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

Methods of Classification and Identification01:28

Methods of Classification and Identification

1.3K
Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
1.3K
Discrete Fourier Transform01:15

Discrete Fourier Transform

924
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
924
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

741
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...
741
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

719
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]...
719
Wave Parameters01:10

Wave Parameters

9.4K
The simplest mechanical waves are associated with simple harmonic motion and repeat themselves for several cycles. These simple harmonic waves can be modeled using a combination of sine and cosine functions. Consider a simplified surface water wave that moves across the water's surface. Unlike complex ocean waves, in surface water waves, water moves vertically, oscillating up and down, whereas the disturbance of the wave moves horizontally through the medium. If a seagull is floating on the...
9.4K

You might also read

Related Articles

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

Sort by
Same author

Assessing psychological variables on mobile devices: an introduction to the experience sampling app <i>ESM-Quest</i>.

Frontiers in psychology·2024
Same author

Reablement - relevant factors for implementation: an exploratory sequential mixed-methods study design.

BMC health services research·2022
Same author

Temporal patterns of roe deer traffic accidents: Effects of season, daytime and lunar phase.

PloS one·2021
Same author

Transoral laser microsurgery for treatment of oropharyngeal cancer in 368 patients.

Head & neck·2019
Same author

A frequency domain approach for parameter identification in multibody dynamics.

Multibody system dynamics·2018
Same author

Optimal input design for multibody systems by using an extended adjoint approach.

Multibody system dynamics·2017
Same journal

Contact dynamics investigation towards microgravity experiment for asteroid-related scenarios.

Multibody system dynamics·2026
Same journal

Dynamic responses of a vibro-impact capsule robot self-propelling in the large intestine via multibody dynamics.

Multibody system dynamics·2025
Same journal

Long-term dynamic simulation of cellular systems with inhomogeneous mass distribution.

Multibody system dynamics·2025
Same journal

Long-term dynamic simulation of adipogenic differentiation of a human mesenchymal stem cell.

Multibody system dynamics·2025
Same journal

A novel motion-reconstruction method for inertial sensors with constraints.

Multibody system dynamics·2023
Same journal

Efficient simulation strategy to design a safer motorcycle.

Multibody system dynamics·2023
See all related articles

Related Experiment Video

Updated: Feb 13, 2026

Identification of Fatty Acids in Bacillus cereus
08:41

Identification of Fatty Acids in Bacillus cereus

Published on: December 5, 2016

10.1K

The discrete adjoint method for parameter identification in multibody system dynamics.

Thomas Lauß1, Stefan Oberpeilsteiner1, Wolfgang Steiner1

  • 1Faculty of Engineering and Environmental Sciences, University of Applied Sciences Upper Austria, Stelzhamerstrasse 23, 4600 Wels, Austria.

Multibody System Dynamics
|March 23, 2018
PubMed
Summary
This summary is machine-generated.

The discrete adjoint method offers an accurate way to compute gradients for parameter identification. It replaces differential equations with algebraic ones, ensuring precise results for discretized systems.

Keywords:
Adjoint methodDiscrete adjoint methodParameter identification

More Related Videos

ODELAY: A Large-scale Method for Multi-parameter Quantification of Yeast Growth
11:19

ODELAY: A Large-scale Method for Multi-parameter Quantification of Yeast Growth

Published on: July 3, 2017

8.7K
A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning
09:22

A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning

Published on: June 22, 2015

15.1K

Related Experiment Videos

Last Updated: Feb 13, 2026

Identification of Fatty Acids in Bacillus cereus
08:41

Identification of Fatty Acids in Bacillus cereus

Published on: December 5, 2016

10.1K
ODELAY: A Large-scale Method for Multi-parameter Quantification of Yeast Growth
11:19

ODELAY: A Large-scale Method for Multi-parameter Quantification of Yeast Growth

Published on: July 3, 2017

8.7K
A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning
09:22

A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning

Published on: June 22, 2015

15.1K

Area of Science:

  • Computational Science
  • Numerical Analysis
  • Optimization

Background:

  • The adjoint method is crucial for gradient computation in parameter identification.
  • Numerical accuracy of adjoint differential equations significantly impacts gradient precision.

Purpose of the Study:

  • To introduce and validate the discrete adjoint method as an accurate alternative to the traditional adjoint method.
  • To address the accuracy limitations in solving adjoint differential equations.

Main Methods:

  • Replacing continuous adjoint differential equations with discrete algebraic equations.
  • Developing a finite difference scheme for the adjoint system directly from time integration.
  • Applying the discrete adjoint method to discretized equations of motion.

Main Results:

  • The discrete adjoint method provides the exact gradient of the discretized cost function.
  • This method bypasses the need to solve differential equations for adjoint variables, enhancing accuracy.

Conclusions:

  • The discrete adjoint method is a robust and accurate approach for gradient computation in discretized systems.
  • It offers a significant improvement over traditional methods when dealing with numerical inaccuracies in differential equations.