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 Experiment Videos

Generalized linear least squares algorithm for non-uniformly sampled biomedical system identification with possible

K P Wong1, D Feng, W C Siu

  • 1Department of Electronic & Information Engineering, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.

Computer Methods and Programs in Biomedicine
|November 21, 1998
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Why methane surged in the atmosphere during the early 2020s.

Science (New York, N.Y.)·2026
Same author

[Identifying risk factors for acute graft-versus-host disease in patients with acute myeloid leukemia undergoing haploidentical hematopoietic stem cell transplantation].

Zhonghua xue ye xue za zhi = Zhonghua xueyexue zazhi·2025
Same author

Presenteeism and physio-psychological disorders among frontline nurses fighting COVID-19.

Occupational medicine (Oxford, England)·2025
Same author

Glutaric Aciduria Type 1: Comparison between Diffusional Kurtosis Imaging and Conventional MR Imaging.

AJNR. American journal of neuroradiology·2023
Same author

A dc Method for the Absolute Determination of Conductivities of the Primary Standard KCl Solutions from 0 °C to 50 °C.

Journal of research of the National Institute of Standards and Technology·2023
Same author

Preoperative prediction of microvascular invasion and perineural invasion in pancreatic ductal adenocarcinoma with <sup>18</sup>F-FDG PET/CT radiomics analysis.

Clinical radiology·2023
Same journal

MMFVS-Net: A triple-symmetric cross-attention network for multimodal optical image fusion and high-accuracy virtual staining of breast cancer tissues.

Computer methods and programs in biomedicine·2026
Same journal

A novel Milstein-stochastic epidemiologically-informed neural network for approaching epidemic dynamics: Application to Mpox disease.

Computer methods and programs in biomedicine·2026
Same journal

Accounting for approximation errors using surrogate-based parameter estimation of cardiac mechanics digital twins.

Computer methods and programs in biomedicine·2026
Same journal

Facial iPPG heatmap patterns based on period-aware autoencoder show association with carotid atherosclerosis towards non-contact hemodynamic assessment.

Computer methods and programs in biomedicine·2026
Same journal

Explainable machine learning models predict liver fibrosis risk and outcome in the general population: Development and multi-cohort external validation.

Computer methods and programs in biomedicine·2026
Same journal

Evaluation of surrogate endpoints for survival outcomes using the surrogate package in R.

Computer methods and programs in biomedicine·2026
See all related articles

This study extends the generalized linear least squares (GLLS) algorithm to handle repeated eigenvalues in biomedical signal processing. The enhanced GLLS algorithm improves system identification from non-uniformly sampled noisy data.

Area of Science:

  • Biomedical Signal Processing
  • System Identification
  • Algorithm Development

Background:

  • Generalized linear least squares (GLLS) is valuable for non-uniformly sampled biomedical signals.
  • Existing GLLS algorithms struggle with signals containing repeated eigenvalues.

Purpose of the Study:

  • To extend the GLLS algorithm to accommodate repeated eigenvalues.
  • To enhance parameter estimation and system identification for non-uniformly sampled signals.

Main Methods:

  • Theoretical derivation of the extended GLLS algorithm.
  • Application of the enhanced algorithm to a case study involving non-uniformly sampled noisy signals.

Main Results:

  • The extended GLLS algorithm successfully processes signals with repeated eigenvalues.

Related Experiment Videos

  • Demonstrated improved model selection capabilities for system identification.

    • Conclusions:

    • The extended GLLS algorithm offers greater flexibility for system identification.
    • It enables more accurate model selection from noisy, non-uniformly sampled biomedical data.