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 Medium Optimization01:28

Methods of Medium Optimization

63
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
63
Hybrid Zones02:29

Hybrid Zones

22.7K
Hybrid zones are narrow regions where two closely related species interact, mate, and produce hybrids. Relative to either parent species, hybrids may possess distinct phenotypic or genetic differences that impact their survival and reproductive success. The genetic variances introduced by hybridization influence species diversity and speciation processes within the hybrid zone.
22.7K
Mismatch Repair01:20

Mismatch Repair

7.2K
Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
The Mutator Protein Family Plays a Key Role in DNA Mismatch Repair
The human genome has more than 3 billion base pairs of DNA per cell. Prior to cell division, that vast amount of genetic...
7.2K
Microbial Biosensors01:17

Microbial Biosensors

71
Microbial biosensors are analytical devices that utilize living microbes to detect specific substances through measurable signals. These devices consist of two main components: biosensing organisms and signal-transducing elements. Biosensing organisms, such as Escherichia coli or Saccharomyces cerevisiae, are typically housed in multiwell plates connected to transducers, enabling rapid, real-time detection of target analytes.Signal Generation MechanismWhen a target analyte—such as...
71

You might also read

Related Articles

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

Sort by
Same author

Capacitive Insect Sensing Under a Single Dual-Arc Geometry: A Laboratory Benchmark of Four CDC Architectures.

Sensors (Basel, Switzerland)·2026
Same author

An Attention-Based Hybrid Deep Learning Approach for Patient-Specific, Cross-Patient, and Patient-Independent Seizure Detection.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Complementary use of visual and olfactory cues to assess capture of Bactrocera dorsalis (Hendel): Implementation and field verification via an IoT-based automatic monitoring system.

Proceedings of the Japan Academy. Series B, Physical and biological sciences·2024
Same author

Potential Applications of Mobile and Wearable Devices for Psychological Support During the COVID-19 Pandemic: A Review.

IEEE sensors journal·2023
Same author

An Alternative Body Temperature Measurement Solution: Combination of a Highly Accurate Monitoring System and a Visualized Public Health Cloud Platform.

IEEE internet of things journal·2023
Same author

Classification of attention deficit/hyperactivity disorder based on EEG signals using a EEG-Transformer model<sup>∗</sup>.

Journal of neural engineering·2023
Same journal

A New Human-Likeness and Comfort Index for Robot Movements Along Prescribed Paths.

IEEE transactions on cybernetics·2026
Same journal

Robust Semiglobal and Global Stabilization for Nonlinear Normal Form Systems by Time-Varying Feedback.

IEEE transactions on cybernetics·2026
Same journal

Adaptive Global Asymptotic Output Stabilization of Uncertain Nonlinear Systems Under Dynamic State/Input Quantization.

IEEE transactions on cybernetics·2026
Same journal

Accelerated Distributed Gradient Tracking for Constrained Aggregative Optimization Over Time-Varying Digraphs.

IEEE transactions on cybernetics·2026
Same journal

Small-Gain-Based Plug-and-Play Distributed Control Framework for DC Microgrids With Decentralized Reconfiguration.

IEEE transactions on cybernetics·2026
Same journal

Prescribed-Time Impulsive Control of High-Order Integrator Systems.

IEEE transactions on cybernetics·2026
See all related articles

Related Experiment Video

Updated: Apr 19, 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

A Hybrid Memetic Framework for Coverage Optimization in Wireless Sensor Networks.

Chia-Pang Chen, Subhas Chandra Mukhopadhyay, Cheng-Long Chuang

    IEEE Transactions on Cybernetics
    |December 23, 2014
    PubMed
    Summary
    This summary is machine-generated.

    Maintaining full sensing coverage in wireless sensor networks (WSNs) is crucial. This study introduces a hybrid framework (Hy-MFCO) for optimized coverage and energy efficiency in WSNs.

    More Related Videos

    Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
    08:58

    Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

    Published on: October 17, 2025

    838

    Related Experiment Videos

    Last Updated: Apr 19, 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
    Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
    08:58

    Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

    Published on: October 17, 2025

    838

    Area of Science:

    • Computer Science
    • Electrical Engineering
    • Network Engineering

    Background:

    • Continuous sensing coverage is critical for wireless sensor networks (WSNs) in applications like intrusion detection and object tracking.
    • Existing methods often rely on redundant nodes and set partitioning for coverage, but dynamic maintenance is needed due to uneven energy consumption and node failures.
    • Sustaining full coverage is a complex problem, combining disjoint set covers and dynamic coverage maintenance, both known to be computationally difficult (NP-complete).

    Purpose of the Study:

    • To present a novel hybrid memetic framework for coverage optimization (Hy-MFCO) in wireless sensor networks.
    • To address the challenge of dynamically maintaining full sensing coverage while maximizing network lifetime and energy efficiency.
    • To develop a solution for the NP-complete problem of hybrid disjoint set covers and dynamic coverage maintenance.

    Main Methods:

    • The proposed Hy-MFCO framework integrates a memetic algorithm (MA)-based scheduling strategy with a heuristic recursive algorithm (HRA).
    • The MA component utilizes a dynamic chromosome structure to generate disjoint sets of sensor nodes.
    • The HRA component dynamically activates hibernated nodes in localized regions to compensate for coverage gaps when a primary set fails.

    Main Results:

    • Real-world experiments and simulations demonstrate that Hy-MFCO effectively maximizes sensing coverage.
    • The framework achieves significant energy efficiency, extending network lifetime.
    • Hy-MFCO outperforms existing methods in both coverage preservation and energy efficiency.

    Conclusions:

    • The hybrid memetic framework (Hy-MFCO) offers an effective solution for dynamic coverage maintenance in wireless sensor networks.
    • The combination of memetic algorithms and heuristic approaches successfully tackles the complexity of coverage optimization.
    • Hy-MFCO provides a robust and efficient method for ensuring continuous sensing coverage and prolonging network operational life.