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

Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

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 organic...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...

You might also read

Related Articles

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

Sort by
Same author

PainFedMVL: A Federated Multi-View Learning Approach for Multi-Level Pain Recognition.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same author

Wavelet-Transformer Attention Network for Accurate Fetal ECG Estimation from Multi-Channel Abdominal Signals.

IEEE journal of biomedical and health informatics·2026
Same author

An Efficient Regenerated Cross-Modal Hashing: Improving Existing Hash Codes with the Arbitrary Length.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Adapting Domain-Aware Knowledge to Vision-Language Model for Zero-Shot Anomaly Detection.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Dual-channel single-spectrometer chromatic confocal coherence sensing to mitigate bandwidth compression in film thickness and refractive-index measurement.

Optics letters·2026
Same author

3D-BNN-accelerated phase tracking with optimal sub-region selection for high-dynamic-range OCE.

Optics letters·2026
Same journal

Universal perceptron and DNA-like learning algorithm for binary neural networks: LSBF and PBF implementations.

IEEE transactions on neural networks·2013
Same journal

Guest editorial: special section on white box nonlinear prediction models.

IEEE transactions on neural networks·2011
Same journal

Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures.

IEEE transactions on neural networks·2011
Same journal

Guest editorial: special section on data-based control, modeling, and optimization.

IEEE transactions on neural networks·2011
Same journal

Neural network-based multiple robot simultaneous localization and mapping.

IEEE transactions on neural networks·2011
Same journal

Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems.

IEEE transactions on neural networks·2011
See all related articles

Related Experiment Video

Updated: Jun 3, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Online blind source separation using incremental nonnegative matrix factorization with volume constraint.

Guoxu Zhou1, Zuyuan Yang, Shengli Xie

  • 1School of Electronics and Information Engineering, South China University of Technology, Guangzhou 510641, China. zhou.guoxu@mail.scut.edu.cn

IEEE Transactions on Neural Networks
|March 9, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces an incremental nonnegative matrix factorization (NMF) with volume constraint for online blind source separation (BSS). This method efficiently separates correlated sources, overcoming limitations of traditional algorithms.

Related Experiment Videos

Last Updated: Jun 3, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Area of Science:

  • Signal Processing
  • Machine Learning
  • Data Analysis

Background:

  • Traditional batch blind source separation (BSS) methods face high computational costs, limiting practical applications.
  • Existing online BSS methods primarily focus on separating independent or uncorrelated sources.
  • Nonnegative matrix factorization (NMF) shows promise for separating correlated sources, but often requires constraints to address non-uniqueness.

Purpose of the Study:

  • To develop an efficient online BSS method capable of separating correlated sources.
  • To reduce the computational cost associated with traditional BSS algorithms.
  • To enhance source identifiability in online BSS through novel constraints.

Main Methods:

  • An incremental nonnegative matrix factorization (NMF) model with a volume constraint was derived.
  • The method utilizes a natural gradient-based multiplication updating rule.
  • The approach is designed for online learning to reduce computational demands.

Main Results:

  • The proposed incremental NMF with volume constraint effectively performs online BSS for correlated sources.
  • The volume constraint improves the identifiability of separated sources.
  • Simulations demonstrated the method's validity in diverse applications, including dual-energy X-ray images, encrypted speech, and face images.

Conclusions:

  • The developed online BSS method using incremental NMF with volume constraint is effective for separating correlated sources.
  • This approach offers a computationally efficient alternative to traditional batch BSS.
  • The technique shows broad applicability across various signal and image processing tasks.