Jove
Visualize
Contact Us

Related Concept Videos

Independent and Dependent Sources01:18

Independent and Dependent Sources

3.0K
In electrical circuits, sources play a crucial role in providing power for the operation of the circuit. These sources can be broadly categorized into two types: independent and dependent.
Independent voltage or current sources supply a fixed amount of voltage or current, respectively, which is unaffected by other elements within the circuit. These are represented using specific symbols. Independent voltage sources are symbolized with polarities (+ and -), indicating the direction of the...
3.0K
Deconvolution01:20

Deconvolution

722
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...
722
Blind Procedures02:07

Blind Procedures

13.9K
Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which...
13.9K
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

5.6K
The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
5.6K
Superposition Theorem01:18

Superposition Theorem

2.0K
The superposition principle is a fundamental concept stating that in a linear circuit, the voltage across (or current through) an element can be determined by summing the individual contributions of each independent source acting in isolation. When dealing with linear circuits containing multiple independent sources, this principle serves as a valuable tool for analysis. To apply the superposition principle effectively, one should focus on a single independent source at a time while...
2.0K
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

1.2K
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
1.2K

You might also read

Related Articles

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

Sort by
Same author

Video-based tracking of single molecules exhibiting directed in-frame motion.

Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canadaยท2012
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: Apr 16, 2026

Cortical Source Analysis of High-Density EEG Recordings in Children
09:32

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

22.1K

Convolutive bounded component analysis algorithms for independent and dependent source separation.

Huseyin A Inan, Alper T Erdogan

    IEEE Transactions on Neural Networks and Learning Systems
    |March 21, 2015
    PubMed
    Summary
    This summary is machine-generated.

    Bounded component analysis (BCA) separates independent and dependent sources from mixtures. New algorithms extend convolutive ICA, improving performance for bounded, correlated sources, especially with short data records.

    More Related Videos

    Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
    11:28

    Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

    Published on: June 30, 2018

    12.5K
    Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI
    10:35

    Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI

    Published on: June 3, 2013

    33.5K

    Related Experiment Videos

    Last Updated: Apr 16, 2026

    Cortical Source Analysis of High-Density EEG Recordings in Children
    09:32

    Cortical Source Analysis of High-Density EEG Recordings in Children

    Published on: June 30, 2014

    22.1K
    Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
    11:28

    Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

    Published on: June 30, 2018

    12.5K
    Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI
    10:35

    Best Current Practice for Obtaining High Quality EEG Data During Simultaneous fMRI

    Published on: June 3, 2013

    33.5K

    Area of Science:

    • Signal Processing
    • Machine Learning
    • Statistical Analysis

    Background:

    • Independent Component Analysis (ICA) is limited to independent sources.
    • Bounded Component Analysis (BCA) offers a more general framework for source separation.
    • Existing BCA methods primarily address instantaneous mixtures.

    Purpose of the Study:

    • To introduce convolutive BCA criteria and algorithms.
    • To extend BCA capabilities to dependent and time-correlated sources.
    • To demonstrate performance advantages over ICA-based methods.

    Main Methods:

    • Development of a family of convolutive BCA criteria.
    • Introduction of corresponding optimization algorithms.
    • Theoretical proof of global optima equivalence to perfect separators.

    Main Results:

    • Algorithms successfully separate independent and dependent/correlated sources in space and time.
    • Demonstrated capability for space-time correlated source separation using a copula distribution example.
    • Outperformed state-of-the-art ICA approaches in MIMO equalization with convolutive mixtures.

    Conclusions:

    • Proposed BCA algorithms extend convolutive ICA with dependent source separation.
    • BCA offers improved performance, particularly for short data records.
    • The framework is effective for complex signal separation tasks like MIMO equalization.