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

Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.2K
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
1.2K
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

541
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
541

You might also read

Related Articles

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

Sort by
Same author

UNI-494 treatment improves measures of renal dysfunction and cardiac pathology in male rats receiving L-NAME and angiotensin II.

Physiological reports·2025
Same author

Aggregation Effects on Optimal Sensor Network Configurations with Distance-Dependent Noise.

Sensors (Basel, Switzerland)·2025
Same author

A Phase 2 Clinical Trial of Oxylanthanum Carbonate in Patients Receiving Maintenance Hemodialysis with Hyperphosphatemia.

Clinical journal of the American Society of Nephrology : CJASN·2025
Same author

Combination Oxylanthanum Carbonate and Tenapanor Lowers Urinary Phosphate Excretion in Rats.

Kidney360·2025
Same author

Two-Way Randomized Crossover Study to Establish Pharmacodynamic Bioequivalence Between Oxylanthanum Carbonate and Lanthanum Carbonate.

Clinical therapeutics·2024
Same author

Single-wavelength bidirectional 4-core MCF self-homodyne coherent link.

Applied optics·2024
Same journal

An Evolutionary Algorithm Assisted by an Ensemble of Pareto-Optimal Surrogate Models.

IEEE transactions on cybernetics·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
See all related articles

Related Experiment Video

Updated: Feb 24, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.2K

POSE: Prediction-Based Opportunistic Sensing for Energy Efficiency in Sensor Networks Using Distributed Supervisors.

James Z Hare, Shalabh Gupta, Thomas A Wettergren

    IEEE Transactions on Cybernetics
    |August 16, 2017
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces the Prediction-based Opportunistic Sensing (POSE) algorithm for energy-efficient target tracking in sensor networks. POSE significantly saves energy by adapting sensing levels based on predicted target presence, improving tracking accuracy.

    Related Experiment Videos

    Last Updated: Feb 24, 2026

    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
    05:30

    Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

    Published on: September 8, 2023

    1.2K

    Area of Science:

    • Computer Science
    • Electrical Engineering
    • Network Engineering

    Background:

    • Sensor networks require efficient energy management for prolonged operation.
    • Target tracking in sensor networks is computationally intensive and energy-consuming.
    • Dynamic adaptation of sensing capabilities is crucial for energy conservation.

    Purpose of the Study:

    • To develop a distributed supervisory control algorithm for energy-efficient target tracking.
    • To minimize energy consumption in sensor nodes through opportunistic sensing.
    • To enhance track estimation accuracy in sensor networks.

    Main Methods:

    • Developed the Prediction-based Opportunistic Sensing (POSE) algorithm.
    • Implemented a distributed node-level energy management approach.
    • Utilized Probabilistic Finite State Automata for dynamic device control based on predicted target location.

    Main Results:

    • POSE demonstrated significant energy savings compared to random scheduling schemes.
    • The algorithm effectively adapts sensing and communication devices based on predicted target trajectories.
    • Track estimation accuracy was improved through fusion-driven state initialization.

    Conclusions:

    • The POSE algorithm offers a viable solution for energy-efficient target tracking in sensor networks.
    • Distributed control and predictive opportunistic sensing are effective strategies for energy management.
    • The approach enhances both energy efficiency and tracking performance.