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

Routh-Hurwitz Criterion II01:19

Routh-Hurwitz Criterion II

433
In the application of the Routh-Hurwitz criterion, two specific scenarios can arise that complicate stability analysis.
The first scenario occurs when a singular zero appears in the first column of the Routh table. This situation creates a division by zero issues. To resolve this, a small positive or negative number, denoted as epsilon (∈), is substituted for the zero. The stability analysis proceeds by assuming a sign for ∈. If ∈ is positive, any sign change in the first...
433
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

508
Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
508
Routh-Hurwitz Criterion I01:15

Routh-Hurwitz Criterion I

343
Consider an electrical power grid, where stability is essential to prevent blackouts. The Routh-Hurwitz criterion is a valuable tool for assessing system stability under varying load conditions or faults. By analyzing the closed-loop transfer function, the Routh-Hurwitz criterion helps determine whether the system remains stable.
To apply the Routh-Hurwitz criterion, a Routh table is constructed. The table's rows are labeled with powers of the complex frequency variable s, starting from the...
343
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

107
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
107
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

990
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
990
Principle of Moments: Problem Solving01:30

Principle of Moments: Problem Solving

949
The principle of moments is a fundamental concept in physics and engineering. It refers to the balancing of forces and moments around a point or axis, also known as the pivot. This principle is used in many real-life scenarios, including construction, sports, and daily activities like opening doors and pushing objects.
One such scenario involves a pole placed in a three-dimensional system with a cable attached. When a tension is applied to the cable, the moment about the z-axis passing through...
949

You might also read

Related Articles

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

Sort by
Same author

Simple and Versatile Toolkit for Genetic Manipulation of <i>Bacillus licheniformis</i>.

ACS synthetic biology·2025
Same author

[Pathogenicity analysis and genetic counseling for a hemizygous c.1042-10G>C variant of SLC9A7 gene].

Zhonghua yi xue yi chuan xue za zhi = Zhonghua yixue yichuanxue zazhi = Chinese journal of medical genetics·2025
Same author

[Pathogenicity analysis of a novel PADI6 gene variant associated with female infertility].

Zhonghua yi xue yi chuan xue za zhi = Zhonghua yixue yichuanxue zazhi = Chinese journal of medical genetics·2025
Same author

Introducing anti-hydrogen evolution sites by hydrophilic metalloporphyrin coatings for stabilizing Zn metal anodes.

Journal of colloid and interface science·2025
Same author

Cardiac surgery timing on the prognosis of patients with infective endocarditis.

Journal of cardiothoracic surgery·2025
Same author

Association of embolization branch selection on middle meningeal artery embolization for chronic subdural hematoma: a secondary analysis of the MAGIC-MT trial.

Neuroradiology·2025

Related Experiment Video

Updated: Sep 21, 2025

Stereoacuity Improvement using Random-Dot Video Games
06:25

Stereoacuity Improvement using Random-Dot Video Games

Published on: January 14, 2020

14.5K

Certifiably Optimal Outlier-Robust Geometric Perception: Semidefinite Relaxations and Scalable Global Optimization.

Heng Yang, Luca Carlone

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |May 31, 2022
    PubMed
    Summary

    This study introduces a new framework for robust geometric perception, reformulating robust estimation as polynomial optimization problems. The developed sparse semidefinite programming relaxation and solver achieve high accuracy with many outliers, improving scalability and certifying optimality.

    More Related Videos

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    3.0K
    Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
    07:05

    Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

    Published on: October 27, 2016

    9.3K

    Related Experiment Videos

    Last Updated: Sep 21, 2025

    Stereoacuity Improvement using Random-Dot Video Games
    06:25

    Stereoacuity Improvement using Random-Dot Video Games

    Published on: January 14, 2020

    14.5K
    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    3.0K
    Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
    07:05

    Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

    Published on: October 27, 2016

    9.3K

    Area of Science:

    • Computer Vision and Machine Learning
    • Optimization and Geometric Algorithms

    Background:

    • Robust geometric perception algorithms are crucial for handling outliers in real-world data.
    • Existing methods often struggle with scalability and lack formal guarantees of optimality.
    • Reformulating robust estimation as polynomial optimization problems (POPs) offers a promising direction.

    Purpose of the Study:

    • To develop a general and scalable framework for designing certifiable algorithms for robust geometric perception.
    • To address the challenge of outliers in estimation tasks.
    • To provide optimality certificates for robust estimation methods.

    Main Methods:

    • Reformulation of robust cost estimations (e.g., truncated least squares) as polynomial optimization problems (POPs).
    • Development of a sparse semidefinite programming (SDP) relaxation for POPs, significantly reducing problem size.
    • Introduction of a novel solver, [Formula: see text], combining global descent on SDPs with local search on POPs for efficient and accurate solutions.

    Main Results:

    • The sparse SDP relaxation demonstrates empirical exactness, recovering optimal solutions with up to 90% outliers across various geometric perception tasks.
    • The [Formula: see text] solver achieves significant speedups (up to 100x) over existing SDP solvers on medium-scale problems and handles large-scale problems with high accuracy.
    • The framework successfully safeguards fast heuristics, providing global optimality certification or detecting and escaping local optima.

    Conclusions:

    • The proposed framework offers a scalable and certifiable approach to robust geometric perception.
    • The sparse SDP relaxation and the [Formula: see text] solver represent significant advancements in solving large-scale robust estimation problems.
    • This work provides a robust foundation for developing reliable geometric perception systems in the presence of significant noise and outliers.