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

Sound Waves: Interference00:53

Sound Waves: Interference

3.9K
Sound waves can be modeled either as longitudinal waves, wherein the molecules of the medium oscillate around an equilibrium position, or as pressure waves. When two identical waves from the same source superimpose on each other, the combination of two crests or two troughs results in amplitude reinforcement known as constructive interference. If two identical waves, that are initially in phase, become out of phase because of different path lengths, the combination of crests with troughs...
3.9K
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

335
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...
335
Classification of Signals01:30

Classification of Signals

878
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
878
Interference and Superposition of Waves01:07

Interference and Superposition of Waves

5.5K
When two waves of the same nature occur in the same region simultaneously, they result in interference. Interference of waves implies that the net effect of the waves is the sum of the individual waves' effects. However, it does not imply that the individual waves affect the propagation of other waves.
Interference occurs in mechanical waves, such as sound waves, waves on a string, and surface water waves. Mechanical waves correspond to the physical displacement of particles. Hence,...
5.5K

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 journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Sep 9, 2025

A Cognitive Paradigm to Investigate Interference in Working Memory by Distractions and Interruptions
10:38

A Cognitive Paradigm to Investigate Interference in Working Memory by Distractions and Interruptions

Published on: July 16, 2015

13.7K

基于CNN-LSTM模型的干扰信号抑制算法

Ningbo Xiao1, Zuxun Song1

  • 1School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China.

Sensors (Basel, Switzerland)
|August 28, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了使用CNN-LSTM的深度学习算法,用于无线系统中的干扰信号抑制. 该方法有效减少干扰,提高传感器可靠性和通信质量.

关键词:
美国CNN-LSTM干扰信号抑制算法

更多相关视频

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.4K

相关实验视频

Last Updated: Sep 9, 2025

A Cognitive Paradigm to Investigate Interference in Working Memory by Distractions and Interruptions
10:38

A Cognitive Paradigm to Investigate Interference in Working Memory by Distractions and Interruptions

Published on: July 16, 2015

13.7K
Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.4K

科学领域:

  • 信号处理
  • 深度学习
  • 无线通信

背景情况:

  • 传感器的抗干扰能力对于测量的准确性,可靠性和稳定性至关重要.
  • 复杂的环境将传感器暴露在各种干扰源中,影响性能.
  • 有效的干扰抑制是改善传感器操作和通信质量的关键.

研究的目的:

  • 提出一种基于CNN-LSTM的算法来抑制无线通信系统中的干扰信号.
  • 通过深度学习增强传感器的防干扰能力.
  • 在各种干扰场景中验证算法的有效性.

主要方法:

  • 使用卷积神经网络 (CNN) 进行空间特征提取.
  • 使用长期短期记忆 (LSTM) 网络来捕获时间动态特征.
  • 开发了一个CNN-LSTM模型用于干扰信号预测和抑制.

主要成果:

  • 与LSTM,BO-LSTM和CNN-GRU相比,CNN-LSTM算法显示出小误差和高回归匹配.
  • 实验模拟证实了在各种干扰条件下算法的性能.
  • 使用ITU-R P.1546和现实噪声数据集的验证证实了显著的干扰抑制.

结论:

  • 拟议的CNN-LSTM算法有效地抑制干扰信号和环境噪声.
  • 这种深度学习方法提高了无线通信系统和传感器的稳定性和可靠性.
  • 这些发现为开发更先进,耐干扰的传感器技术提供了基础.