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

Active Filters01:25

Active Filters

973
Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is:
973
Phase-lead and Phase-lag Controllers01:22

Phase-lead and Phase-lag Controllers

248
Understanding the working function of different types of controllers can be illustrated with practical analogies, such as adjusting a stereo's volume equalizer. Cranking up the bass involves a phase-lead controller, which functions as a high-pass filter, while increasing the treble uses a phase-lag controller, which acts as a low-pass filter. PD controllers, similar to high-pass filters, enhance the system's response to high-frequency components. PI controllers, akin to low-pass...
248
Time and frequency -Domain Interpretation of PI Control01:27

Time and frequency -Domain Interpretation of PI Control

224
Proportional-Integral (PI) controllers are essential in many control systems to improve stability and performance. They are commonly used in everyday devices like thermostats to enhance system damping and reduce steady-state error. When the zero in the controller's transfer function is optimally placed, the system benefits significantly in terms of stability and accuracy.
Acting as a low-pass filter, the PI controller slows the system's response and extends settling times. This requires...
224
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

198
Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
198
Time and frequency -Domain Interpretation of Phase-lag Control01:21

Time and frequency -Domain Interpretation of Phase-lag Control

161
Phase-lag controllers are widely used in control systems to improve stability and reduce steady-state errors. A dimmer switch controlling the brightness of a light bulb serves as a practical example of phase-lag control, gradually adjusting the bulb's brightness. Mathematically, phase-lag control or low-pass filtering is represented when the factor 'a' is less than 1.
Phase-lag controllers do not place a pole at zero, but instead influence the steady-state error by amplifying any...
161
Upsampling01:22

Upsampling

351
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
351

You might also read

Related Articles

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

Sort by
Same author

Machine learning for predicting surgical difficulty of laparoscopic total mesorectal excision for rectal cancer: integrating MR-based pelvimetry and peritoneal reflection.

Frontiers in medicine·2026
Same author

Traditional Chinese exercise for quality of life, cognition, sleep in Parkinson's disease: a systematic review and meta-analysis.

Frontiers in psychology·2026
Same author

Genome-Wide Analysis of <i>PP2C</i> Gene Family and Identification of <i>DlPP2C1</i> as an ABA-Responsive Candidate Regulator During Early Somatic Embryogenesis in Longan (<i>Dimocarpus longan</i> Lour.).

Plants (Basel, Switzerland)·2026
Same author

Physiological and cellular basis of inter-species variation in freeze tolerance between Bombyx mori and Antheraea pernyi.

Cryobiology·2026
Same author

High barrier paper coating with dual-crosslinked networks based on cellulose nanofibrils and epoxidized natural rubber.

International journal of biological macromolecules·2026
Same author

CD98hc controls CNS angiogenesis and blood-brain barrier integrity through localized regulation of the systemic integrin-FAK pathway.

Nature cardiovascular research·2026

Related Experiment Video

Updated: Oct 11, 2025

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
10:51

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

13.9K

An active impulsive noise control algorithm with a post-adaptive filter and variable step size.

Shanjun Li1, Guoyong Jin1, Yukun Chen1

  • 1College of Power and Energy Engineering, Harbin Engineering University, Harbin, 150001, People's Republic of China.

The Journal of the Acoustical Society of America
|December 2, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces new algorithms to reduce impulsive noise, outperforming existing methods. A novel variable step-size approach enhances convergence for active noise control systems dealing with non-Gaussian noise.

More Related Videos

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
09:01

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Published on: April 4, 2017

8.8K
Microparticle Manipulation by Standing Surface Acoustic Waves with Dual-frequency Excitations
06:51

Microparticle Manipulation by Standing Surface Acoustic Waves with Dual-frequency Excitations

Published on: August 21, 2018

7.2K

Related Experiment Videos

Last Updated: Oct 11, 2025

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
10:51

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

13.9K
Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques
09:01

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Published on: April 4, 2017

8.8K
Microparticle Manipulation by Standing Surface Acoustic Waves with Dual-frequency Excitations
06:51

Microparticle Manipulation by Standing Surface Acoustic Waves with Dual-frequency Excitations

Published on: August 21, 2018

7.2K

Area of Science:

  • Signal Processing
  • Acoustics
  • Adaptive Filters

Background:

  • Active noise control (ANC) primarily targets Gaussian noise.
  • Non-Gaussian noise, like impulsive or piling noise, degrades ANC performance.
  • Existing algorithms struggle with impulsive noise reduction.

Purpose of the Study:

  • Propose novel algorithms for effective impulsive noise reduction in ANC.
  • Enhance the performance and convergence of ANC systems facing non-Gaussian noise.
  • Introduce a variable step-size strategy for improved adaptive filtering.

Main Methods:

  • Developed a filtered-x affine projection sign algorithm with a post-adaptive filter.
  • Introduced a variable step-size strategy using a convex combination.
  • Replaced fixed step-size with a ratio of estimated signals for dynamic adaptation.
  • Analyzed computational complexity and conducted numerical simulations.

Main Results:

  • The proposed algorithms effectively reduce impulsive noise.
  • The variable step-size algorithm demonstrates superior convergence performance compared to the fixed step-size version.
  • Simulations validate the practical efficacy of the developed noise control methods.

Conclusions:

  • The novel algorithms provide a robust solution for impulsive noise in ANC.
  • Variable step-size adaptation significantly improves convergence speed and overall performance.
  • The proposed methods offer advancements in adaptive filtering for challenging acoustic environments.