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

Behavior of Concrete Under Compressive Load01:23

Behavior of Concrete Under Compressive Load

898
Concrete exhibits specific behaviors under different compressive loads. Understanding this is crucial for understanding its structural integrity. When concrete undergoes uniaxial compression, it tends to develop cracks that run parallel to the direction of the force. These parallel cracks stem from localized tensile stresses that occur perpendicular to the compression direction. Additionally, angled cracks may appear due to the formation of shear planes.
As the concrete specimen fractures under...
898
Relation Between Tensile Strength and Compressive Strength of Concrete01:30

Relation Between Tensile Strength and Compressive Strength of Concrete

957
Concrete is a fundamental building material, and understanding its strengths is crucial for construction projects. The relationship between its tensile and compressive strengths is intricate, showing that while these strengths are related, they do not increase at the same rate. Tensile strength's growth is slower and is affected by various factors such as the methods used for testing, the size and shape of the specimen, the texture of the aggregate used, and the moisture content of the...
957
Stress: General Loading Conditions01:15

Stress: General Loading Conditions

725
To grasp the intricacy of real-world conditions where multiple loads are applied simultaneously to a structure, one might visualize a section passing through a specific point within a body, aligned parallel to the xy plane. This section is subjected to various forces, including original loads, normal forces, and shearing forces.
The shearing force, possessing potential directionality within the plane of the section, is simplified into two component forces running parallel to the x and y axes....
725
Network Function of a Circuit01:25

Network Function of a Circuit

1.0K
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
1.0K
Mesh Analysis for AC Circuits01:12

Mesh Analysis for AC Circuits

801
In the domain of radio communication, the significance of impedance matching must be considered. It is crucial to ensure the efficient transmission of signals between radio transmitters and receivers. Achieving this balance involves using impedance-matching circuits, with one fundamental configuration comprising a resistor, capacitor, and inductor.
The process of harmonizing these impedances begins with a clear understanding of the input and output signals. Once these signals are known, the...
801
Cable Subjected to a Distributed Load01:24

Cable Subjected to a Distributed Load

1.3K
The analysis of suspension bridges is a complex and critical process that involves multiple factors, including the shape and tension of the main cables. The main cables of suspension bridges are subjected to distributed loads, which result in changes in tensile forces and deformation of the cable. These loads must be carefully considered to ensure that the bridge is safe and capable of supporting the weight of different loads.
1.3K

You might also read

Related Articles

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

Sort by
Same author

Correction: Regulation of focal adhesion dynamics and cell motility by the EB2 and Hax1 protein complex.

The Journal of biological chemistry·2019
Same author

Chemical Syntheses and Chemical Biology of Carboxyl Polyether Ionophores: Recent Highlights.

Angewandte Chemie (International ed. in English)·2019
Same author

Cholesterol content in cell membrane maintains surface levels of ErbB2 and confers a therapeutic vulnerability in ErbB2-positive breast cancer.

Cell communication and signaling : CCS·2019
Same author

Exercise interventions on patients with end-stage renal disease: a systematic review.

Clinical rehabilitation·2019
Same author

Long-term creep deformations in colloidal calcium-silicate-hydrate gels by accelerated aging simulations.

Journal of colloid and interface science·2019
Same author

Microconcave MAPbBr<sub>3</sub> Single Crystal for High-Performance Photodetector.

The journal of physical chemistry letters·2019
Same journal

On the control of recurrent neural networks using constant inputs.

IEEE transactions on automatic control·2026
Same journal

Robust Control Barrier Functions for Uncertain Parameter-Varying Control Affine Systems with Set-Membership Parameter Estimation.

IEEE transactions on automatic control·2026
Same journal

Estimation in Networks with Spatiotemporally Correlated Noise.

IEEE transactions on automatic control·2026
Same journal

Generalized Policy Improvement Algorithms with Theoretically Supported Sample Reuse.

IEEE transactions on automatic control·2025
Same journal

Transient Analysis of Serial Production Lines With Perishable Products: Bernoulli Reliability Model.

IEEE transactions on automatic control·2024
Same journal

Solid Boundary Output Feedback Control of the Stefan Problem: The Enthalpy Approach.

IEEE transactions on automatic control·2024
See all related articles

Related Experiment Video

Updated: Apr 18, 2026

Measurement of Aggregate Cohesion by Tissue Surface Tensiometry
12:49

Measurement of Aggregate Cohesion by Tissue Surface Tensiometry

Published on: April 8, 2011

13.2K

Compressive Network Analysis.

Xiaoye Jiang1, Yuan Yao2, Han Liu3

  • 1Stanford University, Stanford, CA 94305 USA.

IEEE Transactions on Automatic Control
|January 27, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a novel framework connecting network data analysis and compressed sensing for analyzing large network datasets. It provides rigorous conditions for network clique detection and practical algorithms for real-world applications.

Keywords:
Clique detectionRadon basis pursuitcompressive sensingnetwork data analysisrestricted isometry property

More Related Videos

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.7K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.2K

Related Experiment Videos

Last Updated: Apr 18, 2026

Measurement of Aggregate Cohesion by Tissue Surface Tensiometry
12:49

Measurement of Aggregate Cohesion by Tissue Surface Tensiometry

Published on: April 8, 2011

13.2K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.7K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.2K

Area of Science:

  • Network data analysis
  • Compressed sensing
  • Statistical learning
  • Signal processing

Background:

  • Modern data acquisition generates massive network data, but analysis methods are often disconnected from classical statistical learning and signal processing theories.
  • Existing network data analysis techniques lack a unified theoretical framework, hindering rigorous recovery guarantees.

Purpose of the Study:

  • To bridge the gap between network data analysis and compressed sensing.
  • To develop a nonparametric framework for modeling network data using large dictionaries.
  • To establish rigorous recovery conditions for network clique detection problems.

Main Methods:

  • Modeling network data using a large dictionary from a nonparametric perspective.
  • Connecting network clique detection to a novel algebraic tool: Randon basis pursuit in homogeneous spaces.
  • Developing practical approximation algorithms for empirical network data analysis.

Main Results:

  • Established rigorous recovery conditions for clique detection problems by linking network analysis to Randon basis pursuit.
  • Demonstrated the practical utility of developed approximation algorithms on real-world network datasets.
  • Provided a conceptual framework unifying network data analysis with compressed sensing principles.

Conclusions:

  • The proposed framework successfully connects network data analysis and compressed sensing, offering a new perspective for modeling and analyzing complex network data.
  • The study provides theoretical guarantees for network clique detection and practical algorithms, advancing the field of large-scale network analysis.
  • This research opens avenues for applying compressed sensing techniques to a broader range of network data analysis problems.