用于非配置的侧通道分析.

主要方法:

  • 一个基于单一价值分解 (SVD) 的混合值否定框架.
  • 纳入低等级矩阵近似理论来完善SVD等级选择.
  • 硬值 (截断的SVD) 和软值 (单一值收缩阻尼) 的组合.

主要成果:

  • 侧通道轨迹的信号噪声比 (SNR) 显著改善.
  • 预处理的痕迹和正确的加密密钥之间的增强相关性.
  • 使用公共 DPA 竞赛 V2 和 AES_HD 数据集验证框架的有效性.

结论:

  • 拟议的混合值拒绝框架有效预处理侧通道痕迹.
  • 该方法增强了痕迹SNR,并加强了与正确密钥的相关性.
  • 这种方法提高了非配置侧通道攻击的效率和成功率.