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相关概念视频

Upsampling01:22

Upsampling

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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...
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Downsampling01:20

Downsampling

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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...
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Reconstruction of Signal using Interpolation01:10

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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...
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Aliasing01:18

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Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
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Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

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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.
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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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扩展的布拉胡特-阿里莫托算法用于语义速率扭曲函数.

Yuxin Han1,2, Yang Liu1,2, Yaping Sun2,3

  • 1Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Entropy (Basel, Switzerland)
|June 26, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了扩展的Blahut-Arimoto (EBA) 算法用于语义通信,通过关注意义来提高效率. 欧洲银行算法计算了语义速率扭曲函数,其性能优于经典方法.

关键词:
布拉胡特阿里莫托的算法语义信息理论是语义信息理论.语义知识基础是语义知识基础.语义速率扭曲函数是指语义速率扭曲函数.

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科学领域:

  • 信息理论 信息理论
  • 通信系统 通信系统
  • 人工智能的人工智能

背景情况:

  • 语义沟通通过优先考虑意义,提供了比经典方法更高的效率.
  • 布拉胡特-阿里莫托 (BA) 算法是经典信息理论中计算速率扭曲函数的基石.
  • 将经典信息理论扩展到语义通信需要新的分析工具.

研究的目的:

  • 提出扩展的Blahut-Arimoto (EBA) 算法用于计算语义速率扭曲函数.
  • 为未知同义映射的场景开发一个优化框架.
  • 为分析语义知识库 (SKB) 大小及其对沟通的影响提供理论基础.

主要方法:

  • 扩展的Blahut-Arimoto (EBA) 算法反复更新用于语义速率扭曲计算的分布.
  • 一个优化框架将EBA与模拟回火相结合,解决未知的同义映射.
  • 语义知识库 (SKB) 被用作同义映射的一个具体实例.

主要成果:

  • 在EBA算法有效计算语义速率扭曲函数.
  • 对于高斯源,语义速率扭曲函数随着同义数的增加而下降,优于经典方法.
  • 较大的SKB大小与在语义通信中更高的压缩效率相关,如CUB数据集所示.

结论:

  • EBA算法是用于语义通信分析的验证和有效工具.
  • 语义通信,特别是在较大的SKB中,在压缩效率方面提供了显著的优势.
  • 这项工作为理解和优化语义通信系统提供了理论基础.