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

Discrete-time Fourier transform01:26

Discrete-time Fourier transform

1.1K
The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
One of the notable...
1.1K
Basic Discrete Time Signals01:16

Basic Discrete Time Signals

660
The unit step sequence is defined as 1 for zero and positive values of the integer n. This sequence can be graphically displayed using a set of eight sample points, showing a step function starting from n=0 and remaining constant thereafter.
The unit impulse or sample sequence is mathematically expressed as zero for all n values except at n=0, where it is one. The unit impulse sequence, denoted by δ(n), is the first difference of the unit step sequence, while the unit step sequence u(n) is the...
660
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

665
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
665
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

894
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
894
Singularity Functions for Shear01:26

Singularity Functions for Shear

432
In structural analysis, singularity functions are crucial in simplifying the representation of shear forces in beams under discontinuous loading. These functions describe discontinuous  variations in shear force across a beam with varying loads by using a single mathematical expression, regardless of the complexity of the loading conditions. The singularity functions are derived from creating a free-body diagram of the beam and then making conceptual cuts at specific points to examine the...
432
Hydraulic Jump: Problem Solving01:16

Hydraulic Jump: Problem Solving

507
To analyze a hydraulic jump in a rectangular channel with a flow speed of 6 meters per second, follow these steps:Calculate Effective Upstream Velocity:When the downstream gate closes, a hydraulic jump forms, traveling upstream at 2 meters per second. This wave speed combines with the initial channel flow velocity, creating an effective upstream velocity.Identify Flow Velocities Before and After the Hydraulic Jump:Upstream of the hydraulic jump, the effective flow velocity includes both the...
507

You might also read

Related Articles

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

Sort by
Same author

Study on the characteristics of thermo-electrodes of various deposition parameters for the flexible temperature sensor.

The Review of scientific instruments·2020
Same author

Delayed Comparison and Apriori Algorithm (DCAA): A Tool for Discovering Protein-Protein Interactions From Time-Series Phosphoproteomic Data.

Frontiers in molecular biosciences·2020
Same author

The Roles of Inflammation in Keloid and Hypertrophic Scars.

Frontiers in immunology·2020
Same author

Preparation of poly-dopamine-silk fibroin sponge and its dye molecular adsorption.

Water science and technology : a journal of the International Association on Water Pollution Research·2020
Same author

Genomic analysis of Asian honeybee populations in China reveals evolutionary relationships and adaptation to abiotic stress.

Ecology and evolution·2020
Same author

Development and validation of a postoperative nomogram for predicting overall survival after endoscopic surgical management of olfactory neuroblastoma.

EClinicalMedicine·2020

Related Experiment Video

Updated: Jan 22, 2026

A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes
10:04

A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes

Published on: March 3, 2018

7.1K

Event-Based Dissipative Analysis for Discrete Time-Delay Singular Jump Neural Networks.

Yingqi Zhang, Peng Shi, Ramesh K Agarwal

    IEEE Transactions on Neural Networks and Learning Systems
    |June 28, 2019
    PubMed
    Summary

    This study introduces event-triggered filtering for singular jump neural networks with delays. The method ensures stochastic admissibility and strict stochastic dissipativity, improving system stability and performance.

    More Related Videos

    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.9K
    An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents
    07:42

    An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents

    Published on: August 2, 2018

    14.3K

    Related Experiment Videos

    Last Updated: Jan 22, 2026

    A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes
    10:04

    A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes

    Published on: March 3, 2018

    7.1K
    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.9K
    An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents
    07:42

    An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents

    Published on: August 2, 2018

    14.3K

    Area of Science:

    • Control Theory
    • Neural Networks
    • Stochastic Systems

    Background:

    • Discrete-time singular neural networks (SJNNs) are crucial in modeling complex systems.
    • Time-varying delays and Markovian jump parameters introduce significant challenges in network analysis.
    • Event-triggered communication is essential for efficient data transmission in networked systems.

    Purpose of the Study:

    • To investigate the event-triggered dissipative filtering problem for discrete-time SJNNs.
    • To ensure the augmented SJNN system is stochastically admissible and strictly stochastically dissipative (SASSD).
    • To develop a method for designing filters that guarantee SASSD properties under event-triggered conditions.

    Main Methods:

    • Modeling SJNNs with network-induced delays using an event-triggered communication technique.
    • Utilizing a slack matrix scheme to derive sufficient criteria for stochastic admissibility and strict stochastic dissipativity.
    • Employing filter equivalent techniques to co-design filter gains and event-triggered matrices.

    Main Results:

    • Sufficient criteria were established to guarantee the augmented SJNN is stochastically admissible and strictly stochastically dissipative (SASSD).
    • The proposed event-triggered filtering approach effectively ensures the SASSD property for the SJNN model.
    • A practical example demonstrated the efficacy of the developed dissipative filtering method.

    Conclusions:

    • The event-triggered dissipative filtering approach is effective for discrete-time singular jump neural networks.
    • The proposed method guarantees stochastic admissibility and strict stochastic dissipativity, enhancing system reliability.
    • This research contributes to the robust control of complex neural network systems in the presence of uncertainties and delays.