Jove
Visualize
Contact Us

Related Concept Videos

Classification of Signals01:30

Classification of Signals

1.6K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.6K
Classification of Systems-I01:26

Classification of Systems-I

750
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
750
Classification of Systems-II01:31

Classification of Systems-II

658
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
658
Aggregates Classification01:29

Aggregates Classification

1.0K
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
1.0K

You might also read

Related Articles

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

Sort by
Same author

Association between clopidogrel preloading time and post-procedural troponin elevation in patients with stable angina undergoing elective percutaneous coronary intervention: a retrospective cohort study.

Journal of Yeungnam medical science·2026
Same author

The performance of ASpirin-FREE therapy after successful percutaneous coronary intervention for acute coronary syndrome: the ASFREE prospective pilot study.

Journal of Yeungnam medical science·2026
Same author

Reflection-Enhanced Raman Identification of Single Bacterial Cells Patterned Using Capillary Assembly.

ACS sensors·2025
Same author

Prognostic Role of Pan-Immune-Inflammatory Value in Patients with Non-ST-Segment Elevation Acute Coronary Syndrome.

Journal of cardiovascular development and disease·2025
Same author

Laparoscopic removal of a broken acupuncture needle in pancreatic head: a case report.

Journal of surgical case reports·2024
Same author

TopicFM+: Boosting Accuracy and Efficiency of Topic-Assisted Feature Matching.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2024
Same journal

Analysis of End-Tidal CO2 Variability During Plateau Waves Episodes: An Information Theoretic Approach<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

AI and Tomosynthesis for Breast Cancer Molecular Subtyping: A step toward precision medicine<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Towards Sustainable Protein Recovery from Biological Waste: Assessing Polyethersulfone-based Microfiltration.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Analysis of the cardiovascular response to standardized polymicrobial peritonitis experimental model.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

Automated Wrist Ultrasound Image Bone Enhancement and Segmentation Using Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same journal

A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
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: May 7, 2026

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

896

SEMG pattern classification using hierarchical Bayesian model.

Hyonyoung Han, Sungho Jo

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 11, 2013
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a generative model for surface electromyogram (sEMG) muscle pattern classification. The hierarchical Bayesian model achieved 95% accuracy, showing promise for sEMG signal analysis.

    More Related Videos

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
    12:27

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

    Published on: February 15, 2017

    6.3K
    Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
    09:44

    Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

    Published on: March 8, 2024

    4.6K

    Related Experiment Videos

    Last Updated: May 7, 2026

    Decoding Natural Behavior from Neuroethological Embedding
    08:00

    Decoding Natural Behavior from Neuroethological Embedding

    Published on: October 3, 2025

    896
    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
    12:27

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

    Published on: February 15, 2017

    6.3K
    Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
    09:44

    Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

    Published on: March 8, 2024

    4.6K

    Area of Science:

    • Biomedical Engineering
    • Machine Learning
    • Neuroscience

    Background:

    • Surface electromyogram (sEMG) signals are crucial for understanding muscle activity.
    • Accurate muscle pattern classification is essential for applications like prosthetics and human-computer interaction.
    • Existing methods face challenges in robustly modeling the complex nature of sEMG signals.

    Purpose of the Study:

    • To develop and validate a novel generative model for sEMG-based muscle pattern classification.
    • To leverage a hierarchical Bayesian approach for modeling sEMG signal generation.
    • To assess the classification performance of the proposed model on limb action recognition.

    Main Methods:

    • A hierarchical Bayesian generative model was constructed to represent the sEMG signal generation process.
    • Latent neural states governing sEMG data were inferred probabilistically.
    • An eight-class classification task was performed using data from four sEMG sensors across five subjects.

    Main Results:

    • The proposed generative model achieved a high overall accuracy of 95% in the classification experiment.
    • The model successfully classified limb actions based on sEMG patterns.
    • Probabilistic inference of latent neural states proved effective for classification.

    Conclusions:

    • The developed hierarchical Bayesian generative model shows significant promise for sEMG pattern classification.
    • This approach offers a robust method for analyzing complex sEMG data.
    • The high accuracy suggests potential for real-world applications in bio-signal processing.