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

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

1.2K
When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
1.2K
Downsampling01:20

Downsampling

294
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...
294
Discrete Fourier Transform01:15

Discrete Fourier Transform

461
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
461
Discrete-time Fourier transform01:26

Discrete-time Fourier transform

533
The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
One of the notable...
533
Deconvolution01:20

Deconvolution

287
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
287
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

396
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
396

You might also read

Related Articles

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

Sort by
Same author

Dynamic Noise Reduction with Deep Residual Shrinkage Networks for Online Fault Classification.

Sensors (Basel, Switzerland)·2022
Same author

Data-Driven Anomaly Detection in High-Voltage Transformer Bushings with LSTM Auto-Encoder.

Sensors (Basel, Switzerland)·2021
Same author

Entropy-Based Feature Extraction for Electromagnetic Discharges Classification in High-Voltage Power Generation.

Entropy (Basel, Switzerland)·2020
Same author

Imaging Time Series for the Classification of EMI Discharge Sources.

Sensors (Basel, Switzerland)·2018
Same author

Classification of Partial Discharge Signals by Combining Adaptive Local Iterative Filtering and Entropy Features.

Sensors (Basel, Switzerland)·2018
Same author

Trends in cardiac pacemaker batteries.

Indian pacing and electrophysiology journal·2006
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Oct 9, 2025

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section
11:00

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Published on: July 19, 2016

11.7K

Enhanced Partial Discharge Signal Denoising Using Dispersion Entropy Optimized Variational Mode Decomposition.

Ragavesh Dhandapani1, Imene Mitiche2, Scott McMeekin3

  • 1Department of Electrical and Communication Engineering, College of Engineering, National University of Science & Technology, Seeb P.O. Box 2322, Oman.

Entropy (Basel, Switzerland)
|December 24, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a hybrid algorithm for denoising Partial Discharge (PD) signals, effectively removing noise using adaptive decomposition and Group-Sparse Total Variation (GSTV) for improved signal clarity.

Keywords:
dispersion entropygroup-sparse total variationmutual information entropypartial discharge denoisingvariational mode decomposition

More Related Videos

Characterization of Anisotropic Leaky Mode Modulators for Holovideo
09:36

Characterization of Anisotropic Leaky Mode Modulators for Holovideo

Published on: March 19, 2016

8.1K
Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters
14:58

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters

Published on: June 2, 2010

9.7K

Related Experiment Videos

Last Updated: Oct 9, 2025

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section
11:00

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Published on: July 19, 2016

11.7K
Characterization of Anisotropic Leaky Mode Modulators for Holovideo
09:36

Characterization of Anisotropic Leaky Mode Modulators for Holovideo

Published on: March 19, 2016

8.1K
Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters
14:58

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters

Published on: June 2, 2010

9.7K

Area of Science:

  • Electrical Engineering
  • Signal Processing
  • Data Analysis

Background:

  • Partial Discharge (PD) detection is crucial for electrical equipment health monitoring.
  • Traditional denoising methods struggle with complex PD signal noise.
  • Adaptive decomposition and advanced filtering are needed for accurate PD analysis.

Purpose of the Study:

  • To develop and validate a novel hybrid algorithm for effective denoising of Partial Discharge (PD) signals.
  • To enhance the accuracy and reliability of PD signal analysis in noisy environments.
  • To improve the performance of PD detection systems through advanced signal processing.

Main Methods:

  • Hybrid algorithm combining Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD).
  • Mutual Information (MI) analysis for mode selection (K) and BLIMF classification.
  • Group-Sparse Total Variation (GSTV) for denoising noise-dominant BLIMFs.
  • Automatic regularization parameter (λ) selection using Dispersion Entropy.

Main Results:

  • The proposed method effectively denoises synthetic and real PD signals, including Blocks, Bumps, Doppler, Heavy Sine, and PD pulses.
  • Performance metrics (SNR, RMSE, Correlation Coefficient) show significant improvement over EMD variants.
  • Accurate separation and denoising of signal-dominant and noise-dominant components were achieved.

Conclusions:

  • The hybrid EMD-VMD-GSTV approach offers a robust solution for PD signal denoising.
  • The method enhances the Signal-to-Noise Ratio (SNR) and accuracy of PD signal analysis.
  • This technique is highly effective for real-world applications in electrical insulation monitoring.