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

Determination of Expected Frequency01:08

Determination of Expected Frequency

2.1K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.1K
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

79
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....
79
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

56
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,...
56
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

4.9K
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...
4.9K
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

183
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
183
IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

848
Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
848

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

Optimizing retinopathy of prematurity screening in China using a single objective criterion: a 10-year retrospective analysis.

Frontiers in pediatrics·2026
Same author

Dual modal pathomics model for colorectal cancer early recurrence prediction and mutation landscape analysis.

iScience·2026
Same author

Urbanization and climate extremes amplify upstream-downstream water quality disparities across Chinese urban watersheds.

Environmental research·2026
Same authorSame journal

Predicting acoustic field with a separate variable ocean physics-informed neural network.

JASA express letters·2026
Same author

Antibody-drug conjugates in breast cancer brain and leptomeningeal metastases: mechanistic insights and therapeutic progress.

Cancer metastasis reviews·2026
Same author

A structure-function integrated ecological index for ecological quality assessment in urbanizing subtropical regions.

Journal of environmental management·2026
Same journal

Amplitude-invariant phase masking for coherence recovery in scattered wavefields.

JASA express letters·2026
Same journal

Detecting continuous and discrete frequency changes as a function of spectral resolvability and modulation rate.

JASA express letters·2026
Same journal

Bearings-only acoustic source localization method using two distributed gliders and deep ocean experimental validation in the South China Sea.

JASA express letters·2026
Same journal

Block-sparse enhancement and detection of envelope modulation on noise for ship radiated noise.

JASA express letters·2026
Same journal

Extending Sottek Hearing Model loudness to estimate partially-masked sound qualities of loudness, tonality, and sharpness.

JASA express letters·2026
查看所有相关文章

相关实验视频

Updated: May 14, 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

频率差异稀疏贝叶斯式学习用于明确的到达方向估计.

Ze Yuan1,2, Haiqiang Niu1,2, Zhenglin Li3,4

  • 1State Key Laboratory of Acoustics and Marine Information, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China.

JASA express letters
|May 13, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种改进的频差 (FD) 方法来估计到达方向 (DOA),有效地抑制虚假信号,以便更好地分析声场. 改进的技术改善了目标检测,特别是在多个来源的情况下.

更多相关视频

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

906
Flying Insect Detection and Classification with Inexpensive Sensors
05:16

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

25.1K

相关实验视频

Last Updated: May 14, 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
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

906
Flying Insect Detection and Classification with Inexpensive Sensors
05:16

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

25.1K

科学领域:

  • 声学信号处理 声学信号处理
  • 阵列信号处理 阵列信号处理
  • 计算电磁学 计算机电磁学

背景情况:

  • 频率差异 (FD) 方法利用FD哈达马德产物进行有效的到达方向 (DOA) 估计,特别是在空间别名条件下.
  • 压缩传感提高了分辨率,但由于传感矩阵中缺少交叉产品,引入了虚假峰值.
  • 现有的方法在复杂的声学环境中难以准确识别弱点.

研究的目的:

  • 开发一种新的FD方法,有效地抑制压缩传感中的交叉产品引起的虚假DOA.
  • 增强在空间别名的场景中对弱声目标的检测能力.
  • 为了提高DOA估计算法的性能,特别是在处理多个声源时.

主要方法:

  • 使用完整的哈达马德产品重建传感矩阵.
  • 应用稀疏贝叶斯式学习来估计2D超参数矩阵.
  • 从超参数矩阵中提取对角线,以减轻虚假的DOA估计.

主要成果:

  • 拟议的方法成功地抑制了虚假的峰值,从而使得DOA估计更加准确.
  • 与以前的压缩FD方法相比,模拟显示出更高的性能,特别是在检测弱目标方面.
  • 随着声源数量的增加,增强方法的优势变得更加明显.

结论:

  • 开发的稀疏贝叶斯学习方法有效地解决了现有的压缩FD方法的局限性.
  • 这种技术在声信号处理方面提供了显著的进步,用于准确的DOA估计和弱目标检测.
  • 该方法对在具有挑战性的环境中需要高分辨率声场分析的应用具有前景.