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

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.
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...
Op Amp AC Circuits01:18

Op Amp AC Circuits

Within an audio system, the filter circuit plays a pivotal role in processing the amplified audio signal from an amplifier. Its primary function is significantly attenuating signal components with lower frequencies, thereby shaping the audio output. This circuit's operations are examined, focusing on the fundamental filter configuration. This configuration involves an operational amplifier arranged in an inverting setup coupled with resistors (R1 and R2) and a capacitor (C1).
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
Upsampling01:22

Upsampling

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...
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the other increases, and...

You might also read

Related Articles

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

Sort by
Same author

Bio-Inspired Internal Representations of Tactile Sensation, Pain, and Damage for Artificial Skin Using Spatio-Temporal Anomaly Detection.

Sensors (Basel, Switzerland)·2026
Same author

Development of a multi-targeted metabolomics platform for semi-quantification of faecal metabolites: a proof-of-concept analysis in human faeces.

Microbiome research reports·2026
Same author

Comparison between the flavor profiles of milk pasteurized using alternating-current high electric field and ultra-high temperature.

Journal of dairy science·2026
Same author

Gut Microbial Diversity and Community Structure Are Largely Similar Between Apparently Healthy Elderly Japanese Males and Females: A Shotgun Metagenomic Study.

Life (Basel, Switzerland)·2026
Same author

Cross-organ multi-omics profiling of microbiome and metabolome along the gut-liver axis in MASH model mice induced by western diet and MC4R knockout.

Gut pathogens·2026
Same author

Efficacy of GLP-1 analog peptides, semaglutide, tirzepatide, and retatrutide on MC4R deficient obesity and their comparison.

International journal of obesity (2005)·2026

Related Experiment Video

Updated: Jun 16, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

Automatic parameter optimization in epsilon-filter for acoustical signal processing utilizing correlation

Tomomi Abe1, Shuji Hashimoto, Mitsuharu Matsumoto

  • 1Department of Applied Physics, Waseda University, 55N-4F-10A, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan. tomomi@shalab.phys.waseda.ac.jp

The Journal of the Acoustical Society of America
|February 9, 2010
PubMed
Summary

This study introduces an automated method to optimize epsilon-filter parameters for effective noise reduction in signals. The new approach ensures better noise suppression while preserving crucial signal details, improving overall signal processing performance.

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

Related Experiment Videos

Last Updated: Jun 16, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

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

Area of Science:

  • Signal Processing
  • Digital Signal Processing
  • Noise Reduction Techniques

Background:

  • Epsilon-filter effectively reduces various noise types in single-channel signals.
  • Current epsilon-filter applications rely on empirical parameter tuning, lacking rigorous evaluation.
  • Both stationary and non-stationary noise can be reduced, but parameter optimization is challenging.

Purpose of the Study:

  • To develop an automated algorithm for optimal epsilon-filter parameter setting.
  • To evaluate the appropriateness of parameter settings using a novel evaluation function.
  • To enhance the performance and applicability of epsilon-filters in signal processing.

Main Methods:

  • Utilized the correlation coefficient between filter output and input-output difference as an evaluation metric.
  • Developed an algorithm for automatic determination of optimal epsilon-filter parameters.
  • Calculated mean absolute error to assess the adequacy of the optimized parameters.

Main Results:

  • The proposed method successfully determines adequate parameters for the epsilon-filter automatically.
  • Experimental results validate the effectiveness of the automated parameter optimization technique.
  • The automated method provides a reliable way to tune epsilon-filters for diverse noise conditions.

Conclusions:

  • Automated parameter optimization for epsilon-filters is feasible and effective.
  • The developed algorithm enhances the practical application of epsilon-filters.
  • This approach offers a significant improvement over empirical parameter setting for noise reduction.