Jove
Visualize
联系我们

相关概念视频

Classification of Signals01:30

Classification of Signals

1.6K
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...
1.6K

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

A UAV Testbed for Diagnosing Hardware Vulnerabilities: Quantifying Sim-to-Real Discrepancies in PX4 Flight Logs.

Sensors (Basel, Switzerland)·2026
Same author

Robust Federated-Learning-Based Classifier for Smart Grid Power Quality Disturbances.

Sensors (Basel, Switzerland)·2025
Same author

FedECPA: An Efficient Countermeasure Against Scaling-Based Model Poisoning Attacks in Blockchain-Based Federated Learning.

Sensors (Basel, Switzerland)·2025
Same author

Efficient and Accurate Zero-Day Electricity Theft Detection from Smart Meter Sensor Data Using Prototype and Ensemble Learning.

Sensors (Basel, Switzerland)·2025
Same author

Hardware Acceleration-Based Privacy-Aware Authentication Scheme for Internet of Vehicles Using Physical Unclonable Function.

Sensors (Basel, Switzerland)·2025
Same author

Real-Time Anomaly Detection in Physiological Parameters: A Multi-Squad Monitoring and Communication Architecture.

Sensors (Basel, Switzerland)·2025
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
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

相关实验视频

Updated: Apr 12, 2026

Radio Frequency Identification and Motion-sensitive Video Efficiently Automate Recording of Unrewarded Choice Behavior by Bumblebees
09:09

Radio Frequency Identification and Motion-sensitive Video Efficiently Automate Recording of Unrewarded Choice Behavior by Bumblebees

Published on: November 15, 2014

10.9K

在LoRa网络中使用机器学习识别被改的无线电频率传输.

Nurettin Selcuk Senol1, Amar Rasheed1, Mohamed Baza2

  • 1Department of Computer Science, Sam Houston State University, Huntsville, TX 77340, USA.

Sensors (Basel, Switzerland)
|October 26, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种基于图像的方法,使用异常检测算法来识别LoRa网络中被改的射频信号. 当地异常因素实现了最高的准确性,增强了LoRa的安全性.

关键词:
这就是为什么物联网是物联网物联网.洛拉洛拉是什么意思检测异常检测异常检测网络安全 网络安全频率分析频率分析机器学习是机器学习.

更多相关视频

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

490

相关实验视频

Last Updated: Apr 12, 2026

Radio Frequency Identification and Motion-sensitive Video Efficiently Automate Recording of Unrewarded Choice Behavior by Bumblebees
09:09

Radio Frequency Identification and Motion-sensitive Video Efficiently Automate Recording of Unrewarded Choice Behavior by Bumblebees

Published on: November 15, 2014

10.9K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.6K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

490

科学领域:

  • * 物联网 (IoT) 安全性
  • * 无线通信系统无线通信系统
  • * 网络安全和数据完整性

背景情况:

  • *远程 (LoRa) 网络对于物联网至关重要,提供低功耗,远程通信.
  • *LoRa设备容易受到无线电频率干扰和信号操纵的影响,从而损害数据完整性和安全性.
  • *在LoRa网络中检测被改的频率信号是一个重大挑战.

研究的目的:

  • * 提出一种用于检测LoRa网络中被改的无线电频率传输的创新方法.
  • * 评估五种异常检测算法的有效性,用于识别信号操纵.
  • * 增强基于LoRa的物联网系统的安全性和可靠性.

主要方法:

  • *使用了五种异常检测算法:局部异常因子,隔离森林,变异自编码器,传统自编码器和主要组件分析.
  • *采用基于图像的改频率技术,将LoRa传输信号转换为图像.
  • * 创建了一个数据集,包含来自现实世界实验的26,000多张图像,其中包括正常信号和操纵信号.

主要成果:

  • *局部异常因子 (LOF) 显示了最高的检测准确率,为97.78%.
  • *变量自编码器 (VAE),传统自编码器 (AE) 和主要组件分析 (PCA) 实现了97.27%的准确性.
  • * 隔离森林 (IF) 在检测改信号方面取得了84.49%的准确性.

结论:

  • * 提出的基于图像的异常检测方法在识别LoRa网络中被改的无线电频率信号方面是有效的.
  • *本地异常因子在检测信号操纵方面表现出卓越的性能.
  • *这些发现提供了一个有希望的方法来加强基于LoRa的物联网基础设施的安全性和可靠性.