Jove
Visualize
Contact Us

Related Concept Videos

Op Amp AC Circuits01:18

Op Amp AC Circuits

267
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).
267
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

343
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...
343
Passive Filters01:27

Passive Filters

597
Passive filters are utilized to shape the frequency spectrum of signals across a diverse array of applications. These filters, using only passive elements like resistors (R), inductors (L), and capacitors (C), are capable of selectively allowing or blocking certain frequency ranges without the need for external power sources.
Low-Pass Filters
Low-pass filters are designed to transmit signals with frequencies lower than the cutoff frequency, ωc, and attenuate those above it. The cutoff...
597
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

129
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....
129
Basic Continuous Time Signals01:22

Basic Continuous Time Signals

322
Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
322
Linear time-invariant Systems01:23

Linear time-invariant Systems

384
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
384

You might also read

Related Articles

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

Sort by
Same author

Complex Correntropy with Variable Center: Definition, Properties, and Application to Adaptive Filtering.

Entropy (Basel, Switzerland)·2020
Same author

Recursive Minimum Complex Kernel Risk-Sensitive Loss Algorithm.

Entropy (Basel, Switzerland)·2020
Same author

A Robust Adaptive Filter for a Complex Hammerstein System.

Entropy (Basel, Switzerland)·2020
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
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 Experiment Video

Updated: Sep 3, 2025

Interactive and Visualized Online Experimentation System for Engineering Education and Research
08:35

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

2.6K

Generalized Maximum Complex Correntropy Augmented Adaptive IIR Filtering.

Haotian Zheng1, Guobing Qian1

  • 1College of Electronic and Information Engineering, Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Southwest University, Chongqing 400715, China.

Entropy (Basel, Switzerland)
|July 27, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel augmented IIR filter adaptive algorithm using the generalized maximum complex correntropy criterion (GMCCC-AIIR). This new method enhances performance in non-Gaussian noise environments, outperforming existing algorithms.

Keywords:
GMCCCaugmented IIRcomplexnon-Gaussian noisesystem identification

More Related Videos

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

1.8K
X-ray Beam Induced Current Measurements for Multi-Modal X-ray Microscopy of Solar Cells
10:16

X-ray Beam Induced Current Measurements for Multi-Modal X-ray Microscopy of Solar Cells

Published on: August 20, 2019

14.0K

Related Experiment Videos

Last Updated: Sep 3, 2025

Interactive and Visualized Online Experimentation System for Engineering Education and Research
08:35

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

2.6K
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

1.8K
X-ray Beam Induced Current Measurements for Multi-Modal X-ray Microscopy of Solar Cells
10:16

X-ray Beam Induced Current Measurements for Multi-Modal X-ray Microscopy of Solar Cells

Published on: August 20, 2019

14.0K

Area of Science:

  • Signal Processing
  • Adaptive Filtering
  • Complex-Valued Systems

Background:

  • Augmented Infinite Impulse Response (IIR) filter adaptive algorithms are widely studied for complex-valued signals.
  • Existing algorithms often rely on the mean square error (MSE) criterion, which is suboptimal for non-Gaussian noise.
  • Complex correntropy offers robustness against non-Gaussian noise in adaptive filter design.

Purpose of the Study:

  • To propose a new augmented IIR filter adaptive algorithm.
  • To address the limitations of MSE-based algorithms in non-Gaussian noise conditions.
  • To introduce an algorithm based on the generalized maximum complex correntropy criterion (GMCCC).

Main Methods:

  • Development of a novel augmented IIR filter adaptive algorithm.
  • Utilization of the generalized maximum complex correntropy criterion (GMCCC).
  • Employing the complex generalized Gaussian density function as the kernel function for enhanced robustness.

Main Results:

  • The proposed GMCCC-AIIR algorithm demonstrates superior performance compared to existing methods.
  • Stability analysis determined the bounds for the learning rate.
  • Simulation results validated the algorithm's effectiveness and robustness.

Conclusions:

  • The GMCCC-AIIR algorithm provides a robust solution for adaptive filtering in the presence of non-Gaussian noise.
  • The proposed method offers improved performance over traditional MSE-based approaches.
  • The study contributes a novel criterion for designing advanced adaptive filters.