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

Trial and Error and Algorithm01:12

Trial and Error and Algorithm

399
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
399
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

300
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...
300
Range00:59

Range

13.9K
The range is one of the measures of variation. It can be defined as the difference between a dataset's highest and lowest values. For example, in the study of seven 16-ounce soda cans, the filled volume of soda was measured, thus producing the following amount (in ounces) of soda:
15.9; 16.1; 15.2; 14.8; 15.8; 15.9; 16.0; 15.5
Measurements of the amount of soda in a 16-ounce can vary since different subjects record these measurements or since the exact amount - 16 ounces of liquid, was not...
13.9K
Static Equilibrium - I01:05

Static Equilibrium - I

18.7K
A rigid body is said to be in dynamic equilibrium when both its linear and angular acceleration are zero, relative to an inertial frame of reference. This means that a body in equilibrium can be moving, but only when its linear and angular velocities are constant. A rigid body is said to be in static equilibrium when it is at rest in the selected frame of reference. The distinction between static equilibrium (e.g., a state of rest) and dynamic equilibrium (e.g, a state of uniform motion) is...
18.7K
Static Equilibrium - II01:07

Static Equilibrium - II

9.8K
Static equilibrium is a special case in mechanics that is very important in everyday life. It occurs when the net force and the net torque on an object or system are both zero. This means that both the linear and angular accelerations are zero. Thus, the object is at rest, or its center of mass is moving at a constant velocity. However, this does not mean that no forces are acting on the object within the system. In fact, there are very few scenarios on Earth in which no forces are acting upon...
9.8K
Static Friction01:18

Static Friction

1.4K
Static friction is a force that opposes the relative motion or tendency of motion between two surfaces in contact. It plays a crucial role in our daily lives, from walking on the ground to driving a car.
For example, consider a scenario where a truck is connected to a car by a rope, ready to tow it along a road. When no external force is applied by the truck, the car remains stationary and is said to be in static equilibrium. In this case, the forces acting on the car, such as gravity and the...
1.4K

You might also read

Related Articles

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

Sort by
Same author

Multimodal Collaborative Modeling of Molecular Structures and Biomedical Text for Accurate Drug-Drug Interaction Extraction.

Biomedicines·2026
Same author

scMVAF: a multi-view adaptive fusion clustering approach for single-cell RNA-sequencing data.

Briefings in bioinformatics·2026
Same author

Multi-feature fusion network with marginal focal dice loss for multi-label therapeutic peptide prediction.

PLoS computational biology·2025
Same author

MDNN: memetic deep neural network for genomic prediction.

Briefings in bioinformatics·2025
Same author

Inter-view contrastive learning and miRNA fusion for lncRNA-protein interaction prediction in heterogeneous graphs.

Briefings in bioinformatics·2025
Same author

A Fusion Deep Learning Model for Predicting Adverse Drug Reactions Based on Multiple Drug Characteristics.

Life (Basel, Switzerland)·2025

Related Experiment Video

Updated: Jan 21, 2026

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
07:05

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Published on: February 15, 2022

2.9K

A Novel Centralized Range-Free Static Node Localization Algorithm with Memetic Algorithm and Lévy Flight.

Jin Yang1,2, Yongming Cai3,4, Deyu Tang3,4

  • 1School of Medical Information and Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, China. y.jin04@gdpu.edu.cn.

Sensors (Basel, Switzerland)
|July 26, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces LMQPDV-hop, a novel algorithm for precise node localization in wireless sensor networks (WSNs). By improving distance estimation and utilizing an enhanced quantum-behaved particle swarm optimization (QPSO), it significantly boosts positioning accuracy.

Keywords:
Lévy flightmemetic algorithmnode localizationquantum-behaved particle swarm optimizationwireless sensor network

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.4K
Two Algorithms for High-throughput and Multi-parametric Quantification of Drosophila Neuromuscular Junction Morphology
12:29

Two Algorithms for High-throughput and Multi-parametric Quantification of Drosophila Neuromuscular Junction Morphology

Published on: May 3, 2017

11.0K

Related Experiment Videos

Last Updated: Jan 21, 2026

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
07:05

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Published on: February 15, 2022

2.9K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.4K
Two Algorithms for High-throughput and Multi-parametric Quantification of Drosophila Neuromuscular Junction Morphology
12:29

Two Algorithms for High-throughput and Multi-parametric Quantification of Drosophila Neuromuscular Junction Morphology

Published on: May 3, 2017

11.0K

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Node localization is a critical NP-hard problem in wireless sensor networks (WSNs).
  • Swarm intelligent algorithms (SIAs) are increasingly applied to address WSN localization challenges.
  • Existing methods often struggle with precision due to distance estimation errors.

Purpose of the Study:

  • To develop a highly precise node localization algorithm for WSNs.
  • To introduce a novel algorithm, LMQPDV-hop, combining improved DV-Hop and a quantum-behaved particle swarm optimization (QPSO).
  • To enhance global search capabilities and convergence speed for accurate node positioning.

Main Methods:

  • An improved DV-Hop mechanism was used for distance estimation, modifying average hop distance with a defined weight.
  • A quantum-behaved particle swarm optimization (QPSO) algorithm, LMQPSO, was developed for coordinate determination.
  • Memetic algorithm (MA) and Lévy flight were integrated into QPSO to improve global search; a fast local search rule was added for convergence.

Main Results:

  • The LMQPDV-hop algorithm demonstrated significant improvements in position precision.
  • Simulations across various WSN deployment scenarios validated the algorithm's effectiveness.
  • The enhanced QPSO (LMQPSO) successfully improved global searching and convergence speed.

Conclusions:

  • LMQPDV-hop effectively enhances node localization precision in wireless sensor networks.
  • The integration of MA and Lévy flight into QPSO offers superior global search and faster convergence.
  • The modified DV-Hop mechanism reduces distance errors, contributing to overall accuracy.