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相关概念视频

Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

119
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
119
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

109
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
109
Accuracy, limits, and approximation01:28

Accuracy, limits, and approximation

484
Accuracy, limits, and approximations are common in many fields, especially in engineering calculations. These concepts are imperative for ensuring that a given value is as close as possible to its true value.
Accuracy is defined as the closeness of the measured value to the true or actual value. In engineering mechanics, repeated measurements are taken during theoretical or experimental analyses to ensure that the result is precise and accurate.
The accuracy of any solution is based on the...
484
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

256
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
256
Convergence of Fourier Series01:21

Convergence of Fourier Series

183
The Fourier series is a powerful mathematical tool for representing periodic signals as an infinite sum of complex exponentials. In practice, this infinite series is truncated to a finite number of terms, yielding a partial sum. This truncation makes the approximation of the signal feasible but introduces certain challenges, particularly near discontinuities, known as the Gibbs phenomenon.
The Gibbs phenomenon refers to the persistent oscillations and overshoots that occur near discontinuities...
183
Upsampling01:22

Upsampling

267
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
267

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Updated: Jul 29, 2025

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对于信号估计和学习中的应用来说,近似函数与近似隐私.

Naima Tasnim1, Jafar Mohammadi2, Anand D Sarwate3

  • 1Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka P.O. Box 1205, Bangladesh.

Entropy (Basel, Switzerland)
|May 27, 2023
PubMed
概括
此摘要是机器生成的。

我们介绍了Gaussian FM,一个新的算法,改进了功能机制,以便在数据分析中更好地进行隐私-实用性权衡. 这种方法可以显著降低噪音,提高实用性,同时保持近似差异隐私 (DP).

关键词:
分散的数据系统是分散的数据系统.不同的隐私差异 隐私差异一个功能机制的功能机制.

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科学领域:

  • 计算机科学 计算机科学
  • 数据 隐私 数据 隐私 数据
  • 算法设计 算法设计

背景情况:

  • 组织收集敏感的个人数据,需要保护隐私的算法.
  • 不同隐私 (DP) 提供严格的隐私,但往往会损害数据的实用性.
  • 现有的功能机制 (FM) 在DP下面面临着公用事业成本的权衡.

研究的目的:

  • 开发一个改进的功能机制 (FM),提供增强的实用与差异隐私 (DP).
  • 为了减少DP算法中的噪音,从而改善隐私与实用性的平衡.
  • 将拟议的机制扩展到分散的数据设置.

主要方法:

  • 建议高斯FM,使用高斯噪声对功能机制 (FM) 的增强.
  • 通过将Gaussian FM与CAPE协议集成用于分散数据,开发了capeFM.
  • 在分析和经验上分析了降噪和效用改进.

主要成果:

  • 与现有的FM算法相比,Gaussian FM提供了数量级更小的噪音.
  • capeFM在分散的环境中实现了与集中式方法相美的效用.
  • 经验结果表明,在各种数据集上,与最先进的方法相比,其性能优越.

结论:

  • 高斯 FM为数据分析提供了卓越的隐私-实用性权衡.
  • 提出的方法提高了数据的实用性,同时保持了个人的隐私.
  • 这些算法代表了隐私保护数据分析技术的重大进步.