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

Integration of Synaptic Events01:28

Integration of Synaptic Events

Synaptic integration mainly includes the summation of graded potentials. Graded potentials, regardless of their type, cause subtle alterations in membrane voltage, resulting in either depolarization or hyperpolarization. These incremental changes, when combined or summed, can propel the neuron toward its threshold. Consider, for example, a membrane experiencing a +15 mV shift, causing it to depolarize from -70 mV to -55 mV. In this scenario, graded potentials govern the membrane's ability to...
Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)01:15

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT) is an advanced Nuclear Magnetic Resonance (NMR) technique specifically designed to detect and enhance the signals of low-abundance nuclei, such as carbon-13 and nitrogen-15, in small molecules. The fundamental principle behind INEPT is the transfer of polarization from a more abundant and highly polarizable nucleus, typically hydrogen-1, to the low-abundance nucleus of interest. This process effectively boosts the NMR signal of the...
Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...

You might also read

Related Articles

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

Sort by
Same author

The role of long non-coding RNA HCP5 in ulcerative cutaneous tuberculosis therapy introduction.

Frontiers in cellular and infection microbiology·2026
Same author

Ancient Whole-Genome Duplication and Lineage-Specific Retention Shape the Diversification of bZIP Transcription Factors in Pooideae.

Plants (Basel, Switzerland)·2026
Same author

A Zinc Finger Protein-Based Prognostic Model in Lung Adenocarcinoma Identifies FGD3 as a Marker Associated with Lorlatinib Resistance.

Cancers·2026
Same author

Bufalin post-transcriptionally suppresses STAT3 to alleviate renal ferroptosis and tubulointerstitial fibrosis in diabetic kidney disease.

Renal failure·2026
Same author

Comparative analysis and influential factors of embodied carbon emissions across low-rise, multi-story, and high-rise residential buildings in China.

Scientific reports·2026
Same author

Chiral Molecular Intercalation Enables Light-Controlled 2D Multiferroic Heterostructures.

Nano letters·2026
Same journal

A Quantum Self-Attention Neural Network Model on Quantum Circuits.

IEEE transactions on cybernetics·2026
Same journal

Semi-Explicit Solution of Some Discrete-Time Higher-Order-Cost Mean-Field-Type Control.

IEEE transactions on cybernetics·2026
Same journal

A Novel One-Step Small Object Detector for Autonomous Aerial Vehicles.

IEEE transactions on cybernetics·2026
Same journal

Online Data-Driven-Based Optimal Output Tracking Control Without Initial Stabilizing Policy.

IEEE transactions on cybernetics·2026
Same journal

Digital Redesign-Based Interval State Estimation for Continuous Systems With Aperiodic Discrete Measurements.

IEEE transactions on cybernetics·2026
Same journal

Output Prediction-Based Event-Triggered Interval Estimation for Continuous-Time Switched Systems.

IEEE transactions on cybernetics·2026
See all related articles

Related Experiment Video

Updated: Jun 25, 2026

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

Adaptive PD-Like Event-Triggered Secure Synchronization Control for Inertial Neural Networks and Signal Encryption

Junyi Wang, Rui Wang, Jiapeng Han

    IEEE Transactions on Cybernetics
    |June 23, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an adaptive event-triggered mechanism for secure synchronization of inertial neural networks (INNs) facing hybrid attacks, enhancing signal encryption security. The method efficiently manages data sampling while ensuring system performance and robustness against cyber threats.

    Related Experiment Videos

    Last Updated: Jun 25, 2026

    Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
    11:54

    Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

    Published on: May 8, 2021

    Area of Science:

    • Control Theory
    • Cybersecurity
    • Artificial Intelligence

    Background:

    • Markovian jumping delayed inertial neural networks (INNs) are crucial for signal processing.
    • Ensuring secure synchronization under hybrid attacks (Denial-of-Service and Deception Attacks) is a significant challenge.
    • Existing methods may not efficiently handle redundant data sampling in complex network environments.

    Purpose of the Study:

    • To investigate the exponential secure synchronization of Markovian jumping delayed INNs under hybrid attacks.
    • To propose a novel adaptive proportional-derivative (PD)-like event-triggered mechanism (APDETM) for efficient data sampling.
    • To design event-triggered output feedback controllers for secure synchronization control.

    Main Methods:

    • Development of an adaptive proportional-derivative (PD)-like event-triggered mechanism (APDETM) considering state variations.
    • Establishment of INNs with generally uncertain semi-Markovian (GUSM) jumping parameters under hybrid attacks.
    • Design of event-triggered output feedback controllers for secure synchronization.

    Main Results:

    • The proposed APDETM effectively filters redundant sampling data while maintaining system performance.
    • Secure synchronization conditions were derived for INNs with GUSM parameters under hybrid attacks.
    • The developed control strategies demonstrated effectiveness in numerical simulations and audio encryption.

    Conclusions:

    • The proposed event-triggered control approach ensures exponential secure synchronization for delayed INNs under hybrid attacks.
    • The APDETM offers an efficient solution for data sampling in networked systems.
    • The application in signal encryption, particularly audio encryption, highlights the practical utility of the method.