Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Downsampling01:20

Downsampling

108
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...
108
Aliasing01:18

Aliasing

100
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...
100
Upsampling01:22

Upsampling

159
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...
159
Sampling Theorem01:15

Sampling Theorem

244
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
244

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Subspace communication in the hippocampal-retrosplenial axis.

Nature·2026
Same author

Hierarchical Gating of Cortical Population Dynamics Drives Pain.

bioRxiv : the preprint server for biology·2026
Same author

A cautionary tale for AI and machine learning in psychiatry.

Translational psychiatry·2026
Same author

A Holistic and Dynamic Network-Level View of the Autonomic Nervous System.

Annual review of biomedical engineering·2025
Same author

Retinal ganglion cell input to superior colliculus encodes salient information.

bioRxiv : the preprint server for biology·2025
Same author

The efficacy of resveratrol in the treatment of liver fibrosis: a systematic review and meta-analysis of preclinical studies.

Frontiers in nutrition·2025
Same journal

MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)·2026
Same journal

EARLY DETECTION OF COGNITIVE DECLINE USING VOICE ASSISTANT COMMANDS.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)·2025
Same journal

CROSS-DOMAIN DIFFUSION BASED SPEECH ENHANCEMENT FOR VERY NOISY SPEECH.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)·2025
Same journal

CROSS-DOMAIN SPEECH ENHANCEMENT WITH A NEURAL CASCADE ARCHITECTURE.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)·2025
Same journal

NEURAL CASCADE ARCHITECTURE FOR JOINT ACOUSTIC ECHO AND NOISE SUPPRESSION.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)·2025
Same journal

ATTENTION-BASED FUSION FOR BONE-CONDUCTED AND AIR-CONDUCTED SPEECH ENHANCEMENT IN THE COMPLEX DOMAIN.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)·2025
查看所有相关文章

相关实验视频

Updated: May 9, 2025

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
08:42

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Published on: September 3, 2021

2.9K

估计指向的光谱信息在多分辨率时间序列之间的流动.

Qiqi Xian1,2, Zhe Sage Chen1,2

  • 1Dept. Psychiatry, Neuroscience Institute, NYU Grossman School of Medicine, New York, NY, USA.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)
|May 5, 2025
PubMed
概括
此摘要是机器生成的。

我们开发了一种新的方法来测量多式联络时间序列数据中的格兰杰因果关系,采用不同的采样速率. 这种方法量化了依赖频率的定向信息流,克服了传统技术的局限性.

关键词:
光谱格兰杰因果关系的因果关系.准则的相关性分析.多分辨率时间序列.

更多相关视频

Blood Flow Imaging with Ultrafast Doppler
05:57

Blood Flow Imaging with Ultrafast Doppler

Published on: October 14, 2020

7.4K
Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques
10:53

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Published on: March 12, 2019

6.9K

相关实验视频

Last Updated: May 9, 2025

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method
08:42

Measurement of the Directional Information Flow in fNIRS-Hyperscanning Data using the Partial Wavelet Transform Coherence Method

Published on: September 3, 2021

2.9K
Blood Flow Imaging with Ultrafast Doppler
05:57

Blood Flow Imaging with Ultrafast Doppler

Published on: October 14, 2020

7.4K
Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques
10:53

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Published on: March 12, 2019

6.9K

科学领域:

  • 时间序列分析时间序列分析.
  • 信息理论是信息理论.
  • 因果关系推断的推理.

背景情况:

  • 定向信息流和格兰杰因果关系在科学和工程方面至关重要.
  • 现有的方法难以处理多式联运数据和不同的时间分辨率.
  • 在不同数据类型中评估因果关系仍然是一个挑战.

研究的目的:

  • 提出一种新的分析方法,用于多式时序中的格兰杰因果关系.
  • 解决处理不同时间分辨率的传统方法的局限性.
  • 引入依赖频率的定向信息流量的定量表征和统计评估.

主要方法:

  • 开发一个新的分析框架,用于泛化光谱因果关系.
  • 定向信息流量的定量表征.
  • 频率依赖因果关系的统计评估.
  • 使用密集的计算机模拟进行验证.

主要成果:

  • 提出的方法成功量化了依赖频率的定向信息流.
  • 在各种条件下在双变体和三变体系统中证明有效性.
  • 在复杂的数据集中提供了评估格兰杰因果关系的可靠方法.

结论:

  • 这种新方法,即通用光谱因果关系,有效地解决了在具有不同时间分辨率的多模式时间序列中的格兰杰因果关系.
  • 在复杂的科学和工程应用中,为分析定向信息流提供了显著的进步.
  • 通过模拟验证,这种方法提供了可靠的因果洞察.