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Downsampling01:20

Downsampling

251
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...
251
Boundary Conditions: Lossless Lines01:21

Boundary Conditions: Lossless Lines

152
Consider a single-phase, two-wire, lossless transmission line terminated by an impedance at the receiving end and a source with Thevenin voltage and impedance at the sending end. The line, with length, has a surge impedance and wave velocity determined by the line's inductance and capacitance.
At the receiving end, the boundary condition states that the voltage equals the product of the receiving-end impedance and current. This relationship is expressed as a function of the incident and...
152
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

116
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
116
Lossless Lines01:23

Lossless Lines

169
In electrical engineering, a lossless transmission line is characterized by a purely imaginary propagation constant and a resistive characteristic impedance. The ABCD parameters, which describe the relationship between the input and output voltages and currents, indicate an equivalent π circuit with an imaginary series impedance and a shunt admittance. This results in a transmission line that, when the product of the phase constant (beta) and the length of the line is less than pi,...
169
Reducing Line Loss01:18

Reducing Line Loss

193
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
193
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

740
The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
740

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相关实验视频

Updated: Sep 10, 2025

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples
07:30

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

12.2K

数据序列的经验无损压缩

Lei M Li1,2

  • 1State Key Laboratory of Mathematical Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.

Entropy (Basel, Switzerland)
|August 28, 2025
PubMed
概括

本研究探讨了无损数据压缩界限,引入了最佳最小压缩的规范化最大概率 (NML) 分布. 这项研究得出了NML代码长度的精确公式,适用于离散和连续数据,并通过DNA序列压缩来验证. 这项工作促进了对数据压缩限制和生物信息学的实际应用的理解.

科学领域:

  • 信息理论
  • 数据压缩
  • 统计推理

背景情况:

  • 科尔莫戈罗夫复杂性为无损数据压缩提供了一个不可计算的理论边界.
  • 香农的源代码定理确定了平均压缩的约束为nH,其中n是序列长度,H是.
  • 最大概率估计 (MLE) 通常低估了真正的压缩边界.

研究的目的:

  • 导出和分析单个数据序列的无损压缩.
  • 调查数据压缩的正常化最大概率分布 (NML) 的最佳性.
  • 将压缩绑定计算扩展到离散和连续数据,并用于生物信息学.

主要方法:

  • 使用正常化最大概率 (NML) 分布,在最小意义上被证明是最佳的.
  • 应用局部非对称的正常性来导出NML的非对称代码长度.
  • 开发贝叶斯方法来预测最佳代码长度,从而产生混合代码.
  • 使用不同的解析模型计算编码蛋白质DNA序列的压缩边界.

主要成果:

  • NML代码的长度是通过分析推导出来的 nH{\displaystyle \mathrm {θ} n) + (d/2) log{\displaystyle \mathrm {n} 2π) + log{\displaystyle \mathrm {I} \mathrm {θ} 1/2) dθ + o{\displaystyle \mathrm {o} 1} .
关键词:
贝叶斯式在DNA中输入量局部异常常态无损压缩规范化的最大概率预测性

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  • 一个通过贝叶斯预测得到的混合码的长度为nH{\displaystyle \mathrm {n} } + (d/2) log{\displaystyle \mathrm {n} }/2π + log{\displaystyle \mathrm {n} } \mathrm {n} } } 1/2) w{\displaystyle \mathrm {n} } + o{\displaystyle \mathrm {n} } 1).
  • 当解析与氨基酸编码子对齐时,DNA序列的压缩是最大的,这表明了实际应用.
  • 经验压缩边界随着字典大小的增加而改善.
  • 结论:

    • NML 分布为数据压缩界限提供了一个最佳的,可计算的方法.
    • 衍生出来的非对称公式为离散和连续数据压缩提供了准确的估计.
    • 解析策略显著影响压缩效率,特别是对于生物序列.
    • 这项研究为了解和计算各种数据类型的理论压缩极限提供了强大的框架.