Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Classification of Signals01:30

Classification of Signals

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...
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

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, the...
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

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.
Signal and System01:26

Signal and System

A signal x(t) is a set of data or a time function representing a variable of interest. Signals typically convey information about a phenomenon, such as atmospheric temperature, humidity, human voice, television images, a dog's bark, or birdsongs. More generally, a signal can be a function of more than one independent variable. For instance, images depend on horizontal and vertical positions and can be regarded as two-dimensional signals. However, this text will focus on one-dimensional signals...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Experimental Observation of Anomalous Stopping of Mega-ampere Electron Current in Porous Materials.

Physical review letters·2026
Same author

Superchanneling and Radiation of Ultrarelativistic Electron Beams in Disordered Porous Material.

Physical review letters·2026
Same author

Dynamic stabilization and parametric excitation of instabilities in an ablation front by a temporally modulated laser pulse.

Physical review. E·2025
Same author

Dynamic stabilization of ablative Rayleigh-Taylor instability in the presence of a temporally modulated laser pulse.

Physical review. E·2024
Same author

Coherent Subcycle Optical Shock from a Superluminal Plasma Wake.

Physical review letters·2023
Same author

Branching of High-Current Relativistic Electron Beam in Porous Materials.

Physical review letters·2023

Related Experiment Video

Updated: Jul 15, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Nonlinear real-life signal detection with a supervised principal components analysis.

C T Zhou1, T X Cai, T F Cai

  • 1Institute of Applied Physics and Computational Mathematics, P.O. Box 8009, Beijing 100088, People's Republic of China.

Chaos (Woodbury, N.Y.)
|April 7, 2007
PubMed
Summary

A new supervised principal components analysis method effectively detects weak signals in noisy environments. This signal detection strategy demonstrates robustness in extracting frequencies from electromagnetic data.

More Related Videos

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

Related Experiment Videos

Last Updated: Jul 15, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

Area of Science:

  • Signal Processing
  • Data Analysis
  • Electromagnetics

Background:

  • Detecting weak signals in noisy environments is challenging.
  • Traditional methods may struggle with complex, real-world data.
  • Advanced feature extraction is crucial for signal identification.

Purpose of the Study:

  • To investigate a novel supervised principal components analysis (SPCA) strategy for target signal detection.
  • To develop a robust algorithm capable of identifying weak signals within unknown noisy environments.
  • To demonstrate the effectiveness of SPCA using real-life electromagnetic data.

Main Methods:

  • Utilized a two-channel detection scheme, each with nonlinear phase-space reconstruction and principal components analysis.
  • Employed time-series embedding for data matrix creation and feature extraction.
  • Analyzed output error time series using time-frequency tools like frequency spectrum and Wigner-Ville distribution.

Main Results:

  • Successfully detected weak signals previously hidden below the noise floor.
  • Demonstrated the robustness of the detection performance.
  • Showcased the ability to extract signal frequencies when signal power is sufficient.

Conclusions:

  • Supervised principal components analysis offers a powerful approach for detecting weak signals in complex noise.
  • The developed algorithm provides a robust and effective solution for signal detection in electromagnetic environments.
  • Time-frequency analysis of the error time series is key to successful signal identification.